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update samples from Release-149 as a part of 1.0.65 SDK release

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30
.github/ISSUE_TEMPLATE/bug_report.md vendored
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@@ -1,30 +0,0 @@
---
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
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---
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:
```

4
README.md
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@@ -58,8 +58,10 @@ Visit this [community repository](https://github.com/microsoft/MLOps/tree/master
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)
- [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp)
- [Pre-Train BERT 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)
- [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
## Data/Telemetry
This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)

2
configuration.ipynb
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@@ -103,7 +103,7 @@
"source": [
"import azureml.core\n",
"\n",
"print(\"This notebook was created using version 1.0.62 of the Azure ML SDK\")\n",
"print(\"This notebook was created using version 1.0.65 of the Azure ML SDK\")\n",
"print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
]
},

307
contrib/RAPIDS/README.md
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@@ -1,307 +0,0 @@
## How to use the RAPIDS on AzureML materials
### Setting up requirements
The material requires the use of the Azure ML SDK and of the Jupyter Notebook Server to run the interactive execution. Please refer to instructions to [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") Follow the instructions under **Local Computer**, make sure to run the last step: <span style="font-family: Courier New;">pip install \<new package\></span> with <span style="font-family: Courier New;">new package = progressbar2 (pip install progressbar2)</span>
After following the directions, the user should end up setting a conda environment (<span style="font-family: Courier New;">myenv</span>)that can be activated in an Anaconda prompt
The user would also require an Azure Subscription with a Machine Learning Services quota on the desired region for 24 nodes or more (to be able to select a vmSize with 4 GPUs as it is used on the Notebook) on the desired VM family ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)), the specific vmSize to be used within the chosen family would also need to be whitelisted for Machine Learning Services usage.
&nbsp;
### Getting and running the material
Clone the AzureML Notebooks repository in GitHub by running the following command on a local_directory:
* C:\local_directory>git clone https://github.com/Azure/MachineLearningNotebooks.git
On a conda prompt navigate to the local directory, activate the conda environment (<span style="font-family: Courier New;">myenv</span>), where the Azure ML SDK was installed and launch Jupyter Notebook.
* (<span style="font-family: Courier New;">myenv</span>) C:\local_directory>jupyter notebook
From the resulting browser at http://localhost:8888/tree, navigate to the master notebook:
* http://localhost:8888/tree/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
&nbsp;
The following notebook will appear:
![](imgs/NotebookHome.png)
&nbsp;
### Master Jupyter Notebook
The notebook can be executed interactively step by step, by pressing the Run button (In a red circle in the above image.)
The first couple of functional steps import the necessary AzureML libraries. If you experience any errors please refer back to the [setup the environment.](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#local "Local Computer Set Up") instructions.
&nbsp;
#### Setting up a Workspace
The following step gathers the information necessary to set up a workspace to execute the RAPIDS script. This needs to be done only once, or not at all if you already have a workspace you can use set up on the Azure Portal:
![](imgs/WorkSpaceSetUp.png)
It is important to be sure to set the correct values for the subscription\_id, resource\_group, workspace\_name, and region before executing the step. An example is:
subscription_id = os.environ.get("SUBSCRIPTION_ID", "1358e503-xxxx-4043-xxxx-65b83xxxx32d")
resource_group = os.environ.get("RESOURCE_GROUP", "AML-Rapids-Testing")
workspace_name = os.environ.get("WORKSPACE_NAME", "AML_Rapids_Tester")
workspace_region = os.environ.get("WORKSPACE_REGION", "West US 2")
&nbsp;
The resource\_group and workspace_name could take any value, the region should match the region for which the subscription has the required Machine Learning Services node quota.
The first time the code is executed it will redirect to the Azure Portal to validate subscription credentials. After the workspace is created, its related information is stored on a local file so that this step can be subsequently skipped. The immediate step will just load the saved workspace
![](imgs/saved_workspace.png)
Once a workspace has been created the user could skip its creation and just jump to this step. The configuration file resides in:
* C:\local_directory\\MachineLearningNotebooks\contrib\RAPIDS\aml_config\config.json
&nbsp;
#### Creating an AML Compute Target
Following step, creates an AML Compute Target
![](imgs/target_creation.png)
Parameter vm\_size on function call AmlCompute.provisioning\_configuration() has to be a member of the VM families ([NC\_v3](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv3-series), [NC\_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ncv2-series), [ND](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#nd-series) or [ND_v2](https://docs.microsoft.com/en-us/azure/virtual-machines/windows/sizes-gpu#ndv2-series-preview)) that are the ones provided with P40 or V100 GPUs, that are the ones supported by RAPIDS. In this particular case an Standard\_NC24s\_V2 was used.
&nbsp;
If the output of running the step has an error of the form:
![](imgs/targeterror1.png)
It is an indication that even though the subscription has a node quota for VMs for that family, it does not have a node quota for Machine Learning Services for that family.
You will need to request an increase node quota for that family in that region for **Machine Learning Services**.
&nbsp;
Another possible error is the following:
![](imgs/targeterror2.png)
Which indicates that specified vmSize has not been whitelisted for usage on Machine Learning Services and a request to do so should be filled.
The successful creation of the compute target would have an output like the following:
![](imgs/targetsuccess.png)
&nbsp;
#### RAPIDS script uploading and viewing
The next step copies the RAPIDS script process_data.py, which is a slightly modified implementation of the [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb), into a script processing folder and it presents its contents to the user. (The script is discussed in the next section in detail).
If the user wants to use a different RAPIDS script, the references to the <span style="font-family: Courier New;">process_data.py</span> script have to be changed
![](imgs/scriptuploading.png)
&nbsp;
#### Data Uploading
The RAPIDS script loads and extracts features from the Fannie Mae’s Mortgage Dataset to train an XGBoost prediction model. The script uses two years of data
The next few steps download and decompress the data and is made available to the script as an [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data).
&nbsp;
The following functions are used to download and decompress the input data
![](imgs/dcf1.png)
![](imgs/dcf2.png)
![](imgs/dcf3.png)
![](imgs/dcf4.png)
&nbsp;
The next step uses those functions to download locally file:
http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/mortgage_2000-2001.tgz'
And to decompress it, into local folder path = .\mortgage_2000-2001
The step takes several minutes, the intermediate outputs provide progress indicators.
![](imgs/downamddecom.png)
&nbsp;
The decompressed data should have the following structure:
* .\mortgage_2000-2001\acq\Acquisition_<year>Q<num>.txt
* .\mortgage_2000-2001\perf\Performance_<year>Q<num>.txt
* .\mortgage_2000-2001\names.csv
The data is divided in partitions that roughly correspond to yearly quarters. RAPIDS includes support for multi-node, multi-GPU deployments, enabling scaling up and out on much larger dataset sizes. The user will be able to verify that the number of partitions that the script is able to process increases with the number of GPUs used. The RAPIDS script is implemented for single-machine scenarios. An example supporting multiple nodes will be published later.
&nbsp;
The next step upload the data into the [Azure Machine Learning Datastore](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-access-data) under reference <span style="font-family: Courier New;">fileroot = mortgage_2000-2001</span>
The step takes several minutes to load the data, the output provides a progress indicator.
![](imgs/datastore.png)
Once the data has been loaded into the Azure Machine LEarning Data Store, in subsequent run, the user can comment out the ds.upload line and just make reference to the <span style="font-family: Courier New;">mortgage_2000-2001</blog> data store reference
&nbsp;
#### Setting up required libraries and environment to run RAPIDS code
There are two options to setup the environment to run RAPIDS code. The following steps shows how to ues a prebuilt conda environment. A recommended alternative is to specify a base Docker image and package dependencies. You can find sample code for that in the notebook.
![](imgs/install2.png)
&nbsp;
#### Wrapper function to submit the RAPIDS script as an Azure Machine Learning experiment
The next step consists of the definition of a wrapper function to be used when the user attempts to run the RAPIDS script with different arguments. It takes as arguments: <span style="font-family: Times New Roman;">*cpu\_training*</span>; a flag that indicates if the run is meant to be processed with CPU-only, <span style="font-family: Times New Roman;">*gpu\_count*</span>; the number of GPUs to be used if they are meant to be used and part_count: the number of data partitions to be used
![](imgs/wrapper.png)
&nbsp;
The core of the function resides in configuring the run by the instantiation of a ScriptRunConfig object, which defines the source_directory for the script to be executed, the name of the script and the arguments to be passed to the script.
In addition to the wrapper function arguments, two other arguments are passed: <span style="font-family: Times New Roman;">*data\_dir*</span>, the directory where the data is stored and <span style="font-family: Times New Roman;">*end_year*</span> is the largest year to use partition from.
As mentioned earlier the size of the data that can be processed increases with the number of gpus, in the function, dictionary <span style="font-family: Times New Roman;">*max\_gpu\_count\_data\_partition_mapping*</span> maps the maximum number of partitions that we empirically found that the system can handle given the number of GPUs used. The function throws a warning when the number of partitions for a given number of gpus exceeds the maximum but the script is still executed, however the user should expect an error as an out of memory situation would be encountered
If the user wants to use a different RAPIDS script, the reference to the process_data.py script has to be changed
&nbsp;
#### Submitting Experiments
We are ready to submit experiments: launching the RAPIDS script with different sets of parameters.
&nbsp;
The following couple of steps submit experiments under different conditions.
![](imgs/submission1.png)
&nbsp;
The user can change variable num\_gpu between one and the number of GPUs supported by the chosen vmSize. Variable part\_count can take any value between 1 and 11, but if it exceeds the maximum for num_gpu, the run would result in an error
&nbsp;
If the experiment is successfully submitted, it would be placed on a queue for processing, its status would appeared as Queued and an output like the following would appear
![](imgs/queue.png)
&nbsp;
When the experiment starts running, its status would appeared as Running and the output would change to something like this:
![](imgs/running.png)
&nbsp;
#### Reproducing the performance gains plot results on the Blog Post
When the run has finished successfully, its status would appeared as Completed and the output would change to something like this:
&nbsp;
![](imgs/completed.png)
Which is the output for an experiment run with three partitions and one GPU, notice that the reported processing time is 49.16 seconds just as depicted on the performance gains plot on the blog post
&nbsp;
![](imgs/2GPUs.png)
This output corresponds to a run with three partitions and two GPUs, notice that the reported processing time is 37.50 seconds just as depicted on the performance gains plot on the blog post
&nbsp;
![](imgs/3GPUs.png)
This output corresponds to an experiment run with three partitions and three GPUs, notice that the reported processing time is 24.40 seconds just as depicted on the performance gains plot on the blog post
&nbsp;
![](imgs/4gpus.png)
This output corresponds to an experiment run with three partitions and four GPUs, notice that the reported processing time is 23.33 seconds just as depicted on the performance gains plot on the blogpost
&nbsp;
![](imgs/CPUBase.png)
This output corresponds to an experiment run with three partitions and using only CPU, notice that the reported processing time is 9 minutes and 1.21 seconds or 541.21 second just as depicted on the performance gains plot on the blog post
&nbsp;
![](imgs/OOM.png)
This output corresponds to an experiment run with nine partitions and four GPUs, notice that the notebook throws a warning signaling that the number of partitions exceed the maximum that the system can handle with those many GPUs and the run ends up failing, hence having and status of Failed.
&nbsp;
##### Freeing Resources
In the last step the notebook deletes the compute target. (This step is optional especially if the min_nodes in the cluster is set to 0 with which the cluster will scale down to 0 nodes when there is no usage.)
![](imgs/clusterdelete.png)
&nbsp;
### RAPIDS Script
The Master Notebook runs experiments by launching a RAPIDS script with different sets of parameters. In this section, the RAPIDS script, process_data.py in the material, is analyzed
The script first imports all the necessary libraries and parses the arguments passed by the Master Notebook.
The all internal functions to be used by the script are defined.
&nbsp;
#### Wrapper Auxiliary Functions:
The below functions are wrappers for a configuration module for librmm, the RAPIDS Memory Manager python interface:
![](imgs/wap1.png)![](imgs/wap2.png)
&nbsp;
A couple of other functions are wrappers for the submission of jobs to the DASK client:
![](imgs/wap3.png)
![](imgs/wap4.png)
&nbsp;
#### Data Loading Functions:
The data is loaded through the use of the following three functions
![](imgs/DLF1.png)![](imgs/DLF2.png)![](imgs/DLF3.png)
All three functions use library function cudf.read_csv(), cuDF version for the well known counterpart on Pandas.
&nbsp;
#### Data Transformation and Feature Extraction Functions:
The raw data is transformed and processed to extract features by joining, slicing, grouping, aggregating, factoring, etc, the original dataframes just as is done with Pandas. The following functions in the script are used for that purpose:
![](imgs/fef1.png)![](imgs/fef2.png)![](imgs/fef3.png)![](imgs/fef4.png)![](imgs/fef5.png)
![](imgs/fef6.png)![](imgs/fef7.png)![](imgs/fef8.png)![](imgs/fef9.png)
&nbsp;
#### Main() Function
The previous functions are used in the Main function to accomplish several steps: Set up the Dask client, do all ETL operations, set up and train an XGBoost model, the function also assigns which data needs to be processed by each Dask client
&nbsp;
##### Setting Up DASK client:
The following lines:
![](imgs/daskini.png)
&nbsp;
Initialize and set up a DASK client with a number of workers corresponding to the number of GPUs to be used on the run. A successful execution of the set up will result on the following output:
![](imgs/daskoutput.png)
##### All ETL functions are used on single calls to process\_quarter_gpu, one per data partition
![](imgs/ETL.png)
&nbsp;
##### Concentrating the data assigned to each DASK worker
The partitions assigned to each worker are concatenated and set up for training.
![](imgs/Dask2.png)
&nbsp;
##### Setting Training Parameters
The parameters used for the training of a gradient boosted decision tree model are set up in the following code block:
![](imgs/PArameters.png)
Notice how the parameters are modified when using the CPU-only mode.
&nbsp;
##### Launching the training of a gradient boosted decision tree model using XGBoost.
![](imgs/training.png)
The outputs of the script can be observed in the master notebook as the script is executed
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/RAPIDS/README.png)

69
contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb
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@@ -9,13 +9,6 @@
"Licensed under the MIT License."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/RAPIDS/azure-ml-with-nvidia-rapids/azure-ml-with-nvidia-rapids.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -27,7 +20,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model in Azure.\n",
"The [RAPIDS](https://www.developer.nvidia.com/rapids) suite of software libraries from NVIDIA enables the execution of end-to-end data science and analytics pipelines entirely on GPUs. In many machine learning projects, a significant portion of the model training time is spent in setting up the data; this stage of the process is known as Extraction, Transformation and Loading, or ETL. By using the DataFrame API for ETL\u00c3\u201a\u00c2\u00a0and GPU-capable ML algorithms in RAPIDS, data preparation and training models can be done in GPU-accelerated end-to-end pipelines without incurring serialization costs between the pipeline stages. This notebook demonstrates how to use NVIDIA RAPIDS to prepare data and train model\u00c2\u00a0in Azure.\n",
" \n",
"In this notebook, we will do the following:\n",
" \n",
@@ -126,10 +119,8 @@
"outputs": [],
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# if a locally-saved configuration file for the workspace is not available, use the following to load workspace\n",
"# ws = Workspace(subscription_id=subscription_id, resource_group=resource_group, workspace_name=workspace_name)\n",
"\n",
"print('Workspace name: ' + ws.name, \n",
" 'Azure region: ' + ws.location, \n",
" 'Subscription id: ' + ws.subscription_id, \n",
@@ -170,7 +161,7 @@
"if gpu_cluster_name in ws.compute_targets:\n",
" gpu_cluster = ws.compute_targets[gpu_cluster_name]\n",
" if gpu_cluster and type(gpu_cluster) is AmlCompute:\n",
" print('Found compute target. Will use {0} '.format(gpu_cluster_name))\n",
" print('found compute target. just use it. ' + gpu_cluster_name)\n",
"else:\n",
" print(\"creating new cluster\")\n",
" # vm_size parameter below could be modified to one of the RAPIDS-supported VM types\n",
@@ -192,7 +183,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The _process&#95;data.py_ script used in the step below is a slightly modified implementation of [RAPIDS Mortgage E2E example](https://github.com/rapidsai/notebooks-contrib/blob/master/intermediate_notebooks/E2E/mortgage/mortgage_e2e.ipynb)."
"The _process&#95;data.py_ script used in the step below is a slightly modified implementation of [RAPIDS E2E example](https://github.com/rapidsai/notebooks/blob/master/mortgage/E2E.ipynb)."
]
},
{
@@ -203,7 +194,10 @@
"source": [
"# copy process_data.py into the script folder\n",
"import shutil\n",
"shutil.copy('./process_data.py', os.path.join(scripts_folder, 'process_data.py'))"
"shutil.copy('./process_data.py', os.path.join(scripts_folder, 'process_data.py'))\n",
"\n",
"with open(os.path.join(scripts_folder, './process_data.py'), 'r') as process_data_script:\n",
" print(process_data_script.read())"
]
},
{
@@ -227,6 +221,13 @@
"### Downloading Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<font color='red'>Important</font>: Python package progressbar2 is necessary to run the following cell. If it is not available in your environment where this notebook is running, please install it."
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -236,6 +237,7 @@
"import tarfile\n",
"import hashlib\n",
"from urllib.request import urlretrieve\n",
"from progressbar import ProgressBar\n",
"\n",
"def validate_downloaded_data(path):\n",
" if(os.path.isdir(path) and os.path.exists(path + '//names.csv')) :\n",
@@ -265,7 +267,7 @@
" url_format = 'http://rapidsai-data.s3-website.us-east-2.amazonaws.com/notebook-mortgage-data/{0}.tgz'\n",
" url = url_format.format(fileroot)\n",
" print(\"...Downloading file :{0}\".format(filename))\n",
" urlretrieve(url, filename)\n",
" urlretrieve(url, filename,show_progress)\n",
" pbar.finish()\n",
" print(\"...File :{0} finished downloading\".format(filename))\n",
" else:\n",
@@ -280,7 +282,9 @@
" so_far = 0\n",
" for member_info in members:\n",
" tar.extract(member_info,path=path)\n",
" show_progress(so_far, 1, numFiles)\n",
" so_far += 1\n",
" pbar.finish()\n",
" print(\"...All {0} files have been decompressed\".format(numFiles))\n",
" tar.close()"
]
@@ -320,9 +324,7 @@
"\n",
"# download and uncompress data in a local directory before uploading to data store\n",
"# directory specified in src_dir parameter below should have the acq, perf directories with data and names.csv file\n",
"\n",
"# ---->>>> UNCOMMENT THE BELOW LINE TO UPLOAD YOUR DATA IF NOT DONE SO ALREADY <<<<----\n",
"# ds.upload(src_dir=path, target_path=fileroot, overwrite=True, show_progress=True)\n",
"ds.upload(src_dir=path, target_path=fileroot, overwrite=True, show_progress=True)\n",
"\n",
"# data already uploaded to the datastore\n",
"data_ref = DataReference(data_reference_name='data', datastore=ds, path_on_datastore=fileroot)"
@@ -358,7 +360,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The following code shows how to install RAPIDS using conda. The `rapids.yml` file contains the list of packages necessary to run this tutorial. **NOTE:** Initial build of the image might take up to 20 minutes as the service needs to build and cache the new image; once the image is built the subequent runs use the cached image and the overhead is minimal."
"The following code shows how to use an existing image from [Docker Hub](https://hub.docker.com/r/rapidsai/rapidsai/) that has a prebuilt conda environment named 'rapids' when creating a RunConfiguration. Note that this conda environment does not include azureml-defaults package that is required for using AML functionality like metrics tracking, model management etc. This package is automatically installed when you use 'Specify package dependencies' option and that is why it is the recommended option to create RunConfiguraiton in AML."
]
},
{
@@ -367,13 +369,17 @@
"metadata": {},
"outputs": [],
"source": [
"cd = CondaDependencies(conda_dependencies_file_path='rapids.yml')\n",
"run_config = RunConfiguration(conda_dependencies=cd)\n",
"run_config = RunConfiguration()\n",
"run_config.framework = 'python'\n",
"run_config.environment.python.user_managed_dependencies = True\n",
"run_config.environment.python.interpreter_path = '/conda/envs/rapids/bin/python'\n",
"run_config.target = gpu_cluster_name\n",
"run_config.environment.docker.enabled = True\n",
"run_config.environment.docker.gpu_support = True\n",
"run_config.environment.docker.base_image = \"mcr.microsoft.com/azureml/base-gpu:intelmpi2018.3-cuda10.0-cudnn7-ubuntu16.04\"\n",
"run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2-runtime-ubuntu18.04\"\n",
"# run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
"# run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
"# run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
"run_config.environment.spark.precache_packages = False\n",
"run_config.data_references={'data':data_ref.to_config()}"
]
@@ -382,14 +388,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Using Docker"
"#### Specify package dependencies"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, you can specify RAPIDS Docker image."
"The following code shows how to list package dependencies in a conda environment definition file (rapids.yml) when creating a RunConfiguration"
]
},
{
@@ -398,17 +404,16 @@
"metadata": {},
"outputs": [],
"source": [
"# run_config = RunConfiguration()\n",
"# cd = CondaDependencies(conda_dependencies_file_path='rapids.yml')\n",
"# run_config = RunConfiguration(conda_dependencies=cd)\n",
"# run_config.framework = 'python'\n",
"# run_config.environment.python.user_managed_dependencies = True\n",
"# run_config.environment.python.interpreter_path = '/conda/envs/rapids/bin/python'\n",
"# run_config.target = gpu_cluster_name\n",
"# run_config.environment.docker.enabled = True\n",
"# run_config.environment.docker.gpu_support = True\n",
"# run_config.environment.docker.base_image = \"rapidsai/rapidsai:cuda9.2-runtime-ubuntu18.04\"\n",
"# # run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
"# # run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
"# # run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
"# run_config.environment.docker.base_image = \"<image>\"\n",
"# run_config.environment.docker.base_image_registry.address = '<registry_url>' # not required if the base_image is in Docker hub\n",
"# run_config.environment.docker.base_image_registry.username = '<user_name>' # needed only for private images\n",
"# run_config.environment.docker.base_image_registry.password = '<password>' # needed only for private images\n",
"# run_config.environment.spark.precache_packages = False\n",
"# run_config.data_references={'data':data_ref.to_config()}"
]
@@ -546,9 +551,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
"nbformat_minor": 2
}

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89
contrib/RAPIDS/process_data.py
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@@ -15,6 +15,21 @@ from glob import glob
import os
import argparse
def initialize_rmm_pool():
from librmm_cffi import librmm_config as rmm_cfg
rmm_cfg.use_pool_allocator = True
#rmm_cfg.initial_pool_size = 2<<30 # set to 2GiB. Default is 1/2 total GPU memory
import cudf
return cudf._gdf.rmm_initialize()
def initialize_rmm_no_pool():
from librmm_cffi import librmm_config as rmm_cfg
rmm_cfg.use_pool_allocator = False
import cudf
return cudf._gdf.rmm_initialize()
def run_dask_task(func, **kwargs):
task = func(**kwargs)
return task
@@ -192,26 +207,26 @@ def gpu_load_names(col_path):
def create_ever_features(gdf, **kwargs):
everdf = gdf[['loan_id', 'current_loan_delinquency_status']]
everdf = everdf.groupby('loan_id', method='hash').max().reset_index()
everdf = everdf.groupby('loan_id', method='hash').max()
del(gdf)
everdf['ever_30'] = (everdf['current_loan_delinquency_status'] >= 1).astype('int8')
everdf['ever_90'] = (everdf['current_loan_delinquency_status'] >= 3).astype('int8')
everdf['ever_180'] = (everdf['current_loan_delinquency_status'] >= 6).astype('int8')
everdf.drop_column('current_loan_delinquency_status')
everdf['ever_30'] = (everdf['max_current_loan_delinquency_status'] >= 1).astype('int8')
everdf['ever_90'] = (everdf['max_current_loan_delinquency_status'] >= 3).astype('int8')
everdf['ever_180'] = (everdf['max_current_loan_delinquency_status'] >= 6).astype('int8')
everdf.drop_column('max_current_loan_delinquency_status')
return everdf
def create_delinq_features(gdf, **kwargs):
delinq_gdf = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status']]
del(gdf)
delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
delinq_30['delinquency_30'] = delinq_30['monthly_reporting_period']
delinq_30.drop_column('monthly_reporting_period')
delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
delinq_90['delinquency_90'] = delinq_90['monthly_reporting_period']
delinq_90.drop_column('monthly_reporting_period')
delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
delinq_180['delinquency_180'] = delinq_180['monthly_reporting_period']
delinq_180.drop_column('monthly_reporting_period')
delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
delinq_30['delinquency_30'] = delinq_30['min_monthly_reporting_period']
delinq_30.drop_column('min_monthly_reporting_period')
delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
delinq_90['delinquency_90'] = delinq_90['min_monthly_reporting_period']
delinq_90.drop_column('min_monthly_reporting_period')
delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
delinq_180['delinquency_180'] = delinq_180['min_monthly_reporting_period']
delinq_180.drop_column('min_monthly_reporting_period')
del(delinq_gdf)
delinq_merge = delinq_30.merge(delinq_90, how='left', on=['loan_id'], type='hash')
delinq_merge['delinquency_90'] = delinq_merge['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
@@ -264,15 +279,16 @@ def create_joined_df(gdf, everdf, **kwargs):
def create_12_mon_features(joined_df, **kwargs):
testdfs = []
n_months = 12
for y in range(1, n_months + 1):
tmpdf = joined_df[['loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12', 'upb_12']]
tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf['timestamp_month']
tmpdf['josh_mody_n'] = ((tmpdf['josh_months'].astype('float64') - 24000 - y) / 12).floor()
tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'}).reset_index()
tmpdf['delinquency_12'] = (tmpdf['delinquency_12']>3).astype('int32')
tmpdf['delinquency_12'] +=(tmpdf['upb_12']==0).astype('int32')
tmpdf['upb_12'] = tmpdf['upb_12']
tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'})
tmpdf['delinquency_12'] = (tmpdf['max_delinquency_12']>3).astype('int32')
tmpdf['delinquency_12'] +=(tmpdf['min_upb_12']==0).astype('int32')
tmpdf.drop_column('max_delinquency_12')
tmpdf['upb_12'] = tmpdf['min_upb_12']
tmpdf.drop_column('min_upb_12')
tmpdf['timestamp_year'] = (((tmpdf['josh_mody_n'] * n_months) + 24000 + (y - 1)) / 12).floor().astype('int16')
tmpdf['timestamp_month'] = np.int8(y)
tmpdf.drop_column('josh_mody_n')
@@ -313,7 +329,6 @@ def last_mile_cleaning(df, **kwargs):
'delinquency_30', 'delinquency_90', 'delinquency_180', 'upb_12',
'zero_balance_effective_date','foreclosed_after', 'disposition_date','timestamp'
]
for column in drop_list:
df.drop_column(column)
for col, dtype in df.dtypes.iteritems():
@@ -327,6 +342,7 @@ def last_mile_cleaning(df, **kwargs):
return df.to_arrow(preserve_index=False)
def main():
#print('XGBOOST_BUILD_DOC is ' + os.environ['XGBOOST_BUILD_DOC'])
parser = argparse.ArgumentParser("rapidssample")
parser.add_argument("--data_dir", type=str, help="location of data")
parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1)
@@ -348,6 +364,7 @@ def main():
print('data_dir = {0}'.format(data_dir))
print('num_gpu = {0}'.format(num_gpu))
print('part_count = {0}'.format(part_count))
#part_count = part_count + 1 # adding one because the usage below is not inclusive
print('end_year = {0}'.format(end_year))
print('cpu_predictor = {0}'.format(cpu_predictor))
@@ -363,17 +380,19 @@ def main():
client
print(client.ncores())
# to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
# to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq"
perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf"
col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv"
start_year = 2000
#end_year = 2000 # end_year is inclusive -- converted to parameter
#part_count = 2 # the number of data files to train against -- converted to parameter
client.run(initialize_rmm_pool)
client
print('--->>> Workers used: {0}'.format(client.ncores()))
# NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
# This can be optimized to avoid calculating the dropped features.
print(client.ncores())
# NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
# This can be optimized to avoid calculating the dropped features.
print("Reading ...")
t1 = datetime.datetime.now()
gpu_dfs = []
@@ -395,9 +414,14 @@ def main():
wait(gpu_dfs)
t2 = datetime.datetime.now()
print("Reading time: {0}".format(str(t2-t1)))
print('--->>> Number of data parts: {0}'.format(len(gpu_dfs)))
print("Reading time ...")
print(t2-t1)
print('len(gpu_dfs) is {0}'.format(len(gpu_dfs)))
client.run(cudf._gdf.rmm_finalize)
client.run(initialize_rmm_no_pool)
client
print(client.ncores())
dxgb_gpu_params = {
'nround': 100,
'max_depth': 8,
@@ -414,7 +438,7 @@ def main():
'n_gpus': 1,
'distributed_dask': True,
'loss': 'ls',
'objective': 'reg:squarederror',
'objective': 'gpu:reg:linear',
'max_features': 'auto',
'criterion': 'friedman_mse',
'grow_policy': 'lossguide',
@@ -422,13 +446,13 @@ def main():
}
if cpu_predictor:
print('\n---->>>> Training using CPUs <<<<----\n')
print('Training using CPUs')
dxgb_gpu_params['predictor'] = 'cpu_predictor'
dxgb_gpu_params['tree_method'] = 'hist'
dxgb_gpu_params['objective'] = 'reg:linear'
else:
print('\n---->>>> Training using GPUs <<<<----\n')
print('Training using GPUs')
print('Training parameters are {0}'.format(dxgb_gpu_params))
@@ -458,12 +482,13 @@ def main():
gc.collect()
wait(gpu_dfs)
# TRAIN THE MODEL
labels = None
t1 = datetime.datetime.now()
bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround'])
t2 = datetime.datetime.now()
print('\n---->>>> Training time: {0} <<<<----\n'.format(str(t2-t1)))
print("Training time ...")
print(t2-t1)
print('str(bst) is {0}'.format(str(bst)))
print('Exiting script')
if __name__ == '__main__':

48
contrib/RAPIDS/rapids.yml
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@@ -1,48 +0,0 @@
name: rapids0.9
channels:
- nvidia
- rapidsai/label/xgboost
- rapidsai
- conda-forge
- numba
- pytorch
dependencies:
- python=3.7
- pytorch
- cudatoolkit=10.0
- dask-cuda=0.9.1
- cudf=0.9.*
- cuml=0.9.*
- cugraph=0.9.*
- rapidsai/label/xgboost::xgboost=0.90.rapidsdev1
- rapidsai/label/xgboost::dask-xgboost=0.2.*
- conda-forge::numpy=1.16.4
- cython
- dask
- distributed=2.3.2
- pynvml=8.0.2
- gcsfs
- requests
- jupyterhub
- jupyterlab
- matplotlib
- ipywidgets
- ipyvolume
- seaborn
- scipy
- pandas
- boost
- nodejs
- pytest
- pip
- pip:
- git+https://github.com/cupy/cupy.git
- setuptools
- torch
- torchvision
- pytorch-ignite
- graphviz
- networkx
- dask-kubernetes
- dask_labextension
- jupyterlab-nvdashboard

3
how-to-use-azureml/automated-machine-learning/automl_env.yml
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@@ -6,7 +6,7 @@ dependencies:
- python>=3.5.2,<3.6.8
- nb_conda
- matplotlib==2.1.0
- numpy>=1.11.0,<=1.16.2
- numpy>=1.16.0,<=1.16.2
- cython
- urllib3<1.24
- scipy>=1.0.0,<=1.1.0
@@ -14,6 +14,7 @@ dependencies:
- pandas>=0.22.0,<=0.23.4
- py-xgboost<=0.80
- pyarrow>=0.11.0
- conda-forge::fbprophet==0.5
- pip:
# Required packages for AzureML execution, history, and data preparation.

3
how-to-use-azureml/automated-machine-learning/automl_env_mac.yml
View File

@@ -7,7 +7,7 @@ dependencies:
- python>=3.5.2,<3.6.8
- nb_conda
- matplotlib==2.1.0
- numpy>=1.11.0,<=1.16.2
- numpy>=1.16.0,<=1.16.2
- cython
- urllib3<1.24
- scipy>=1.0.0,<=1.1.0
@@ -15,6 +15,7 @@ dependencies:
- pandas>=0.22.0,<0.23.0
- py-xgboost<=0.80
- pyarrow>=0.11.0
- conda-forge::fbprophet==0.5
- pip:
# Required packages for AzureML execution, history, and data preparation.

77
how-to-use-azureml/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.ipynb
View File

@@ -191,7 +191,7 @@
"source": [
"### Load Data\n",
"\n",
"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."
"Load the bank marketing dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model."
]
},
{
@@ -202,8 +202,6 @@
"source": [
"data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
"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()"
]
},
@@ -222,8 +220,8 @@
"|**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",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**training_data**|Input dataset, containing both features and label column.|\n",
"|**label_column_name**|The name of the label column.|\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)"
]
@@ -247,8 +245,8 @@
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" run_configuration=conda_run_config,\n",
" X = X_train,\n",
" y = y_train,\n",
" training_data = dataset,\n",
" label_column_name = 'y',\n",
" **automl_settings\n",
" )"
]
@@ -428,7 +426,7 @@
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
" pip_packages=['azureml-train-automl'])\n",
" pip_packages=['azureml-defaults','azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -465,45 +463,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image\n",
"\n",
"Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
"or when testing a model that is under development."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"bmData\", 'type': \"automl_classification\"},\n",
" description = \"Image for automl classification sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\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)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance\n",
"\n",
"Deploy an image that contains the model and other assets needed by the service."
"### Deploy the model as a Web Service on Azure Container Instance"
]
},
{
@@ -512,28 +472,23 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.webservice import AciWebservice\n",
"from azureml.core.webservice import Webservice\n",
"from azureml.core.model import Model\n",
"\n",
"inference_config = InferenceConfig(runtime = \"python\", \n",
" entry_script = script_file_name,\n",
" conda_file = conda_env_file_name)\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
" description = 'sample service for Automl Classification')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
" description = 'sample service for Automl Classification')\n",
"\n",
"aci_service_name = 'automl-sample-bankmarketing'\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, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

83
how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb
View File

@@ -191,7 +191,7 @@
"source": [
"### Load Data\n",
"\n",
"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."
"Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model."
]
},
{
@@ -202,10 +202,10 @@
"source": [
"data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\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)"
"training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n",
"label_column_name = 'Class'\n",
"X_test = validation_data.drop_columns(columns=[label_column_name])\n",
"y_test = validation_data.keep_columns(columns=[label_column_name], validate=True)\n"
]
},
{
@@ -223,8 +223,8 @@
"|**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",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
"|**training_data**|Input dataset, containing both features and label column.|\n",
"|**label_column_name**|The name of the label column.|\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)"
]
@@ -255,8 +255,8 @@
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" run_configuration=conda_run_config,\n",
" X = X_train,\n",
" y = y_train,\n",
" training_data = training_data,\n",
" label_column_name = label_column_name,\n",
" **automl_settings\n",
" )"
]
@@ -435,7 +435,7 @@
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
" pip_packages=['azureml-train-automl'])\n",
" pip_packages=['azureml-defaults','azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -472,45 +472,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image\n",
"\n",
"Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
"or when testing a model that is under development."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"cards\", 'type': \"automl_classification\"},\n",
" description = \"Image for automl classification sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\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)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance\n",
"\n",
"Deploy an image that contains the model and other assets needed by the service."
"### Deploy the model as a Web Service on Azure Container Instance"
]
},
{
@@ -519,28 +481,23 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.webservice import AciWebservice\n",
"from azureml.core.webservice import Webservice\n",
"from azureml.core.model import Model\n",
"\n",
"inference_config = InferenceConfig(runtime = \"python\", \n",
" entry_script = script_file_name,\n",
" conda_file = conda_env_file_name)\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"cards\", 'type': \"automl_classification\"}, \n",
" description = 'sample service for Automl Classification')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
" description = 'sample service for Automl Classification')\n",
"\n",
"aci_service_name = 'automl-sample-creditcard'\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, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

84
how-to-use-azureml/automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb
View File

@@ -305,7 +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-train-automl'])\n",
" pip_packages=['azureml-defaults','azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -342,40 +342,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
" description = \"Image for automl classification sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\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)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance"
"### Deploy the model as a Web Service on Azure Container Instance\n",
"\n",
"Create the configuration needed for deploying the model as a web service service."
]
},
{
@@ -384,8 +353,13 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.webservice import AciWebservice\n",
"\n",
"inference_config = InferenceConfig(runtime = \"python\", \n",
" entry_script = script_file_name,\n",
" conda_file = conda_env_file_name)\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"digits\", 'type': \"automl_classification\"}, \n",
@@ -399,17 +373,33 @@
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"from azureml.core.model import Model\n",
"\n",
"aci_service_name = 'automl-sample-01'\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, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get the logs from service deployment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if aci_service.state != 'Healthy':\n",
" # run this command for debugging.\n",
" print(aci_service.get_logs())"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -426,22 +416,6 @@
"#aci_service.delete()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get Logs from a Deployed Web Service"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#aci_service.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},

8
how-to-use-azureml/automated-machine-learning/dataset-remote-execution/auto-ml-dataset-remote-execution.ipynb
View File

@@ -138,8 +138,8 @@
"metadata": {},
"outputs": [],
"source": [
"X = dataset.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
"y = dataset.keep_columns(columns=['Primary Type'], validate=True)"
"training_data = dataset.drop_columns(columns=['FBI Code'])\n",
"label_column_name = 'Primary Type'"
]
},
{
@@ -251,8 +251,8 @@
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" run_configuration=conda_run_config,\n",
" X = X,\n",
" y = y,\n",
" training_data = training_data,\n",
" label_column_name = label_column_name,\n",
" **automl_settings)"
]
},

8
how-to-use-azureml/automated-machine-learning/dataset/auto-ml-dataset.ipynb
View File

@@ -138,8 +138,8 @@
"metadata": {},
"outputs": [],
"source": [
"X = dataset.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
"y = dataset.keep_columns(columns=['Primary Type'], validate=True)"
"training_data = dataset.drop_columns(columns=['FBI Code'])\n",
"label_column_name = 'Primary Type'"
]
},
{
@@ -183,8 +183,8 @@
"source": [
"automl_config = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
" X = X,\n",
" y = y,\n",
" training_data = training_data,\n",
" label_column_name = label_column_name,\n",
" **automl_settings)"
]
},

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

@@ -6,3 +6,4 @@ dependencies:
- azureml-widgets
- matplotlib
- pandas_ml
- azureml-dataprep[pandas]

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

@@ -218,8 +218,8 @@
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
"|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
"|**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",
"|**training_data**|Input dataset, containing both features and label column.|\n",
"|**label_column_name**|The name of the label column.|\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",
"\n",
@@ -247,9 +247,9 @@
" 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",
" training_data=train,\n",
" label_column_name=target_column_name,\n",
" n_cross_validations=3, \n",
" verbosity=logging.INFO,\n",
" **automl_settings)"
]

2
how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb
View File

@@ -227,7 +227,7 @@
"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",
" blacklist_models = ['ExtremeRandomTrees', 'AutoArima'],\n",
" iterations=10,\n",
" iteration_timeout_minutes=5,\n",
" X=X_train,\n",

61
how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb
View File

@@ -663,7 +663,7 @@
"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-train-automl'])\n",
"myenv = CondaDependencies.create(conda_packages=['numpy>=1.16.0,<=1.16.2','scikit-learn','fbprophet==0.5'], pip_packages=['azureml-defaults','azureml-train-automl'])\n",
"\n",
"myenv.save_to_file('.', conda_env_file_name)"
]
@@ -700,40 +700,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'type': \"automl-forecasting\"},\n",
" description = \"Image for automl forecasting sample\")\n",
"\n",
"image = Image.create(name = \"automl-fcast-image\",\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)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance"
"### Deploy the model as a Web Service on Azure Container Instance"
]
},
{
@@ -742,29 +709,23 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.webservice import AciWebservice\n",
"from azureml.core.webservice import Webservice\n",
"from azureml.core.model import Model\n",
"\n",
"inference_config = InferenceConfig(runtime = \"python\", \n",
" entry_script = script_file_name,\n",
" conda_file = conda_env_file_name)\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 2, \n",
" tags = {'type': \"automl-forecasting\"},\n",
" description = \"Automl forecasting sample service\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
" description = \"Automl forecasting sample service\")\n",
"\n",
"aci_service_name = 'automl-forecast-01'\n",
"print(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",
"aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

593
how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.ipynb Normal file
View File

@@ -0,0 +1,593 @@
{
"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/classification-bank-marketing/auto-ml-classification-bank-marketing.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Regression on remote compute using Computer Hardware dataset with model explanations**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)\n",
"1. [Explanations](#Explanations)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. After training AutoML models for this regression data set, we show how you can compute model explanations on your remote compute using a sample explainer script.\n",
"\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration](../../../configuration.ipynb) notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
"\n",
"In this notebook you will learn how to:\n",
"1. Create an `Experiment` in an existing `Workspace`.\n",
"2. Configure AutoML using `AutoMLConfig`.\n",
"3. Train the model using remote compute.\n",
"4. Explore the results.\n",
"5. Setup remote compute for computing the model explanations for a given AutoML model.\n",
"6. Start an AzureML experiment on your remote compute to compute explanations for an AutoML model.\n",
"7. Download the feature importance for engineered features and visualize the explanations for engineered features. \n",
"8. Download the feature importance for raw features and visualize the explanations for raw features. \n"
]
},
{
"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",
"\n",
"from matplotlib import pyplot as plt\n",
"import pandas as pd\n",
"import os\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 experiment\n",
"experiment_name = 'automl-regression-computer-hardware'\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'] = ws.name\n",
"output['Resource Group'] = ws.resource_group\n",
"output['Location'] = ws.location\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 for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
"#### Creation of AmlCompute takes approximately 5 minutes. \n",
"If the AmlCompute with that name is already in your workspace this code will skip the creation process.\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 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 = \"automlcl\"\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",
" 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": [
"### Conda Dependecies for AutoML training experiment\n",
"\n",
"Create the conda dependencies for running AutoML experiment on remote compute."
]
},
{
"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": [
"### Setup Training and Test Data for AutoML experiment\n",
"\n",
"Here we create the train and test datasets for hardware performance dataset. We also register the datasets in your workspace using a name so that these datasets may be accessed from the remote compute."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Data source\n",
"data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
"\n",
"# Create dataset from the url\n",
"dataset = Dataset.Tabular.from_delimited_files(data)\n",
"\n",
"# Split the dataset into train and test datasets\n",
"train_dataset, test_dataset = dataset.random_split(percentage=0.8, seed=223)\n",
"\n",
"# Register the train dataset with your workspace\n",
"train_dataset.register(workspace = ws, name = 'hardware_performance_train_dataset',\n",
" description = 'hardware performance training data',\n",
" create_new_version=True)\n",
"\n",
"# Register the test dataset with your workspace\n",
"test_dataset.register(workspace = ws, name = 'hardware_performance_test_dataset',\n",
" description = 'hardware performance test data',\n",
" create_new_version=True)\n",
"\n",
"# Drop the labeled column from the train dataset\n",
"X_train = train_dataset.drop_columns(columns=['ERP'])\n",
"y_train = train_dataset.keep_columns(columns=['ERP'], validate=True)\n",
"\n",
"# Drop the labeled column from the test dataset\n",
"X_test = test_dataset.drop_columns(columns=['ERP']) \n",
"\n",
"# Display the top rows in the train dataset\n",
"X_train.take(5).to_pandas_dataframe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train\n",
"\n",
"Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
"\n",
"|Property|Description|\n",
"|-|-|\n",
"|**task**|classification or regression|\n",
"|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</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",
"|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
"|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 5,\n",
" \"iterations\": 10,\n",
" \"n_cross_validations\": 2,\n",
" \"primary_metric\": 'spearman_correlation',\n",
" \"preprocess\": True,\n",
" \"max_concurrent_iterations\": 1,\n",
" \"verbosity\": logging.INFO,\n",
"}\n",
"\n",
"automl_config = AutoMLConfig(task = 'regression',\n",
" debug_log = 'automl_errors_model_exp.log',\n",
" run_configuration=conda_run_config,\n",
" X = X_train,\n",
" y = y_train,\n",
" **automl_settings\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
"In this example, we specify `show_output = True` to print currently running iterations to the console."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run = experiment.submit(automl_config, show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"remote_run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results"
]
},
{
"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",
"**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": [
"from azureml.widgets import RunDetails\n",
"RunDetails(remote_run).show() "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explanations\n",
"This section will walk you through the workflow to compute model explanations for an AutoML model on your remote compute.\n",
"\n",
"### Retrieve any AutoML Model for explanations\n",
"\n",
"Below we select the some AutoML pipeline from our iterations. The `get_output` method returns the a AutoML run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_run, fitted_model = remote_run.get_output(iteration=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Setup model explanation run on the remote compute\n",
"The following section provides details on how to setup an AzureML experiment to run model explanations for an AutoML model on your remote compute."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Sample script used for computing explanations\n",
"View the sample script for computing the model explanations for your AutoML model on remote compute."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('train_explainer.py', 'r') as cefr:\n",
" print(cefr.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Substitute values in your sample script\n",
"The following cell shows how you change the values in the sample script so that you can change the sample script according to your experiment and dataset."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"# create script folder\n",
"script_folder = './sample_projects/automl-regression-computer-hardware'\n",
"if not os.path.exists(script_folder):\n",
" os.makedirs(script_folder)\n",
"\n",
"# Copy the sample script to script folder.\n",
"shutil.copy('train_explainer.py', script_folder)\n",
"\n",
"# Create the explainer script that will run on the remote compute.\n",
"script_file_name = script_folder + '/train_explainer.py'\n",
"\n",
"# Open the sample script for modification\n",
"with open(script_file_name, 'r') as cefr:\n",
" content = cefr.read()\n",
"\n",
"# Replace the values in train_explainer.py file with the appropriate values\n",
"content = content.replace('<<experimnet_name>>', automl_run.experiment.name) # your experiment name.\n",
"content = content.replace('<<run_id>>', automl_run.id) # Run-id of the AutoML run for which you want to explain the model.\n",
"content = content.replace('<<target_column_name>>', 'ERP') # Your target column name\n",
"content = content.replace('<<task>>', 'regression') # Training task type\n",
"# Name of your training dataset register with your workspace\n",
"content = content.replace('<<train_dataset_name>>', 'hardware_performance_train_dataset') \n",
"# Name of your test dataset register with your workspace\n",
"content = content.replace('<<test_dataset_name>>', 'hardware_performance_test_dataset')\n",
"\n",
"# Write sample file into your script folder.\n",
"with open(script_file_name, 'w') as cefw:\n",
" cefw.write(content)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create conda configuration for model explanations experiment\n",
"We need `azureml-explain-model`, `azureml-train-automl` and `azureml-core` packages for computing model explanations for your AutoML model on remote compute."
]
},
{
"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",
"azureml_pip_packages = [\n",
" 'azureml-train-automl', 'azureml-core', 'azureml-explain-model'\n",
"]\n",
"\n",
"# specify CondaDependencies obj\n",
"conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
" conda_packages=['scikit-learn', 'numpy','py-xgboost<=0.80'],\n",
" pip_packages=azureml_pip_packages)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Submit the experiment for model explanations\n",
"Submit the experiment with the above `run_config` and the sample script for computing explanations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Now submit a run on AmlCompute for model explanations\n",
"from azureml.core.script_run_config import ScriptRunConfig\n",
"\n",
"script_run_config = ScriptRunConfig(source_directory=script_folder,\n",
" script='train_explainer.py',\n",
" run_config=conda_run_config)\n",
"\n",
"run = experiment.submit(script_run_config)\n",
"\n",
"# Show run details\n",
"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": "markdown",
"metadata": {},
"source": [
"### Feature importance and explanation dashboard\n",
"In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Setup for visualizing the model explanation results\n",
"For visualizing the explanation results for the *fitted_model* we need to perform the following steps:-\n",
"1. Featurize test data samples.\n",
"\n",
"The *automl_explainer_setup_obj* contains all the structures from above list. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
"explainer_setup_class = automl_setup_model_explanations(fitted_model, 'regression', X_test=X_test)"
]
},
{
"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 *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the engineered features."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
"from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
"client = ExplanationClient.from_run(automl_run)\n",
"engineered_explanations = client.download_model_explanation(raw=False)\n",
"print(engineered_explanations.get_feature_importance_dict())\n",
"ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, explainer_setup_class.X_test_transform)"
]
},
{
"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 *automl_run*. 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 = client.download_model_explanation(raw=True)\n",
"print(raw_explanations.get_feature_importance_dict())\n",
"ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, explainer_setup_class.X_test_raw)"
]
}
],
"metadata": {
"authors": [
{
"name": "v-rasav"
}
],
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

10
how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.yml Normal file
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@@ -0,0 +1,10 @@
name: auto-ml-model-explanations-remote-compute
dependencies:
- pip:
- azureml-sdk
- azureml-train-automl
- azureml-widgets
- matplotlib
- pandas_ml
- azureml-explain-model
- azureml-contrib-explain-model

64
how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/train_explainer.py Normal file
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@@ -0,0 +1,64 @@
# Copyright (c) Microsoft. All rights reserved.
# Licensed under the MIT license.
import os
from azureml.core.run import Run
from azureml.core.experiment import Experiment
from sklearn.externals import joblib
from azureml.core.dataset import Dataset
from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations
from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel
from azureml.explain.model.mimic_wrapper import MimicWrapper
from automl.client.core.common.constants import MODEL_PATH
OUTPUT_DIR = './outputs/'
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Get workspace from the run context
run = Run.get_context()
ws = run.experiment.workspace
# Get the AutoML run object from the experiment name and the workspace
experiment = Experiment(ws, '<<experimnet_name>>')
automl_run = Run(experiment=experiment, run_id='<<run_id>>')
# Download the best model from the artifact store
automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')
# Load the AutoML model into memory
fitted_model = joblib.load('model.pkl')
# Get the train dataset from the workspace
train_dataset = Dataset.get_by_name(workspace=ws, name='<<train_dataset_name>>')
# Drop the lablled column to get the training set.
X_train = train_dataset.drop_columns(columns=['<<target_column_name>>'])
y_train = train_dataset.keep_columns(columns=['<<target_column_name>>'], validate=True)
# Get the train dataset from the workspace
test_dataset = Dataset.get_by_name(workspace=ws, name='<<test_dataset_name>>')
# Drop the lablled column to get the testing set.
X_test = test_dataset.drop_columns(columns=['<<target_column_name>>'])
# Setup the class for explaining the AtuoML models
automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, '<<task>>',
X=X_train, X_test=X_test,
y=y_train)
# Initialize the Mimic Explainer
explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel,
init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,
features=automl_explainer_setup_obj.engineered_feature_names,
feature_maps=[automl_explainer_setup_obj.feature_map],
classes=automl_explainer_setup_obj.classes)
# Compute the engineered explanations
engineered_explanations = explainer.explain(['local', 'global'],
eval_dataset=automl_explainer_setup_obj.X_test_transform)
# Compute the raw explanations
raw_explanations = explainer.explain(['local', 'global'], get_raw=True,
raw_feature_names=automl_explainer_setup_obj.raw_feature_names,
eval_dataset=automl_explainer_setup_obj.X_test_transform)
print("Engineered and raw explanations computed successfully")

297
how-to-use-azureml/automated-machine-learning/model-explanation/auto-ml-model-explanation.ipynb
View File

@@ -21,14 +21,16 @@
"metadata": {},
"source": [
"# Automated Machine Learning\n",
"_**Explain classification model and visualize the explanation**_\n",
"_**Explain classification model, visualize the explanation and operationalize the explainer along with AutoML model**_\n",
"\n",
"## Contents\n",
"1. [Introduction](#Introduction)\n",
"1. [Setup](#Setup)\n",
"1. [Data](#Data)\n",
"1. [Train](#Train)\n",
"1. [Results](#Results)"
"1. [Results](#Results)\n",
"1. [Explanations](#Explanations)\n",
"1. [Operationailze](#Operationailze)"
]
},
{
@@ -45,7 +47,8 @@
"2. Instantiating AutoMLConfig\n",
"3. Training the Model using local compute and explain the model\n",
"4. Visualization model's feature importance in widget\n",
"5. Explore best model's explanation"
"5. Explore any model's explanation\n",
"6. Operationalize the AutoML model and the explaination model"
]
},
{
@@ -70,7 +73,8 @@
"from azureml.core.experiment import Experiment\n",
"from azureml.core.workspace import Workspace\n",
"from azureml.train.automl import AutoMLConfig\n",
"from azureml.core.dataset import Dataset"
"from azureml.core.dataset import Dataset\n",
"from azureml.explain.model._internal.explanation_client import ExplanationClient"
]
},
{
@@ -83,8 +87,6 @@
"\n",
"# choose a name for experiment\n",
"experiment_name = 'automl-model-explanation'\n",
"# project folder\n",
"project_folder = './sample_projects/automl-model-explanation'\n",
"\n",
"experiment=Experiment(ws, experiment_name)\n",
"\n",
@@ -94,7 +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",
@@ -140,7 +141,7 @@
"metadata": {},
"outputs": [],
"source": [
"test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
"test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_test.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()"
@@ -162,8 +163,7 @@
"|**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",
"|**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. |"
"|**model_explainability**|Indicate to explain each trained pipeline or not |"
]
},
{
@@ -182,8 +182,7 @@
" X = X_train, \n",
" y = y_train,\n",
" n_cross_validations = 5,\n",
" model_explainability=True,\n",
" path=project_folder)"
" model_explainability=True)"
]
},
{
@@ -209,7 +208,7 @@
"metadata": {},
"outputs": [],
"source": [
"best_run, fitted_model = local_run.get_output()"
"local_run"
]
},
{
@@ -266,56 +265,69 @@
"source": [
"### Best Model 's explanation\n",
"\n",
"Retrieve the explanation from the best_run. And explanation information includes:\n",
"\n",
"1.\tshap_values: The explanation information generated by shap lib\n",
"2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
"3.\toverall_summary: The model level feature importance values sorted in descending order\n",
"4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
"5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
"6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case\n",
"\n",
"Note:- The **retrieve_model_explanation()** API only works in case AutoML has been configured with **'model_explainability'** flag set to **True**. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
"\n",
"shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
" retrieve_model_explanation(best_run)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(overall_summary)\n",
"print(overall_imp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(per_class_summary)\n",
"print(per_class_imp)"
"Retrieve the explanation from the *best_run* which includes explanations for engineered features and raw features."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 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. "
"#### 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": [
"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": [
"client = ExplanationClient.from_run(best_run)\n",
"raw_explanations = client.download_model_explanation(raw=True)\n",
"print(raw_explanations.get_feature_importance_dict())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explanations\n",
"In this section, we will show how to compute model explanations and visualize the explanations using azureml-explain-model package. Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different 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": "markdown",
"metadata": {},
"source": [
"#### Retrieve any other AutoML model from training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"automl_run, fitted_model = local_run.get_output(iteration=0)"
]
},
{
@@ -349,7 +361,7 @@
"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."
"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 *automl_run* object where the raw and engineered explanations will be uploaded."
]
},
{
@@ -361,7 +373,7 @@
"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",
" init_dataset=automl_explainer_setup_obj.X_transform, run=automl_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)"
@@ -413,8 +425,13 @@
"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*."
"### Operationailze\n",
"In this section we will show how you can operationalize an AutoML model and the explainer which was used to compute the explanations in the previous section.\n",
"\n",
"#### Register the AutoML model and the scoring explainer\n",
"We use the *TreeScoringExplainer* from *azureml.explain.model* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. Note that, we initialize the scoring explainer with the *feature_map* that was computed previously. The *feature_map* will be used by the scoring explainer to return the raw feature importance.\n",
"\n",
"In the cell below, we pickle the scoring explainer and register the AutoML model and the scoring explainer with the Model Management Service."
]
},
{
@@ -423,18 +440,29 @@
"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())"
"from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save\n",
"\n",
"# Initialize the ScoringExplainer\n",
"scoring_explainer = TreeScoringExplainer(explainer._internal_explainer, feature_maps=[automl_explainer_setup_obj.feature_map])\n",
"\n",
"# Pickle scoring explainer locally\n",
"save(scoring_explainer, exist_ok=True)\n",
"\n",
"# Register trained automl model present in the 'outputs' folder in the artifacts\n",
"original_model = automl_run.register_model(model_name='automl_model', \n",
" model_path='outputs/model.pkl')\n",
"\n",
"# Register scoring explainer\n",
"automl_run.upload_file('scoring_explainer.pkl', 'scoring_explainer.pkl')\n",
"scoring_explainer_model = automl_run.register_model(model_name='scoring_explainer', model_path='scoring_explainer.pkl')"
]
},
{
"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*."
"#### Create the conda dependencies for setting up the service\n",
"We need to create the conda dependencies comprising of the *azureml-explain-model*, *azureml-train-automl* and *azureml-defaults* packages. "
]
},
{
@@ -443,10 +471,135 @@
"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())"
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"azureml_pip_packages = [\n",
" 'azureml-explain-model', 'azureml-train-automl', 'azureml-defaults'\n",
"]\n",
" \n",
"\n",
"# specify CondaDependencies obj\n",
"myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas', 'numpy', 'py-xgboost<=0.80'],\n",
" pip_packages=azureml_pip_packages,\n",
" pin_sdk_version=True)\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": "markdown",
"metadata": {},
"source": [
"#### View your scoring file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open(\"score_local_explain.py\",\"r\") as f:\n",
" print(f.read())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Deploy the service\n",
"In the cell below, we deploy the service using the conda file and the scoring file from the previous steps. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.webservice import AciWebservice\n",
"from azureml.core.model import Model\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
" memory_gb=1, \n",
" tags={\"data\": \"Bank Marketing\", \n",
" \"method\" : \"local_explanation\"}, \n",
" description='Get local explanations for Bank marketing test data')\n",
"\n",
"inference_config = InferenceConfig(runtime= \"python\", \n",
" entry_script=\"score_local_explain.py\",\n",
" conda_file=\"myenv.yml\")\n",
"\n",
"# Use configs and models generated above\n",
"service = Model.deploy(ws, 'model-scoring', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
"service.wait_for_deployment(show_output=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### View the service logs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"service.get_logs()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Inference using some test data\n",
"Inference using some test data to see the predicted value from autml model, view the engineered feature importance for the predicted value and raw feature importance for the predicted value."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"if service.state == 'Healthy':\n",
" # Serialize the first row of the test data into json\n",
" X_test_json = X_test[:1].to_json(orient='records')\n",
" print(X_test_json)\n",
" # Call the service to get the predictions and the engineered and raw explanations\n",
" output = service.run(X_test_json)\n",
" # Print the predicted value\n",
" print(output['predictions'])\n",
" # Print the engineered feature importances for the predicted value\n",
" print(output['engineered_local_importance_values'])\n",
" # Print the raw feature importances for the predicted value\n",
" print(output['raw_local_importance_values'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Delete the service\n",
"Delete the service once you have finished inferencing."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"service.delete()"
]
}
],
@@ -471,7 +624,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
"version": "3.6.7"
}
},
"nbformat": 4,

42
how-to-use-azureml/automated-machine-learning/model-explanation/score_local_explain.py Normal file
View File

@@ -0,0 +1,42 @@
import json
import numpy as np
import pandas as pd
import os
import pickle
import azureml.train.automl
import azureml.explain.model
from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations
from sklearn.externals import joblib
from azureml.core.model import Model
def init():
global automl_model
global scoring_explainer
# Retrieve the path to the model file using the model name
# Assume original model is named original_prediction_model
automl_model_path = Model.get_model_path('automl_model')
scoring_explainer_path = Model.get_model_path('scoring_explainer')
automl_model = joblib.load(automl_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, orient='records')
# Make prediction
predictions = automl_model.predict(data)
# Setup for inferencing explanations
automl_explainer_setup_obj = automl_setup_model_explanations(automl_model,
X_test=data, task='classification')
# Retrieve model explanations for engineered explanations
engineered_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform)
# Retrieve model explanations for raw explanations
raw_local_importance_values = scoring_explainer.explain(automl_explainer_setup_obj.X_test_transform, get_raw=True)
# You can return any data type as long as it is JSON-serializable
return {'predictions': predictions.tolist(),
'engineered_local_importance_values': engineered_local_importance_values,
'raw_local_importance_values': raw_local_importance_values}

65
how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb
View File

@@ -473,7 +473,7 @@
"metadata": {},
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-train-automl'])\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-defaults','azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -510,45 +510,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image\n",
"\n",
"Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
"or when testing a model that is under development."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"digits\", 'type': \"automl_regression\"},\n",
" description = \"Image for automl regression sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\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)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance\n",
"\n",
"Deploy an image that contains the model and other assets needed by the service."
"### Deploy the model as a Web Service on Azure Container Instance"
]
},
{
@@ -557,28 +519,23 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.webservice import AciWebservice\n",
"from azureml.core.webservice import Webservice\n",
"from azureml.core.model import Model\n",
"\n",
"inference_config = InferenceConfig(runtime = \"python\", \n",
" entry_script = script_file_name,\n",
" conda_file = conda_env_file_name)\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"digits\", 'type': \"automl_regression\"}, \n",
" description = 'sample service for Automl Regression')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
" description = 'sample service for Automl Regression')\n",
"\n",
"aci_service_name = 'automl-sample-concrete'\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, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

1
how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.yml
View File

@@ -8,3 +8,4 @@ dependencies:
- azureml-widgets
- matplotlib
- pandas_ml
- azureml-dataprep[pandas]

65
how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.ipynb
View File

@@ -491,7 +491,7 @@
"metadata": {},
"outputs": [],
"source": [
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-train-automl'])\n",
"myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-defaults','azureml-train-automl'])\n",
"\n",
"conda_env_file_name = 'myenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -528,45 +528,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a Container Image\n",
"\n",
"Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
"or when testing a model that is under development."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image, ContainerImage\n",
"\n",
"image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
" execution_script = script_file_name,\n",
" conda_file = conda_env_file_name,\n",
" tags = {'area': \"digits\", 'type': \"automl_regression\"},\n",
" description = \"Image for automl regression sample\")\n",
"\n",
"image = Image.create(name = \"automlsampleimage\",\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)\n",
"\n",
"if image.creation_state == 'Failed':\n",
" print(\"Image build log at: \" + image.image_build_log_uri)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy the Image as a Web Service on Azure Container Instance\n",
"\n",
"Deploy an image that contains the model and other assets needed by the service."
"### Deploy the model as a Web Service on Azure Container Instance"
]
},
{
@@ -575,28 +537,23 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.webservice import AciWebservice\n",
"from azureml.core.webservice import Webservice\n",
"from azureml.core.model import Model\n",
"\n",
"inference_config = InferenceConfig(runtime = \"python\", \n",
" entry_script = script_file_name,\n",
" conda_file = conda_env_file_name)\n",
"\n",
"aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
" memory_gb = 1, \n",
" tags = {'area': \"digits\", 'type': \"automl_regression\"}, \n",
" description = 'sample service for Automl Regression')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
" description = 'sample service for Automl Regression')\n",
"\n",
"aci_service_name = 'automl-sample-hardware'\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, aciconfig)\n",
"aci_service.wait_for_deployment(True)\n",
"print(aci_service.state)"
]

1
how-to-use-azureml/automated-machine-learning/regression-hardware-performance/auto-ml-regression-hardware-performance.yml
View File

@@ -8,3 +8,4 @@ dependencies:
- azureml-widgets
- matplotlib
- pandas_ml
- azureml-dataprep[pandas]

18
how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.ipynb
View File

@@ -93,9 +93,8 @@
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the run history container in the workspace.\n",
"# Choose an experiment name.\n",
"experiment_name = 'automl-remote-amlcompute-with-onnx'\n",
"project_folder = './project'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -105,7 +104,6 @@
"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",
@@ -179,12 +177,6 @@
"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",
@@ -211,6 +203,9 @@
"X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])\n",
"y_train = pd.DataFrame(y_train, columns=['label'])\n",
"\n",
"if not os.path.isdir('data'):\n",
" os.mkdir('data')\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",
@@ -264,7 +259,7 @@
"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",
"You can specify `automl_settings` as `**kwargs` as well. \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",
@@ -276,7 +271,7 @@
"|**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",
"|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of nodes in the AmlCompute cluster.|\n",
"|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|"
]
},
@@ -305,7 +300,6 @@
"\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,\n",
" y = y,\n",

16
how-to-use-azureml/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.ipynb
View File

@@ -95,9 +95,8 @@
"source": [
"ws = Workspace.from_config()\n",
"\n",
"# Choose a name for the run history container in the workspace.\n",
"# Choose an experiment name.\n",
"experiment_name = 'automl-remote-amlcompute'\n",
"project_folder = './project'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -107,7 +106,6 @@
"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",
@@ -183,10 +181,7 @@
"\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",
"\n",
"pd.DataFrame(data_train.data[100:,:]).to_csv(\"data/X_train.csv\", index=False)\n",
"pd.DataFrame(data_train.target[100:]).to_csv(\"data/y_train.csv\", index=False)\n",
"\n",
@@ -240,7 +235,7 @@
"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",
"You can specify `automl_settings` as `**kwargs` as well.\n",
"\n",
"**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
"\n",
@@ -250,7 +245,7 @@
"|**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.|"
"|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of nodes in the AmlCompute cluster.|"
]
},
{
@@ -261,7 +256,7 @@
"source": [
"automl_settings = {\n",
" \"iteration_timeout_minutes\": 10,\n",
" \"iterations\": 20,\n",
" \"iterations\": 10,\n",
" \"n_cross_validations\": 5,\n",
" \"primary_metric\": 'AUC_weighted',\n",
" \"preprocess\": False,\n",
@@ -271,7 +266,6 @@
"\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,\n",
" y = y,\n",

9
how-to-use-azureml/automated-machine-learning/sample-weight/auto-ml-sample-weight.ipynb
View File

@@ -82,8 +82,6 @@
"experiment_name = 'non_sample_weight_experiment'\n",
"sample_weight_experiment_name = 'sample_weight_experiment'\n",
"\n",
"project_folder = './sample_projects/sample_weight'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
"\n",
@@ -93,7 +91,6 @@
"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",
@@ -131,8 +128,7 @@
" n_cross_validations = 2,\n",
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" path = project_folder)\n",
" y = y_train)\n",
"\n",
"automl_sample_weight = AutoMLConfig(task = 'classification',\n",
" debug_log = 'automl_errors.log',\n",
@@ -143,8 +139,7 @@
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" sample_weight = sample_weight,\n",
" path = project_folder)"
" sample_weight = sample_weight)"
]
},
{

9
how-to-use-azureml/automated-machine-learning/sparse-data-train-test-split/auto-ml-sparse-data-train-test-split.ipynb
View File

@@ -87,8 +87,6 @@
"\n",
"# choose a name for the experiment\n",
"experiment_name = 'sparse-data-train-test-split'\n",
"# project folder\n",
"project_folder = './sample_projects/sparse-data-train-test-split'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -98,7 +96,6 @@
"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",
@@ -165,8 +162,7 @@
"|**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] for the custom validation set.|\n",
"|**y_valid**|(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_valid**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
]
},
{
@@ -185,8 +181,7 @@
" X = X_train, \n",
" y = y_train,\n",
" X_valid = X_valid, \n",
" y_valid = y_valid, \n",
" path = project_folder)"
" y_valid = y_valid)"
]
},
{

17
how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/PredictEnergyDemand.sql
View File

@@ -1,17 +0,0 @@
-- This shows using the AutoMLPredict stored procedure to predict using a forecasting model for the nyc_energy dataset.
DECLARE @Model NVARCHAR(MAX) = (SELECT TOP 1 Model FROM dbo.aml_model
WHERE ExperimentName = 'automl-sql-forecast'
ORDER BY CreatedDate DESC)
EXEC dbo.AutoMLPredict @input_query='
SELECT CAST(timeStamp AS NVARCHAR(30)) AS timeStamp,
demand,
precip,
temp
FROM nyc_energy
WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL
AND timeStamp >= ''2017-02-01''',
@label_column='demand',
@model=@model
WITH RESULT SETS ((timeStamp NVARCHAR(30), actual_demand FLOAT, precip FLOAT, temp FLOAT, predicted_demand FLOAT))

14
how-to-use-azureml/automated-machine-learning/subsampling/auto-ml-subsampling-local.ipynb
View File

@@ -77,9 +77,8 @@
"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-subsampling'\n",
"project_folder = './sample_projects/automl-subsampling'\n",
"\n",
"experiment = Experiment(ws, experiment_name)\n",
"\n",
@@ -89,7 +88,6 @@
"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",
"pd.DataFrame(data = output, index = ['']).T"
@@ -150,8 +148,7 @@
" verbosity = logging.INFO,\n",
" X = X_train, \n",
" y = y_train,\n",
" enable_subsampling=True,\n",
" path = project_folder)"
" enable_subsampling=True)"
]
},
{
@@ -170,13 +167,6 @@
"source": [
"local_run = experiment.submit(automl_config, show_output = True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {

BIN
how-to-use-azureml/azure-databricks/Databricks_AMLSDK_1-4_6.dbc
View File

Binary file not shown.

42
how-to-use-azureml/azure-databricks/README.md
View File

@@ -21,49 +21,9 @@ 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 Azure ML Pipelines**
**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).
# 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.**

7
how-to-use-azureml/azure-databricks/amlsdk/build-model-run-history-03.ipynb
View File

@@ -11,13 +11,6 @@
"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/azure-databricks/amlsdk/build-model-run-history-03.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},

45
how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aci-04.ipynb
View File

@@ -11,13 +11,6 @@
"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/azure-databricks/amlsdk/deploy-to-aci-04.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -178,42 +171,18 @@
"source": [
"#deploy to ACI\n",
"from azureml.core.webservice import AciWebservice, Webservice\n",
"from azureml.core.model import InferenceConfig\n",
"\n",
"myaci_config = AciWebservice.deploy_configuration(\n",
" cpu_cores = 2, \n",
"myaci_config = AciWebservice.deploy_configuration(cpu_cores = 2, \n",
" memory_gb = 2, \n",
" tags = {'name':'Databricks Azure ML ACI'}, \n",
" description = 'This is for ADB and AML example. Azure Databricks & Azure ML SDK demo with ACI by Parashar.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# this will take 10-15 minutes to finish\n",
" description = 'This is for ADB and AML example.')\n",
"\n",
"service_name = \"aciws\"\n",
"runtime = \"spark-py\" \n",
"driver_file = \"score_sparkml.py\"\n",
"my_conda_file = \"mydeployenv.yml\"\n",
"\n",
"# image creation\n",
"from azureml.core.image import ContainerImage\n",
"myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
" runtime = runtime, \n",
" conda_file = my_conda_file)\n",
"\n",
"# Webservice creation\n",
"myservice = Webservice.deploy_from_model(\n",
" workspace=ws, \n",
" name=service_name,\n",
" deployment_config = myaci_config,\n",
" models = [mymodel],\n",
" image_config = myimage_config\n",
" )\n",
"inference_config = InferenceConfig(runtime= 'spark-py', \n",
" entry_script='score_sparkml.py',\n",
" conda_file='mydeployenv.yml')\n",
"\n",
"myservice = Model.deploy(ws, 'aciws', [mymodel], inference_config, myaci_config)\n",
"myservice.wait_for_deployment(show_output=True)"
]
},

124
how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-existingimage-05.ipynb → how-to-use-azureml/azure-databricks/amlsdk/deploy-to-aks-05.ipynb
View File

@@ -11,13 +11,6 @@
"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/azure-databricks/amlsdk/deploy-to-aks-existingimage-05.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -70,11 +63,23 @@
"metadata": {},
"outputs": [],
"source": [
"# List images by ws\n",
"#Register the model\n",
"import os\n",
"from azureml.core.model import Model\n",
"\n",
"from azureml.core.image import ContainerImage\n",
"for i in ContainerImage.list(workspace = ws):\n",
" print('{}(v.{} [{}]) stored at {} with build log {}'.format(i.name, i.version, i.creation_state, i.image_location, i.image_build_log_uri))"
"model_name = \"AdultCensus_runHistory_aks.mml\" # \n",
"model_name_dbfs = os.path.join(\"/dbfs\", model_name)\n",
"\n",
"print(\"copy model from dbfs to local\")\n",
"model_local = \"file:\" + os.getcwd() + \"/\" + model_name\n",
"dbutils.fs.cp(model_name, model_local, True)\n",
"\n",
"mymodel = Model.register(model_path = model_name, # this points to a local file\n",
" model_name = model_name, # this is the name the model is registered as, am using same name for both path and name. \n",
" description = \"ADB trained model by Parashar\",\n",
" workspace = ws)\n",
"\n",
"print(mymodel.name, mymodel.description, mymodel.version)"
]
},
{
@@ -83,8 +88,69 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.image import Image\n",
"myimage = Image(workspace=ws, name=\"aciws\")"
"#%%writefile score_sparkml.py\n",
"score_sparkml = \"\"\"\n",
" \n",
"import json\n",
" \n",
"def init():\n",
" # One-time initialization of PySpark and predictive model\n",
" import pyspark\n",
" from azureml.core.model import Model\n",
" from pyspark.ml import PipelineModel\n",
" \n",
" global trainedModel\n",
" global spark\n",
" \n",
" spark = pyspark.sql.SparkSession.builder.appName(\"ADB and AML notebook by Parashar\").getOrCreate()\n",
" model_name = \"{model_name}\" #interpolated\n",
" model_path = Model.get_model_path(model_name)\n",
" trainedModel = PipelineModel.load(model_path)\n",
" \n",
"def run(input_json):\n",
" if isinstance(trainedModel, Exception):\n",
" return json.dumps({{\"trainedModel\":str(trainedModel)}})\n",
" \n",
" try:\n",
" sc = spark.sparkContext\n",
" input_list = json.loads(input_json)\n",
" input_rdd = sc.parallelize(input_list)\n",
" input_df = spark.read.json(input_rdd)\n",
" \n",
" # Compute prediction\n",
" prediction = trainedModel.transform(input_df)\n",
" #result = prediction.first().prediction\n",
" predictions = prediction.collect()\n",
" \n",
" #Get each scored result\n",
" preds = [str(x['prediction']) for x in predictions]\n",
" result = \",\".join(preds)\n",
" # you can return any data type as long as it is JSON-serializable\n",
" return result.tolist()\n",
" except Exception as e:\n",
" result = str(e)\n",
" return result\n",
" \n",
"\"\"\".format(model_name=model_name)\n",
" \n",
"exec(score_sparkml)\n",
" \n",
"with open(\"score_sparkml.py\", \"w\") as file:\n",
" file.write(score_sparkml)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.conda_dependencies import CondaDependencies \n",
"\n",
"myacienv = CondaDependencies.create(conda_packages=['scikit-learn','numpy','pandas']) #showing how to add libs as an eg. - not needed for this model.\n",
"\n",
"with open(\"mydeployenv.yml\",\"w\") as f:\n",
" f.write(myacienv.serialize_to_string())"
]
},
{
@@ -120,34 +186,17 @@
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice\n",
"help( Webservice.deploy_from_image)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.core.webservice import Webservice, AksWebservice\n",
"from azureml.core.image import ContainerImage\n",
"#deploy to AKS\n",
"from azureml.core.webservice import AksWebservice, Webservice\n",
"from azureml.core.model import InferenceConfig\n",
"\n",
"#Set the web service configuration (using default here with app insights)\n",
"aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)\n",
"\n",
"#unique service name\n",
"service_name ='ps-aks-service'\n",
"\n",
"# Webservice creation using single command, there is a variant to use image directly as well.\n",
"aks_service = Webservice.deploy_from_image(\n",
" workspace=ws, \n",
" name=service_name,\n",
" deployment_config = aks_config,\n",
" image = myimage,\n",
" deployment_target = aks_target\n",
" )\n",
"inference_config = InferenceConfig(runtime = 'spark-py', \n",
" entry_script ='score_sparkml.py',\n",
" conda_file ='mydeployenv.yml')\n",
"\n",
"aks_service = Model.deploy(ws, 'ps-aks-service', [mymodel], inference_config, aks_config, aks_target)\n",
"aks_service.wait_for_deployment(show_output=True)"
]
},
@@ -206,7 +255,6 @@
"source": [
"#comment to not delete the web service\n",
"aks_service.delete()\n",
"#image.delete()\n",
"#model.delete()\n",
"aks_target.delete() "
]

7
how-to-use-azureml/azure-databricks/amlsdk/ingest-data-02.ipynb
View File

@@ -11,13 +11,6 @@
"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/azure-databricks/amlsdk/ingest-data-02.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},

7
how-to-use-azureml/azure-databricks/amlsdk/installation-and-configuration-01.ipynb
View File

@@ -11,13 +11,6 @@
"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/azure-databricks/amlsdk/installation-and-configuration-01.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},

59
how-to-use-azureml/azure-databricks/automl/automl-databricks-local-with-deployment.ipynb
View File

@@ -638,7 +638,7 @@
"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'], pip_packages=['azureml-defaults', 'azureml-sdk[automl]'])\n",
"\n",
"conda_env_file_name = 'mydeployenv.yml'\n",
"myenv.save_to_file('.', conda_env_file_name)"
@@ -648,30 +648,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Create ACI config"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#deploy to ACI\n",
"from azureml.core.webservice import AciWebservice, Webservice\n",
"\n",
"myaci_config = AciWebservice.deploy_configuration(\n",
" cpu_cores = 2, \n",
" memory_gb = 2, \n",
" tags = {'name':'Databricks Azure ML ACI'}, \n",
" description = 'This is for ADB and AutoML example.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy the Image as a Web Service on Azure Container Instance\n",
"## Deploy the model as a Web Service on Azure Container Instance\n",
"Replace servicename with any meaningful name of service"
]
},
@@ -683,30 +660,26 @@
"source": [
"# this will take 10-15 minutes to finish\n",
"\n",
"from azureml.core.webservice import AciWebservice, Webservice\n",
"from azureml.core.model import InferenceConfig\n",
"from azureml.core.model import Model\n",
"import uuid\n",
"from azureml.core.image import ContainerImage\n",
"\n",
"myaci_config = AciWebservice.deploy_configuration(\n",
" cpu_cores = 2, \n",
" memory_gb = 2, \n",
" tags = {'name':'Databricks Azure ML ACI'}, \n",
" description = 'This is for ADB and AutoML example.')\n",
"\n",
"inference_config = InferenceConfig(runtime= 'spark-py', \n",
" entry_script='score.py',\n",
" conda_file='mydeployenv.yml')\n",
"\n",
"guid = str(uuid.uuid4()).split(\"-\")[0]\n",
"service_name = \"myservice-{}\".format(guid)\n",
"print(\"Creating service with name: {}\".format(service_name))\n",
"runtime = \"spark-py\" \n",
"driver_file = \"score.py\"\n",
"my_conda_file = \"mydeployenv.yml\"\n",
"\n",
"# image creation\n",
"myimage_config = ContainerImage.image_configuration(execution_script = driver_file, \n",
" runtime = runtime, \n",
" conda_file = 'mydeployenv.yml')\n",
"\n",
"# Webservice creation\n",
"myservice = Webservice.deploy_from_model(\n",
" workspace=ws, \n",
" name=service_name,\n",
" deployment_config = myaci_config,\n",
" models = [model],\n",
" image_config = myimage_config\n",
" )\n",
"\n",
"myservice = Model.deploy(ws, service_name, [model], inference_config, myaci_config)\n",
"myservice.wait_for_deployment(show_output=True)"
]
},

16
how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/README.md
View File

@@ -1,16 +0,0 @@
# Using Databricks as a Compute Target from Azure Machine Learning Pipeline
To use Databricks as a compute target from Azure Machine Learning Pipeline, a DatabricksStep is used. This notebook demonstrates the use of DatabricksStep in Azure Machine Learning Pipeline.
The notebook will show:
1. Running an arbitrary Databricks notebook that the customer has in Databricks workspace
2. Running an arbitrary Python script that the customer has in DBFS
3. Running an arbitrary Python script that is available on local computer (will upload to DBFS, and then run in Databricks)
4. Running a JAR job that the customer has in DBFS.
## Before you begin:
1. **Create an Azure Databricks workspace** in the same subscription where you have your Azure Machine Learning workspace.
You will need details of this workspace later on to define DatabricksStep. [More information](https://ms.portal.azure.com/#blade/HubsExtension/Resources/resourceType/Microsoft.Databricks%2Fworkspaces).
2. **Create PAT (access token)** at the Azure Databricks portal. [More information](https://docs.databricks.com/api/latest/authentication.html#generate-a-token).
3. **Add demo notebook to ADB** This notebook has a sample you can use as is. Launch Azure Databricks attached to your Azure Machine Learning workspace and add a new notebook.
4. **Create/attach a Blob storage** for use from ADB

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how-to-use-azureml/azure-databricks/databricks-as-remote-compute-target/aml-pipelines-use-databricks-as-compute-target.ipynb
View File

@@ -403,7 +403,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"metadata": {
"tags": [
"databricksstep-remarks-sample"
]
},
"outputs": [],
"source": [
"notebook_path=os.getenv(\"DATABRICKS_NOTEBOOK_PATH\", \"<my-databricks-notebook-path>\") # Databricks notebook path\n",

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56
how-to-use-azureml/azure-databricks/linking/README.md
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@@ -1,56 +0,0 @@
# 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
View File

@@ -1,24 +0,0 @@
#!/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

55
how-to-use-azureml/azure-hdi/README.md
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@@ -1,55 +0,0 @@
**Azure HDInsight**
Azure HDInsight is a fully managed cloud Hadoop & Spark offering the gives
optimized open-source analytic clusters for Spark, Hive, MapReduce, HBase,
Storm, and Kafka. HDInsight Spark clusters provide kernels that you can use with
the Jupyter notebook on [Apache Spark](https://spark.apache.org/) for testing
your applications. 
How Azure HDInsight works with Azure Machine Learning service
- You can train a model using Spark clusters and deploy the model to ACI/AKS
from within Azure HDInsight.
- You can also use [automated machine
learning](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml) capabilities
integrated within Azure HDInsight.
You can use Azure HDInsight as a compute target from an [Azure Machine Learning
pipeline](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-ml-pipelines).
**Set up your HDInsight cluster**
Create [HDInsight
cluster](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters)
**Quick create: Basic cluster setup**
This article walks you through setup in the [Azure
portal](https://portal.azure.com/), where you can create an HDInsight cluster
using *Quick create* or *Custom*.
![hdinsight create options custom quick create](media/0a235b34c0b881117e51dc31a232dbe1.png)
Follow instructions on the screen to do a basic cluster setup. Details are
provided below for:
- [Resource group
name](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters#resource-group-name)
- [Cluster types and
configuration](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters#cluster-types)
(Cluster must be Spark 2.3 (HDI 3.6) or greater)
- Cluster login and SSH username
- [Location](https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters#location)
**Import the sample HDI notebook in Jupyter**
**Important links:**
Create HDI cluster:
<https://docs.microsoft.com/en-us/azure/hdinsight/hdinsight-hadoop-provision-linux-clusters>
![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/azure-hdi/README.png)

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