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Merge pull request #1 from Azure/master

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merge latest changes from Azure/MLNB repo
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Kaarthik Sivashanmugam
2019-09-24 20:40:43 -07:00
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parent 4fe8c1702d 6b19e2f263
commit 6622a6c5f2
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30
.github/ISSUE_TEMPLATE/bug_report.md vendored Normal file
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@@ -0,0 +1,30 @@
---
name: Bug report
about: Create a report to help us improve
title: "[Notebook issue]"
labels: ''
assignees: ''
---
**Describe the bug**
A clear and concise description of what the bug is.
Provide the following if applicable:
+ Your Python & SDK version
+ Python Scripts or the full notebook name
+ Pipeline definition
+ Environment definition
+ Example data
+ Any log files.
+ Run and Workspace Id
**To Reproduce**
Steps to reproduce the behavior:
1.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Additional context**
Add any other context about the problem here.

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.github/ISSUE_TEMPLATE/notebook-issue.md vendored Normal file
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@@ -0,0 +1,43 @@
---
name: Notebook issue
about: Describe your notebook issue
title: "[Notebook] DESCRIPTIVE TITLE"
labels: notebook
assignees: ''
---
### DESCRIPTION: Describe clearly + concisely
.
### REPRODUCIBLE: Steps
.
### EXPECTATION: Clear description
.
### CONFIG/ENVIRONMENT:
```Provide where applicable
## Your Python & SDK version:
## Environment definition:
## Notebook name or Python scripts:
## Run and Workspace Id:
## Pipeline definition:
## Example data:
## Any log files:
```

10
README.md
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@@ -2,7 +2,8 @@
This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure. The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/concept-azure-machine-learning-architecture/workflow.png)
## Quick installation
```sh
@@ -38,6 +39,7 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
- [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
- [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
- [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
- [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
---
## Documentation
@@ -48,10 +50,14 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
---
## Community Repository
Visit this [community repository](https://github.com/microsoft/MLOps/tree/master/examples) to find useful end-to-end sample notebooks. Also, please follow these [contribution guidelines](https://github.com/microsoft/MLOps/blob/master/contributing.md) when contributing to this repository.
## Projects using Azure Machine Learning
Visit following repos to see projects contributed by Azure ML users:
- [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
- [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
- [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)

2
configuration.ipynb
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@@ -103,7 +103,7 @@
"source": [
"import azureml.core\n",
"\n",
"print(\"This notebook was created using version 1.0.48\r\n of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.0.62 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

0
model-deployment/README.md → end-to-end-samples/README.md
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2
how-to-use-azureml/README.md
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@@ -8,7 +8,7 @@ As a pre-requisite, run the [configuration Notebook](../configuration.ipynb) not
* [train-on-local](./training/train-on-local): Learn how to submit a run to local computer and use Azure ML managed run configuration.
* [train-on-amlcompute](./training/train-on-amlcompute): Use a 1-n node Azure ML managed compute cluster for remote runs on Azure CPU or GPU infrastructure.
* [train-on-remote-vm](./training/train-on-remote-vm): Use Data Science Virtual Machine as a target for remote runs.
* [logging-api](./training/logging-api): Learn about the details of logging metrics to run history.
* [logging-api](./track-and-monitor-experiments/logging-api): Learn about the details of logging metrics to run history.
* [register-model-create-image-deploy-service](./deployment/register-model-create-image-deploy-service): Learn about the details of model management.
* [production-deploy-to-aks](./deployment/production-deploy-to-aks) Deploy a model to production at scale on Azure Kubernetes Service.
* [enable-data-collection-for-models-in-aks](./deployment/enable-data-collection-for-models-in-aks) Learn about data collection APIs for deployed model.

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how-to-use-azureml/automated-machine-learning/README.md
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@@ -155,11 +155,11 @@ jupyter notebook
- [auto-ml-subsampling-local.ipynb](subsampling/auto-ml-subsampling-local.ipynb)
- How to enable subsampling
- [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
- Using DataPrep for reading data
- [auto-ml-dataset.ipynb](dataprep/auto-ml-dataset.ipynb)
- Using Dataset for reading data
- [auto-ml-dataprep-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb)
- Using DataPrep for reading data with remote execution
- [auto-ml-dataset-remote-execution.ipynb](dataprep-remote-execution/auto-ml-dataset-remote-execution.ipynb)
- Using Dataset for reading data with remote execution
- [auto-ml-classification-with-whitelisting.ipynb](classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
@@ -175,10 +175,19 @@ jupyter notebook
- Example of training an automated ML forecasting model on multiple time-series
- [auto-ml-classification-with-onnx.ipynb](classification-with-onnx/auto-ml-classification-with-onnx.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
- Dataset: scikit learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
- Simple example of using automated ML for classification with ONNX models
- Uses local compute for training
- [auto-ml-remote-amlcompute-with-onnx.ipynb](remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb)
- Dataset: scikit learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html)
- Example of using automated ML for classification using remote AmlCompute for training
- Train the models with ONNX compatible config on
- Parallel execution of iterations
- Async tracking of progress
- Cancelling individual iterations or entire run
- Retrieving the ONNX models and do the inference with them
- [auto-ml-bank-marketing-subscribers-with-deployment.ipynb](bank-marketing-subscribers-with-deployment/auto-ml-bank-marketing-with-deployment.ipynb)
- Dataset: UCI's [bank marketing dataset](https://www.kaggle.com/janiobachmann/bank-marketing-dataset)
- Simple example of using automated ML for classification to predict term deposit subscriptions for a bank
@@ -220,7 +229,7 @@ The main code of the file must be indented so that it is under this condition.
2. Check that you have conda 64-bit installed rather than 32-bit. You can check this with the command `conda info`. The `platform` should be `win-64` for Windows or `osx-64` for Mac.
3. Check that you have conda 4.4.10 or later. You can check the version with the command `conda -V`. If you have a previous version installed, you can update it using the command: `conda update conda`.
4. On Linux, if the error is `gcc: error trying to exec 'cc1plus': execvp: No such file or directory`, install build essentials using the command `sudo apt-get install build-essential`.
5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`.
5. Pass a new name as the first parameter to automl_setup so that it creates a new conda environment. You can view existing conda environments using `conda env list` and remove them with `conda env remove -n <environmentname>`.
## automl_setup_linux.sh fails
If automl_setup_linux.sh fails on Ubuntu Linux with the error: `unable to execute 'gcc': No such file or directory`
@@ -255,13 +264,13 @@ Some Windows environments see an error loading numpy with the latest Python vers
Check the tensorflow version in the automated ml conda environment. Supported versions are < 1.13. Uninstall tensorflow from the environment if version is >= 1.13
You may check the version of tensorflow and uninstall as follows
1) start a command shell, activate conda environment where automated ml packages are installed
2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
3) If the listed version is a not a supported version, `pip uninstall tensorflow` in the command shell and enter y for confirmation.
2) enter `pip freeze` and look for `tensorflow` , if found, the version listed should be < 1.13
3) If the listed version is a not a supported version, `pip uninstall tensorflow` in the command shell and enter y for confirmation.
## Remote run: DsvmCompute.create fails
## Remote run: DsvmCompute.create fails
There are several reasons why the DsvmCompute.create can fail. The reason is usually in the error message but you have to look at the end of the error message for the detailed reason. Some common reasons are:
1) `Compute name is invalid, it should start with a letter, be between 2 and 16 character, and only include letters (a-zA-Z), numbers (0-9) and \'-\'.` Note that underscore is not allowed in the name.
2) `The requested VM size xxxxx is not available in the current region.` You can select a different region or vm_size.
2) `The requested VM size xxxxx is not available in the current region.` You can select a different region or vm_size.
## Remote run: Unable to establish SSH connection
Automated ML uses the SSH protocol to communicate with remote DSVMs. This defaults to port 22. Possible causes for this error are:
@@ -287,4 +296,4 @@ To resolve this issue, allocate a DSVM with more memory or reduce the value spec
## Remote run: Iterations show as "Not Responding" in the RunDetails widget.
This can be caused by too many concurrent iterations for a remote DSVM. Each concurrent iteration usually takes 100% of a core when it is running. Some iterations can use multiple cores. So, the max_concurrent_iterations setting should always be less than the number of cores of the DSVM.
To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.
To resolve this issue, try reducing the value specified for the max_concurrent_iterations setting.

6
how-to-use-azureml/automated-machine-learning/automl_env.yml
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@@ -13,10 +13,14 @@ dependencies:
- scikit-learn>=0.19.0,<=0.20.3
- pandas>=0.22.0,<=0.23.4
- py-xgboost<=0.80
- pyarrow>=0.11.0
- pip:
# Required packages for AzureML execution, history, and data preparation.
- azureml-sdk[automl,explain]
- azureml-defaults
- azureml-train-automl
- azureml-widgets
- azureml-explain-model
- azureml-contrib-explain-model
- pandas_ml

7
how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
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@@ -2,6 +2,7 @@ name: azure_automl
dependencies:
# The python interpreter version.
# Currently Azure ML only supports 3.5.2 and later.
- pip
- nomkl
- python>=3.5.2,<3.6.8
- nb_conda
@@ -13,10 +14,14 @@ dependencies:
- scikit-learn>=0.19.0,<=0.20.3
- pandas>=0.22.0,<0.23.0
- py-xgboost<=0.80
- pyarrow>=0.11.0
- pip:
# Required packages for AzureML execution, history, and data preparation.
- azureml-sdk[automl,explain]
- azureml-defaults
- azureml-train-automl
- azureml-widgets
- azureml-explain-model
- azureml-contrib-explain-model
- pandas_ml

75
how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb
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@@ -69,22 +69,17 @@
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import logging\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"from sklearn import datasets\n",
"import azureml.dataprep as dprep\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun"
"from azureml.core.dataset import Dataset\n",
"from azureml.train.automl import AutoMLConfig"
]
},
{
@@ -97,8 +92,6 @@
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-classification-bmarketing'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-classification-bankmarketing'\n",
"\n",
"experiment=Experiment(ws, experiment_name)\n",
"\n",
@@ -108,7 +101,6 @@
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -155,11 +147,12 @@
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"print('Checking cluster status...')\n",
"# Can poll for a minimum number of nodes and for a specific timeout.\n",
"# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
"compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()."
"# For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
@@ -168,20 +161,7 @@
"source": [
"# Data\n",
"\n",
"Here load the data in the get_data() script to be utilized in azure compute. To do this first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_Run_config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
" \n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
"Create a run configuration for the remote run."
]
},
{
@@ -200,11 +180,8 @@
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
"cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
@@ -214,7 +191,7 @@
"source": [
"### Load Data\n",
"\n",
"Here we create the script to be run in azure comput for loading the data, we load the bank marketing dataset into X_train and y_train. Next X_train and y_train is returned for training the model."
"Load the bank marketing dataset into X_train and y_train. X_train contains the training features, which are inputs to the model. y_train contains the training labels, which are the expected output of the model."
]
},
{
@@ -224,11 +201,10 @@
"outputs": [],
"source": [
"data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
"dflow = dprep.auto_read_file(data)\n",
"dflow.get_profile()\n",
"X_train = dflow.drop_columns(columns=['y'])\n",
"y_train = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
"dflow.head()"
"dataset = Dataset.Tabular.from_delimited_files(data)\n",
"X_train = dataset.drop_columns(columns=['y'])\n",
"y_train = dataset.keep_columns(columns=['y'], validate=True)\n",
"dataset.take(5).to_pandas_dataframe()"
]
},
{
@@ -248,7 +224,6 @@
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
]
@@ -271,7 +246,6 @@
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" X = X_train,\n",
" y = y_train,\n",
@@ -406,7 +380,7 @@
"def run(rawdata):\n",
" try:\n",
" data = json.loads(rawdata)['data']\n",
" data = numpy.array(data)\n",
" data = np.array(data)\n",
" result = model.predict(data)\n",
" except Exception as e:\n",
" result = str(e)\n",
@@ -443,7 +417,7 @@
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
"for p in ['azureml-train-automl', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
@@ -453,10 +427,8 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
" pip_packages=['azureml-sdk[automl]'])\n",
" pip_packages=['azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -476,7 +448,7 @@
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",
@@ -618,8 +590,6 @@
"outputs": [],
"source": [
"# Load the bank marketing datasets.\n",
"from sklearn.datasets import load_diabetes\n",
"from sklearn.model_selection import train_test_split\n",
"from numpy import array"
]
},
@@ -630,11 +600,10 @@
"outputs": [],
"source": [
"data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
"dflow = dprep.auto_read_file(data)\n",
"dflow.get_profile()\n",
"X_test = dflow.drop_columns(columns=['y'])\n",
"y_test = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
"dflow.head()"
"dataset = Dataset.Tabular.from_delimited_files(data)\n",
"X_test = dataset.drop_columns(columns=['y'])\n",
"y_test = dataset.keep_columns(columns=['y'], validate=True)\n",
"dataset.take(5).to_pandas_dataframe()"
]
},
{

2
how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.yml
View File

@@ -2,6 +2,8 @@ name: auto-ml-classification-bank-marketing
dependencies:
- pip:
- azureml-sdk
- azureml-defaults
- azureml-explain-model
- azureml-train-automl
- azureml-widgets
- matplotlib

59
how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
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@@ -74,14 +74,12 @@
"from matplotlib import pyplot as plt\n",
"import pandas as pd\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"import azureml.dataprep as dprep\n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.train.automl.run import AutoMLRun"
"from azureml.core.dataset import Dataset\n",
"from azureml.train.automl import AutoMLConfig"
]
},
{
@@ -94,8 +92,6 @@
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-classification-ccard'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-classification-creditcard'\n",
"\n",
"experiment=Experiment(ws, experiment_name)\n",
"\n",
@@ -105,7 +101,6 @@
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -152,11 +147,12 @@
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()."
"print('Checking cluster status...')\n",
"# Can poll for a minimum number of nodes and for a specific timeout.\n",
"# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
"compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"\n",
"# For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
@@ -165,20 +161,7 @@
"source": [
"# Data\n",
"\n",
"Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
" \n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
"Create a run configuration for the remote run."
]
},
{
@@ -197,11 +180,8 @@
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
"cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
@@ -211,7 +191,7 @@
"source": [
"### Load Data\n",
"\n",
"Here create the script to be run in azure compute for loading the data, load the credit card dataset into cards and store the Class column (y) in the y variable and store the remaining data in the x variable. Next split the data using train_test_split and return X_train and y_train for training the model."
"Load the credit card dataset into X and y. X contains the features, which are inputs to the model. y contains the labels, which are the expected output of the model. Next split the data using random_split and return X_train and y_train for training the model."
]
},
{
@@ -221,10 +201,9 @@
"outputs": [],
"source": [
"data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
"dflow = dprep.auto_read_file(data)\n",
"dflow.get_profile()\n",
"X = dflow.drop_columns(columns=['Class'])\n",
"y = dflow.keep_columns(columns=['Class'], validate_column_exists=True)\n",
"dataset = Dataset.Tabular.from_delimited_files(data)\n",
"X = dataset.drop_columns(columns=['Class'])\n",
"y = dataset.keep_columns(columns=['Class'], validate=True)\n",
"X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
"y_train, y_test = y.random_split(percentage=0.8, seed=223)"
]
@@ -246,7 +225,6 @@
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
]
@@ -275,8 +253,7 @@
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors_20190417.log',\n",
" path = project_folder,\n",
" debug_log = 'automl_errors.log',\n",
" run_configuration=conda_run_config,\n",
" X = X_train,\n",
" y = y_train,\n",
@@ -447,7 +424,7 @@
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
"for p in ['azureml-train-automl', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
@@ -458,7 +435,7 @@
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
" pip_packages=['azureml-sdk[automl]'])\n",
" pip_packages=['azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -478,7 +455,7 @@
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",

2
how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.yml
View File

@@ -2,6 +2,8 @@ name: auto-ml-classification-credit-card-fraud
dependencies:
- pip:
- azureml-sdk
- azureml-defaults
- azureml-explain-model
- azureml-train-automl
- azureml-widgets
- matplotlib

15
how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
View File

@@ -92,8 +92,6 @@
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-classification-deployment'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-classification-deployment'\n",
"\n",
"experiment=Experiment(ws, experiment_name)\n",
"\n",
@@ -103,7 +101,6 @@
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -126,8 +123,7 @@
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
]
},
{
@@ -148,8 +144,7 @@
" iterations = 10,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)"
" y = y_train)"
]
},
{
@@ -297,7 +292,7 @@
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
"for p in ['azureml-train-automl', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
@@ -310,7 +305,7 @@
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
" pip_packages=['azureml-sdk[automl]'])\n",
" pip_packages=['azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -330,7 +325,7 @@
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",

24
how-to-use-azureml/automated-machine-learning/classification-with-onnx/auto-ml-classification-with-onnx.ipynb
View File

@@ -29,7 +29,6 @@
"1. [Data](#Data)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)\n",
"1. [Test](#Test)\n",
"\n"
]
},
@@ -39,7 +38,7 @@
"source": [
"## Introduction\n",
"\n",
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
"In this example we use the scikit-learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use AutoML for a simple classification problem.\n",
"\n",
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
"\n",
@@ -49,7 +48,8 @@
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Configure AutoML using `AutoMLConfig`.\n",
"3. Train the model using local compute with ONNX compatible config on.\n",
"4. Explore the results and save the ONNX model."
"4. Explore the results and save the ONNX model.\n",
"5. Inference with the ONNX model."
]
},
{
@@ -89,9 +89,8 @@
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"# Choose a name for the experiment.\n",
"experiment_name = 'automl-classification-onnx'\n",
"project_folder = './sample_projects/automl-classification-onnx'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -101,7 +100,6 @@
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -127,9 +125,7 @@
"X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
" iris.target, \n",
" test_size=0.2, \n",
" random_state=0)\n",
"\n",
"\n"
" random_state=0)"
]
},
{
@@ -156,11 +152,11 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train with enable ONNX compatible models config on\n",
"## Train\n",
"\n",
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
"\n",
"Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
"**Note:** Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
@@ -170,8 +166,7 @@
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
"|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|"
]
},
{
@@ -196,8 +191,7 @@
" X = X_train, \n",
" y = y_train,\n",
" preprocess=True,\n",
" enable_onnx_compatible_models=True,\n",
" path = project_folder)"
" enable_onnx_compatible_models=True)"
]
},
{

10
how-to-use-azureml/automated-machine-learning/classification-with-whitelisting/auto-ml-classification-with-whitelisting.ipynb
View File

@@ -41,7 +41,7 @@
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
"\n",
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
"This notebooks shows how can automl can be trained on a a selected list of models,see the readme.md for the models.\n",
"This notebooks shows how can automl can be trained on a selected list of models, see the readme.md for the models.\n",
"This trains the model exclusively on tensorflow based models.\n",
"\n",
"In this notebook you will learn how to:\n",
@@ -100,9 +100,8 @@
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"# Choose a name for the experiment.\n",
"experiment_name = 'automl-local-whitelist'\n",
"project_folder = './sample_projects/automl-local-whitelist'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -112,7 +111,6 @@
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -158,7 +156,6 @@
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"|**whitelist_models**|List of models that AutoML should use. The possible values are listed [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#configure-your-experiment-settings).|"
]
},
@@ -177,8 +174,7 @@
" X = X_train, \n",
" y = y_train,\n",
" enable_tf=True,\n",
" whitelist_models=whitelist_models,\n",
" path = project_folder)"
" whitelist_models=whitelist_models)"
]
},
{

10
how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb
View File

@@ -113,9 +113,8 @@
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"# Choose a name for the experiment.\n",
"experiment_name = 'automl-classification'\n",
"project_folder = './sample_projects/automl-classification'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -125,7 +124,6 @@
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -258,7 +256,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"widget-rundetails-sample"
]
},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",

86
how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb → how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.ipynb
View File

@@ -21,7 +21,7 @@
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Prepare Data using `azureml.dataprep` for Remote Execution (AmlCompute)**_\n",
"_**Load Data using `TabularDataset` for Remote Execution (AmlCompute)**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
@@ -37,23 +37,20 @@
"metadata": {},
"source": [
"## Introduction\n",
"In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
"In this example we showcase how you can use AzureML Dataset to load data for AutoML.\n",
"\n",
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
"2. Pass the `Dataflow` to AutoML for a local run.\n",
"3. Pass the `Dataflow` to AutoML for a remote run."
"1. Create a `TabularDataset` pointing to the training data.\n",
"2. Pass the `TabularDataset` to AutoML for a remote run."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
"## Setup"
]
},
{
@@ -70,15 +67,13 @@
"outputs": [],
"source": [
"import logging\n",
"import time\n",
"\n",
"import pandas as pd\n",
"\n",
"import azureml.core\n",
"from azureml.core.compute import DsvmCompute\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"import azureml.dataprep as dprep\n",
"from azureml.core.dataset import Dataset\n",
"from azureml.train.automl import AutoMLConfig"
]
},
@@ -89,11 +84,9 @@
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
" \n",
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-dataprep-remote-dsvm'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-dataprep-remote-dsvm'\n",
"experiment_name = 'automl-dataset-remote-bai'\n",
" \n",
"experiment = Experiment(ws, experiment_name)\n",
" \n",
@@ -103,7 +96,6 @@
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -123,35 +115,21 @@
"metadata": {},
"outputs": [],
"source": [
"# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
"# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
"# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
"# and convert column types manually.\n",
"example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
"dflow = dprep.auto_read_file(example_data).skip(1) # Remove the header row.\n",
"dflow.get_profile()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
"dflow = dflow.drop_nulls('Primary Type')\n",
"dflow.head(5)"
"dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
"dataset.take(5).to_pandas_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Review the Data Preparation Result\n",
"### Review the data\n",
"\n",
"You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets.\n",
"You can peek the result of a `TabularDataset` at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records, which makes it fast even against large datasets.\n",
"\n",
"`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
"`TabularDataset` objects are immutable and are composed of a list of subsetting transformations (optional)."
]
},
{
@@ -160,8 +138,8 @@
"metadata": {},
"outputs": [],
"source": [
"X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
"y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
"X = dataset.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
"y = dataset.keep_columns(columns=['Primary Type'], validate=True)"
]
},
{
@@ -205,7 +183,7 @@
"from azureml.core.compute import ComputeTarget\n",
"\n",
"# Choose a name for your cluster.\n",
"amlcompute_cluster_name = \"cpu-cluster\"\n",
"amlcompute_cluster_name = \"automlc2\"\n",
"\n",
"found = False\n",
"\n",
@@ -226,11 +204,12 @@
" # Create the cluster.\\n\",\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
"\n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"print('Checking cluster status...')\n",
"# Can poll for a minimum number of nodes and for a specific timeout.\n",
"# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
"compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"\n",
" # For a more detailed view of current AmlCompute status, use get_status()."
"# For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
@@ -249,11 +228,8 @@
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
"cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
@@ -261,9 +237,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pass Data with `Dataflow` Objects\n",
"### Pass Data with `TabularDataset` Objects\n",
"\n",
"The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
"The `TabularDataset` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `TabularDataset` object and send it to the remote compute target. The `TabularDataset` will not be evaluated locally."
]
},
{
@@ -274,7 +250,6 @@
"source": [
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" X = X,\n",
" y = y,\n",
@@ -466,8 +441,13 @@
"metadata": {},
"outputs": [],
"source": [
"dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
"dflow_test = dflow_test.drop_nulls('Primary Type')"
"dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
"\n",
"df_test = dataset_test.to_pandas_dataframe()\n",
"df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
"\n",
"y_test = df_test[['Primary Type']]\n",
"X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
]
},
{
@@ -486,10 +466,6 @@
"source": [
"from pandas_ml import ConfusionMatrix\n",
"\n",
"y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
"X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
"\n",
"\n",
"ypred = fitted_model.predict(X_test)\n",
"\n",
"cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",

6
tutorials/regression-part2-automated-ml.yml → how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.yml
View File

@@ -1,10 +1,10 @@
name: regression-part2-automated-ml
name: auto-ml-dataset-remote-execution
dependencies:
- pip:
- azureml-sdk
- azureml-defaults
- azureml-explain-model
- azureml-train-automl
- azureml-widgets
- azureml-explain-model
- matplotlib
- pandas_ml
- seaborn

64
how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb → how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.ipynb
View File

@@ -21,7 +21,7 @@
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Prepare Data using `azureml.dataprep` for Local Execution**_\n",
"_**Load Data using `TabularDataset` for Local Execution**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
@@ -37,23 +37,20 @@
"metadata": {},
"source": [
"## Introduction\n",
"In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
"In this example we showcase how you can use AzureML Dataset to load data for AutoML.\n",
"\n",
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you will learn how to:\n",
"1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
"2. Pass the `Dataflow` to AutoML for a local run.\n",
"3. Pass the `Dataflow` to AutoML for a remote run."
"1. Create a `TabularDataset` pointing to the training data.\n",
"2. Pass the `TabularDataset` to AutoML for a local run."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
"## Setup"
]
},
{
@@ -76,7 +73,7 @@
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"import azureml.dataprep as dprep\n",
"from azureml.core.dataset import Dataset\n",
"from azureml.train.automl import AutoMLConfig"
]
},
@@ -89,9 +86,7 @@
"ws = Workspace.from_config()\n",
" \n",
"# choose a name for experiment\n",
"experiment_name = 'automl-dataprep-local'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-dataprep-local'\n",
"experiment_name = 'automl-dataset-local'\n",
" \n",
"experiment = Experiment(ws, experiment_name)\n",
" \n",
@@ -101,7 +96,6 @@
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -121,35 +115,21 @@
"metadata": {},
"outputs": [],
"source": [
"# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
"# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
"# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
"# and convert column types manually.\n",
"example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
"dflow = dprep.auto_read_file(example_data).skip(1) # Remove the header row.\n",
"dflow.get_profile()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
"dflow = dflow.drop_nulls('Primary Type')\n",
"dflow.head(5)"
"dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
"dataset.take(5).to_pandas_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Review the Data Preparation Result\n",
"### Review the data\n",
"\n",
"You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets.\n",
"You can peek the result of a `TabularDataset` at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records, which makes it fast even against large datasets.\n",
"\n",
"`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
"`TabularDataset` objects are immutable and are composed of a list of subsetting transformations (optional)."
]
},
{
@@ -158,8 +138,8 @@
"metadata": {},
"outputs": [],
"source": [
"X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
"y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
"X = dataset.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
"y = dataset.keep_columns(columns=['Primary Type'], validate=True)"
]
},
{
@@ -190,9 +170,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pass Data with `Dataflow` Objects\n",
"### Pass Data with `TabularDataset` Objects\n",
"\n",
"The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
"The `TabularDataset` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `TabularDataset` for model training."
]
},
{
@@ -355,8 +335,13 @@
"metadata": {},
"outputs": [],
"source": [
"dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
"dflow_test = dflow_test.drop_nulls('Primary Type')"
"dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
"\n",
"df_test = dataset_test.to_pandas_dataframe()\n",
"df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
"\n",
"y_test = df_test[['Primary Type']]\n",
"X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
]
},
{
@@ -375,9 +360,6 @@
"source": [
"from pandas_ml import ConfusionMatrix\n",
"\n",
"y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
"X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
"\n",
"ypred = fitted_model.predict(X_test)\n",
"\n",
"cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",

2
how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.yml → how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.yml
View File

@@ -1,4 +1,4 @@
name: auto-ml-dataprep
name: auto-ml-dataset
dependencies:
- pip:
- azureml-sdk

10
how-to-use-azureml/automated-machine-learning/exploring-previous-runs/auto-ml-exploring-previous-runs.ipynb
View File

@@ -197,12 +197,12 @@
"display(HTML('<h3>Iterations</h3>'))\n",
"RunDetails(ml_run).show() \n",
"\n",
"children = list(ml_run.get_children())\n",
"all_metrics = ml_run.get_metrics(recursive=True)\n",
"metricslist = {}\n",
"for run in children:\n",
" properties = run.get_properties()\n",
" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
" metricslist[int(properties['iteration'])] = metrics\n",
"for run_id, metrics in all_metrics.items():\n",
" iteration = int(run_id.split('_')[-1])\n",
" float_metrics = {k: v for k, v in metrics.items() if isinstance(v, float)}\n",
" metricslist[iteration] = float_metrics\n",
"\n",
"rundata = pd.DataFrame(metricslist).sort_index(1)\n",
"display(HTML('<h3>Metrics</h3>'))\n",

21
how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb
View File

@@ -97,8 +97,6 @@
"\n",
"# choose a name for the run history container in the workspace\n",
"experiment_name = 'automl-bikeshareforecasting'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-bikeshareforecasting'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -108,7 +106,6 @@
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -225,7 +222,8 @@
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**country_or_region**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
"\n",
"This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
]
},
{
@@ -246,12 +244,12 @@
"\n",
"automl_config = AutoMLConfig(task='forecasting', \n",
" primary_metric='normalized_root_mean_squared_error',\n",
" blacklist_models = ['ExtremeRandomTrees'],\n",
" iterations=10,\n",
" iteration_timeout_minutes=5,\n",
" X=X_train,\n",
" y=y_train,\n",
" n_cross_validations=3, \n",
" path=project_folder,\n",
" n_cross_validations=3,\n",
" verbosity=logging.INFO,\n",
" **automl_settings)"
]
@@ -345,7 +343,10 @@
"metadata": {},
"outputs": [],
"source": [
"fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()"
"# Get the featurization summary as a list of JSON\n",
"featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
"# View the featurization summary as a pandas dataframe\n",
"pd.DataFrame.from_records(featurization_summary)"
]
},
{
@@ -522,7 +523,7 @@
"print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
"\n",
"# Plot outputs\n",
"%matplotlib notebook\n",
"%matplotlib inline\n",
"test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
@@ -564,7 +565,7 @@
"df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
"APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
"\n",
"%matplotlib notebook\n",
"%matplotlib inline\n",
"plt.boxplot(APEs)\n",
"plt.yscale('log')\n",
"plt.xlabel('horizon')\n",
@@ -578,7 +579,7 @@
"metadata": {
"authors": [
{
"name": "xiaga@microsoft.com, tosingli@microsoft.com, erwright@microsoft.com"
"name": "erwright"
}
],
"kernelspec": {

113
how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
View File

@@ -93,8 +93,6 @@
"\n",
"# choose a name for the run history container in the workspace\n",
"experiment_name = 'automl-energydemandforecasting'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -104,7 +102,6 @@
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -213,8 +210,7 @@
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
"|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
"|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|"
]
},
{
@@ -231,12 +227,12 @@
"automl_config = AutoMLConfig(task='forecasting',\n",
" debug_log='automl_nyc_energy_errors.log',\n",
" primary_metric='normalized_root_mean_squared_error',\n",
" blacklist_models = ['ExtremeRandomTrees'],\n",
" iterations=10,\n",
" iteration_timeout_minutes=5,\n",
" X=X_train,\n",
" y=y_train,\n",
" n_cross_validations=3,\n",
" path=project_folder,\n",
" verbosity = logging.INFO,\n",
" **time_series_settings)"
]
@@ -432,7 +428,7 @@
"print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
"\n",
"# Plot outputs\n",
"%matplotlib notebook\n",
"%matplotlib inline\n",
"pred, = plt.plot(df_all[time_column_name], df_all['predicted'], color='b')\n",
"actual, = plt.plot(df_all[time_column_name], df_all[target_column_name], color='g')\n",
"plt.xticks(fontsize=8)\n",
@@ -462,7 +458,9 @@
"source": [
"We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation.\n",
"\n",
"Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features."
"Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
"\n",
"This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
]
},
{
@@ -481,13 +479,12 @@
"automl_config_lags = AutoMLConfig(task='forecasting',\n",
" debug_log='automl_nyc_energy_errors.log',\n",
" primary_metric='normalized_root_mean_squared_error',\n",
" blacklist_models=['ElasticNet'],\n",
" blacklist_models=['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor'],\n",
" iterations=10,\n",
" iteration_timeout_minutes=10,\n",
" X=X_train,\n",
" y=y_train,\n",
" n_cross_validations=3,\n",
" path=project_folder,\n",
" verbosity=logging.INFO,\n",
" **time_series_settings_with_lags)"
]
@@ -543,7 +540,7 @@
"print('MAPE: %.2f' % MAPE(df_lags[target_column_name], df_lags['predicted']))\n",
"\n",
"# Plot outputs\n",
"%matplotlib notebook\n",
"%matplotlib inline\n",
"pred, = plt.plot(df_lags[time_column_name], df_lags['predicted'], color='b')\n",
"actual, = plt.plot(df_lags[time_column_name], df_lags[target_column_name], color='g')\n",
"plt.xticks(fontsize=8)\n",
@@ -555,7 +552,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### What features matter for the forecast?"
"### What features matter for the forecast?\n",
"The following steps will allow you to compute and visualize engineered feature importance based on your test data for forecasting. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Setup the model explanations for AutoML models\n",
"The *fitted_model* can generate the following which will be used for getting the engineered and raw feature explanations using *automl_setup_model_explanations*:-\n",
"1. Featurized data from train samples/test samples \n",
"2. Gather engineered and raw feature name lists\n",
"3. Find the classes in your labeled column in classification scenarios\n",
"\n",
"The *automl_explainer_setup_obj* contains all the structures from above list. "
]
},
{
@@ -564,14 +575,74 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.automl.automlexplainer import explain_model\n",
"\n",
"# feature names are everything in the transformed data except the target\n",
"features = X_trans_lags.columns[:-1]\n",
"expl = explain_model(fitted_model_lags, X_train.copy(), X_test.copy(), features=features, best_run=best_run_lags, y_train=y_train)\n",
"# unpack the tuple\n",
"shap_values, expected_values, feat_overall_imp, feat_names, per_class_summary, per_class_imp = expl\n",
"best_run_lags"
"from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
"automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train.copy(), \n",
" X_test=X_test.copy(), y=y_train, \n",
" task='forecasting')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Initialize the Mimic Explainer for feature importance\n",
"For explaining the AutoML models, use the *MimicWrapper* from *azureml.explain.model* package. The *MimicWrapper* can be initialized with fields in *automl_explainer_setup_obj*, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (*fitted_model* here). The *MimicWrapper* also takes the *best_run* object where the raw and engineered explanations will be uploaded."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
"from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
"explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
" init_dataset=automl_explainer_setup_obj.X_transform, run=best_run,\n",
" features=automl_explainer_setup_obj.engineered_feature_names, \n",
" feature_maps=[automl_explainer_setup_obj.feature_map])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
"The *explain()* method in *MimicWrapper* can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the generated engineered features by AutoML featurizers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
"print(engineered_explanations.get_feature_importance_dict())\n",
"from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
"ExplanationDashboard(engineered_explanations, automl_explainer_setup_obj.automl_estimator, automl_explainer_setup_obj.X_test_transform)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
"The *explain()* method in *MimicWrapper* can be again called with the transformed test samples and setting *get_raw* to *True* to get the feature importance for the raw features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the raw features."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"raw_explanations = explainer.explain(['local', 'global'], get_raw=True, \n",
" raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
" eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
"print(raw_explanations.get_feature_importance_dict())\n",
"from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
"ExplanationDashboard(raw_explanations, automl_explainer_setup_obj.automl_pipeline, automl_explainer_setup_obj.X_test_raw)"
]
},
{
@@ -587,7 +658,7 @@
"metadata": {
"authors": [
{
"name": "xiaga, tosingli, erwright"
"name": "erwright"
}
],
"kernelspec": {

1
how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.yml
View File

@@ -8,3 +8,4 @@ dependencies:
- pandas_ml
- statsmodels
- azureml-explain-model
- azureml-contrib-explain-model

21
how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
View File

@@ -89,8 +89,6 @@
"\n",
"# choose a name for the run history container in the workspace\n",
"experiment_name = 'automl-ojforecasting'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-local-ojforecasting'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -100,7 +98,6 @@
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Run History Name'] = experiment_name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -244,9 +241,9 @@
"|**X**|Training matrix of features as a pandas DataFrame, shape = [n_training_samples, n_features]|\n",
"|**y**|Target values as a numpy.ndarray, shape = [n_training_samples, ]|\n",
"|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection|\n",
"|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
"|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models\n",
"|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models\n",
"|**debug_log**|Log file path for writing debugging information\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"|**time_column_name**|Name of the datetime column in the input data|\n",
"|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
"|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
@@ -273,8 +270,8 @@
" X=X_train,\n",
" y=y_train,\n",
" n_cross_validations=3,\n",
" enable_ensembling=False,\n",
" path=project_folder,\n",
" enable_voting_ensemble=False,\n",
" enable_stack_ensemble=False,\n",
" verbosity=logging.INFO,\n",
" **time_series_settings)"
]
@@ -463,7 +460,7 @@
"# Plot outputs\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib notebook\n",
"%matplotlib inline\n",
"test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
"test_test = plt.scatter(y_test, y_test, color='g')\n",
"plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
@@ -663,10 +660,10 @@
"conda_env_file_name = 'fcast_env.yml'\n",
"\n",
"dependencies = ml_run.get_run_sdk_dependencies(iteration = best_iteration)\n",
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
"for p in ['azureml-train-automl', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))\n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-train-automl'])\n",
"\n",
"myenv.save_to_file('.', conda_env_file_name)"
]
@@ -688,7 +685,7 @@
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",
@@ -829,7 +826,7 @@
"metadata": {
"authors": [
{
"name": "erwright, tosingli"
"name": "erwright"
}
],
"kernelspec": {

13
how-to-use-azureml/automated-machine-learning/missing-data-blacklist-early-termination/auto-ml-missing-data-blacklist-early-termination.ipynb
View File

@@ -93,7 +93,6 @@
"\n",
"# Choose a name for the experiment.\n",
"experiment_name = 'automl-local-missing-data'\n",
"project_folder = './sample_projects/automl-local-missing-data'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -103,7 +102,6 @@
"output['Workspace'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -166,8 +164,7 @@
"|**experiment_exit_score**|*double* value indicating the target for *primary_metric*. <br>Once the target is surpassed the run terminates.|\n",
"|**blacklist_models**|*List* of *strings* indicating machine learning algorithms for AutoML to avoid in this run.<br><br> Allowed values for **Classification**<br><i>LogisticRegression</i><br><i>SGD</i><br><i>MultinomialNaiveBayes</i><br><i>BernoulliNaiveBayes</i><br><i>SVM</i><br><i>LinearSVM</i><br><i>KNN</i><br><i>DecisionTree</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>GradientBoosting</i><br><i>TensorFlowDNN</i><br><i>TensorFlowLinearClassifier</i><br><br>Allowed values for **Regression**<br><i>ElasticNet</i><br><i>GradientBoosting</i><br><i>DecisionTree</i><br><i>KNN</i><br><i>LassoLars</i><br><i>SGD</i><br><i>RandomForest</i><br><i>ExtremeRandomTrees</i><br><i>LightGBM</i><br><i>TensorFlowLinearRegressor</i><br><i>TensorFlowDNN</i>|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
]
},
{
@@ -186,8 +183,7 @@
" blacklist_models = ['KNN','LinearSVM'],\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)"
" y = y_train)"
]
},
{
@@ -360,7 +356,10 @@
"metadata": {},
"outputs": [],
"source": [
"fitted_model.named_steps['datatransformer'].get_featurization_summary()"
"# Get the featurization summary as a list of JSON\n",
"featurization_summary = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
"# View the featurization summary as a pandas dataframe\n",
"pd.DataFrame.from_records(featurization_summary)"
]
},
{

186
how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
View File

@@ -69,7 +69,8 @@
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig"
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.core.dataset import Dataset"
]
},
{
@@ -107,29 +108,42 @@
"## Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"\n",
"iris = datasets.load_iris()\n",
"y = iris.target\n",
"X = iris.data\n",
"\n",
"features = iris.feature_names\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X,\n",
" y,\n",
" test_size=0.1,\n",
" random_state=100,\n",
" stratify=y)\n",
"\n",
"X_train = pd.DataFrame(X_train, columns=features)\n",
"X_test = pd.DataFrame(X_test, columns=features)"
"train_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
"train_dataset = Dataset.Tabular.from_delimited_files(train_data)\n",
"X_train = train_dataset.drop_columns(columns=['y']).to_pandas_dataframe()\n",
"y_train = train_dataset.keep_columns(columns=['y'], validate=True).to_pandas_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
"test_dataset = Dataset.Tabular.from_delimited_files(test_data)\n",
"X_test = test_dataset.drop_columns(columns=['y']).to_pandas_dataframe()\n",
"y_test = test_dataset.keep_columns(columns=['y'], validate=True).to_pandas_dataframe()"
]
},
{
@@ -148,8 +162,6 @@
"|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y_valid**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**model_explainability**|Indicate to explain each trained pipeline or not |\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
]
@@ -166,10 +178,10 @@
" iteration_timeout_minutes = 200,\n",
" iterations = 10,\n",
" verbosity = logging.INFO,\n",
" preprocess = True,\n",
" X = X_train, \n",
" y = y_train,\n",
" X_valid = X_test,\n",
" y_valid = y_test,\n",
" n_cross_validations = 5,\n",
" model_explainability=True,\n",
" path=project_folder)"
]
@@ -197,7 +209,7 @@
"metadata": {},
"outputs": [],
"source": [
"local_run"
"best_run, fitted_model = local_run.get_output()"
]
},
{
@@ -302,19 +314,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Beside retrieve the existed model explanation information, explain the model with different train/test data"
"### Computing model explanations and visualizing the explanations using azureml-explain-model package\n",
"Beside retrieve the existed model explanation information, explain the model with different train/test data. The following steps will allow you to compute and visualize engineered feature importance and raw feature importance based on your test data. "
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.automl.automlexplainer import explain_model\n",
"#### Setup the model explanations for AutoML models\n",
"The *fitted_model* can generate the following which will be used for getting the engineered and raw feature explanations using *automl_setup_model_explanations*:-\n",
"1. Featurized data from train samples/test samples \n",
"2. Gather engineered and raw feature name lists\n",
"3. Find the classes in your labeled column in classification scenarios\n",
"\n",
"shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
" explain_model(fitted_model, X_train, X_test, features=features)"
"The *automl_explainer_setup_obj* contains all the structures from above list. "
]
},
{
@@ -323,8 +337,116 @@
"metadata": {},
"outputs": [],
"source": [
"print(overall_summary)\n",
"print(overall_imp)"
"from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
"\n",
"automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
" X_test=X_test, y=y_train, \n",
" task='classification')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Initialize the Mimic Explainer for feature importance\n",
"For explaining the AutoML models, use the *MimicWrapper* from *azureml.explain.model* package. The *MimicWrapper* can be initialized with fields in *automl_explainer_setup_obj*, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (*fitted_model* here). The *MimicWrapper* also takes the *best_run* object where the raw and engineered explanations will be uploaded."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
"from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
"explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
" init_dataset=automl_explainer_setup_obj.X_transform, run=best_run,\n",
" features=automl_explainer_setup_obj.engineered_feature_names, \n",
" feature_maps=[automl_explainer_setup_obj.feature_map],\n",
" classes=automl_explainer_setup_obj.classes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
"The *explain()* method in *MimicWrapper* can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the generated engineered features by AutoML featurizers."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
"print(engineered_explanations.get_feature_importance_dict())\n",
"from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
"ExplanationDashboard(engineered_explanations, automl_explainer_setup_obj.automl_estimator, automl_explainer_setup_obj.X_test_transform)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
"The *explain()* method in *MimicWrapper* can be again called with the transformed test samples and setting *get_raw* to *True* to get the feature importance for the raw features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the raw features."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"raw_explanations = explainer.explain(['local', 'global'], get_raw=True, \n",
" raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
" eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
"print(raw_explanations.get_feature_importance_dict())\n",
"from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
"ExplanationDashboard(raw_explanations, automl_explainer_setup_obj.automl_pipeline, automl_explainer_setup_obj.X_test_raw)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Download engineered feature importance from artifact store\n",
"You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *best_run*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
"client = ExplanationClient.from_run(best_run)\n",
"engineered_explanations = client.download_model_explanation(raw=False)\n",
"print(engineered_explanations.get_feature_importance_dict())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Download raw feature importance from artifact store\n",
"You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *best_run*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
"client = ExplanationClient.from_run(best_run)\n",
"raw_explanations = client.download_model_explanation(raw=True)\n",
"print(raw_explanations.get_feature_importance_dict())"
]
}
],

1
how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.yml
View File

@@ -7,3 +7,4 @@ dependencies:
- matplotlib
- pandas_ml
- azureml-explain-model
- azureml-contrib-explain-model

57
how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb
View File

@@ -70,13 +70,12 @@
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"import azureml.dataprep as dprep\n",
" \n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.dataset import Dataset\n",
"from azureml.train.automl import AutoMLConfig"
]
},
@@ -88,9 +87,8 @@
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"# Choose a name for the experiment.\n",
"experiment_name = 'automl-regression-concrete'\n",
"project_folder = './sample_projects/automl-regression-concrete'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -100,7 +98,6 @@
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -147,11 +144,12 @@
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"print('Checking cluster status...')\n",
"# Can poll for a minimum number of nodes and for a specific timeout.\n",
"# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
"compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()."
"# For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
@@ -160,20 +158,7 @@
"source": [
"# Data\n",
"\n",
"Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
" \n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
"Create a run configuration for the remote run."
]
},
{
@@ -192,11 +177,8 @@
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy'])\n",
"cd = CondaDependencies.create(conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
@@ -206,7 +188,7 @@
"source": [
"### Load Data\n",
"\n",
"Here create the script to be run in azure compute for loading the data, load the concrete strength dataset into the X and y variables. Next, split the data using train_test_split and return X_train and y_train for training the model. Finally, return X_train and y_train for training the model."
"Load the concrete strength dataset into X and y. X contains the training features, which are inputs to the model. y contains the training labels, which are the expected output of the model."
]
},
{
@@ -216,13 +198,12 @@
"outputs": [],
"source": [
"data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/compresive_strength_concrete.csv\"\n",
"dflow = dprep.auto_read_file(data)\n",
"dflow.get_profile()\n",
"X = dflow.drop_columns(columns=['CONCRETE'])\n",
"y = dflow.keep_columns(columns=['CONCRETE'], validate_column_exists=True)\n",
"dataset = Dataset.Tabular.from_delimited_files(data)\n",
"X = dataset.drop_columns(columns=['CONCRETE'])\n",
"y = dataset.keep_columns(columns=['CONCRETE'], validate=True)\n",
"X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
"y_train, y_test = y.random_split(percentage=0.8, seed=223) \n",
"dflow.head()"
"dataset.take(5).to_pandas_dataframe()"
]
},
{
@@ -242,7 +223,6 @@
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
]
@@ -272,7 +252,6 @@
"\n",
"automl_config = AutoMLConfig(task = 'regression',\n",
" debug_log = 'automl.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" X = X_train,\n",
" y = y_train,\n",
@@ -484,7 +463,7 @@
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
"for p in ['azureml-train-automl', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
@@ -494,9 +473,7 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies\n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -516,7 +493,7 @@
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",

2
how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.yml
View File

@@ -2,6 +2,8 @@ name: auto-ml-regression-concrete-strength
dependencies:
- pip:
- azureml-sdk
- azureml-defaults
- azureml-explain-model
- azureml-train-automl
- azureml-widgets
- matplotlib

59
how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.ipynb
View File

@@ -70,13 +70,12 @@
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"from sklearn.model_selection import train_test_split\n",
"import azureml.dataprep as dprep\n",
" \n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.dataset import Dataset\n",
"from azureml.train.automl import AutoMLConfig"
]
},
@@ -88,9 +87,8 @@
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"# Choose a name for the experiment.\n",
"experiment_name = 'automl-regression-hardware'\n",
"project_folder = './sample_projects/automl-remote-regression'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -100,7 +98,6 @@
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -147,11 +144,12 @@
" # Create the cluster.\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
" \n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"print('Checking cluster status...')\n",
"# Can poll for a minimum number of nodes and for a specific timeout.\n",
"# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
"compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
" \n",
" # For a more detailed view of current AmlCompute status, use get_status()."
"# For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
@@ -160,20 +158,7 @@
"source": [
"# Data\n",
"\n",
"Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
" \n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)"
"Create a run configuration for the remote run."
]
},
{
@@ -192,11 +177,8 @@
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy'])\n",
"cd = CondaDependencies.create(conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
@@ -206,7 +188,7 @@
"source": [
"### Load Data\n",
"\n",
"Here create the script to be run in azure compute for loading the data, load the hardware dataset into the X and y variables. Next split the data using train_test_split and return X_train and y_train for training the model."
"Load the hardware performance dataset into X and y. X contains the training features, which are inputs to the model. y contains the training labels, which are the expected output of the model."
]
},
{
@@ -216,13 +198,12 @@
"outputs": [],
"source": [
"data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
"dflow = dprep.auto_read_file(data)\n",
"dflow.get_profile()\n",
"X = dflow.drop_columns(columns=['ERP'])\n",
"y = dflow.keep_columns(columns=['ERP'], validate_column_exists=True)\n",
"dataset = Dataset.Tabular.from_delimited_files(data)\n",
"X = dataset.drop_columns(columns=['ERP'])\n",
"y = dataset.keep_columns(columns=['ERP'], validate=True)\n",
"X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
"y_train, y_test = y.random_split(percentage=0.8, seed=223) \n",
"dflow.head()"
"y_train, y_test = y.random_split(percentage=0.8, seed=223)\n",
"dataset.take(5).to_pandas_dataframe()"
]
},
{
@@ -243,7 +224,6 @@
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
"\n",
"**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
]
@@ -272,8 +252,7 @@
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'regression',\n",
" debug_log = 'automl_errors_20190417.log',\n",
" path = project_folder,\n",
" debug_log = 'automl_errors.log',\n",
" run_configuration=conda_run_config,\n",
" X = X_train,\n",
" y = y_train,\n",
@@ -502,7 +481,7 @@
"metadata": {},
"outputs": [],
"source": [
"for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
"for p in ['azureml-train-automl', 'azureml-core']:\n",
" print('{}\\t{}'.format(p, dependencies[p]))"
]
},
@@ -512,7 +491,7 @@
"metadata": {},
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -532,7 +511,7 @@
" content = cefr.read()\n",
"\n",
"with open(conda_env_file_name, 'w') as cefw:\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
" cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
"\n",
"# Substitute the actual model id in the script file.\n",
"\n",

2
how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.yml
View File

@@ -2,6 +2,8 @@ name: auto-ml-regression-hardware-performance
dependencies:
- pip:
- azureml-sdk
- azureml-defaults
- azureml-explain-model
- azureml-train-automl
- azureml-widgets
- matplotlib

10
how-to-use-azureml/automated-machine-learning/regression/auto-ml-regression.ipynb
View File

@@ -84,9 +84,8 @@
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the experiment and specify the project folder.\n",
"# Choose a name for the experiment.\n",
"experiment_name = 'automl-local-regression'\n",
"project_folder = './sample_projects/automl-local-regression'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -96,7 +95,6 @@
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -144,8 +142,7 @@
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
"|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|"
]
},
{
@@ -162,8 +159,7 @@
" debug_log = 'automl.log',\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)"
" y = y_train)"
]
},
{

548
how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb Normal file
View File

@@ -0,0 +1,548 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Remote Execution using AmlCompute**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Data](#Data)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"In this example we use the scikit-learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use AutoML for a simple classification problem.\n",
"\n",
"Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
"\n",
"In this notebook you would see\n",
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Create or Attach existing AmlCompute to a workspace.\n",
"3. Configure AutoML using `AutoMLConfig`.\n",
"4. Train the model using AmlCompute with ONNX compatible config on.\n",
"5. Explore the results and save the ONNX model.\n",
"6. Inference with the ONNX model.\n",
"\n",
"In addition this notebook showcases the following features\n",
"- **Parallel** executions for iterations\n",
"- **Asynchronous** tracking of progress\n",
"- **Cancellation** of individual iterations or the entire run\n",
"- Retrieving models for any iteration or logged metric\n",
"- Specifying AutoML settings as `**kwargs`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"import os\n",
"\n",
"import pandas as pd\n",
"from sklearn import datasets\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.core.dataset import Dataset\n",
"from azureml.train.automl import AutoMLConfig"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the run history container in the workspace.\n",
"experiment_name = 'automl-remote-amlcompute-with-onnx'\n",
"project_folder = './project'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
"output = {}\n",
"output['SDK version'] = azureml.core.VERSION\n",
"output['Subscription ID'] = ws.subscription_id\n",
"output['Workspace Name'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\n",
"output['Project Directory'] = project_folder\n",
"output['Experiment Name'] = experiment.name\n",
"pd.set_option('display.max_colwidth', -1)\n",
"outputDf = pd.DataFrame(data = output, index = [''])\n",
"outputDf.T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create or Attach existing AmlCompute\n",
"You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
"\n",
"**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
"\n",
"As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import AmlCompute\n",
"from azureml.core.compute import ComputeTarget\n",
"\n",
"# Choose a name for your cluster.\n",
"amlcompute_cluster_name = \"automlc2\"\n",
"\n",
"found = False\n",
"# Check if this compute target already exists in the workspace.\n",
"cts = ws.compute_targets\n",
"if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
" found = True\n",
" print('Found existing compute target.')\n",
" compute_target = cts[amlcompute_cluster_name]\n",
"\n",
"if not found:\n",
" print('Creating a new compute target...')\n",
" provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
" #vm_priority = 'lowpriority', # optional\n",
" max_nodes = 6)\n",
"\n",
" # Create the cluster.\\n\",\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
"\n",
"print('Checking cluster status...')\n",
"# Can poll for a minimum number of nodes and for a specific timeout.\n",
"# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
"compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"\n",
"# For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data\n",
"For remote executions, you need to make the data accessible from the remote compute.\n",
"This can be done by uploading the data to DataStore.\n",
"In this example, we upload scikit-learn's [load_iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iris = datasets.load_iris()\n",
"\n",
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\n",
"\n",
"if not os.path.exists(project_folder):\n",
" os.makedirs(project_folder)\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
" iris.target, \n",
" test_size=0.2, \n",
" random_state=0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ensure the x_train and x_test are pandas DataFrame."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
"# This is needed for initializing the input variable names of ONNX model, \n",
"# and the prediction with the ONNX model using the inference helper.\n",
"X_train = pd.DataFrame(X_train, columns=['c1', 'c2', 'c3', 'c4'])\n",
"X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])\n",
"y_train = pd.DataFrame(y_train, columns=['label'])\n",
"\n",
"X_train.to_csv(\"data/X_train.csv\", index=False)\n",
"y_train.to_csv(\"data/y_train.csv\", index=False)\n",
"\n",
"ds = ws.get_default_datastore()\n",
"ds.upload(src_dir='./data', target_path='irisdata', overwrite=True, show_progress=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"import pkg_resources\n",
"\n",
"# create a new RunConfig object\n",
"conda_run_config = RunConfiguration(framework=\"python\")\n",
"\n",
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"\n",
"cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating a TabularDataset\n",
"\n",
"Defined X and y as `TabularDataset`s, which are passed to automated machine learning in the AutoMLConfig."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X = Dataset.Tabular.from_delimited_files(path=ds.path('irisdata/X_train.csv'))\n",
"y = Dataset.Tabular.from_delimited_files(path=ds.path('irisdata/y_train.csv'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train\n",
"\n",
"You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
"\n",
"**Note:** Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
"\n",
"**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
"|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
"|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
"|**n_cross_validations**|Number of cross validation splits.|\n",
"|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|\n",
"|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set the preprocess=True, currently the InferenceHelper only supports this mode."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 10,\n",
" \"iterations\": 10,\n",
" \"n_cross_validations\": 5,\n",
" \"primary_metric\": 'AUC_weighted',\n",
" \"preprocess\": True,\n",
" \"max_concurrent_iterations\": 5,\n",
" \"verbosity\": logging.INFO\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" path = project_folder,\n",
" run_configuration=conda_run_config,\n",
" X = X,\n",
" y = y,\n",
" enable_onnx_compatible_models=True, # This will generate ONNX compatible models.\n",
" **automl_settings\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
"In this example, we specify `show_output = False` to suppress console output while the run is in progress."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results\n",
"\n",
"#### Loading executed runs\n",
"In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Widget for Monitoring Runs\n",
"\n",
"The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
"\n",
"You can click on a pipeline to see run properties and output logs. Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
"\n",
"**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.widgets import RunDetails\n",
"RunDetails(remote_run).show() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Wait until the run finishes.\n",
"remote_run.wait_for_completion(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cancelling Runs\n",
"\n",
"You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Cancel the ongoing experiment and stop scheduling new iterations.\n",
"# remote_run.cancel()\n",
"\n",
"# Cancel iteration 1 and move onto iteration 2.\n",
"# remote_run.cancel_iteration(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Retrieve the Best ONNX Model\n",
"\n",
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*.\n",
"\n",
"Set the parameter return_onnx_model=True to retrieve the best ONNX model, instead of the Python model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"best_run, onnx_mdl = remote_run.get_output(return_onnx_model=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save the best ONNX model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.automl.core.onnx_convert import OnnxConverter\n",
"onnx_fl_path = \"./best_model.onnx\"\n",
"OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predict with the ONNX model, using onnxruntime package"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"import json\n",
"from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
"from azureml.train.automl import constants\n",
"\n",
"if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
" python_version_compatible = True\n",
"else:\n",
" python_version_compatible = False\n",
"\n",
"try:\n",
" import onnxruntime\n",
" from azureml.automl.core.onnx_convert import OnnxInferenceHelper \n",
" onnxrt_present = True\n",
"except ImportError:\n",
" onnxrt_present = False\n",
"\n",
"def get_onnx_res(run):\n",
" res_path = 'onnx_resource.json'\n",
" run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
" with open(res_path) as f:\n",
" return json.load(f)\n",
"\n",
"if onnxrt_present and python_version_compatible: \n",
" mdl_bytes = onnx_mdl.SerializeToString()\n",
" onnx_res = get_onnx_res(best_run)\n",
"\n",
" onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
" pred_onnx, pred_prob_onnx = onnxrt_helper.predict(X_test)\n",
"\n",
" print(pred_onnx)\n",
" print(pred_prob_onnx)\n",
"else:\n",
" if not python_version_compatible:\n",
" print('Please use Python version 3.6 or 3.7 to run the inference helper.') \n",
" if not onnxrt_present:\n",
" print('Please install the onnxruntime package to do the prediction with ONNX model.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

11
how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.yml Normal file
View File

@@ -0,0 +1,11 @@
name: auto-ml-remote-amlcompute-with-onnx
dependencies:
- pip:
- azureml-sdk
- azureml-defaults
- azureml-explain-model
- azureml-train-automl
- azureml-widgets
- matplotlib
- pandas_ml
- onnxruntime

29
how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.ipynb
View File

@@ -74,7 +74,6 @@
"source": [
"import logging\n",
"import os\n",
"import csv\n",
"\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
@@ -84,8 +83,8 @@
"import azureml.core\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig\n",
"import azureml.dataprep as dprep"
"from azureml.core.dataset import Dataset\n",
"from azureml.train.automl import AutoMLConfig"
]
},
{
@@ -137,7 +136,7 @@
"from azureml.core.compute import ComputeTarget\n",
"\n",
"# Choose a name for your cluster.\n",
"amlcompute_cluster_name = \"cpu-cluster\"\n",
"amlcompute_cluster_name = \"automlc2\"\n",
"\n",
"found = False\n",
"# Check if this compute target already exists in the workspace.\n",
@@ -156,11 +155,12 @@
" # Create the cluster.\\n\",\n",
" compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
"\n",
" # Can poll for a minimum number of nodes and for a specific timeout.\n",
" # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
" compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"print('Checking cluster status...')\n",
"# Can poll for a minimum number of nodes and for a specific timeout.\n",
"# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
"compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
"\n",
" # For a more detailed view of current AmlCompute status, use get_status()."
"# For a more detailed view of current AmlCompute status, use get_status()."
]
},
{
@@ -210,11 +210,8 @@
"# Set compute target to AmlCompute\n",
"conda_run_config.target = compute_target\n",
"conda_run_config.environment.docker.enabled = True\n",
"conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
"\n",
"dprep_dependency = 'azureml-dataprep==' + pkg_resources.get_distribution(\"azureml-dataprep\").version\n",
"\n",
"cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]', dprep_dependency], conda_packages=['numpy','py-xgboost<=0.80'])\n",
"cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
"conda_run_config.environment.python.conda_dependencies = cd"
]
},
@@ -222,9 +219,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dprep reference\n",
"### Creating TabularDataset\n",
"\n",
"Defined X and y as dprep references, which are passed to automated machine learning in the AutoMLConfig."
"Defined X and y as `TabularDataset`s, which are passed to Automated ML in the AutoMLConfig. `from_delimited_files` by default sets the `infer_column_types` to true, which will infer the columns type automatically. If you do wish to manually set the column types, you can set the `set_column_types` argument to manually set the type of each columns."
]
},
{
@@ -233,8 +230,8 @@
"metadata": {},
"outputs": [],
"source": [
"X = dprep.auto_read_file(path=ds.path('digitsdata/X_train.csv'))\n",
"y = dprep.auto_read_file(path=ds.path('digitsdata/y_train.csv'))"
"X = Dataset.Tabular.from_delimited_files(path=ds.path('digitsdata/X_train.csv'))\n",
"y = Dataset.Tabular.from_delimited_files(path=ds.path('digitsdata/y_train.csv'))"
]
},
{

2
how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.yml
View File

@@ -2,6 +2,8 @@ name: auto-ml-remote-amlcompute
dependencies:
- pip:
- azureml-sdk
- azureml-defaults
- azureml-explain-model
- azureml-train-automl
- azureml-widgets
- matplotlib

4
how-to-use-azureml/automated-machine-learning/sql-server/README.md
View File

@@ -87,7 +87,7 @@ These instruction setup the integration for SQL Server 2017 on Windows.
sudo /opt/mssql/mlservices/bin/python/python -m pip install --upgrade sklearn
```
7. Start SQL Server.
8. Execute the files aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql and AutoMLTrain.sql in SQL Server Management Studio.
8. Execute the files aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql, AutoMLForecast.sql and AutoMLTrain.sql in SQL Server Management Studio.
9. Create an Azure Machine Learning Workspace. You can use the instructions at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace)
10. Create a config.json file file using the subscription id, resource group name and workspace name that you use to create the workspace. The file is described at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace)
11. Create an Azure service principal. You can do this with the commands:
@@ -109,5 +109,5 @@ First you need to load the sample data in the database.
You can then run the queries in the energy-demand folder:
* TrainEnergyDemand.sql runs AutoML, trains multiple models on data and selects the best model.
* PredictEnergyDemand.sql predicts based on the most recent training run.
* ForecastEnergyDemand.sql forecasts based on the most recent training run.
* GetMetrics.sql returns all the metrics for each model in the most recent training run.

1
how-to-use-azureml/automated-machine-learning/sql-server/setup/auto-ml-sql-setup.ipynb
View File

@@ -342,7 +342,6 @@
" n_cross_validations = n_cross_validations, \r\n",
" preprocess = preprocess,\r\n",
" verbosity = logging.INFO, \r\n",
" enable_ensembling = False,\r\n",
" X = X_train, \r\n",
" y = y_train, \r\n",
" path = project_folder,\r\n",

68
how-to-use-azureml/azure-databricks/README.md
View File

@@ -1,33 +1,73 @@
Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster.
Azure Databricks is a managed Spark offering on Azure and customers already use it for advanced analytics. It provides a collaborative Notebook based environment with CPU or GPU based compute cluster.
In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks.
In this section, you will find sample notebooks on how to use Azure Machine Learning SDK with Azure Databricks. You can train a model using Spark MLlib and then deploy the model to ACI/AKS from within Azure Databricks. You can also use Automated ML capability (**public preview**) of Azure ML SDK with Azure Databricks.
- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning.
- You can keep the data within the same cluster.
- You can leverage the local worker nodes with autoscale and auto termination capabilities.
- You can use multiple cores of your Azure Databricks cluster to perform simultenous training.
- You can further tune the model generated by automated machine learning if you chose to.
- Every run (including the best run) is available as a pipeline, which you can tune further if needed.
- Customers who use Azure Databricks for advanced analytics can now use the same cluster to run experiments with or without automated machine learning.
- You can keep the data within the same cluster.
- You can leverage the local worker nodes with autoscale and auto termination capabilities.
- You can use multiple cores of your Azure Databricks cluster to perform simultenous training.
- You can further tune the model generated by automated machine learning if you chose to.
- Every run (including the best run) is available as a pipeline, which you can tune further if needed.
- The model trained using Azure Databricks can be registered in Azure ML SDK workspace and then deployed to Azure managed compute (ACI or AKS) using the Azure Machine learning SDK.
Please follow our [Azure doc](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#azure-databricks) to install the sdk in your Azure Databricks cluster before trying any of the sample notebooks.
**Single file** -
**Single file** -
The following archive contains all the sample notebooks. You can the run notebooks after importing [DBC](Databricks_AMLSDK_1-4_6.dbc) in your Databricks workspace instead of downloading individually.
Notebooks 1-4 have to be run sequentially & are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks.
Notebooks 1-4 have to be run sequentially & are related to Income prediction experiment based on this [dataset](https://archive.ics.uci.edu/ml/datasets/adult) and demonstrate how to data prep, train and operationalize a Spark ML model with Azure ML Python SDK from within Azure Databricks.
Notebook 6 is an Automated ML sample notebook for Classification.
Learn more about [how to use Azure Databricks as a development environment](https://docs.microsoft.com/azure/machine-learning/service/how-to-configure-environment#azure-databricks) for Azure Machine Learning service.
**Databricks as a Compute Target from AML Pipelines**
You can use Azure Databricks as a compute target from [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines). Take a look at this notebook for details: [aml-pipelines-use-databricks-as-compute-target.ipynb](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb).
**Databricks as a Compute Target from Azure ML Pipelines**
You can use Azure Databricks as a compute target from [Azure Machine Learning Pipelines](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines). Take a look at this notebook for details: [aml-pipelines-use-databricks-as-compute-target.ipynb](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb).
# Linked Azure Databricks and Azure Machine Learning Workspaces (Preview)
Customers can now link Azure Databricks and AzureML Workspaces to better enable cross-Azure ML scenarios by [managing their tracking data in a single place when using the MLflow client](https://mlflow.org/docs/latest/tracking.html#mlflow-tracking) - the Azure ML workspace.
## Linking the Workspaces (Admin operation)
1. The Azure Databricks Azure portal blade now includes a new button to link an Azure ML workspace.
![New ADB Portal Link button](./img/adb-link-button.png)
2. Both a new or existing Azure ML Workspace can be linked in the resulting prompt. Follow any instructions to set up the Azure ML Workspace.
![Link Prompt](./img/link-prompt.png)
3. After a successful link operation, you should see the Azure Databricks overview reflect the linked status
![Linked Successfully](./img/adb-successful-link.png)
## Configure MLflow to send data to Azure ML (All roles)
1. Add azureml-mlflow as a library to any notebook or cluster that should send data to Azure ML. You can do this via:
1. [DBUtils](https://docs.azuredatabricks.net/user-guide/dev-tools/dbutils.html#dbutils-library)
```
dbutils.library.installPyPI("azureml-mlflow")
dbutils.library.restartPython() # Removes Python state
```
2. [Cluster Libraries](https://docs.azuredatabricks.net/user-guide/libraries.html#install-a-library-on-a-cluster)
![Cluster Library](./img/cluster-library.png)
2. [Set the MLflow tracking URI](https://mlflow.org/docs/latest/tracking.html#where-runs-are-recorded) to the following scheme:
```
adbazureml://${azuremlRegion}.experiments.azureml.net/history/v1.0/subscriptions/${azuremlSubscriptionId}/resourceGroups/${azuremlResourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/${azuremlWorkspaceName}
```
1. You can automatically configure this on your clusters for all subsequent notebook sessions using this helper script instead of manually setting the tracking URI in the notebook:
* [AzureML Tracking Cluster Init Script](./linking/README.md)
3. If configured correctly, you'll now be able to see your MLflow tracking data in both Azure ML (via the REST API and all clients) and Azure Databricks (in the MLflow UI and using the MLflow client)
## Known Preview Limitations
While we roll this experience out to customers for feedback, there are some known limitations we'd love comments on in addition to any other issues seen in your workflow.
### 1-to-1 Workspace linking
Currently, an Azure ML Workspace can only be linked to one Azure Databricks Workspace at a time.
### Data synchronization
At the moment, data is only generated in the Azure Machine Learning workspace for tracking. Editing tags via the Azure Databricks MLflow UI won't be reflected in the Azure ML UI.
### Java and R support
The experience currently is only available from the Python MLflow client.
For more on SDK concepts, please refer to [notebooks](https://github.com/Azure/MachineLearningNotebooks).
**Please let us know your feedback.**
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/README.png)
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-databricks/README.png)

34
how-to-use-azureml/azure-databricks/automl/automl-databricks-local-01.ipynb
View File

@@ -314,25 +314,18 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Training Data Using DataPrep"
"## Load Training Data Using Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Automated ML takes a Dataflow as input.\n",
"Automated ML takes a `TabularDataset` as input.\n",
"\n",
"If you are familiar with Pandas and have done your data preparation work in Pandas already, you can use the `read_pandas_dataframe` method in dprep to convert the DataFrame to a Dataflow.\n",
"```python\n",
"df = pd.read_csv(...)\n",
"# apply some transforms\n",
"dprep.read_pandas_dataframe(df, temp_folder='/path/accessible/by/both/driver/and/worker')\n",
"```\n",
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
"\n",
"If you just need to ingest data without doing any preparation, you can directly use AzureML Data Prep (Data Prep) to do so. The code below demonstrates this scenario. Data Prep also has data preparation capabilities, we have many [sample notebooks](https://github.com/Microsoft/AMLDataPrepDocs) demonstrating the capabilities.\n",
"\n",
"You will get the datastore you registered previously and pass it to Data Prep for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
"You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
]
},
{
@@ -341,21 +334,21 @@
"metadata": {},
"outputs": [],
"source": [
"import azureml.dataprep as dprep\n",
"from azureml.core.dataset import Dataset\n",
"from azureml.data.datapath import DataPath\n",
"\n",
"datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
"\n",
"X_train = dprep.read_csv(datastore.path('X.csv'))\n",
"y_train = dprep.read_csv(datastore.path('y.csv')).to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
"X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
"y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Review the Data Preparation Result\n",
"You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
"## Review the TabularDataset\n",
"You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only j records for all the steps in the TabularDataset, which makes it fast even against large datasets."
]
},
{
@@ -364,7 +357,7 @@
"metadata": {},
"outputs": [],
"source": [
"X_train.get_profile()"
"X_train.take(5).to_pandas_dataframe()"
]
},
{
@@ -373,7 +366,7 @@
"metadata": {},
"outputs": [],
"source": [
"y_train.get_profile()"
"y_train.take(5).to_pandas_dataframe()"
]
},
{
@@ -593,7 +586,10 @@
"metadata": {},
"outputs": [],
"source": [
"fitted_model.named_steps['datatransformer'].get_featurization_summary()"
"# Get the featurization summary as a list of JSON\n",
"featurization_summary = fitted_model.named_steps['datatransformer'].get_featurization_summary()\n",
"# View the featurization summary as a pandas dataframe\n",
"pd.DataFrame.from_records(featurization_summary)"
]
},
{

29
how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
View File

@@ -331,25 +331,18 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Training Data Using DataPrep"
"## Load Training Data Using Dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Automated ML takes a Dataflow as input.\n",
"Automated ML takes a `TabularDataset` as input.\n",
"\n",
"If you are familiar with Pandas and have done your data preparation work in Pandas already, you can use the `read_pandas_dataframe` method in dprep to convert the DataFrame to a Dataflow.\n",
"```python\n",
"df = pd.read_csv(...)\n",
"# apply some transforms\n",
"dprep.read_pandas_dataframe(df, temp_folder='/path/accessible/by/both/driver/and/worker')\n",
"```\n",
"You are free to use the data preparation libraries/tools of your choice to do the require preparation and once you are done, you can write it to a datastore and create a TabularDataset from it.\n",
"\n",
"If you just need to ingest data without doing any preparation, you can directly use AzureML Data Prep (Data Prep) to do so. The code below demonstrates this scenario. Data Prep also has data preparation capabilities, we have many [sample notebooks](https://github.com/Microsoft/AMLDataPrepDocs) demonstrating the capabilities.\n",
"\n",
"You will get the datastore you registered previously and pass it to Data Prep for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
"You will get the datastore you registered previously and pass it to Dataset for reading. The data comes from the digits dataset: `sklearn.datasets.load_digits()`. `DataPath` points to a specific location within a datastore. "
]
},
{
@@ -358,21 +351,21 @@
"metadata": {},
"outputs": [],
"source": [
"import azureml.dataprep as dprep\n",
"from azureml.core.dataset import Dataset\n",
"from azureml.data.datapath import DataPath\n",
"\n",
"datastore = Datastore.get(workspace = ws, datastore_name = datastore_name)\n",
"\n",
"X_train = dprep.read_csv(datastore.path('X.csv'))\n",
"y_train = dprep.read_csv(datastore.path('y.csv')).to_long(dprep.ColumnSelector(term='.*', use_regex = True))"
"X_train = Dataset.Tabular.from_delimited_files(datastore.path('X.csv'))\n",
"y_train = Dataset.Tabular.from_delimited_files(datastore.path('y.csv'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Review the Data Preparation Result\n",
"You can peek the result of a Dataflow at any range using skip(i) and head(j). Doing so evaluates only j records for all the steps in the Dataflow, which makes it fast even against large datasets."
"## Review the TabularDataset\n",
"You can peek the result of a TabularDataset at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only j records for all the steps in the TabularDataset, which makes it fast even against large datasets."
]
},
{
@@ -381,7 +374,7 @@
"metadata": {},
"outputs": [],
"source": [
"X_train.get_profile()"
"X_train.take(5).to_pandas_dataframe()"
]
},
{
@@ -390,7 +383,7 @@
"metadata": {},
"outputs": [],
"source": [
"y_train.get_profile()"
"y_train.take(5).to_pandas_dataframe()"
]
},
{

4
how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb
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@@ -13,7 +13,7 @@
"metadata": {},
"source": [
"# Using Databricks as a Compute Target from Azure Machine Learning Pipeline\n",
"To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
"To use Databricks as a compute target from [Azure Machine Learning Pipeline](https://aka.ms/pl-concept), a [DatabricksStep](https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep?view=azure-ml-py) is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.\n",
"\n",
"The notebook will show:\n",
"1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace\n",
@@ -675,7 +675,7 @@
"metadata": {},
"source": [
"# Next: ADLA as a Compute Target\n",
"To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](./aml-pipelines-use-adla-as-compute-target.ipynb) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
"To use ADLA as a compute target from Azure Machine Learning Pipeline, a AdlaStep is used. This [notebook](https://aka.ms/pl-adla) demonstrates the use of AdlaStep in Azure Machine Learning Pipeline."
]
},
{

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# Adding an init script to an Azure Databricks cluster
The [azureml-cluster-init.sh](./azureml-cluster-init.sh) script configures the environment to
1. Use the configured AzureML Workspace with Workspace.from_config()
2. Set the default MLflow Tracking Server to be the AzureML managed one
Modify azureml-cluster-init.sh by providing the values for region, subscriptionId, resourceGroupName, and workspaceName of your target Azure ML workspace in the highlighted section at the top of the script.
To create the Azure Databricks cluster-scoped init script
1. Create the base directory you want to store the init script in if it does not exist.
```
dbutils.fs.mkdirs("dbfs:/databricks/<directory>/")
```
2. Create the script by copying the contents of azureml-cluster-init.sh
```
dbutils.fs.put("/databricks/<directory>/azureml-cluster-init.sh","""
<configured_contents_of_azureml-cluster-init.sh>
""", True)
3. Check that the script exists.
```
display(dbutils.fs.ls("dbfs:/databricks/<directory>/azureml-cluster-init.sh"))
```
1. Configure the cluster to run the script.
* Using the cluster configuration page
1. On the cluster configuration page, click the Advanced Options toggle.
1. At the bottom of the page, click the Init Scripts tab.
1. In the Destination drop-down, select a destination type. Example: 'DBFS'
1. Specify a path to the init script.
```
dbfs:/databricks/<directory>/azureml-cluster-init.sh
```
1. Click Add
* Using the API.
```
curl -n -X POST -H 'Content-Type: application/json' -d '{
"cluster_id": "<cluster_id>",
"num_workers": <num_workers>,
"spark_version": "<spark_version>",
"node_type_id": "<node_type_id>",
"cluster_log_conf": {
"dbfs" : {
"destination": "dbfs:/cluster-logs"
}
},
"init_scripts": [ {
"dbfs": {
"destination": "dbfs:/databricks/<directory>/azureml-cluster-init.sh"
}
} ]
}' https://<databricks-instance>/api/2.0/clusters/edit
```

24
how-to-use-azureml/azure-databricks/linking/azureml-cluster-init.sh Normal file
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@@ -0,0 +1,24 @@
#!/bin/bash
# This script configures the environment to
# 1. Use the configured AzureML Workspace with azureml.core.Workspace.from_config()
# 2. Set the default MLflow Tracking Server to be the AzureML managed one
############## START CONFIGURATION #################
# Provide the required *AzureML* workspace information
region="" # example: westus2
subscriptionId="" # example: bcb65f42-f234-4bff-91cf-9ef816cd9936
resourceGroupName="" # example: dev-rg
workspaceName="" # example: myazuremlws
# Optional config directory
configLocation="/databricks/config.json"
############### END CONFIGURATION #################
# Drop the workspace configuration on the cluster
sudo touch $configLocation
sudo echo {\\"subscription_id\\": \\"${subscriptionId}\\", \\"resource_group\\": \\"${resourceGroupName}\\", \\"workspace_name\\": \\"${workspaceName}\\"} > $configLocation
# Set the MLflow Tracking URI
trackingUri="adbazureml://${region}.experiments.azureml.net/history/v1.0/subscriptions/${subscriptionId}/resourceGroups/${resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/${workspaceName}"
sudo echo export MLFLOW_TRACKING_URI=${trackingUri} >> /databricks/spark/conf/spark-env.sh

709
how-to-use-azureml/data-drift/azure-ml-datadrift.ipynb
View File

@@ -1,709 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Track Data Drift between Training and Inference Data in Production \n",
"\n",
"With this notebook, you will learn how to enable the DataDrift service to automatically track and determine whether your inference data is drifting from the data your model was initially trained on. The DataDrift service provides metrics and visualizations to help stakeholders identify which specific features cause the concept drift to occur.\n",
"\n",
"Please email driftfeedback@microsoft.com with any issues. A member from the DataDrift team will respond shortly. \n",
"\n",
"The DataDrift Public Preview API can be found [here](https://docs.microsoft.com/en-us/python/api/azureml-contrib-datadrift/?view=azure-ml-py). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/datadrift/azureml-datadrift.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prerequisites and Setup"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Install the DataDrift package\n",
"\n",
"Install the azureml-contrib-datadrift, azureml-contrib-opendatasets and lightgbm packages before running this notebook.\n",
"```\n",
"pip install azureml-contrib-datadrift\n",
"pip install azureml-contrib-datasets\n",
"pip install lightgbm\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import os\n",
"import time\n",
"from datetime import datetime, timedelta\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import requests\n",
"from azureml.contrib.datadrift import DataDriftDetector, AlertConfiguration\n",
"from azureml.contrib.opendatasets import NoaaIsdWeather\n",
"from azureml.core import Dataset, Workspace, Run\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"from azureml.core.experiment import Experiment\n",
"from azureml.core.image import ContainerImage\n",
"from azureml.core.model import Model\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.widgets import RunDetails\n",
"from sklearn.externals import joblib\n",
"from sklearn.model_selection import train_test_split\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up Configuraton and Create Azure ML Workspace\n",
"\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Please type in your initials/alias. The prefix is prepended to the names of resources created by this notebook. \n",
"prefix = \"dd\"\n",
"\n",
"# NOTE: Please do not change the model_name, as it's required by the score.py file\n",
"model_name = \"driftmodel\"\n",
"image_name = \"{}driftimage\".format(prefix)\n",
"service_name = \"{}driftservice\".format(prefix)\n",
"\n",
"# optionally, set email address to receive an email alert for DataDrift\n",
"email_address = \"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate Train/Testing Data\n",
"\n",
"For this demo, we will use NOAA weather data from [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/). You may replace this step with your own dataset. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"usaf_list = ['725724', '722149', '723090', '722159', '723910', '720279',\n",
" '725513', '725254', '726430', '720381', '723074', '726682',\n",
" '725486', '727883', '723177', '722075', '723086', '724053',\n",
" '725070', '722073', '726060', '725224', '725260', '724520',\n",
" '720305', '724020', '726510', '725126', '722523', '703333',\n",
" '722249', '722728', '725483', '722972', '724975', '742079',\n",
" '727468', '722193', '725624', '722030', '726380', '720309',\n",
" '722071', '720326', '725415', '724504', '725665', '725424',\n",
" '725066']\n",
"\n",
"columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation', 'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']\n",
"\n",
"def enrich_weather_noaa_data(noaa_df):\n",
" hours_in_day = 23\n",
" week_in_year = 52\n",
" \n",
"\n",
" noaa_df = noaa_df.assign(hour=noaa_df[\"datetime\"].dt.hour,\n",
" weekofyear=noaa_df[\"datetime\"].dt.week,\n",
" sine_weekofyear=noaa_df['datetime'].transform(lambda x: np.sin((2*np.pi*x.dt.week-1)/week_in_year)),\n",
" cosine_weekofyear=noaa_df['datetime'].transform(lambda x: np.cos((2*np.pi*x.dt.week-1)/week_in_year)),\n",
" sine_hourofday=noaa_df['datetime'].transform(lambda x: np.sin(2*np.pi*x.dt.hour/hours_in_day)),\n",
" cosine_hourofday=noaa_df['datetime'].transform(lambda x: np.cos(2*np.pi*x.dt.hour/hours_in_day))\n",
" )\n",
" \n",
" return noaa_df\n",
"\n",
"\n",
"def add_window_col(input_df):\n",
" shift_interval = pd.Timedelta('-7 days') # your X days interval\n",
" df_shifted = input_df.copy()\n",
" df_shifted.loc[:,'datetime'] = df_shifted['datetime'] - shift_interval\n",
" df_shifted.drop(list(input_df.columns.difference(['datetime', 'usaf', 'wban', 'sine_hourofday', 'temperature'])), axis=1, inplace=True)\n",
"\n",
" # merge, keeping only observations where -1 lag is present\n",
" df2 = pd.merge(input_df,\n",
" df_shifted,\n",
" on=['datetime', 'usaf', 'wban', 'sine_hourofday'],\n",
" how='inner', # use 'left' to keep observations without lags\n",
" suffixes=['', '-7'])\n",
" return df2\n",
"\n",
"def get_noaa_data(start_time, end_time, cols, station_list):\n",
" isd = NoaaIsdWeather(start_time, end_time, cols=cols)\n",
" # Read into Pandas data frame.\n",
" noaa_df = isd.to_pandas_dataframe()\n",
" noaa_df = noaa_df.rename(columns={\"stationName\": \"station_name\"})\n",
" \n",
" df_filtered = noaa_df[noaa_df[\"usaf\"].isin(station_list)]\n",
" df_filtered.reset_index(drop=True)\n",
" \n",
" # Enrich with time features\n",
" df_enriched = enrich_weather_noaa_data(df_filtered)\n",
" \n",
" return df_enriched\n",
"\n",
"def get_featurized_noaa_df(start_time, end_time, cols, station_list):\n",
" df_1 = get_noaa_data(start_time - timedelta(days=7), start_time - timedelta(seconds=1), cols, station_list)\n",
" df_2 = get_noaa_data(start_time, end_time, cols, station_list)\n",
" noaa_df = pd.concat([df_1, df_2])\n",
" \n",
" print(\"Adding window feature\")\n",
" df_window = add_window_col(noaa_df)\n",
" \n",
" cat_columns = df_window.dtypes == object\n",
" cat_columns = cat_columns[cat_columns == True]\n",
" \n",
" print(\"Encoding categorical columns\")\n",
" df_encoded = pd.get_dummies(df_window, columns=cat_columns.keys().tolist())\n",
" \n",
" print(\"Dropping unnecessary columns\")\n",
" df_featurized = df_encoded.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna().drop_duplicates()\n",
" \n",
" return df_featurized"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Train model on Jan 1 - 14, 2009 data\n",
"df = get_featurized_noaa_df(datetime(2009, 1, 1), datetime(2009, 1, 14, 23, 59, 59), columns, usaf_list)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"label = \"temperature\"\n",
"x_df = df.drop(label, axis=1)\n",
"y_df = df[[label]]\n",
"x_train, x_test, y_train, y_test = train_test_split(df, y_df, test_size=0.2, random_state=223)\n",
"print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n",
"\n",
"training_dir = 'outputs/training'\n",
"training_file = \"training.csv\"\n",
"\n",
"# Generate training dataframe to register as Training Dataset\n",
"os.makedirs(training_dir, exist_ok=True)\n",
"training_df = pd.merge(x_train.drop(label, axis=1), y_train, left_index=True, right_index=True)\n",
"training_df.to_csv(training_dir + \"/\" + training_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create/Register Training Dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset_name = \"dataset\"\n",
"name_suffix = datetime.utcnow().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
"snapshot_name = \"snapshot-{}\".format(name_suffix)\n",
"\n",
"dstore = ws.get_default_datastore()\n",
"dstore.upload(training_dir, \"data/training\", show_progress=True)\n",
"dpath = dstore.path(\"data/training/training.csv\")\n",
"trainingDataset = Dataset.auto_read_files(dpath, include_path=True)\n",
"trainingDataset = trainingDataset.register(workspace=ws, name=dataset_name, description=\"dset\", exist_ok=True)\n",
"\n",
"trainingDataSnapshot = trainingDataset.create_snapshot(snapshot_name=snapshot_name, compute_target=None, create_data_snapshot=True)\n",
"datasets = [(Dataset.Scenario.TRAINING, trainingDataSnapshot)]\n",
"print(\"dataset registration done.\\n\")\n",
"datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train and Save Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import lightgbm as lgb\n",
"\n",
"train = lgb.Dataset(data=x_train, \n",
" label=y_train)\n",
"\n",
"test = lgb.Dataset(data=x_test, \n",
" label=y_test,\n",
" reference=train)\n",
"\n",
"params = {'learning_rate' : 0.1,\n",
" 'boosting' : 'gbdt',\n",
" 'metric' : 'rmse',\n",
" 'feature_fraction' : 1,\n",
" 'bagging_fraction' : 1,\n",
" 'max_depth': 6,\n",
" 'num_leaves' : 31,\n",
" 'objective' : 'regression',\n",
" 'bagging_freq' : 1,\n",
" \"verbose\": -1,\n",
" 'min_data_per_leaf': 100}\n",
"\n",
"model = lgb.train(params, \n",
" num_boost_round=500,\n",
" train_set=train,\n",
" valid_sets=[train, test],\n",
" verbose_eval=50,\n",
" early_stopping_rounds=25)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model_file = 'outputs/{}.pkl'.format(model_name)\n",
"\n",
"os.makedirs('outputs', exist_ok=True)\n",
"joblib.dump(model, model_file)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Register Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = Model.register(model_path=model_file,\n",
" model_name=model_name,\n",
" workspace=ws,\n",
" datasets=datasets)\n",
"\n",
"print(model_name, image_name, service_name, model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Deploy Model To AKS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prepare Environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn', 'joblib', 'lightgbm', 'pandas'],\n",
" pip_packages=['azureml-monitoring', 'azureml-sdk[automl]'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Image creation may take up to 15 minutes.\n",
"\n",
"image_name = image_name + str(model.version)\n",
"\n",
"if not image_name in ws.images:\n",
" # Use the score.py defined in this directory as the execution script\n",
" # NOTE: The Model Data Collector must be enabled in the execution script for DataDrift to run correctly\n",
" image_config = ContainerImage.image_configuration(execution_script=\"score.py\",\n",
" runtime=\"python\",\n",
" conda_file=\"myenv.yml\",\n",
" description=\"Image with weather dataset model\")\n",
" image = ContainerImage.create(name=image_name,\n",
" models=[model],\n",
" image_config=image_config,\n",
" workspace=ws)\n",
"\n",
" image.wait_for_creation(show_output=True)\n",
"else:\n",
" image = ws.images[image_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Compute Target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_name = 'dd-demo-e2e'\n",
"prov_config = AksCompute.provisioning_configuration()\n",
"\n",
"if not aks_name in ws.compute_targets:\n",
" aks_target = ComputeTarget.create(workspace=ws,\n",
" name=aks_name,\n",
" provisioning_configuration=prov_config)\n",
"\n",
" aks_target.wait_for_completion(show_output=True)\n",
" print(aks_target.provisioning_state)\n",
" print(aks_target.provisioning_errors)\n",
"else:\n",
" aks_target=ws.compute_targets[aks_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy Service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"aks_service_name = service_name\n",
"\n",
"if not aks_service_name in ws.webservices:\n",
" aks_config = AksWebservice.deploy_configuration(collect_model_data=True, enable_app_insights=True)\n",
" aks_service = Webservice.deploy_from_image(workspace=ws,\n",
" name=aks_service_name,\n",
" image=image,\n",
" deployment_config=aks_config,\n",
" deployment_target=aks_target)\n",
" aks_service.wait_for_deployment(show_output=True)\n",
" print(aks_service.state)\n",
"else:\n",
" aks_service = ws.webservices[aks_service_name]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run DataDrift Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Send Scoring Data to Service"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download Scoring Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Score Model on March 15, 2016 data\n",
"scoring_df = get_noaa_data(datetime(2016, 3, 15) - timedelta(days=7), datetime(2016, 3, 16), columns, usaf_list)\n",
"# Add the window feature column\n",
"scoring_df = add_window_col(scoring_df)\n",
"\n",
"# Drop features not used by the model\n",
"print(\"Dropping unnecessary columns\")\n",
"scoring_df = scoring_df.drop(['windAngle', 'windSpeed', 'datetime', 'elevation'], axis=1).dropna()\n",
"scoring_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# One Hot Encode the scoring dataset to match the training dataset schema\n",
"columns_dict = model.datasets[\"training\"][0].get_profile().columns\n",
"extra_cols = ('Path', 'Column1')\n",
"for k in extra_cols:\n",
" columns_dict.pop(k, None)\n",
"training_columns = list(columns_dict.keys())\n",
"\n",
"categorical_columns = scoring_df.dtypes == object\n",
"categorical_columns = categorical_columns[categorical_columns == True]\n",
"\n",
"test_df = pd.get_dummies(scoring_df[categorical_columns.keys().tolist()])\n",
"encoded_df = scoring_df.join(test_df)\n",
"\n",
"# Populate missing OHE columns with 0 values to match traning dataset schema\n",
"difference = list(set(training_columns) - set(encoded_df.columns.tolist()))\n",
"for col in difference:\n",
" encoded_df[col] = 0\n",
"encoded_df.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Serialize dataframe to list of row dictionaries\n",
"encoded_dict = encoded_df.to_dict('records')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit Scoring Data to Service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"\n",
"# retreive the API keys. AML generates two keys.\n",
"key1, key2 = aks_service.get_keys()\n",
"\n",
"total_count = len(scoring_df)\n",
"i = 0\n",
"load = []\n",
"for row in encoded_dict:\n",
" load.append(row)\n",
" i = i + 1\n",
" if i % 100 == 0:\n",
" payload = json.dumps({\"data\": load})\n",
" \n",
" # construct raw HTTP request and send to the service\n",
" payload_binary = bytes(payload,encoding = 'utf8')\n",
" headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
" resp = requests.post(aks_service.scoring_uri, payload_binary, headers=headers)\n",
" \n",
" print(\"prediction:\", resp.content, \"Progress: {}/{}\".format(i, total_count)) \n",
"\n",
" load = []\n",
" time.sleep(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure DataDrift"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"services = [service_name]\n",
"start = datetime.now() - timedelta(days=2)\n",
"end = datetime(year=2020, month=1, day=22, hour=15, minute=16)\n",
"feature_list = ['usaf', 'wban', 'latitude', 'longitude', 'station_name', 'p_k', 'sine_hourofday', 'cosine_hourofday', 'temperature-7']\n",
"alert_config = AlertConfiguration([email_address]) if email_address else None\n",
"\n",
"# there will be an exception indicating using get() method if DataDrift object already exist\n",
"try:\n",
" datadrift = DataDriftDetector.create(ws, model.name, model.version, services, frequency=\"Day\", alert_config=alert_config)\n",
"except KeyError:\n",
" datadrift = DataDriftDetector.get(ws, model.name, model.version)\n",
" \n",
"print(\"Details of DataDrift Object:\\n{}\".format(datadrift))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run an Adhoc DataDriftDetector Run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"target_date = datetime.today()\n",
"run = datadrift.run(target_date, services, feature_list=feature_list, create_compute_target=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"exp = Experiment(ws, datadrift._id)\n",
"dd_run = Run(experiment=exp, run_id=run)\n",
"RunDetails(dd_run).show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get Drift Analysis Results"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"children = list(dd_run.get_children())\n",
"for child in children:\n",
" child.wait_for_completion()\n",
"\n",
"drift_metrics = datadrift.get_output(start_time=start, end_time=end)\n",
"drift_metrics"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Show all drift figures, one per serivice.\n",
"# If setting with_details is False (by default), only drift will be shown; if it's True, all details will be shown.\n",
"\n",
"drift_figures = datadrift.show(with_details=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Enable DataDrift Schedule"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datadrift.enable_schedule()"
]
}
],
"metadata": {
"authors": [
{
"name": "rafarmah"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

3
how-to-use-azureml/data-drift/readme.md
View File

@@ -1,3 +0,0 @@
## Using data drift APIs
1. [Detect data drift for a model](azure-ml-datadrift.ipynb): Detect data drift for a deployed model.

58
how-to-use-azureml/data-drift/score.py
View File

@@ -1,58 +0,0 @@
import pickle
import json
import numpy
import azureml.train.automl
from sklearn.externals import joblib
from sklearn.linear_model import Ridge
from azureml.core.model import Model
from azureml.core.run import Run
from azureml.monitoring import ModelDataCollector
import time
import pandas as pd
def init():
global model, inputs_dc, prediction_dc, feature_names, categorical_features
print("Model is initialized" + time.strftime("%H:%M:%S"))
model_path = Model.get_model_path(model_name="driftmodel")
model = joblib.load(model_path)
feature_names = ["usaf", "wban", "latitude", "longitude", "station_name", "p_k",
"sine_weekofyear", "cosine_weekofyear", "sine_hourofday", "cosine_hourofday",
"temperature-7"]
categorical_features = ["usaf", "wban", "p_k", "station_name"]
inputs_dc = ModelDataCollector(model_name="driftmodel",
identifier="inputs",
feature_names=feature_names)
prediction_dc = ModelDataCollector("driftmodel",
identifier="predictions",
feature_names=["temperature"])
def run(raw_data):
global inputs_dc, prediction_dc
try:
data = json.loads(raw_data)["data"]
data = pd.DataFrame(data)
# Remove the categorical features as the model expects OHE values
input_data = data.drop(categorical_features, axis=1)
result = model.predict(input_data)
# Collect the non-OHE dataframe
collected_df = data[feature_names]
inputs_dc.collect(collected_df.values)
prediction_dc.collect(result)
return result.tolist()
except Exception as e:
error = str(e)
print(error + time.strftime("%H:%M:%S"))
return error

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217
how-to-use-azureml/deployment/accelerated-models/NOTICE.txt Normal file
View File

@@ -0,0 +1,217 @@
NOTICES AND INFORMATION
Do Not Translate or Localize
This Azure Machine Learning service example notebooks repository includes material from the projects listed below.
1. SSD-Tensorflow (https://github.com/balancap/ssd-tensorflow)
%% SSD-Tensorflow NOTICES AND INFORMATION BEGIN HERE
=========================================
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where such license applies only to those patent claims licensable
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or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
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as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
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may provide additional or different license terms and conditions
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Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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Unless required by applicable law or agreed to in writing, software
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END OF SSD-Tensorflow NOTICES AND INFORMATION

2
how-to-use-azureml/deployment/accelerated-models/README.md
View File

@@ -12,7 +12,7 @@ Easily create and train a model using various deep neural networks (DNNs) as a f
To learn more about the azureml-accel-model classes, see the section [Model Classes](#model-classes) below or the [Azure ML Accel Models SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py).
### Step 1: Create an Azure ML workspace
Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/quickstart-create-workspace-with-python) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
### Step 2: Check your FPGA quota
Use the Azure CLI to check whether you have quota.

43
how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.ipynb
View File

@@ -1,5 +1,12 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -230,11 +237,14 @@
"\n",
"# Convert model\n",
"convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors_str)\n",
"# If it fails, you can run wait_for_completion again with show_output=True.\n",
"convert_request.wait_for_completion(show_output=False)\n",
"converted_model = convert_request.result\n",
"print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')\n",
"if convert_request.wait_for_completion(show_output = False):\n",
" # If the above call succeeded, get the converted model\n",
" converted_model = convert_request.result\n",
" print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')\n",
"else:\n",
" print(\"Model conversion failed. Showing output.\")\n",
" convert_request.wait_for_completion(show_output = True)\n",
"\n",
"# Package into AccelContainerImage\n",
"image_config = AccelContainerImage.image_configuration()\n",
@@ -298,6 +308,7 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
@@ -316,6 +327,7 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"\n",
"# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
@@ -324,7 +336,7 @@
" num_replicas=1,\n",
" auth_enabled = False)\n",
"\n",
"aks_service_name ='my-aks-service'\n",
"aks_service_name ='my-aks-service-3'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws,\n",
" name = aks_service_name,\n",
@@ -342,10 +354,9 @@
"## 5. Test the service\n",
"<a id=\"create-client\"></a>\n",
"### 5.a. Create Client\n",
"The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions. \n",
"\n",
"**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).",
"The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
"\n",
"**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
"**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
]
},
@@ -356,18 +367,10 @@
"outputs": [],
"source": [
"# Using the grpc client in AzureML Accelerated Models SDK\n",
"from azureml.accel.client import PredictionClient\n",
"\n",
"address = aks_service.scoring_uri\n",
"ssl_enabled = address.startswith(\"https\")\n",
"address = address[address.find('/')+2:].strip('/')\n",
"port = 443 if ssl_enabled else 80\n",
"from azureml.accel import client_from_service\n",
"\n",
"# Initialize AzureML Accelerated Models client\n",
"client = PredictionClient(address=address,\n",
" port=port,\n",
" use_ssl=ssl_enabled,\n",
" service_name=aks_service.name)"
"client = client_from_service(aks_service)"
]
},
{
@@ -486,7 +489,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
"version": "3.5.6"
}
},
"nbformat": 4,

8
how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.yml Normal file
View File

@@ -0,0 +1,8 @@
name: accelerated-models-object-detection
dependencies:
- pip:
- azureml-sdk
- azureml-accel-models
- tensorflow
- opencv-python
- matplotlib

47
how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.ipynb
View File

@@ -1,5 +1,12 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -270,12 +277,15 @@
"from azureml.accel import AccelOnnxConverter\n",
"\n",
"convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
"# If it fails, you can run wait_for_completion again with show_output=True.\n",
"convert_request.wait_for_completion(show_output = False)\n",
"# If the above call succeeded, get the converted model\n",
"converted_model = convert_request.result\n",
"print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')"
"\n",
"if convert_request.wait_for_completion(show_output = False):\n",
" # If the above call succeeded, get the converted model\n",
" converted_model = convert_request.result\n",
" print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')\n",
"else:\n",
" print(\"Model conversion failed. Showing output.\")\n",
" convert_request.wait_for_completion(show_output = True)"
]
},
{
@@ -366,6 +376,7 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
@@ -384,15 +395,16 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"\n",
"#Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
"# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
"# Authentication is enabled by default, but for testing we specify False\n",
"aks_config = AksWebservice.deploy_configuration(autoscale_enabled=False,\n",
" num_replicas=1,\n",
" auth_enabled = False)\n",
"\n",
"aks_service_name ='my-aks-service'\n",
"aks_service_name ='my-aks-service-1'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws,\n",
" name = aks_service_name,\n",
@@ -415,10 +427,9 @@
"metadata": {},
"source": [
"### 7.a. Create Client\n",
"The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions.\n",
"\n",
"**Note:** If you chose to use auth_enabled=True when creating your AksWebservice, see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).",
"The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
"\n",
"**Note:** If you chose to use auth_enabled=True when creating your AksWebservice, see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
"**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
]
},
@@ -429,18 +440,10 @@
"outputs": [],
"source": [
"# Using the grpc client in AzureML Accelerated Models SDK\n",
"from azureml.accel.client import PredictionClient\n",
"\n",
"address = aks_service.scoring_uri\n",
"ssl_enabled = address.startswith(\"https\")\n",
"address = address[address.find('/')+2:].strip('/')\n",
"port = 443 if ssl_enabled else 80\n",
"from azureml.accel import client_from_service\n",
"\n",
"# Initialize AzureML Accelerated Models client\n",
"client = PredictionClient(address=address,\n",
" port=port,\n",
" use_ssl=ssl_enabled,\n",
" service_name=aks_service.name)"
"client = client_from_service(aks_service)"
]
},
{
@@ -540,7 +543,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
"version": "3.7.3"
}
},
"nbformat": 4,

6
how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.yml Normal file
View File

@@ -0,0 +1,6 @@
name: accelerated-models-quickstart
dependencies:
- pip:
- azureml-sdk
- azureml-accel-models
- tensorflow

44
how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.ipynb
View File

@@ -1,5 +1,12 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -410,6 +417,7 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"# Launch the training\n",
"tf.reset_default_graph()\n",
"sess = tf.Session(graph=tf.get_default_graph())\n",
@@ -582,11 +590,14 @@
"\n",
"# Convert model\n",
"convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensors, output_tensors)\n",
"# If it fails, you can run wait_for_completion again with show_output=True.\n",
"convert_request.wait_for_completion(show_output=False)\n",
"converted_model = convert_request.result\n",
"print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')\n",
"if convert_request.wait_for_completion(show_output = False):\n",
" # If the above call succeeded, get the converted model\n",
" converted_model = convert_request.result\n",
" print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')\n",
"else:\n",
" print(\"Model conversion failed. Showing output.\")\n",
" convert_request.wait_for_completion(show_output = True)\n",
"\n",
"# Package into AccelContainerImage\n",
"image_config = AccelContainerImage.image_configuration()\n",
@@ -655,6 +666,7 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"aks_target.wait_for_completion(show_output = True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
@@ -673,6 +685,7 @@
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"\n",
"# Set the web service configuration (for creating a test service, we don't want autoscale enabled)\n",
@@ -681,7 +694,7 @@
" num_replicas=1,\n",
" auth_enabled = False)\n",
"\n",
"aks_service_name ='my-aks-service'\n",
"aks_service_name ='my-aks-service-2'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws,\n",
" name = aks_service_name,\n",
@@ -700,10 +713,9 @@
"\n",
"<a id=\"create-client\"></a>\n",
"### 9.a. Create Client\n",
"The image supports gRPC and the TensorFlow Serving \"predict\" API. We have a client that can call into the docker image to get predictions. \n",
"\n",
"**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).",
"The image supports gRPC and the TensorFlow Serving \"predict\" API. We will create a PredictionClient from the Webservice object that can call into the docker image to get predictions. If you do not have the Webservice object, you can also create [PredictionClient](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.predictionclient?view=azure-ml-py) directly.\n",
"\n",
"**Note:** If you chose to use auth_enabled=True when creating your AksWebservice.deploy_configuration(), see documentation [here](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.webservice(class)?view=azure-ml-py#get-keys--) on how to retrieve your keys and use either key as an argument to PredictionClient(...,access_token=key).\n",
"**WARNING:** If you are running on Azure Notebooks free compute, you will not be able to make outgoing calls to your service. Try locating your client on a different machine to consume it."
]
},
@@ -714,18 +726,10 @@
"outputs": [],
"source": [
"# Using the grpc client in AzureML Accelerated Models SDK\n",
"from azureml.accel.client import PredictionClient\n",
"\n",
"address = aks_service.scoring_uri\n",
"ssl_enabled = address.startswith(\"https\")\n",
"address = address[address.find('/')+2:].strip('/')\n",
"port = 443 if ssl_enabled else 80\n",
"from azureml.accel import client_from_service\n",
"\n",
"# Initialize AzureML Accelerated Models client\n",
"client = PredictionClient(address=address,\n",
" port=port,\n",
" use_ssl=ssl_enabled,\n",
" service_name=aks_service.name)"
"client = client_from_service(aks_service)"
]
},
{
@@ -854,7 +858,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
"version": "3.5.6"
}
},
"nbformat": 4,

9
how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.yml Normal file
View File

@@ -0,0 +1,9 @@
name: accelerated-models-training
dependencies:
- pip:
- azureml-sdk
- azureml-accel-models
- tensorflow
- keras
- tqdm
- sklearn

0
how-to-use-azureml/deploy-to-cloud/README.md → how-to-use-azureml/deployment/deploy-to-cloud/README.md
View File

0
how-to-use-azureml/deploy-to-cloud/helloworld.txt → how-to-use-azureml/deployment/deploy-to-cloud/helloworld.txt
View File

88
how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.ipynb → how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
View File

@@ -13,7 +13,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/deploy-to-cloud/model-register-and-deploy.png)"
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.png)"
]
},
{
@@ -77,7 +77,7 @@
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
]
},
{
@@ -108,11 +108,41 @@
"source": [
"from azureml.core.model import Model\n",
"\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
" model_name = \"sklearn_regression_model.pkl\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)"
"model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
" model_name=\"sklearn_regression_model.pkl\",\n",
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
" description=\"Ridge regression model to predict diabetes\",\n",
" workspace=ws)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Environment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can now create and/or use an Environment object when deploying a Webservice. The Environment can have been previously registered with your Workspace, or it will be registered with it as a part of the Webservice deployment. Only Environments that were created using azureml-defaults version 1.0.48 or later will work with this new handling however.\n",
"\n",
"More information can be found in our [using environments notebook](../training/using-environments/using-environments.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Environment\n",
"\n",
"env = Environment.from_conda_specification(name='deploytocloudenv', file_path='myenv.yml')\n",
"\n",
"# This is optional at this point\n",
"# env.register(workspace=ws)"
]
},
{
@@ -153,10 +183,7 @@
"source": [
"from azureml.core.model import InferenceConfig\n",
"\n",
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score.py\",\n",
" conda_file=\"myenv.yml\", \n",
" extra_docker_file_steps=\"helloworld.txt\")"
"inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)"
]
},
{
@@ -171,13 +198,17 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"azuremlexception-remarks-sample"
]
},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice, Webservice\n",
"from azureml.exceptions import WebserviceException\n",
"\n",
"deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)\n",
"deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)\n",
"aci_service_name = 'aciservice1'\n",
"\n",
"try:\n",
@@ -215,7 +246,7 @@
" [10,9,8,7,6,5,4,3,2,1]\n",
"]})\n",
"\n",
"test_sample_encoded = bytes(test_sample,encoding = 'utf8')\n",
"test_sample_encoded = bytes(test_sample, encoding='utf8')\n",
"prediction = service.run(input_data=test_sample_encoded)\n",
"print(prediction)"
]
@@ -247,15 +278,38 @@
"source": [
"### Model Profiling\n",
"\n",
"you can also take advantage of profiling feature for model\n",
"You can also take advantage of the profiling feature to estimate CPU and memory requirements for models.\n",
"\n",
"```python\n",
"\n",
"profile = model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
"profile = Model.profile(ws, \"profilename\", [model], inference_config, test_sample)\n",
"profile.wait_for_profiling(True)\n",
"profiling_results = profile.get_results()\n",
"print(profiling_results)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Model Packaging\n",
"\n",
"If you want to build a Docker image that encapsulates your model and its dependencies, you can use the model packaging option. The output image will be pushed to your workspace's ACR.\n",
"\n",
"You must include an Environment object in your inference configuration to use `Model.package()`.\n",
"\n",
"```python\n",
"package = Model.package(ws, [model], inference_config)\n",
"package.wait_for_creation(show_output=True) # Or show_output=False to hide the Docker build logs.\n",
"package.pull()\n",
"```\n",
"\n",
"Instead of a fully-built image, you can also generate a Dockerfile and download all the assets needed to build an image on top of your Environment.\n",
"\n",
"```python\n",
"package = Model.package(ws, [model], inference_config, generate_dockerfile=True)\n",
"package.wait_for_creation(show_output=True)\n",
"package.save(\"./local_context_dir\")\n",
"```"
]
}

0
how-to-use-azureml/deploy-to-cloud/model-register-and-deploy.yml → how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.yml
View File

0
how-to-use-azureml/deploy-to-cloud/myenv.yml → how-to-use-azureml/deployment/deploy-to-cloud/myenv.yml
View File

0
how-to-use-azureml/deploy-to-cloud/score.py → how-to-use-azureml/deployment/deploy-to-cloud/score.py
View File

0
how-to-use-azureml/deploy-to-cloud/sklearn_regression_model.pkl → how-to-use-azureml/deployment/deploy-to-cloud/sklearn_regression_model.pkl
View File

0
how-to-use-azureml/deploy-to-local/README.md → how-to-use-azureml/deployment/deploy-to-local/README.md
View File

0
how-to-use-azureml/deploy-to-local/helloworld.txt → how-to-use-azureml/deployment/deploy-to-local/helloworld.txt
View File

0
how-to-use-azureml/deploy-to-local/myenv.yml → how-to-use-azureml/deployment/deploy-to-local/myenv.yml
View File

50
how-to-use-azureml/deploy-to-local/register-model-deploy-local-advanced.ipynb → how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.ipynb
View File

@@ -13,7 +13,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deploy-to-local/register-model-deploy-local-advanced.png)"
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local-advanced.png)"
]
},
{
@@ -72,7 +72,7 @@
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
]
},
{
@@ -103,11 +103,11 @@
"source": [
"from azureml.core.model import Model\n",
"\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
" model_name = \"sklearn_regression_model.pkl\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)"
"model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
" model_name=\"sklearn_regression_model.pkl\",\n",
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
" description=\"Ridge regression model to predict diabetes\",\n",
" workspace=ws)"
]
},
{
@@ -127,10 +127,10 @@
"\n",
"source_directory = \"C:/abc\"\n",
"\n",
"os.makedirs(source_directory, exist_ok = True)\n",
"os.makedirs(\"C:/abc/x/y\", exist_ok = True)\n",
"os.makedirs(\"C:/abc/env\", exist_ok = True)\n",
"os.makedirs(\"C:/abc/dockerstep\", exist_ok = True)"
"os.makedirs(source_directory, exist_ok=True)\n",
"os.makedirs(\"C:/abc/x/y\", exist_ok=True)\n",
"os.makedirs(\"C:/abc/env\", exist_ok=True)\n",
"os.makedirs(\"C:/abc/dockerstep\", exist_ok=True)"
]
},
{
@@ -253,7 +253,7 @@
"from azureml.core.model import InferenceConfig\n",
"\n",
"inference_config = InferenceConfig(source_directory=\"C:/abc\",\n",
" runtime= \"python\", \n",
" runtime=\"python\", \n",
" entry_script=\"x/y/score.py\",\n",
" conda_file=\"env/myenv.yml\", \n",
" extra_docker_file_steps=\"dockerstep/customDockerStep.txt\")"
@@ -271,15 +271,10 @@
"\n",
"NOTE:\n",
"\n",
"we require docker running with linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running\n",
"The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
"\n",
" powershell command to switch to linux engine\n",
" & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine\n",
"\n",
"and c drive is shared https://docs.docker.com/docker-for-windows/#shared-drives\n",
"sometimes you have to reshare c drive as docker \n",
"\n",
"<img src=\"./dockerSharedDrive.JPG\" align=\"left\"/>"
" # PowerShell command to switch to Linux engine\n",
" & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
]
},
{
@@ -295,7 +290,7 @@
"source": [
"from azureml.core.webservice import LocalWebservice\n",
"\n",
"#this is optional, if not provided we choose random port\n",
"# This is optional, if not provided Docker will choose a random unused port.\n",
"deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
"\n",
"local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
@@ -427,9 +422,8 @@
"local_service.reload()\n",
"print(\"--------------------------------------------------------------\")\n",
"\n",
"# after reload now if you call run this will return updated return message\n",
"\n",
"print(local_service.run(input_data=sample_input))"
"# After calling reload(), run() will return the updated message.\n",
"local_service.run(input_data=sample_input)"
]
},
{
@@ -442,9 +436,9 @@
"\n",
"```python\n",
"\n",
"local_service.update(models = [SomeOtherModelObject],\n",
" deployment_config = local_config,\n",
" inference_config = inference_config)\n",
"local_service.update(models=[SomeOtherModelObject],\n",
" deployment_config=local_config,\n",
" inference_config=inference_config)\n",
"```"
]
},
@@ -468,7 +462,7 @@
"metadata": {
"authors": [
{
"name": "raymondl"
"name": "keriehm"
}
],
"kernelspec": {

68
how-to-use-azureml/deploy-to-local/register-model-deploy-local.ipynb → how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb
View File

@@ -13,7 +13,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deploy-to-local/register-model-deploy-local.png)"
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.png)"
]
},
{
@@ -68,7 +68,7 @@
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
]
},
{
@@ -99,11 +99,31 @@
"source": [
"from azureml.core.model import Model\n",
"\n",
"model = Model.register(model_path = \"sklearn_regression_model.pkl\",\n",
" model_name = \"sklearn_regression_model.pkl\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"},\n",
" description = \"Ridge regression model to predict diabetes\",\n",
" workspace = ws)"
"model = Model.register(model_path=\"sklearn_regression_model.pkl\",\n",
" model_name=\"sklearn_regression_model.pkl\",\n",
" tags={'area': \"diabetes\", 'type': \"regression\"},\n",
" description=\"Ridge regression model to predict diabetes\",\n",
" workspace=ws)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies\n",
"from azureml.core.environment import Environment\n",
"\n",
"environment = Environment(\"LocalDeploy\")\n",
"environment.python.conda_dependencies = CondaDependencies(\"myenv.yml\")"
]
},
{
@@ -121,9 +141,8 @@
"source": [
"from azureml.core.model import InferenceConfig\n",
"\n",
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score.py\",\n",
" conda_file=\"myenv.yml\")"
"inference_config = InferenceConfig(entry_script=\"score.py\",\n",
" environment=environment)"
]
},
{
@@ -138,15 +157,10 @@
"\n",
"NOTE:\n",
"\n",
"we require docker running with linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running\n",
"The Docker image runs as a Linux container. If you are running Docker for Windows, you need to ensure the Linux Engine is running:\n",
"\n",
" powershell command to switch to linux engine\n",
" & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine\n",
"\n",
"and c drive is shared https://docs.docker.com/docker-for-windows/#shared-drives\n",
"sometimes you have to reshare c drive as docker \n",
"\n",
"<img src=\"./dockerSharedDrive.JPG\" align=\"left\"/>"
" # PowerShell command to switch to Linux engine\n",
" & 'C:\\Program Files\\Docker\\Docker\\DockerCli.exe' -SwitchLinuxEngine"
]
},
{
@@ -157,7 +171,7 @@
"source": [
"from azureml.core.webservice import LocalWebservice\n",
"\n",
"#this is optional, if not provided we choose random port\n",
"# This is optional, if not provided Docker will choose a random unused port.\n",
"deployment_config = LocalWebservice.deploy_configuration(port=6789)\n",
"\n",
"local_service = Model.deploy(ws, \"test\", [model], inference_config, deployment_config)\n",
@@ -221,7 +235,7 @@
"\n",
"sample_input = bytes(sample_input, encoding='utf-8')\n",
"\n",
"print(local_service.run(input_data=sample_input))"
"local_service.run(input_data=sample_input)"
]
},
{
@@ -282,9 +296,8 @@
"local_service.reload()\n",
"print(\"--------------------------------------------------------------\")\n",
"\n",
"# after reload now if you call run this will return updated return message\n",
"\n",
"print(local_service.run(input_data=sample_input))"
"# After calling reload(), run() will return the updated message.\n",
"local_service.run(input_data=sample_input)"
]
},
{
@@ -296,10 +309,9 @@
"If you want to change your model(s), Conda dependencies, or deployment configuration, call `update()` to rebuild the Docker image.\n",
"\n",
"```python\n",
"\n",
"local_service.update(models = [SomeOtherModelObject],\n",
" deployment_config = local_config,\n",
" inference_config = inference_config)\n",
"local_service.update(models=[SomeOtherModelObject],\n",
" inference_config=inference_config,\n",
" deployment_config=local_config)\n",
"```"
]
},
@@ -323,7 +335,7 @@
"metadata": {
"authors": [
{
"name": "raymondl"
"name": "keriehm"
}
],
"kernelspec": {

0
how-to-use-azureml/deploy-to-local/score.py → how-to-use-azureml/deployment/deploy-to-local/score.py
View File

0
how-to-use-azureml/deploy-to-local/sklearn_regression_model.pkl → how-to-use-azureml/deployment/deploy-to-local/sklearn_regression_model.pkl
View File

46
how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb
View File

@@ -22,7 +22,7 @@
"If you want to log custom traces, you will follow the standard deplyment process for AKS and you will:\n",
"1. Update scoring file.\n",
"2. Update aks configuration.\n",
"3. Build new image and deploy it. "
"3. Deploy the model with this new configuration. "
]
},
{
@@ -178,7 +178,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Create your new Image"
"## 6. Create Inference Configuration"
]
},
{
@@ -187,22 +187,11 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"from azureml.core.model import InferenceConfig\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" description = \"Image with ridge regression model\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"myimage1\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score.py\",\n",
" conda_file=\"myenv.yml\")"
]
},
{
@@ -220,7 +209,7 @@
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
"aci_deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}, \n",
" description = 'Predict diabetes using regression model',\n",
@@ -236,11 +225,7 @@
"from azureml.core.webservice import Webservice\n",
"\n",
"aci_service_name = 'my-aci-service-4'\n",
"print(aci_service_name)\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aci_deployment_config)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
@@ -361,7 +346,7 @@
"outputs": [],
"source": [
"#Set the web service configuration\n",
"aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
"aks_deployment_config = AksWebservice.deploy_configuration(enable_app_insights=True)"
]
},
{
@@ -379,12 +364,12 @@
"source": [
"if aks_target.provisioning_state== \"Succeeded\": \n",
" aks_service_name ='aks-w-dc5'\n",
" aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target\n",
" )\n",
" aks_service = Model.deploy(ws,\n",
" aks_service_name, \n",
" [model], \n",
" inference_config, \n",
" aks_deployment_config, \n",
" deployment_target = aks_target) \n",
" aks_service.wait_for_deployment(show_output = True)\n",
" print(aks_service.state)\n",
"else:\n",
@@ -464,7 +449,6 @@
"%%time\n",
"aks_service.delete()\n",
"aci_service.delete()\n",
"image.delete()\n",
"model.delete()"
]
}

56
how-to-use-azureml/deployment/onnx/onnx-convert-aml-deploy-tinyyolo.ipynb
View File

@@ -243,7 +243,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create container image\n",
"### Setting up inference configuration\n",
"First we create a YAML file that specifies which dependencies we would like to see in our container."
]
},
@@ -265,7 +265,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we have Azure ML create the container. This step will likely take a few minutes."
"Then we create the inference configuration."
]
},
{
@@ -274,48 +274,19 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"from azureml.core.model import InferenceConfig\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" docker_file = \"Dockerfile\",\n",
" description = \"TinyYOLO ONNX Demo\",\n",
" tags = {\"demo\": \"onnx\"}\n",
" )\n",
"\n",
"\n",
"image = ContainerImage.create(name = \"onnxyolo\",\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score.py\",\n",
" conda_file=\"myenv.yml\",\n",
" extra_docker_file_steps = \"Dockerfile\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case you need to debug your code, the next line of code accesses the log file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're all set! Let's get our model chugging.\n",
"\n",
"### Deploy the container image"
"### Deploy the model"
]
},
{
@@ -336,7 +307,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The following cell will likely take a few minutes to run as well."
"The following cell will take a few minutes to run as the model gets packaged up and deployed to ACI."
]
},
{
@@ -348,14 +319,9 @@
"from azureml.core.webservice import Webservice\n",
"from random import randint\n",
"\n",
"aci_service_name = 'onnx-tinyyolo'+str(randint(0,100))\n",
"aci_service_name = 'my-aci-service-15ad'\n",
"print(\"Service\", aci_service_name)\n",
"\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"\n",
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

72
how-to-use-azureml/deployment/onnx/onnx-inference-facial-expression-recognition-deploy.ipynb
View File

@@ -54,7 +54,7 @@
"\n",
"### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
"\n",
"In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
"In the following lines of code, we download [the trained ONNX Emotion FER+ model and corresponding test data](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) and place them in the same folder as this tutorial notebook. For more information about the FER+ dataset, please visit Microsoft Researcher Emad Barsoum's [FER+ source data repository](https://github.com/ebarsoum/FERPlus)."
]
},
{
@@ -176,7 +176,7 @@
"source": [
"### ONNX FER+ Model Methodology\n",
"\n",
"The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/emotion_ferplus) in the ONNX model zoo.\n",
"The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the well-known FER+ data set, provided as part of the [trained Emotion Recognition model](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus) in the ONNX model zoo.\n",
"\n",
"The original Facial Emotion Recognition (FER) Dataset was released in 2013 by Pierre-Luc Carrier and Aaron Courville as part of a [Kaggle Competition](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data), but some of the labels are not entirely appropriate for the expression. In the FER+ Dataset, each photo was evaluated by at least 10 croud sourced reviewers, creating a more accurate basis for ground truth. \n",
"\n",
@@ -341,9 +341,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create the Container Image\n",
"\n",
"This step will likely take a few minutes."
"### Setup inference configuration"
]
},
{
@@ -352,48 +350,19 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"from azureml.core.model import InferenceConfig\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" docker_file = \"Dockerfile\",\n",
" description = \"Emotion ONNX Runtime container\",\n",
" tags = {\"demo\": \"onnx\"})\n",
"\n",
"\n",
"image = ContainerImage.create(name = \"onnximage\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score.py\",\n",
" conda_file=\"myenv.yml\",\n",
" extra_docker_file_steps = \"Dockerfile\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case you need to debug your code, the next line of code accesses the log file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
"\n",
"### Deploy the container image"
"### Deploy the model"
]
},
{
@@ -410,6 +379,13 @@
" description = 'ONNX for emotion recognition model')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following cell will likely take a few minutes to run as well."
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -420,23 +396,11 @@
"\n",
"aci_service_name = 'onnx-demo-emotion'\n",
"print(\"Service\", aci_service_name)\n",
"\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"\n",
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following cell will likely take a few minutes to run as well."
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -470,7 +434,7 @@
"\n",
"### Useful Helper Functions\n",
"\n",
"We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/emotion_ferplus)."
"We preprocess and postprocess our data (see score.py file) using the helper functions specified in the [ONNX FER+ Model page in the Model Zoo repository](https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus)."
]
},
{

57
how-to-use-azureml/deployment/onnx/onnx-inference-mnist-deploy.ipynb
View File

@@ -54,7 +54,7 @@
"\n",
"### 3. Download sample data and pre-trained ONNX model from ONNX Model Zoo.\n",
"\n",
"In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
"In the following lines of code, we download [the trained ONNX MNIST model and corresponding test data](https://github.com/onnx/models/tree/master/vision/classification/mnist) and place them in the same folder as this tutorial notebook. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/)."
]
},
{
@@ -187,7 +187,7 @@
"source": [
"### ONNX MNIST Model Methodology\n",
"\n",
"The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/mnist) in the ONNX model zoo.\n",
"The image classification model we are using is pre-trained using Microsoft's deep learning cognitive toolkit, [CNTK](https://github.com/Microsoft/CNTK), from the [ONNX model zoo](http://github.com/onnx/models). The model zoo has many other models that can be deployed on cloud providers like AzureML without any additional training. To ensure that our cloud deployed model works, we use testing data from the famous MNIST data set, provided as part of the [trained MNIST model](https://github.com/onnx/models/tree/master/vision/classification/mnist) in the ONNX model zoo.\n",
"\n",
"***Input: Handwritten Images from MNIST Dataset***\n",
"\n",
@@ -325,8 +325,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create the Container Image\n",
"This step will likely take a few minutes."
"### Create Inference Configuration"
]
},
{
@@ -335,48 +334,19 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"from azureml.core.model import InferenceConfig\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" docker_file = \"Dockerfile\",\n",
" description = \"MNIST ONNX Runtime container\",\n",
" tags = {\"demo\": \"onnx\"}) \n",
"\n",
"\n",
"image = ContainerImage.create(name = \"onnximage\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score.py\",\n",
" extra_docker_file_steps = \"Dockerfile\",\n",
" conda_file=\"myenv.yml\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case you need to debug your code, the next line of code accesses the log file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're all done specifying what we want our virtual machine to do. Let's configure and deploy our container image.\n",
"\n",
"### Deploy the container image"
"### Deploy the model"
]
},
{
@@ -397,7 +367,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The following cell will likely take a few minutes to run as well."
"The following cell will likely take a few minutes to run."
]
},
{
@@ -410,12 +380,7 @@
"\n",
"aci_service_name = 'onnx-demo-mnist'\n",
"print(\"Service\", aci_service_name)\n",
"\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"\n",
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

54
how-to-use-azureml/deployment/onnx/onnx-modelzoo-aml-deploy-resnet50.ipynb
View File

@@ -28,7 +28,7 @@
"ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
"\n",
"## ResNet50 Details\n",
"ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/models/image_classification/resnet). "
"ResNet classifies the major object in an input image into a set of 1000 pre-defined classes. For more information about the ResNet50 model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/resnet). "
]
},
{
@@ -221,7 +221,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create container image"
"### Create inference configuration"
]
},
{
@@ -249,7 +249,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we have Azure ML create the container. This step will likely take a few minutes."
"Create the inference configuration object"
]
},
{
@@ -258,48 +258,19 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"from azureml.core.model import InferenceConfig\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" docker_file = \"Dockerfile\",\n",
" description = \"ONNX ResNet50 Demo\",\n",
" tags = {\"demo\": \"onnx\"}\n",
" )\n",
"\n",
"\n",
"image = ContainerImage.create(name = \"onnxresnet50v2\",\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score.py\",\n",
" conda_file=\"myenv.yml\",\n",
" extra_docker_file_steps = \"Dockerfile\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case you need to debug your code, the next line of code accesses the log file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're all set! Let's get our model chugging.\n",
"\n",
"### Deploy the container image"
"### Deploy the model"
]
},
{
@@ -334,12 +305,7 @@
"\n",
"aci_service_name = 'onnx-demo-resnet50'+str(randint(0,100))\n",
"print(\"Service\", aci_service_name)\n",
"\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"\n",
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

55
how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb
View File

@@ -28,7 +28,7 @@
"ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
"\n",
"## MNIST Details\n",
"The Modified National Institute of Standards and Technology (MNIST) dataset consists of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing numbers from 0 to 9. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/). For more information about the MNIST model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/mnist). "
"The Modified National Institute of Standards and Technology (MNIST) dataset consists of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing numbers from 0 to 9. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/). For more information about the MNIST model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/mnist). "
]
},
{
@@ -401,7 +401,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create container image\n",
"### Create inference configuration\n",
"First we create a YAML file that specifies which dependencies we would like to see in our container."
]
},
@@ -423,7 +423,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we have Azure ML create the container. This step will likely take a few minutes."
"Then we setup the inference configuration "
]
},
{
@@ -432,48 +432,19 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"from azureml.core.model import InferenceConfig\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" docker_file = \"Dockerfile\",\n",
" description = \"MNIST ONNX Demo\",\n",
" tags = {\"demo\": \"onnx\"}\n",
" )\n",
"\n",
"\n",
"image = ContainerImage.create(name = \"onnxmnistdemo\",\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score.py\",\n",
" conda_file=\"myenv.yml\",\n",
" extra_docker_file_steps = \"Dockerfile\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case you need to debug your code, the next line of code accesses the log file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We're all set! Let's get our model chugging.\n",
"\n",
"### Deploy the container image"
"### Deploy the model"
]
},
{
@@ -504,16 +475,12 @@
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"from azureml.core.model import Model\n",
"from random import randint\n",
"\n",
"aci_service_name = 'onnx-demo-mnist'+str(randint(0,100))\n",
"print(\"Service\", aci_service_name)\n",
"\n",
"aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
" image = image,\n",
" name = aci_service_name,\n",
" workspace = ws)\n",
"\n",
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

222
how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb
View File

@@ -34,7 +34,6 @@
"from azureml.core import Workspace\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import Image\n",
"from azureml.core.model import Model"
]
},
@@ -97,8 +96,51 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create an image\n",
"Create an image using the registered model the script that will load and run the model."
"# Create the Environment\n",
"Create an environment that the model will be deployed with"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Environment\n",
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-defaults'])\n",
"myenv = Environment(name='myenv')\n",
"myenv.python.conda_dependencies = conda_deps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use a custom Docker image\n",
"\n",
"You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
"\n",
"Only supported with `python` runtime.\n",
"```python\n",
"# use an image available in public Container Registry without authentication\n",
"myenv.docker.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
"\n",
"# or, use an image available in a private Container Registry\n",
"myenv.docker.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
"myenv.docker.base_image_registry.address = \"myregistry.azurecr.io\"\n",
"myenv.docker.base_image_registry.username = \"username\"\n",
"myenv.docker.base_image_registry.password = \"password\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Write the Entry Script\n",
"Write the script that will be used to predict on your model"
]
},
{
@@ -136,67 +178,23 @@
" return error"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'])\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(execution_script = \"score.py\",\n",
" runtime = \"python\",\n",
" conda_file = \"myenv.yml\",\n",
" description = \"Image with ridge regression model\",\n",
" tags = {'area': \"diabetes\", 'type': \"regression\"}\n",
" )\n",
"\n",
"image = ContainerImage.create(name = \"myimage1\",\n",
" # this is the model object\n",
" models = [model],\n",
" image_config = image_config,\n",
" workspace = ws)\n",
"\n",
"image.wait_for_creation(show_output = True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Use a custom Docker image\n",
"# Create the InferenceConfig\n",
"Create the inference config that will be used when deploying the model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import InferenceConfig\n",
"\n",
"You can also specify a custom Docker image to be used as base image if you don't want to use the default base image provided by Azure ML. Please make sure the custom Docker image has Ubuntu >= 16.04, Conda >= 4.5.\\* and Python(3.5.\\* or 3.6.\\*).\n",
"\n",
"Only Supported for `ContainerImage`(from azureml.core.image) with `python` runtime.\n",
"```python\n",
"# use an image available in public Container Registry without authentication\n",
"image_config.base_image = \"mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda\"\n",
"\n",
"# or, use an image available in a private Container Registry\n",
"image_config.base_image = \"myregistry.azurecr.io/mycustomimage:1.0\"\n",
"image_config.base_image_registry.address = \"myregistry.azurecr.io\"\n",
"image_config.base_image_registry.username = \"username\"\n",
"image_config.base_image_registry.password = \"password\"\n",
"\n",
"# or, use an image built during training.\n",
"image_config.base_image = run.properties[\"AzureML.DerivedImageName\"]\n",
"```\n",
"You can get the address of training image from the properties of a Run object. Only new runs submitted with azureml-sdk>=1.0.22 to AMLCompute targets will have the 'AzureML.DerivedImageName' property. Instructions on how to get a Run can be found in [manage-runs](../../training/manage-runs/manage-runs.ipynb). \n"
"inf_config = InferenceConfig(entry_script='score.py', environment=myenv)"
]
},
{
@@ -237,23 +235,21 @@
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"from azureml.core.compute import ComputeTarget, AksCompute\n",
"# from azureml.core.compute import ComputeTarget, AksCompute\n",
"\n",
"# Create the compute configuration and set virtual network information\n",
"config = AksCompute.provisioning_configuration(location=\"eastus2\")\n",
"config.vnet_resourcegroup_name = \"mygroup\"\n",
"config.vnet_name = \"mynetwork\"\n",
"config.subnet_name = \"default\"\n",
"config.service_cidr = \"10.0.0.0/16\"\n",
"config.dns_service_ip = \"10.0.0.10\"\n",
"config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
"# # Create the compute configuration and set virtual network information\n",
"# config = AksCompute.provisioning_configuration(location=\"eastus2\")\n",
"# config.vnet_resourcegroup_name = \"mygroup\"\n",
"# config.vnet_name = \"mynetwork\"\n",
"# config.subnet_name = \"default\"\n",
"# config.service_cidr = \"10.0.0.0/16\"\n",
"# config.dns_service_ip = \"10.0.0.10\"\n",
"# config.docker_bridge_cidr = \"172.17.0.1/16\"\n",
"\n",
"# Create the compute target\n",
"aks_target = ComputeTarget.create(workspace = ws,\n",
" name = \"myaks\",\n",
" provisioning_configuration = config)\n",
"'''"
"# # Create the compute target\n",
"# aks_target = ComputeTarget.create(workspace = ws,\n",
"# name = \"myaks\",\n",
"# provisioning_configuration = config)"
]
},
{
@@ -300,17 +296,15 @@
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# Use the default configuration (can also provide parameters to customize)\n",
"resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
"# # Use the default configuration (can also provide parameters to customize)\n",
"# resource_id = '/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourcegroups/raymondsdk0604/providers/Microsoft.ContainerService/managedClusters/my-aks-0605d37425356b7d01'\n",
"\n",
"create_name='my-existing-aks' \n",
"# Create the cluster\n",
"attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
"aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
"# Wait for the operation to complete\n",
"aks_target.wait_for_completion(True)\n",
"'''"
"# create_name='my-existing-aks' \n",
"# # Create the cluster\n",
"# attach_config = AksCompute.attach_configuration(resource_id=resource_id)\n",
"# aks_target = ComputeTarget.attach(workspace=ws, name=create_name, attach_configuration=attach_config)\n",
"# # Wait for the operation to complete\n",
"# aks_target.wait_for_completion(True)"
]
},
{
@@ -326,8 +320,11 @@
"metadata": {},
"outputs": [],
"source": [
"#Set the web service configuration (using default here)\n",
"aks_config = AksWebservice.deploy_configuration()"
"# Set the web service configuration (using default here)\n",
"aks_config = AksWebservice.deploy_configuration()\n",
"\n",
"# # Enable token auth and disable (key) auth on the webservice\n",
"# aks_config = AksWebservice.deploy_configuration(token_auth_enabled=True, auth_enabled=False)\n"
]
},
{
@@ -339,11 +336,13 @@
"%%time\n",
"aks_service_name ='aks-service-1'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws, \n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target)\n",
"aks_service = Model.deploy(workspace=ws,\n",
" name=aks_service_name,\n",
" models=[model],\n",
" inference_config=inf_config,\n",
" deployment_config=aks_config,\n",
" deployment_target=aks_target)\n",
"\n",
"aks_service.wait_for_deployment(show_output = True)\n",
"print(aks_service.state)"
]
@@ -390,11 +389,12 @@
"metadata": {},
"outputs": [],
"source": [
"# retreive the API keys. AML generates two keys.\n",
"'''\n",
"key1, Key2 = aks_service.get_keys()\n",
"print(key1)\n",
"'''"
"# # if (key) auth is enabled, retrieve the API keys. AML generates two keys.\n",
"# key1, Key2 = aks_service.get_keys()\n",
"# print(key1)\n",
"\n",
"# # if token auth is enabled, retrieve the token.\n",
"# access_token, refresh_after = aks_service.get_token()"
]
},
{
@@ -404,27 +404,28 @@
"outputs": [],
"source": [
"# construct raw HTTP request and send to the service\n",
"'''\n",
"%%time\n",
"# %%time\n",
"\n",
"import requests\n",
"# import requests\n",
"\n",
"import json\n",
"# import json\n",
"\n",
"test_sample = json.dumps({'data': [\n",
" [1,2,3,4,5,6,7,8,9,10], \n",
" [10,9,8,7,6,5,4,3,2,1]\n",
"]})\n",
"test_sample = bytes(test_sample,encoding = 'utf8')\n",
"# test_sample = json.dumps({'data': [\n",
"# [1,2,3,4,5,6,7,8,9,10], \n",
"# [10,9,8,7,6,5,4,3,2,1]\n",
"# ]})\n",
"# test_sample = bytes(test_sample,encoding = 'utf8')\n",
"\n",
"# Don't forget to add key to the HTTP header.\n",
"headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
"# # If (key) auth is enabled, don't forget to add key to the HTTP header.\n",
"# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
"\n",
"resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)\n",
"# # If token auth is enabled, don't forget to add token to the HTTP header.\n",
"# headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + access_token}\n",
"\n",
"# resp = requests.post(aks_service.scoring_uri, test_sample, headers=headers)\n",
"\n",
"\n",
"print(\"prediction:\", resp.text)\n",
"'''"
"# print(\"prediction:\", resp.text)"
]
},
{
@@ -443,7 +444,6 @@
"source": [
"%%time\n",
"aks_service.delete()\n",
"image.delete()\n",
"model.delete()"
]
}

11
how-to-use-azureml/explain-model/README.md
View File

@@ -1,8 +1,11 @@
## Using explain model APIs
<a name="samples"></a>
# Explain Model SDK Sample Notebooks
Follow these sample notebooks to learn:
1. [Explain tabular data locally](explain-tabular-data-local): Basic example of explaining model trained on tabular data.
4. [Explain on remote AMLCompute](explain-on-amlcompute): Explain a model on a remote AMLCompute target.
5. [Explain tabular data with Run History](explain-tabular-data-run-history): Explain a model with Run History.
7. [Explain raw features](explain-tabular-data-raw-features): Explain the raw features of a trained model.
1. [Explain tabular data locally](tabular-data): Basic examples of explaining model trained on tabular data.
2. [Explain on remote AMLCompute](azure-integration/remote-explanation): Explain a model on a remote AMLCompute target.
3. [Explain tabular data with Run History](azure-integration/run-history): Explain a model with Run History.
4. [Operationalize model explanation](azure-integration/scoring-time): Operationalize model explanation as a web service.

204
how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.ipynb → how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.ipynb
View File

@@ -13,33 +13,68 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/explain-on-amlcompute/regression-sklearn-on-amlcompute.png)"
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train using Azure Machine Learning Compute\n",
"# Train and explain models remotely via Azure Machine Learning Compute\n",
"\n",
"* Initialize a Workspace\n",
"* Create an Experiment\n",
"* Introduction to AmlCompute\n",
"* Submit an AmlCompute run in a few different ways\n",
" - Provision as a run based compute target \n",
" - Provision as a persistent compute target (Basic)\n",
" - Provision as a persistent compute target (Advanced)\n",
"* Additional operations to perform on AmlCompute\n",
"* Download model explanation data from the Run History Portal\n",
"* Print the explanation data"
"\n",
"_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to train and explain a regression model remotely on an Azure Machine Leanrning Compute Target (AMLCompute).**_\n",
"\n",
"\n",
"\n",
"\n",
"## Table of Contents\n",
"\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
" 1. Initialize a Workspace\n",
" 1. Create an Experiment\n",
" 1. Introduction to AmlCompute\n",
" 1. Submit an AmlCompute run in a few different ways\n",
" 1. Option 1: Provision as a run based compute target \n",
" 1. Option 2: Provision as a persistent compute target (Basic)\n",
" 1. Option 3: Provision as a persistent compute target (Advanced)\n",
"1. Additional operations to perform on AmlCompute\n",
"1. [Download model explanations from Azure Machine Learning Run History](#Download)\n",
"1. [Visualize explanations](#Visualize)\n",
"1. [Next steps](#Next)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Prerequisites\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't."
"## Introduction\n",
"\n",
"This notebook showcases how to train and explain a regression model remotely via Azure Machine Learning Compute (AMLCompute), and download the calculated explanations locally for visualization.\n",
"It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations.\n",
"\n",
"We will showcase one of the tabular data explainers: TabularExplainer (SHAP).\n",
"\n",
"Problem: Boston Housing Price Prediction with scikit-learn (train a model and run an explainer remotely via AMLCompute, and download and visualize the remotely-calculated explanations.)\n",
"\n",
"| ![explanations-run-history](./img/explanations-run-history.PNG) |\n",
"|:--:|\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the [configuration notebook](../../../configuration.ipynb) first if you haven't.\n",
"\n",
"\n",
"If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
"If you are using Jupyter Labs run the following command:\n",
"```\n",
"(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
"```\n"
]
},
{
@@ -116,7 +151,7 @@
"**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
"\n",
"\n",
"The training script `run_explainer.py` is already created for you. Let's have a look."
"The training script `train_explain.py` is already created for you. Let's have a look."
]
},
{
@@ -162,14 +197,14 @@
"\n",
"project_folder = './explainer-remote-run-on-amlcompute'\n",
"os.makedirs(project_folder, exist_ok=True)\n",
"shutil.copy('run_explainer.py', project_folder)"
"shutil.copy('train_explain.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Provision as a run based compute target\n",
"### Option 1: Provision as a run based compute target\n",
"\n",
"You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
]
@@ -205,7 +240,7 @@
"\n",
"azureml_pip_packages = [\n",
" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
" 'azureml-explain-model'\n",
" 'azureml-explain-model', 'sklearn-pandas', 'azureml-dataprep'\n",
"]\n",
"\n",
"# specify CondaDependencies obj\n",
@@ -216,7 +251,7 @@
"from azureml.core.script_run_config import ScriptRunConfig\n",
"\n",
"script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
" script='run_explainer.py',\n",
" script='train_explain.py',\n",
" run_config=run_config)\n",
"\n",
"run = experiment.submit(script_run_config)\n",
@@ -247,7 +282,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Provision as a persistent compute target (Basic)\n",
"### Option 2: Provision as a persistent compute target (Basic)\n",
"\n",
"You can provision a persistent AmlCompute resource by simply defining two parameters thanks to smart defaults. By default it autoscales from 0 nodes and provisions dedicated VMs to run your job in a container. This is useful when you want to continously re-use the same target, debug it between jobs or simply share the resource with other users of your workspace.\n",
"\n",
@@ -306,7 +341,7 @@
"\n",
"azureml_pip_packages = [\n",
" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
" 'azureml-explain-model'\n",
" 'azureml-explain-model', 'azureml-dataprep'\n",
"]\n",
"\n",
"# specify CondaDependencies obj\n",
@@ -317,7 +352,7 @@
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=project_folder, \n",
" script='run_explainer.py', \n",
" script='train_explain.py', \n",
" run_config=run_config) \n",
"run = experiment.submit(config=src)\n",
"run"
@@ -347,7 +382,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Provision as a persistent compute target (Advanced)\n",
"### Option 3: Provision as a persistent compute target (Advanced)\n",
"\n",
"You can also specify additional properties or change defaults while provisioning AmlCompute using a more advanced configuration. This is useful when you want a dedicated cluster of 4 nodes (for example you can set the min_nodes and max_nodes to 4), or want the compute to be within an existing VNet in your subscription.\n",
"\n",
@@ -417,9 +452,11 @@
"\n",
"azureml_pip_packages = [\n",
" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
" 'azureml-explain-model'\n",
" 'azureml-explain-model', 'azureml-dataprep'\n",
"]\n",
"\n",
"\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
" pip_packages=azureml_pip_packages)\n",
@@ -428,7 +465,7 @@
"from azureml.core import ScriptRunConfig\n",
"\n",
"src = ScriptRunConfig(source_directory=project_folder, \n",
" script='run_explainer.py', \n",
" script='train_explain.py', \n",
" run_config=run_config) \n",
"run = experiment.submit(config=src)\n",
"run"
@@ -515,7 +552,8 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Model Explanation Data"
"## Download \n",
"1. Download model explanation data."
]
},
{
@@ -528,9 +566,9 @@
"\n",
"# Get model explanation data\n",
"client = ExplanationClient.from_run(run)\n",
"explanation = client.download_model_explanation()\n",
"local_importance_values = explanation.local_importance_values\n",
"expected_values = explanation.expected_values\n"
"global_explanation = client.download_model_explanation()\n",
"local_importance_values = global_explanation.local_importance_values\n",
"expected_values = global_explanation.expected_values\n"
]
},
{
@@ -541,9 +579,9 @@
"source": [
"# Or you can use the saved run.id to retrive the feature importance values\n",
"client = ExplanationClient.from_run_id(ws, experiment_name, run.id)\n",
"explanation = client.download_model_explanation()\n",
"local_importance_values = explanation.local_importance_values\n",
"expected_values = explanation.expected_values"
"global_explanation = client.download_model_explanation()\n",
"local_importance_values = global_explanation.local_importance_values\n",
"expected_values = global_explanation.expected_values"
]
},
{
@@ -553,9 +591,9 @@
"outputs": [],
"source": [
"# Get the top k (e.g., 4) most important features with their importance values\n",
"explanation = client.download_model_explanation(top_k=4)\n",
"global_importance_values = explanation.get_ranked_global_values()\n",
"global_importance_names = explanation.get_ranked_global_names()"
"global_explanation_topk = client.download_model_explanation(top_k=4)\n",
"global_importance_values = global_explanation_topk.get_ranked_global_values()\n",
"global_importance_names = global_explanation_topk.get_ranked_global_names()"
]
},
{
@@ -572,9 +610,101 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Success!\n",
"Great, you are ready to move on to the remaining notebooks."
"2. Download model file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# retrieve model for visualization and deployment\n",
"from azureml.core.model import Model\n",
"from sklearn.externals import joblib\n",
"original_model = Model(ws, 'model_explain_model_on_amlcomp')\n",
"model_path = original_model.download(exist_ok=True)\n",
"original_model = joblib.load(model_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. Download test dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# retrieve x_test for visualization\n",
"from sklearn.externals import joblib\n",
"x_test_path = './x_test_boston_housing.pkl'\n",
"run.download_file('x_test_boston_housing.pkl', output_file_path=x_test_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"x_test = joblib.load('x_test_boston_housing.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize\n",
"Load the visualization dashboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.contrib.explain.model.visualize import ExplanationDashboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ExplanationDashboard(global_explanation, original_model, x_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next\n",
"Learn about other use cases of the explain package on a:\n",
"1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb) \n",
"1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
"1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
"1. Explain models with engineered features:\n",
" 1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
" 1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
"1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
"1. Inferencing time: deploy a classification model and explainer:\n",
" 1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
" 1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

8
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@@ -0,0 +1,8 @@
name: explain-model-on-amlcompute
dependencies:
- pip:
- azureml-sdk
- azureml-explain-model
- azureml-contrib-explain-model
- sklearn-pandas
- azureml-dataprep

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16
how-to-use-azureml/explain-model/explain-on-amlcompute/run_explainer.py → how-to-use-azureml/explain-model/azure-integration/remote-explanation/train_explain.py
View File

@@ -11,7 +11,8 @@ from sklearn.externals import joblib
import os
import numpy as np
os.makedirs('./outputs', exist_ok=True)
OUTPUT_DIR = './outputs/'
os.makedirs(OUTPUT_DIR, exist_ok=True)
boston_data = datasets.load_boston()
@@ -22,6 +23,12 @@ X_train, X_test, y_train, y_test = train_test_split(boston_data.data,
boston_data.target,
test_size=0.2,
random_state=0)
# write x_test out as a pickle file for later visualization
x_test_pkl = 'x_test.pkl'
with open(x_test_pkl, 'wb') as file:
joblib.dump(value=X_test, filename=os.path.join(OUTPUT_DIR, x_test_pkl))
run.upload_file('x_test_boston_housing.pkl', os.path.join(OUTPUT_DIR, x_test_pkl))
alpha = 0.5
# Use Ridge algorithm to create a regression model
@@ -34,9 +41,14 @@ run.log('alpha', alpha)
model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
# save model in the outputs folder so it automatically get uploaded
with open(model_file_name, 'wb') as file:
joblib.dump(value=reg, filename=os.path.join('./outputs/',
joblib.dump(value=reg, filename=os.path.join(OUTPUT_DIR,
model_file_name))
# register the model
run.upload_file('original_model.pkl', os.path.join('./outputs/', model_file_name))
original_model = run.register_model(model_name='model_explain_model_on_amlcomp',
model_path='original_model.pkl')
# Explain predictions on your local machine
tabular_explainer = TabularExplainer(model, X_train, features=boston_data.feature_names)

631
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View File

@@ -0,0 +1,631 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Save and retrieve explanations via Azure Machine Learning Run History\n",
"\n",
"_**This notebook showcases how to use the Azure Machine Learning Interpretability SDK to save and retrieve classification model explanations to/from Azure Machine Learning Run History.**_\n",
"\n",
"\n",
"## Table of Contents\n",
"\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Run model explainer locally at training time](#Explain)\n",
" 1. Apply feature transformations\n",
" 1. Train a binary classification model\n",
" 1. Explain the model on raw features\n",
" 1. Generate global explanations\n",
" 1. Generate local explanations\n",
"1. [Upload model explanations to Azure Machine Learning Run History](#Upload)\n",
"1. [Download model explanations from Azure Machine Learning Run History](#Download)\n",
"1. [Visualize explanations](#Visualize)\n",
"1. [Next steps](#Next)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"This notebook showcases how to explain a classification model predictions locally at training time, upload explanations to the Azure Machine Learning's run history, and download previously-uploaded explanations from the Run History.\n",
"It demonstrates the API calls that you need to make to upload/download the global and local explanations and a visualization dashboard that provides an interactive way of discovering patterns in data and downloaded explanations.\n",
"\n",
"We will showcase three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
"\n",
"\n",
"\n",
"Problem: IBM employee attrition classification with scikit-learn (run model explainer locally and upload explanation to the Azure Machine Learning Run History)\n",
"\n",
"1. Train a SVM classification model using Scikit-learn\n",
"2. Run 'explain_model' with AML Run History, which leverages run history service to store and manage the explanation data\n",
"---\n",
"\n",
"Setup: If you are using Jupyter notebooks, the extensions should be installed automatically with the package.\n",
"If you are using Jupyter Labs run the following command:\n",
"```\n",
"(myenv) $ jupyter labextension install @jupyter-widgets/jupyterlab-manager\n",
"```\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explain\n",
"\n",
"### Run model explainer locally at training time"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.pipeline import Pipeline\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.svm import SVC\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# Explainers:\n",
"# 1. SHAP Tabular Explainer\n",
"from azureml.explain.model.tabular_explainer import TabularExplainer\n",
"\n",
"# OR\n",
"\n",
"# 2. Mimic Explainer\n",
"from azureml.explain.model.mimic.mimic_explainer import MimicExplainer\n",
"# You can use one of the following four interpretable models as a global surrogate to the black box model\n",
"from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
"from azureml.explain.model.mimic.models.linear_model import LinearExplainableModel\n",
"from azureml.explain.model.mimic.models.linear_model import SGDExplainableModel\n",
"from azureml.explain.model.mimic.models.tree_model import DecisionTreeExplainableModel\n",
"\n",
"# OR\n",
"\n",
"# 3. PFI Explainer\n",
"from azureml.explain.model.permutation.permutation_importance import PFIExplainer "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load the IBM employee attrition data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get the IBM employee attrition dataset\n",
"outdirname = 'dataset.6.21.19'\n",
"try:\n",
" from urllib import urlretrieve\n",
"except ImportError:\n",
" from urllib.request import urlretrieve\n",
"import zipfile\n",
"zipfilename = outdirname + '.zip'\n",
"urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
"with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
" unzip.extractall('.')\n",
"attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')\n",
"\n",
"# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
"attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
"# Dropping Employee Number since it is merely an identifier\n",
"attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
"\n",
"attritionData = attritionData.drop(['Over18'], axis=1)\n",
"\n",
"# Since all values are 80\n",
"attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
"\n",
"# Converting target variables from string to numerical values\n",
"target_map = {'Yes': 1, 'No': 0}\n",
"attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
"target = attritionData[\"Attrition_numerical\"]\n",
"\n",
"attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Split data into train and test\n",
"from sklearn.model_selection import train_test_split\n",
"x_train, x_test, y_train, y_test = train_test_split(attritionXData, \n",
" target, \n",
" test_size = 0.2,\n",
" random_state=0,\n",
" stratify=target)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Creating dummy columns for each categorical feature\n",
"categorical = []\n",
"for col, value in attritionXData.iteritems():\n",
" if value.dtype == 'object':\n",
" categorical.append(col)\n",
" \n",
"# Store the numerical columns in a list numerical\n",
"numerical = attritionXData.columns.difference(categorical) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Transform raw features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can explain raw features by either using a `sklearn.compose.ColumnTransformer` or a list of fitted transformer tuples. The cell below uses `sklearn.compose.ColumnTransformer`. In case you want to run the example with the list of fitted transformer tuples, comment the cell below and uncomment the cell that follows after. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.compose import ColumnTransformer\n",
"\n",
"# We create the preprocessing pipelines for both numeric and categorical data.\n",
"numeric_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())])\n",
"\n",
"categorical_transformer = Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
" ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n",
"\n",
"transformations = ColumnTransformer(\n",
" transformers=[\n",
" ('num', numeric_transformer, numerical),\n",
" ('cat', categorical_transformer, categorical)])\n",
"\n",
"# Append classifier to preprocessing pipeline.\n",
"# Now we have a full prediction pipeline.\n",
"clf = Pipeline(steps=[('preprocessor', transformations),\n",
" ('classifier', SVC(kernel='linear', C = 1.0, probability=True))])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"'''\n",
"# Uncomment below if sklearn-pandas is not installed\n",
"#!pip install sklearn-pandas\n",
"from sklearn_pandas import DataFrameMapper\n",
"\n",
"# Impute, standardize the numeric features and one-hot encode the categorical features. \n",
"\n",
"\n",
"numeric_transformations = [([f], Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])) for f in numerical]\n",
"\n",
"categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
"\n",
"transformations = numeric_transformations + categorical_transformations\n",
"\n",
"# Append classifier to preprocessing pipeline.\n",
"# Now we have a full prediction pipeline.\n",
"clf = Pipeline(steps=[('preprocessor', transformations),\n",
" ('classifier', SVC(kernel='linear', C = 1.0, probability=True))]) \n",
"\n",
"\n",
"\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Train a SVM classification model, which you want to explain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = clf.fit(x_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explain predictions on your local machine"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 1. Using SHAP TabularExplainer\n",
"# clf.steps[-1][1] returns the trained classification model\n",
"explainer = TabularExplainer(clf.steps[-1][1], \n",
" initialization_examples=x_train, \n",
" features=attritionXData.columns, \n",
" classes=[\"Not leaving\", \"leaving\"], \n",
" transformations=transformations)\n",
"\n",
"\n",
"\n",
"\n",
"# 2. Using MimicExplainer\n",
"# augment_data is optional and if true, oversamples the initialization examples to improve surrogate model accuracy to fit original model. Useful for high-dimensional data where the number of rows is less than the number of columns. \n",
"# max_num_of_augmentations is optional and defines max number of times we can increase the input data size.\n",
"# LGBMExplainableModel can be replaced with LinearExplainableModel, SGDExplainableModel, or DecisionTreeExplainableModel\n",
"# explainer = MimicExplainer(clf.steps[-1][1], \n",
"# x_train, \n",
"# LGBMExplainableModel, \n",
"# augment_data=True, \n",
"# max_num_of_augmentations=10, \n",
"# features=attritionXData.columns, \n",
"# classes=[\"Not leaving\", \"leaving\"], \n",
"# transformations=transformations)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"# 3. Using PFIExplainer\n",
"\n",
"# Use the parameter \"metric\" to pass a metric name or function to evaluate the permutation. \n",
"# Note that if a metric function is provided a higher value must be better.\n",
"# Otherwise, take the negative of the function or set the parameter \"is_error_metric\" to True.\n",
"# Default metrics: \n",
"# F1 Score for binary classification, F1 Score with micro average for multiclass classification and\n",
"# Mean absolute error for regression\n",
"\n",
"# explainer = PFIExplainer(clf.steps[-1][1], \n",
"# features=x_train.columns, \n",
"# transformations=transformations,\n",
"# classes=[\"Not leaving\", \"leaving\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate global explanations\n",
"Explain overall model predictions (global explanation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
"# x_train can be passed as well, but with more examples explanations will take longer although they may be more accurate\n",
"global_explanation = explainer.explain_global(x_test)\n",
"\n",
"# Note: if you used the PFIExplainer in the previous step, use the next line of code instead\n",
"# global_explanation = explainer.explain_global(x_test, true_labels=y_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Sorted SHAP values\n",
"print('ranked global importance values: {}'.format(global_explanation.get_ranked_global_values()))\n",
"# Corresponding feature names\n",
"print('ranked global importance names: {}'.format(global_explanation.get_ranked_global_names()))\n",
"# Feature ranks (based on original order of features)\n",
"print('global importance rank: {}'.format(global_explanation.global_importance_rank))\n",
"\n",
"# Note: PFIExplainer does not support per class explanations\n",
"# Per class feature names\n",
"print('ranked per class feature names: {}'.format(global_explanation.get_ranked_per_class_names()))\n",
"# Per class feature importance values\n",
"print('ranked per class feature values: {}'.format(global_explanation.get_ranked_per_class_values()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Print out a dictionary that holds the sorted feature importance names and values\n",
"print('global importance rank: {}'.format(global_explanation.get_feature_importance_dict()))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explain overall model predictions as a collection of local (instance-level) explanations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# feature shap values for all features and all data points in the training data\n",
"print('local importance values: {}'.format(global_explanation.local_importance_values))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Generate local explanations\n",
"Explain local data points (individual instances)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Note: PFIExplainer does not support local explanations\n",
"# You can pass a specific data point or a group of data points to the explain_local function\n",
"\n",
"# E.g., Explain the first data point in the test set\n",
"instance_num = 1\n",
"local_explanation = explainer.explain_local(x_test[:instance_num])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get the prediction for the first member of the test set and explain why model made that prediction\n",
"prediction_value = clf.predict(x_test)[instance_num]\n",
"\n",
"sorted_local_importance_values = local_explanation.get_ranked_local_values()[prediction_value]\n",
"sorted_local_importance_names = local_explanation.get_ranked_local_names()[prediction_value]\n",
"\n",
"print('local importance values: {}'.format(sorted_local_importance_values))\n",
"print('local importance names: {}'.format(sorted_local_importance_names))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Upload\n",
"Upload explanations to Azure Machine Learning Run History"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import azureml.core\n",
"from azureml.core import Workspace, Experiment, Run\n",
"from azureml.explain.model.tabular_explainer import TabularExplainer\n",
"from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
"# Check core SDK version number\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"print('Workspace name: ' + ws.name, \n",
" 'Azure region: ' + ws.location, \n",
" 'Subscription id: ' + ws.subscription_id, \n",
" 'Resource group: ' + ws.resource_group, sep = '\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'explain_model'\n",
"experiment = Experiment(ws, experiment_name)\n",
"run = experiment.start_logging()\n",
"client = ExplanationClient.from_run(run)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uploading model explanation data for storage or visualization in webUX\n",
"# The explanation can then be downloaded on any compute\n",
"# Multiple explanations can be uploaded\n",
"client.upload_model_explanation(global_explanation, comment='global explanation: all features')\n",
"# Or you can only upload the explanation object with the top k feature info\n",
"#client.upload_model_explanation(global_explanation, top_k=2, comment='global explanation: Only top 2 features')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Uploading model explanation data for storage or visualization in webUX\n",
"# The explanation can then be downloaded on any compute\n",
"# Multiple explanations can be uploaded\n",
"client.upload_model_explanation(local_explanation, comment='local explanation for test point 1: all features')\n",
"\n",
"# Alterntively, you can only upload the local explanation object with the top k feature info\n",
"#client.upload_model_explanation(local_explanation, top_k=2, comment='local explanation: top 2 features')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download\n",
"Download explanations from Azure Machine Learning Run History"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# List uploaded explanations\n",
"client.list_model_explanations()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for explanation in client.list_model_explanations():\n",
" \n",
" if explanation['comment'] == 'local explanation for test point 1: all features':\n",
" downloaded_local_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n",
" # You can pass a k value to only download the top k feature importance values\n",
" downloaded_local_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n",
" \n",
" \n",
" elif explanation['comment'] == 'global explanation: all features':\n",
" downloaded_global_explanation = client.download_model_explanation(explanation_id=explanation['id'])\n",
" # You can pass a k value to only download the top k feature importance values\n",
" downloaded_global_explanation_top2 = client.download_model_explanation(top_k=2, explanation_id=explanation['id'])\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize\n",
"Load the visualization dashboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.contrib.explain.model.visualize import ExplanationDashboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ExplanationDashboard(downloaded_global_explanation, model, x_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next\n",
"Learn about other use cases of the explain package on a:\n",
"1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb) \n",
"1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
"1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
"1. Explain models with engineered features:\n",
" 1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
" 1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
"1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
"1. Inferencing time: deploy a classification model and explainer:\n",
" 1. [Deploy a locally-trained model and explainer](../scoring-time/train-explain-model-locally-and-deploy.ipynb)\n",
" 1. [Deploy a remotely-trained model and explainer](../scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "mesameki"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

2
how-to-use-azureml/explain-model/explain-tabular-data-run-history/explain-run-history-sklearn-regression.yml → how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml
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name: explain-run-history-sklearn-regression
name: save-retrieve-explanations-run-history
dependencies:
- pip:
- azureml-sdk

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import json
import numpy as np
import pandas as pd
import os
import pickle
from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
from azureml.core.model import Model
def init():
global original_model
global scoring_explainer
# Retrieve the path to the model file using the model name
# Assume original model is named original_prediction_model
original_model_path = Model.get_model_path('original_model')
scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
original_model = joblib.load(original_model_path)
scoring_explainer = joblib.load(scoring_explainer_path)
def run(raw_data):
# Get predictions and explanations for each data point
data = pd.read_json(raw_data)
# Make prediction
predictions = original_model.predict(data)
# Retrieve model explanations
local_importance_values = scoring_explainer.explain(data)
# You can return any data type as long as it is JSON-serializable
return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}

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@@ -0,0 +1,33 @@
import json
import numpy as np
import pandas as pd
import os
import pickle
from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
from azureml.core.model import Model
def init():
global original_model
global scoring_explainer
# Retrieve the path to the model file using the model name
# Assume original model is named original_prediction_model
original_model_path = Model.get_model_path('local_deploy_model')
scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
original_model = joblib.load(original_model_path)
scoring_explainer = joblib.load(scoring_explainer_path)
def run(raw_data):
# Get predictions and explanations for each data point
data = pd.read_json(raw_data)
# Make prediction
predictions = original_model.predict(data)
# Retrieve model explanations
local_importance_values = scoring_explainer.explain(data)
# You can return any data type as long as it is JSON-serializable
return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}

33
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@@ -0,0 +1,33 @@
import json
import numpy as np
import pandas as pd
import os
import pickle
from sklearn.externals import joblib
from sklearn.linear_model import LogisticRegression
from azureml.core.model import Model
def init():
global original_model
global scoring_explainer
# Retrieve the path to the model file using the model name
# Assume original model is named original_prediction_model
original_model_path = Model.get_model_path('amlcompute_deploy_model')
scoring_explainer_path = Model.get_model_path('IBM_attrition_explainer')
original_model = joblib.load(original_model_path)
scoring_explainer = joblib.load(scoring_explainer_path)
def run(raw_data):
# Get predictions and explanations for each data point
data = pd.read_json(raw_data)
# Make prediction
predictions = original_model.predict(data)
# Retrieve model explanations
local_importance_values = scoring_explainer.explain(data)
# You can return any data type as long as it is JSON-serializable
return {'predictions': predictions.tolist(), 'local_importance_values': local_importance_values}

514
how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.ipynb Normal file
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@@ -0,0 +1,514 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train and explain models locally and deploy model and scoring explainer\n",
"\n",
"\n",
"_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to deploy a locally-trained model and its corresponding scoring explainer to Azure Container Instances (ACI) as a web service.**_\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"Problem: IBM employee attrition classification with scikit-learn (train and explain a model locally and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.)\n",
"\n",
"---\n",
"\n",
"## Table of Contents\n",
"\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Run model explainer locally at training time](#Explain)\n",
" 1. Apply feature transformations\n",
" 1. Train a binary classification model\n",
" 1. Explain the model on raw features\n",
" 1. Generate global explanations\n",
" 1. Generate local explanations\n",
"1. [Visualize explanations](#Visualize)\n",
"1. [Deploy model and scoring explainer](#Deploy)\n",
"1. [Next steps](#Next)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"\n",
"This notebook showcases how to train and explain a classification model locally, and deploy the trained model and its corresponding explainer to Azure Container Instances (ACI).\n",
"It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations. It also demonstrates how to use Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n",
"\n",
"We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n",
"1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n",
"2.\tRun the script locally.\n",
"3.\tUse the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n",
"5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n",
"6.\tDevelop a scoring script.\n",
"7.\tCreate an image and register it in the image registry.\n",
"8.\tDeploy the image as a web service in Azure.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize a Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explain\n",
"Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"experiment_name = 'explain_model_at_scoring_time'\n",
"experiment = Experiment(workspace=ws, name=experiment_name)\n",
"run = experiment.start_logging()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# get IBM attrition data\n",
"import os\n",
"import pandas as pd\n",
"\n",
"outdirname = 'dataset.6.21.19'\n",
"try:\n",
" from urllib import urlretrieve\n",
"except ImportError:\n",
" from urllib.request import urlretrieve\n",
"import zipfile\n",
"zipfilename = outdirname + '.zip'\n",
"urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)\n",
"with zipfile.ZipFile(zipfilename, 'r') as unzip:\n",
" unzip.extractall('.')\n",
"attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.externals import joblib\n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn_pandas import DataFrameMapper\n",
"\n",
"from azureml.explain.model.tabular_explainer import TabularExplainer\n",
"\n",
"os.makedirs('./outputs', exist_ok=True)\n",
"\n",
"# Dropping Employee count as all values are 1 and hence attrition is independent of this feature\n",
"attritionData = attritionData.drop(['EmployeeCount'], axis=1)\n",
"# Dropping Employee Number since it is merely an identifier\n",
"attritionData = attritionData.drop(['EmployeeNumber'], axis=1)\n",
"attritionData = attritionData.drop(['Over18'], axis=1)\n",
"# Since all values are 80\n",
"attritionData = attritionData.drop(['StandardHours'], axis=1)\n",
"\n",
"# Converting target variables from string to numerical values\n",
"target_map = {'Yes': 1, 'No': 0}\n",
"attritionData[\"Attrition_numerical\"] = attritionData[\"Attrition\"].apply(lambda x: target_map[x])\n",
"target = attritionData[\"Attrition_numerical\"]\n",
"\n",
"attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)\n",
"\n",
"# Creating dummy columns for each categorical feature\n",
"categorical = []\n",
"for col, value in attritionXData.iteritems():\n",
" if value.dtype == 'object':\n",
" categorical.append(col)\n",
"\n",
"# Store the numerical columns in a list numerical\n",
"numerical = attritionXData.columns.difference(categorical)\n",
"\n",
"numeric_transformations = [([f], Pipeline(steps=[\n",
" ('imputer', SimpleImputer(strategy='median')),\n",
" ('scaler', StandardScaler())])) for f in numerical]\n",
"\n",
"categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]\n",
"\n",
"transformations = numeric_transformations + categorical_transformations\n",
"\n",
"# Append classifier to preprocessing pipeline.\n",
"# Now we have a full prediction pipeline.\n",
"clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),\n",
" ('classifier', RandomForestClassifier())])\n",
"\n",
"# Split data into train and test\n",
"from sklearn.model_selection import train_test_split\n",
"x_train, x_test, y_train, y_test = train_test_split(attritionXData,\n",
" target,\n",
" test_size = 0.2,\n",
" random_state=0,\n",
" stratify=target)\n",
"\n",
"# preprocess the data and fit the classification model\n",
"clf.fit(x_train, y_train)\n",
"model = clf.steps[-1][1]\n",
"\n",
"model_file_name = 'log_reg.pkl'\n",
"\n",
"# save model in the outputs folder so it automatically get uploaded\n",
"with open(model_file_name, 'wb') as file:\n",
" joblib.dump(value=clf, filename=os.path.join('./outputs/',\n",
" model_file_name))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Explain predictions on your local machine\n",
"tabular_explainer = TabularExplainer(model, \n",
" initialization_examples=x_train, \n",
" features=attritionXData.columns, \n",
" classes=[\"Not leaving\", \"leaving\"], \n",
" transformations=transformations)\n",
"\n",
"# Explain overall model predictions (global explanation)\n",
"# Passing in test dataset for evaluation examples - note it must be a representative sample of the original data\n",
"# x_train can be passed as well, but with more examples explanations it will\n",
"# take longer although they may be more accurate\n",
"global_explanation = tabular_explainer.explain_global(x_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save\n",
"# ScoringExplainer\n",
"scoring_explainer = TreeScoringExplainer(tabular_explainer)\n",
"# Pickle scoring explainer locally\n",
"save(scoring_explainer, exist_ok=True)\n",
"\n",
"# Register original model\n",
"run.upload_file('original_model.pkl', os.path.join('./outputs/', model_file_name))\n",
"original_model = run.register_model(model_name='local_deploy_model', \n",
" model_path='original_model.pkl')\n",
"\n",
"# Register scoring explainer\n",
"run.upload_file('IBM_attrition_explainer.pkl', 'scoring_explainer.pkl')\n",
"scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer', model_path='IBM_attrition_explainer.pkl')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize\n",
"Visualize the explanations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.contrib.explain.model.visualize import ExplanationDashboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ExplanationDashboard(global_explanation, clf, x_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy \n",
"\n",
"Deploy Model and ScoringExplainer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
" memory_gb=1, \n",
" tags={\"data\": \"IBM_Attrition\", \n",
" \"method\" : \"local_explanation\"}, \n",
" description='Get local explanations for IBM Employee Attrition data')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
"azureml_pip_packages = [\n",
" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
" 'azureml-explain-model'\n",
"]\n",
" \n",
"\n",
"# specify CondaDependencies obj\n",
"myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas'],\n",
" pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
" pin_sdk_version=False)\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())\n",
"\n",
"with open(\"myenv.yml\",\"r\") as f:\n",
" print(f.read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile dockerfile\n",
"RUN apt-get update && apt-get install -y g++ "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import Model\n",
"# retrieve scoring explainer for deployment\n",
"scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"from azureml.core.image import ContainerImage\n",
"\n",
"# Use the custom scoring, docker, and conda files we created above\n",
"image_config = ContainerImage.image_configuration(execution_script=\"score_local_explain.py\",\n",
" docker_file=\"dockerfile\", \n",
" runtime=\"python\", \n",
" conda_file=\"myenv.yml\")\n",
"\n",
"# Use configs and models generated above\n",
"service = Webservice.deploy_from_model(workspace=ws,\n",
" name='model-scoring',\n",
" deployment_config=aciconfig,\n",
" models=[scoring_explainer_model, original_model],\n",
" image_config=image_config)\n",
"\n",
"service.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"import json\n",
"\n",
"\n",
"# Create data to test service with\n",
"sample_data = '{\"Age\":{\"899\":49},\"BusinessTravel\":{\"899\":\"Travel_Rarely\"},\"DailyRate\":{\"899\":1098},\"Department\":{\"899\":\"Research & Development\"},\"DistanceFromHome\":{\"899\":4},\"Education\":{\"899\":2},\"EducationField\":{\"899\":\"Medical\"},\"EnvironmentSatisfaction\":{\"899\":1},\"Gender\":{\"899\":\"Male\"},\"HourlyRate\":{\"899\":85},\"JobInvolvement\":{\"899\":2},\"JobLevel\":{\"899\":5},\"JobRole\":{\"899\":\"Manager\"},\"JobSatisfaction\":{\"899\":3},\"MaritalStatus\":{\"899\":\"Married\"},\"MonthlyIncome\":{\"899\":18711},\"MonthlyRate\":{\"899\":12124},\"NumCompaniesWorked\":{\"899\":2},\"OverTime\":{\"899\":\"No\"},\"PercentSalaryHike\":{\"899\":13},\"PerformanceRating\":{\"899\":3},\"RelationshipSatisfaction\":{\"899\":3},\"StockOptionLevel\":{\"899\":1},\"TotalWorkingYears\":{\"899\":23},\"TrainingTimesLastYear\":{\"899\":2},\"WorkLifeBalance\":{\"899\":4},\"YearsAtCompany\":{\"899\":1},\"YearsInCurrentRole\":{\"899\":0},\"YearsSinceLastPromotion\":{\"899\":0},\"YearsWithCurrManager\":{\"899\":0}}'\n",
"\n",
"\n",
"\n",
"headers = {'Content-Type':'application/json'}\n",
"\n",
"# send request to service\n",
"resp = requests.post(service.scoring_uri, sample_data, headers=headers)\n",
"\n",
"print(\"POST to url\", service.scoring_uri)\n",
"# can covert back to Python objects from json string if desired\n",
"print(\"prediction:\", resp.text)\n",
"result = json.loads(resp.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#plot the feature importance for the prediction\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt; plt.rcdefaults()\n",
"\n",
"labels = json.loads(sample_data)\n",
"labels = labels.keys()\n",
"objects = labels\n",
"y_pos = np.arange(len(objects))\n",
"performance = result[\"local_importance_values\"][0][0]\n",
"\n",
"plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
"plt.xticks(y_pos, objects)\n",
"locs, labels = plt.xticks()\n",
"plt.setp(labels, rotation=90)\n",
"plt.ylabel('Feature impact - leaving vs not leaving')\n",
"plt.title('Local feature importance for prediction')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next\n",
"Learn about other use cases of the explain package on a:\n",
"1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb) \n",
"1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
"1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
"1. Explain models with engineered features:\n",
" 1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
" 1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
"1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
"1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
"1. [Inferencing time: deploy a remotely-trained model and explainer](./train-explain-model-on-amlcompute-and-deploy.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "mesameki"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

2
how-to-use-azureml/explain-model/explain-tabular-data-raw-features/explain-sklearn-raw-features.yml → how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml
View File

@@ -1,4 +1,4 @@
name: explain-sklearn-raw-features
name: train-explain-model-locally-and-deploy
dependencies:
- pip:
- azureml-sdk

548
how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.ipynb Normal file
View File

@@ -0,0 +1,548 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright (c) Microsoft Corporation. All rights reserved.\n",
"\n",
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Train and explain models remotely via Azure Machine Learning Compute and deploy model and scoring explainer\n",
"\n",
"\n",
"_**This notebook illustrates how to use the Azure Machine Learning Interpretability SDK to train and explain a classification model remotely on an Azure Machine Leanrning Compute Target (AMLCompute), and use Azure Container Instances (ACI) for deploying your model and its corresponding scoring explainer as a web service.**_\n",
"\n",
"Problem: IBM employee attrition classification with scikit-learn (train a model and run an explainer remotely via AMLCompute, and deploy model and its corresponding explainer.)\n",
"\n",
"---\n",
"\n",
"## Table of Contents\n",
"\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Run model explainer locally at training time](#Explain)\n",
" 1. Apply feature transformations\n",
" 1. Train a binary classification model\n",
" 1. Explain the model on raw features\n",
" 1. Generate global explanations\n",
" 1. Generate local explanations\n",
"1. [Visualize results](#Visualize)\n",
"1. [Deploy model and scoring explainer](#Deploy)\n",
"1. [Next steps](#Next)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"This notebook showcases how to train and explain a classification model remotely via Azure Machine Learning Compute (AMLCompute), download the calculated explanations locally for visualization and inspection, and deploy the final model and its corresponding explainer to Azure Container Instances (ACI).\n",
"It demonstrates the API calls that you need to make to submit a run for training and explaining a model to AMLCompute, download the compute explanations remotely, and visualizing the global and local explanations via a visualization dashboard that provides an interactive way of discovering patterns in model predictions and downloaded explanations, and using Azure Machine Learning MLOps capabilities to deploy your model and its corresponding explainer.\n",
"\n",
"We will showcase one of the tabular data explainers: TabularExplainer (SHAP) and follow these steps:\n",
"1.\tDevelop a machine learning script in Python which involves the training script and the explanation script.\n",
"2.\tCreate and configure a compute target.\n",
"3.\tSubmit the scripts to the configured compute target to run in that environment. During training, the scripts can read from or write to datastore. And the records of execution (e.g., model, metrics, prediction explanations) are saved as runs in the workspace and grouped under experiments.\n",
"4.\tQuery the experiment for logged metrics and explanations from the current and past runs. Use the interpretability toolkit\u00e2\u20ac\u2122s visualization dashboard to visualize predictions and their explanation. If the metrics and explanations don't indicate a desired outcome, loop back to step 1 and iterate on your scripts.\n",
"5.\tAfter a satisfactory run is found, create a scoring explainer and register the persisted model and its corresponding explainer in the model registry.\n",
"6.\tDevelop a scoring script.\n",
"7.\tCreate an image and register it in the image registry.\n",
"8.\tDeploy the image as a web service in Azure.\n",
"\n",
"| ![azure-machine-learning-cycle](./img/azure-machine-learning-cycle.PNG) |\n",
"|:--:|"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup\n",
"Make sure you go through the [configuration notebook](../../../../configuration.ipynb) first if you haven't."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Check core SDK version number\n",
"import azureml.core\n",
"\n",
"print(\"SDK version:\", azureml.core.VERSION)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize a Workspace\n",
"\n",
"Initialize a workspace object from persisted configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"create workspace"
]
},
"outputs": [],
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explain\n",
"\n",
"Create An Experiment: **Experiment** is a logical container in an Azure ML Workspace. It hosts run records which can include run metrics and output artifacts from your experiments."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core import Experiment\n",
"experiment_name = 'explainer-remote-run-on-amlcompute'\n",
"experiment = Experiment(workspace=ws, name=experiment_name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction to AmlCompute\n",
"\n",
"Azure Machine Learning Compute is managed compute infrastructure that allows the user to easily create single to multi-node compute of the appropriate VM Family. It is created **within your workspace region** and is a resource that can be used by other users in your workspace. It autoscales by default to the max_nodes, when a job is submitted, and executes in a containerized environment packaging the dependencies as specified by the user. \n",
"\n",
"Since it is managed compute, job scheduling and cluster management are handled internally by Azure Machine Learning service. \n",
"\n",
"For more information on Azure Machine Learning Compute, please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)\n",
"\n",
"If you are an existing BatchAI customer who is migrating to Azure Machine Learning, please read [this article](https://aka.ms/batchai-retirement)\n",
"\n",
"**Note**: As with other Azure services, there are limits on certain resources (for eg. AmlCompute quota) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota.\n",
"\n",
"\n",
"The training script `run_explainer.py` is already created for you. Let's have a look."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Submit an AmlCompute run in a few different ways\n",
"\n",
"First lets check which VM families are available in your region. Azure is a regional service and some specialized SKUs (especially GPUs) are only available in certain regions. Since AmlCompute is created in the region of your workspace, we will use the supported_vms () function to see if the VM family we want to use ('STANDARD_D2_V2') is supported.\n",
"\n",
"You can also pass a different region to check availability and then re-create your workspace in that region through the [configuration notebook](../../../configuration.ipynb)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.compute import ComputeTarget, AmlCompute\n",
"\n",
"AmlCompute.supported_vmsizes(workspace=ws)\n",
"# AmlCompute.supported_vmsizes(workspace=ws, location='southcentralus')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create project directory\n",
"\n",
"Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script, and any additional files your training script depends on"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"\n",
"project_folder = './explainer-remote-run-on-amlcompute'\n",
"os.makedirs(project_folder, exist_ok=True)\n",
"shutil.copy('train_explain.py', project_folder)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Provision as a run based compute target\n",
"\n",
"You can provision AmlCompute as a compute target at run-time. In this case, the compute is auto-created for your run, scales up to max_nodes that you specify, and then **deleted automatically** after the run completes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.runconfig import RunConfiguration\n",
"from azureml.core.conda_dependencies import CondaDependencies\n",
"from azureml.core.runconfig import DEFAULT_CPU_IMAGE\n",
"\n",
"# create a new runconfig object\n",
"run_config = RunConfiguration()\n",
"\n",
"# signal that you want to use AmlCompute to execute script.\n",
"run_config.target = \"amlcompute\"\n",
"\n",
"# AmlCompute will be created in the same region as workspace\n",
"# Set vm size for AmlCompute\n",
"run_config.amlcompute.vm_size = 'STANDARD_D2_V2'\n",
"\n",
"# enable Docker \n",
"run_config.environment.docker.enabled = True\n",
"\n",
"# set Docker base image to the default CPU-based image\n",
"run_config.environment.docker.base_image = DEFAULT_CPU_IMAGE\n",
"\n",
"# use conda_dependencies.yml to create a conda environment in the Docker image for execution\n",
"run_config.environment.python.user_managed_dependencies = False\n",
"\n",
"# auto-prepare the Docker image when used for execution (if it is not already prepared)\n",
"run_config.auto_prepare_environment = True\n",
"\n",
"azureml_pip_packages = [\n",
" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
" 'azureml-explain-model', 'azureml-dataprep'\n",
"]\n",
" \n",
"\n",
"\n",
"# specify CondaDependencies obj\n",
"run_config.environment.python.conda_dependencies = CondaDependencies.create(conda_packages=['scikit-learn'],\n",
" pip_packages=['sklearn_pandas', 'pyyaml'] + azureml_pip_packages,\n",
" pin_sdk_version=False)\n",
"# Now submit a run on AmlCompute\n",
"from azureml.core.script_run_config import ScriptRunConfig\n",
"\n",
"script_run_config = ScriptRunConfig(source_directory=project_folder,\n",
" script='train_explain.py',\n",
" run_config=run_config)\n",
"\n",
"run = experiment.submit(script_run_config)\n",
"\n",
"# Show run details\n",
"run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: if you need to cancel a run, you can follow [these instructions](https://aka.ms/aml-docs-cancel-run)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%time\n",
"# Shows output of the run on stdout.\n",
"run.wait_for_completion(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Delete () is used to deprovision and delete the AmlCompute target. Useful if you want to re-use the compute name \n",
"# 'cpucluster' in this case but use a different VM family for instance.\n",
"\n",
"# cpu_cluster.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download Model Explanation, Model, and Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# retrieve model for visualization and deployment\n",
"from azureml.core.model import Model\n",
"from sklearn.externals import joblib\n",
"original_model = Model(ws, 'amlcompute_deploy_model')\n",
"model_path = original_model.download(exist_ok=True)\n",
"original_svm_model = joblib.load(model_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# retrieve global explanation for visualization\n",
"from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
"\n",
"# get model explanation data\n",
"client = ExplanationClient.from_run(run)\n",
"global_explanation = client.download_model_explanation()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# retrieve x_test for visualization\n",
"from sklearn.externals import joblib\n",
"x_test_path = './x_test.pkl'\n",
"run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n",
"x_test = joblib.load(x_test_path)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize\n",
"Visualize the explanations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.contrib.explain.model.visualize import ExplanationDashboard"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ExplanationDashboard(global_explanation, original_svm_model, x_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy\n",
"Deploy Model and ScoringExplainer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import AciWebservice\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
" memory_gb=1, \n",
" tags={\"data\": \"IBM_Attrition\", \n",
" \"method\" : \"local_explanation\"}, \n",
" description='Get local explanations for IBM Employee Attrition data')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"# WARNING: to install this, g++ needs to be available on the Docker image and is not by default (look at the next cell)\n",
"azureml_pip_packages = [\n",
" 'azureml-defaults', 'azureml-contrib-explain-model', 'azureml-core', 'azureml-telemetry',\n",
" 'azureml-explain-model'\n",
"]\n",
" \n",
"\n",
"# specify CondaDependencies obj\n",
"myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas'],\n",
" pip_packages=['sklearn-pandas', 'pyyaml'] + azureml_pip_packages,\n",
" pin_sdk_version=False)\n",
"\n",
"with open(\"myenv.yml\",\"w\") as f:\n",
" f.write(myenv.serialize_to_string())\n",
"\n",
"with open(\"myenv.yml\",\"r\") as f:\n",
" print(f.read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%writefile dockerfile\n",
"RUN apt-get update && apt-get install -y g++ "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# retrieve scoring explainer for deployment\n",
"scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"from azureml.core.image import ContainerImage\n",
"\n",
"# Use the custom scoring, docker, and conda files we created above\n",
"image_config = ContainerImage.image_configuration(execution_script=\"score_remote_explain.py\",\n",
" docker_file=\"dockerfile\", \n",
" runtime=\"python\", \n",
" conda_file=\"myenv.yml\")\n",
"\n",
"# Use configs and models generated above\n",
"service = Webservice.deploy_from_model(workspace=ws,\n",
" name='model-scoring-service',\n",
" deployment_config=aciconfig,\n",
" models=[scoring_explainer_model, original_model],\n",
" image_config=image_config)\n",
"\n",
"service.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import requests\n",
"\n",
"# create data to test service with\n",
"examples = x_test[:4]\n",
"input_data = examples.to_json()\n",
"\n",
"headers = {'Content-Type':'application/json'}\n",
"\n",
"# send request to service\n",
"resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
"\n",
"print(\"POST to url\", service.scoring_uri)\n",
"# can covert back to Python objects from json string if desired\n",
"print(\"prediction:\", resp.text)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next\n",
"Learn about other use cases of the explain package on a:\n",
"1. [Training time: regression problem](../../tabular-data/explain-binary-classification-local.ipynb) \n",
"1. [Training time: binary classification problem](../../tabular-data/explain-binary-classification-local.ipynb)\n",
"1. [Training time: multiclass classification problem](../../tabular-data/explain-multiclass-classification-local.ipynb)\n",
"1. Explain models with engineered features:\n",
" 1. [Simple feature transformations](../../tabular-data/simple-feature-transformations-explain-local.ipynb)\n",
" 1. [Advanced feature transformations](../../tabular-data/advanced-feature-transformations-explain-local.ipynb)\n",
"1. [Save model explanations via Azure Machine Learning Run History](../run-history/save-retrieve-explanations-run-history.ipynb)\n",
"1. [Run explainers remotely on Azure Machine Learning Compute (AMLCompute)](../remote-explanation/explain-model-on-amlcompute.ipynb)\n",
"1. [Inferencing time: deploy a locally-trained model and explainer](./train-explain-model-locally-and-deploy.ipynb)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "mesameki"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python36"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

8
how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml Normal file
View File

@@ -0,0 +1,8 @@
name: train-explain-model-on-amlcompute-and-deploy
dependencies:
- pip:
- azureml-sdk
- azureml-explain-model
- azureml-contrib-explain-model
- sklearn-pandas
- azureml-dataprep

129
how-to-use-azureml/explain-model/azure-integration/scoring-time/train_explain.py Normal file
View File

@@ -0,0 +1,129 @@
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import os
import pandas as pd
import zipfile
from sklearn.model_selection import train_test_split
from sklearn.externals import joblib
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn_pandas import DataFrameMapper
from azureml.core.run import Run
from azureml.explain.model.tabular_explainer import TabularExplainer
from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient
from azureml.explain.model.scoring.scoring_explainer import LinearScoringExplainer, save
OUTPUT_DIR = './outputs/'
os.makedirs(OUTPUT_DIR, exist_ok=True)
# get the IBM employee attrition dataset
outdirname = 'dataset.6.21.19'
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
zipfilename = outdirname + '.zip'
urlretrieve('https://publictestdatasets.blob.core.windows.net/data/' + zipfilename, zipfilename)
with zipfile.ZipFile(zipfilename, 'r') as unzip:
unzip.extractall('.')
attritionData = pd.read_csv('./WA_Fn-UseC_-HR-Employee-Attrition.csv')
# dropping Employee count as all values are 1 and hence attrition is independent of this feature
attritionData = attritionData.drop(['EmployeeCount'], axis=1)
# dropping Employee Number since it is merely an identifier
attritionData = attritionData.drop(['EmployeeNumber'], axis=1)
attritionData = attritionData.drop(['Over18'], axis=1)
# since all values are 80
attritionData = attritionData.drop(['StandardHours'], axis=1)
# converting target variables from string to numerical values
target_map = {'Yes': 1, 'No': 0}
attritionData["Attrition_numerical"] = attritionData["Attrition"].apply(lambda x: target_map[x])
target = attritionData["Attrition_numerical"]
attritionXData = attritionData.drop(['Attrition_numerical', 'Attrition'], axis=1)
# creating dummy columns for each categorical feature
categorical = []
for col, value in attritionXData.iteritems():
if value.dtype == 'object':
categorical.append(col)
# store the numerical columns
numerical = attritionXData.columns.difference(categorical)
numeric_transformations = [([f], Pipeline(steps=[
('imputer', SimpleImputer(strategy='median')),
('scaler', StandardScaler())])) for f in numerical]
categorical_transformations = [([f], OneHotEncoder(handle_unknown='ignore', sparse=False)) for f in categorical]
transformations = numeric_transformations + categorical_transformations
# append classifier to preprocessing pipeline
clf = Pipeline(steps=[('preprocessor', DataFrameMapper(transformations)),
('classifier', LogisticRegression(solver='lbfgs'))])
# get the run this was submitted from to interact with run history
run = Run.get_context()
# create an explanation client to store the explanation (contrib API)
client = ExplanationClient.from_run(run)
# Split data into train and test
x_train, x_test, y_train, y_test = train_test_split(attritionXData,
target,
test_size=0.2,
random_state=0,
stratify=target)
# write x_test out as a pickle file for later visualization
x_test_pkl = 'x_test.pkl'
with open(x_test_pkl, 'wb') as file:
joblib.dump(value=x_test, filename=os.path.join(OUTPUT_DIR, x_test_pkl))
run.upload_file('x_test_ibm.pkl', os.path.join(OUTPUT_DIR, x_test_pkl))
# preprocess the data and fit the classification model
clf.fit(x_train, y_train)
model = clf.steps[-1][1]
# save model for use outside the script
model_file_name = 'log_reg.pkl'
with open(model_file_name, 'wb') as file:
joblib.dump(value=clf, filename=os.path.join(OUTPUT_DIR, model_file_name))
# register the model with the model management service for later use
run.upload_file('original_model.pkl', os.path.join(OUTPUT_DIR, model_file_name))
original_model = run.register_model(model_name='amlcompute_deploy_model',
model_path='original_model.pkl')
# create an explainer to validate or debug the model
tabular_explainer = TabularExplainer(model,
initialization_examples=x_train,
features=attritionXData.columns,
classes=["Not leaving", "leaving"],
transformations=transformations)
# explain overall model predictions (global explanation)
# passing in test dataset for evaluation examples - note it must be a representative sample of the original data
# more data (e.g. x_train) will likely lead to higher accuracy, but at a time cost
global_explanation = tabular_explainer.explain_global(x_test)
# uploading model explanation data for storage or visualization
comment = 'Global explanation on classification model trained on IBM employee attrition dataset'
client.upload_model_explanation(global_explanation, comment=comment)
# also create a lightweight explainer for scoring time
scoring_explainer = LinearScoringExplainer(tabular_explainer)
# pickle scoring explainer locally
save(scoring_explainer, directory=OUTPUT_DIR, exist_ok=True)
# register scoring explainer
run.upload_file('IBM_attrition_explainer.pkl', os.path.join(OUTPUT_DIR, 'scoring_explainer.pkl'))
scoring_explainer_model = run.register_model(model_name='IBM_attrition_explainer',
model_path='IBM_attrition_explainer.pkl')

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