diff --git a/configuration.ipynb b/configuration.ipynb index 20ba6f0c..208d8c63 100644 --- a/configuration.ipynb +++ b/configuration.ipynb @@ -103,7 +103,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.41.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/contrib/fairness/fairlearn-azureml-mitigation.yml b/contrib/fairness/fairlearn-azureml-mitigation.yml index 0230d1a0..1f91c727 100644 --- a/contrib/fairness/fairlearn-azureml-mitigation.yml +++ b/contrib/fairness/fairlearn-azureml-mitigation.yml @@ -6,6 +6,6 @@ dependencies: - fairlearn>=0.6.2 - joblib - liac-arff - - raiwidgets~=0.17.0 + - raiwidgets~=0.18.1 - itsdangerous==2.0.1 - markupsafe<2.1.0 diff --git a/contrib/fairness/upload-fairness-dashboard.yml b/contrib/fairness/upload-fairness-dashboard.yml index cab53a4d..93463a2c 100644 --- a/contrib/fairness/upload-fairness-dashboard.yml +++ b/contrib/fairness/upload-fairness-dashboard.yml @@ -6,6 +6,6 @@ dependencies: - fairlearn>=0.6.2 - joblib - liac-arff - - raiwidgets~=0.17.0 + - raiwidgets~=0.18.1 - itsdangerous==2.0.1 - markupsafe<2.1.0 diff --git a/how-to-use-azureml/automated-machine-learning/automl_env.yml b/how-to-use-azureml/automated-machine-learning/automl_env.yml index 1dfed013..9fb4c214 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env.yml @@ -22,10 +22,10 @@ dependencies: - pip: # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.41.0 + - azureml-widgets~=1.42.0 - pytorch-transformers==1.0.0 - spacy==2.2.4 - pystan==2.19.1.1 - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.41.0/validated_win32_requirements.txt [--no-deps] + - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_win32_requirements.txt [--no-deps] - arch==4.14 diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml index def7e69d..179ec5e4 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env_linux.yml @@ -24,10 +24,10 @@ dependencies: - pip: # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.41.0 + - azureml-widgets~=1.42.0 - pytorch-transformers==1.0.0 - spacy==2.2.4 - pystan==2.19.1.1 - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.41.0/validated_linux_requirements.txt [--no-deps] + - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_linux_requirements.txt [--no-deps] - arch==4.14 diff --git a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml index 1eade730..6a46d8bc 100644 --- a/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml +++ b/how-to-use-azureml/automated-machine-learning/automl_env_mac.yml @@ -25,10 +25,10 @@ dependencies: - pip: # Required packages for AzureML execution, history, and data preparation. - - azureml-widgets~=1.41.0 + - azureml-widgets~=1.42.0 - pytorch-transformers==1.0.0 - spacy==2.2.4 - pystan==2.19.1.1 - https://aka.ms/automl-resources/packages/en_core_web_sm-2.1.0.tar.gz - - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.41.0/validated_darwin_requirements.txt [--no-deps] + - -r https://automlsdkdataresources.blob.core.windows.net/validated-requirements/1.42.0/validated_darwin_requirements.txt [--no-deps] - arch==4.14 diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb index 13d82f0d..da471818 100644 --- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb +++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/auto-ml-continuous-retraining.ipynb @@ -179,7 +179,7 @@ " \"azureml-opendatasets\",\n", " \"azureml-defaults\",\n", " ],\n", - " conda_packages=[\"numpy==1.16.2\"],\n", + " conda_packages=[\"numpy==1.19.5\"],\n", " pin_sdk_version=False,\n", ")\n", "conda_run_config.environment.python.conda_dependencies = cd\n", diff --git a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb index 0f215e2b..d765487c 100644 --- a/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb +++ b/how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb @@ -92,7 +92,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.41.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb index c1e99ba8..a6c9ec67 100644 --- a/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb +++ b/how-to-use-azureml/automated-machine-learning/experimental/regression-model-proxy/auto-ml-regression-model-proxy.ipynb @@ -91,7 +91,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.41.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb new file mode 100644 index 00000000..f3e27d9d --- /dev/null +++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb @@ -0,0 +1,823 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training and Inferencing AutoML Forecasting Model Using Pipelines" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "In this notebook, we demonstrate how to use piplines to train and inference on AutoML Forecasting model. Two pipelines will be created: one for training AutoML model, and the other is for inference on AutoML model. We'll also demonstrate how to schedule the inference pipeline so you can get inference results periodically (with refreshed test dataset). Make sure you have executed the configuration notebook before running this notebook. In this notebook you will learn how to:\n", + "\n", + "- Configure AutoML using AutoMLConfig for forecasting tasks using pipeline AutoMLSteps.\n", + "- Create and register an AutoML model using AzureML pipeline.\n", + "- Inference and schdelue the pipeline using registered model." + ] + }, + { + "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 json\n", + "import logging\n", + "import os\n", + "\n", + "from matplotlib import pyplot as plt\n", + "import pandas as pd\n", + "\n", + "import azureml.core\n", + "from azureml.core.experiment import Experiment\n", + "from azureml.core.workspace import Workspace\n", + "from azureml.train.automl import AutoMLConfig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"This notebook was created using version 1.38.0 of the Azure ML SDK\")\n", + "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Accessing the Azure ML workspace requires authentication with Azure.\n", + "\n", + "The default authentication is interactive authentication using the default tenant. Executing the ws = Workspace.from_config() line in the cell below will prompt for authentication the first time that it is run.\n", + "\n", + "If you have multiple Azure tenants, you can specify the tenant by replacing the ws = Workspace.from_config() line in the cell below with the following:\n", + "```\n", + "from azureml.core.authentication import InteractiveLoginAuthentication\n", + "auth = InteractiveLoginAuthentication(tenant_id = 'mytenantid')\n", + "ws = Workspace.from_config(auth = auth)\n", + "```\n", + "If you need to run in an environment where interactive login is not possible, you can use Service Principal authentication by replacing the ws = Workspace.from_config() line in the cell below with the following:\n", + "```\n", + "from azureml.core.authentication import ServicePrincipalAuthentication\n", + "auth = ServicePrincipalAuthentication('mytenantid', 'myappid', 'mypassword')\n", + "ws = Workspace.from_config(auth = auth)\n", + "```\n", + "For more details, see aka.ms/aml-notebook-auth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ws = Workspace.from_config()\n", + "dstor = ws.get_default_datastore()\n", + "\n", + "# Choose a name for the run history container in the workspace.\n", + "experiment_name = \"forecasting-pipeline\"\n", + "experiment = Experiment(ws, experiment_name)\n", + "\n", + "output = {}\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[\"Run History Name\"] = experiment_name\n", + "pd.set_option(\"display.max_colwidth\", None)\n", + "outputDf = pd.DataFrame(data=output, index=[\"\"])\n", + "outputDf.T" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Compute \n", + "\n", + "#### Create or Attach existing AmlCompute\n", + "\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", + "\n", + "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", + "\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](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.compute import ComputeTarget, AmlCompute\n", + "from azureml.core.compute_target import ComputeTargetException\n", + "\n", + "# Choose a name for your CPU cluster\n", + "amlcompute_cluster_name = \"forecast-step-cluster\"\n", + "\n", + "# Verify that cluster does not exist already\n", + "try:\n", + " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", + " print(\"Found existing cluster, use it.\")\n", + "except ComputeTargetException:\n", + " compute_config = AmlCompute.provisioning_configuration(\n", + " vm_size=\"STANDARD_DS12_V2\", max_nodes=4\n", + " )\n", + " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", + "compute_target.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "You are now ready to load the historical orange juice sales data. For demonstration purposes, we extract sales time-series for just a few of the stores. We will load the CSV file into a plain pandas DataFrame; the time column in the CSV is called _WeekStarting_, so it will be specially parsed into the datetime type." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_column_name = \"WeekStarting\"\n", + "train = pd.read_csv(\"oj-train.csv\", parse_dates=[time_column_name])\n", + "\n", + "train.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each row in the DataFrame holds a quantity of weekly sales for an OJ brand at a single store. The data also includes the sales price, a flag indicating if the OJ brand was advertised in the store that week, and some customer demographic information based on the store location. For historical reasons, the data also include the logarithm of the sales quantity. The Dominick's grocery data is commonly used to illustrate econometric modeling techniques where logarithms of quantities are generally preferred. \n", + "\n", + "The task is now to build a time-series model for the _Quantity_ column. It is important to note that this dataset is comprised of many individual time-series - one for each unique combination of _Store_ and _Brand_. To distinguish the individual time-series, we define the **time_series_id_column_names** - the columns whose values determine the boundaries between time-series: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_series_id_column_names = [\"Store\", \"Brand\"]\n", + "nseries = train.groupby(time_series_id_column_names).ngroups\n", + "print(\"Data contains {0} individual time-series.\".format(nseries))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test Splitting\n", + "We now split the data into a training and a testing set for later forecast prediction. The test set will contain the final 4 weeks of observed sales for each time-series. The splits should be stratified by series, so we use a group-by statement on the time series identifier columns." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_test_periods = 4\n", + "\n", + "test = pd.read_csv(\"oj-test.csv\", parse_dates=[time_column_name])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Upload data to datastore\n", + "The [Machine Learning service workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-workspace), is paired with the storage account, which contains the default data store. We will use it to upload the train and test data and create [tabular datasets](https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) for training and testing. A tabular dataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.data.dataset_factory import TabularDatasetFactory\n", + "\n", + "datastore = ws.get_default_datastore()\n", + "train_dataset = TabularDatasetFactory.register_pandas_dataframe(\n", + " train, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_train\"\n", + ")\n", + "\n", + "test_dataset = TabularDatasetFactory.register_pandas_dataframe(\n", + " test, target=(datastore, \"dataset/\"), name=\"dominicks_OJ_test\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling\n", + "\n", + "For forecasting tasks, AutoML uses pre-processing and estimation steps that are specific to time-series. AutoML will undertake the following pre-processing steps:\n", + "* Detect time-series sample frequency (e.g. hourly, daily, weekly) and create new records for absent time points to make the series regular. A regular time series has a well-defined frequency and has a value at every sample point in a contiguous time span \n", + "* Impute missing values in the target (via forward-fill) and feature columns (using median column values) \n", + "* Create features based on time series identifiers to enable fixed effects across different series\n", + "* Create time-based features to assist in learning seasonal patterns\n", + "* Encode categorical variables to numeric quantities\n", + "\n", + "In this notebook, AutoML will train a single, regression-type model across **all** time-series in a given training set. This allows the model to generalize across related series. If you're looking for training multiple models for different time-series, please see the many-models notebook.\n", + "\n", + "You are almost ready to start an AutoML training job. First, we need to define the target column." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "target_column_name = \"Quantity\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Forecasting Parameters\n", + "To define forecasting parameters for your experiment training, you can leverage the ForecastingParameters class. The table below details the forecasting parameter we will be passing into our experiment.\n", + "\n", + "\n", + "|Property|Description|\n", + "|-|-|\n", + "|**time_column_name**|The name of your time column.|\n", + "|**forecast_horizon**|The forecast horizon is how many periods forward you would like to forecast. This integer horizon is in units of the timeseries frequency (e.g. daily, weekly).|\n", + "|**time_series_id_column_names**|The column names used to uniquely identify the time series in data that has multiple rows with the same timestamp. If the time series identifiers are not defined, the data set is assumed to be one time series.|\n", + "|**freq**|Forecast frequency. This optional parameter represents the period with which the forecast is desired, for example, daily, weekly, yearly, etc. Use this parameter for the correction of time series containing irregular data points or for padding of short time series. The frequency needs to be a pandas offset alias. Please refer to [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) for more information." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.automl.core.forecasting_parameters import ForecastingParameters\n", + "\n", + "forecasting_parameters = ForecastingParameters(\n", + " time_column_name=time_column_name,\n", + " forecast_horizon=n_test_periods,\n", + " time_series_id_column_names=time_series_id_column_names,\n", + " freq=\"W-THU\", # Set the forecast frequency to be weekly (start on each Thursday)\n", + ")\n", + "\n", + "automl_config = AutoMLConfig(\n", + " task=\"forecasting\",\n", + " debug_log=\"automl_oj_sales_errors.log\",\n", + " primary_metric=\"normalized_mean_absolute_error\",\n", + " experiment_timeout_hours=0.25,\n", + " training_data=train_dataset,\n", + " label_column_name=target_column_name,\n", + " compute_target=compute_target,\n", + " enable_early_stopping=True,\n", + " n_cross_validations=5,\n", + " verbosity=logging.INFO,\n", + " max_cores_per_iteration=-1,\n", + " forecasting_parameters=forecasting_parameters,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineData, TrainingOutput\n", + "from azureml.pipeline.steps import AutoMLStep\n", + "from azureml.pipeline.core import Pipeline, PipelineParameter\n", + "from azureml.pipeline.steps import PythonScriptStep\n", + "\n", + "metrics_output_name = \"metrics_output\"\n", + "best_model_output_name = \"best_model_output\"\n", + "model_file_name = \"model_file\"\n", + "metrics_data_name = \"metrics_data\"\n", + "\n", + "metrics_data = PipelineData(\n", + " name=metrics_data_name,\n", + " datastore=datastore,\n", + " pipeline_output_name=metrics_output_name,\n", + " training_output=TrainingOutput(type=\"Metrics\"),\n", + ")\n", + "model_data = PipelineData(\n", + " name=model_file_name,\n", + " datastore=datastore,\n", + " pipeline_output_name=best_model_output_name,\n", + " training_output=TrainingOutput(type=\"Model\"),\n", + ")\n", + "\n", + "automl_step = AutoMLStep(\n", + " name=\"automl_module\",\n", + " automl_config=automl_config,\n", + " outputs=[metrics_data, model_data],\n", + " allow_reuse=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Register Model Step" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Run Configuration and Environment\n", + "To have a pipeline step run, we first need an environment to run the jobs. The environment can be build using the following code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core.runconfig import CondaDependencies, RunConfiguration\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", + "\n", + "conda_run_config.docker.use_docker = True\n", + "\n", + "cd = CondaDependencies.create(\n", + " pip_packages=[\n", + " \"azureml-sdk[automl]\",\n", + " \"applicationinsights\",\n", + " \"azureml-opendatasets\",\n", + " \"azureml-defaults\",\n", + " ],\n", + " conda_packages=[\"numpy==1.19.5\"],\n", + " pin_sdk_version=False,\n", + ")\n", + "conda_run_config.environment.python.conda_dependencies = cd\n", + "\n", + "print(\"run config is ready\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Step to register the model.\n", + "The following code generates a step to register the model to the workspace from previous step. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.pipeline.core import PipelineData\n", + "\n", + "# The model name with which to register the trained model in the workspace.\n", + "model_name_str = \"ojmodel\"\n", + "model_name = PipelineParameter(\"model_name\", default_value=model_name_str)\n", + "\n", + "\n", + "register_model_step = PythonScriptStep(\n", + " script_name=\"register_model.py\",\n", + " name=\"register_model\",\n", + " source_directory=\"scripts\",\n", + " allow_reuse=False,\n", + " arguments=[\n", + " \"--model_name\",\n", + " model_name,\n", + " \"--model_path\",\n", + " model_data,\n", + " \"--ds_name\",\n", + " \"dominicks_OJ_train\",\n", + " ],\n", + " inputs=[model_data],\n", + " compute_target=compute_target,\n", + " runconfig=conda_run_config,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build the Pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "training_pipeline = Pipeline(\n", + " description=\"training_pipeline\",\n", + " workspace=ws,\n", + " steps=[automl_step, register_model_step],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Submit Pipeline Run" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "training_pipeline_run = experiment.submit(training_pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "training_pipeline_run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get metrics for each runs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output_dir = \"train_output\"\n", + "pipeline_output = training_pipeline_run.get_pipeline_output(\"metrics_output\")\n", + "pipeline_output.download(output_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_path = os.path.join(output_dir, pipeline_output.path_on_datastore)\n", + "with open(file_path) as f:\n", + " metrics = json.load(f)\n", + "for run_id, metrics in metrics.items():\n", + " print(\"{}: {}\".format(run_id, metrics[\"normalized_root_mean_squared_error\"][0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inference" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are several ways to do the inference, for here we will demonstrate how to use the registered model and pipeline to do the inference. (how to register a model https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model?view=azure-ml-py)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Inference Pipeline Environment\n", + "To trigger an inference pipeline run, we first need a running environment for run that contains all the appropriate packages for the model unpickling. This environment can be either assess from the training run or using the `yml` file that comes with the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Model\n", + "\n", + "model = Model(ws, model_name_str)\n", + "download_path = model.download(model_name_str, exist_ok=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After all the files are downloaded, we can generate the run config for inference runs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Environment, RunConfiguration\n", + "from azureml.core.conda_dependencies import CondaDependencies\n", + "\n", + "env_file = os.path.join(download_path, \"conda_env_v_1_0_0.yml\")\n", + "inference_env = Environment(\"oj-inference-env\")\n", + "inference_env.python.conda_dependencies = CondaDependencies(\n", + " conda_dependencies_file_path=env_file\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[Optional] The enviroment can also be assessed from the training run using `get_environment()` API." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After we have the environment for the inference, we could build run config based on this environment." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "run_config = RunConfiguration()\n", + "run_config.environment = inference_env" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Build and submit the inference pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The inference pipeline will create two different format of outputs, 1) a tabular dataset that contains the prediction and 2) an `OutputFileDatasetConfig` that can be used for the sequential pipeline steps." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.data import OutputFileDatasetConfig\n", + "\n", + "output_data = OutputFileDatasetConfig(name=\"prediction_result\")\n", + "\n", + "output_ds_name = \"oj-output\"\n", + "\n", + "inference_step = PythonScriptStep(\n", + " name=\"infer-results\",\n", + " source_directory=\"scripts\",\n", + " script_name=\"infer.py\",\n", + " arguments=[\n", + " \"--model_name\",\n", + " model_name_str,\n", + " \"--ouput_dataset_name\",\n", + " output_ds_name,\n", + " \"--test_dataset_name\",\n", + " test_dataset.name,\n", + " \"--target_column_name\",\n", + " target_column_name,\n", + " \"--output_path\",\n", + " output_data,\n", + " ],\n", + " compute_target=compute_target,\n", + " allow_reuse=False,\n", + " runconfig=run_config,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inference_pipeline = Pipeline(ws, [inference_step])\n", + "inference_run = experiment.submit(inference_pipeline)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inference_run.wait_for_completion(show_output=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get the predicted data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azureml.core import Dataset\n", + "\n", + "inference_ds = Dataset.get_by_name(ws, output_ds_name)\n", + "inference_df = inference_ds.to_pandas_dataframe()\n", + "inference_df.tail(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Schedule Pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This section is about how to schedule a pipeline for periodically predictions. For more info about pipeline schedule and pipeline endpoint, please follow this [notebook](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inference_published_pipeline = inference_pipeline.publish(\n", + " name=\"OJ Inference Test\", description=\"OJ Inference Test\"\n", + ")\n", + "print(\"Newly published pipeline id: {}\".format(inference_published_pipeline.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If `test_dataset` is going to refresh every 4 weeks before Friday 16:00 and we want to predict every 4 weeks (forecast_horizon), we can schedule our pipeline to run every 4 weeks at 16:00 to get daily inference results. You can refresh your test dataset (a newer version will be created) periodically when new data is available (i.e. target column in test dataset would have values in the beginning as context data, and followed by NaNs to be predicted). The inference pipeline will pick up context to further improve the forecast accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# schedule\n", + "\n", + "from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule\n", + "\n", + "recurrence = ScheduleRecurrence(\n", + " frequency=\"Week\", interval=4, week_days=[\"Friday\"], hours=[16], minutes=[0]\n", + ")\n", + "\n", + "schedule = Schedule.create(\n", + " workspace=ws,\n", + " name=\"OJ_Inference_schedule\",\n", + " pipeline_id=inference_published_pipeline.id,\n", + " experiment_name=\"Schedule-run-OJ\",\n", + " recurrence=recurrence,\n", + " wait_for_provisioning=True,\n", + " description=\"Schedule Run\",\n", + ")\n", + "\n", + "# You may want to make sure that the schedule is provisioned properly\n", + "# before making any further changes to the schedule\n", + "\n", + "print(\"Created schedule with id: {}\".format(schedule.id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### [Optional] Disable schedule" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "schedule.disable()" + ] + } + ], + "metadata": { + "authors": [ + { + "name": "jialiu" + } + ], + "category": "tutorial", + "celltoolbar": "Raw Cell Format", + "compute": [ + "Remote" + ], + "datasets": [ + "Orange Juice Sales" + ], + "deployment": [ + "Azure Container Instance" + ], + "exclude_from_index": false, + "framework": [ + "Azure ML AutoML" + ], + "friendly_name": "Forecasting orange juice sales with deployment", + "index_order": 1, + "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": "3.6.9" + }, + "tags": [ + "None" + ], + "task": "Forecasting" + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.yml b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.yml new file mode 100644 index 00000000..48d7667f --- /dev/null +++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.yml @@ -0,0 +1,4 @@ +name: auto-ml-forecasting-pipelines +dependencies: +- pip: + - azureml-sdk diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/oj-test.csv b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/oj-test.csv new file mode 100644 index 00000000..0bb8c7d1 --- /dev/null +++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/oj-test.csv @@ -0,0 +1,37 @@ +WeekStarting,Store,Brand,Advert,Price,Age60,COLLEGE,INCOME,Hincome150,Large HH,Minorities,WorkingWoman,SSTRDIST,SSTRVOL,CPDIST5,CPWVOL5 +1992-09-10,2,dominicks,0,2.09,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997 +1992-09-10,2,minute.maid,1,1.99,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997 +1992-09-10,2,tropicana,0,2.64,0.232864734,0.248934934,10.55320518,0.463887065,0.103953406,0.114279949,0.303585347,2.110122129,1.142857143,1.927279669,0.37692661299999997 +1992-09-10,5,dominicks,0,1.85,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 +1992-09-10,5,minute.maid,1,1.99,0.117368032,0.32122573,10.92237097,0.535883355,0.103091585,0.053875277,0.410568032,3.801997814,0.681818182,1.600573425,0.736306837 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+1,170 @@ +import argparse +from datetime import datetime +import os +import uuid +import numpy as np +import pandas as pd + +from pandas.tseries.frequencies import to_offset +from sklearn.externals import joblib +from sklearn.metrics import mean_absolute_error, mean_squared_error + +from azureml.data.dataset_factory import TabularDatasetFactory +from azureml.automl.runtime.shared.score import scoring, constants as metrics_constants +import azureml.automl.core.shared.constants as constants +from azureml.core import Run, Dataset, Model + +try: + import torch + + _torch_present = True +except ImportError: + _torch_present = False + + +def infer_forecasting_dataset_tcn( + X_test, + y_test, + model, + output_path, + output_dataset_name="results", +): + + y_pred, df_all = model.forecast(X_test, y_test) + + run = Run.get_context() + + registered_train = TabularDatasetFactory.register_pandas_dataframe( + df_all, + target=( + run.experiment.workspace.get_default_datastore(), + datetime.now().strftime("%Y-%m-%d-") + str(uuid.uuid4())[:6], + ), + name=output_dataset_name, + ) + df_all.to_csv(os.path.join(output_path, output_dataset_name + ".csv"), index=False) + + +def map_location_cuda(storage, loc): + return storage.cuda() + + +def get_model(model_path, model_file_name): + # _, ext = os.path.splitext(model_path) + model_full_path = os.path.join(model_path, model_file_name) + print(model_full_path) + if model_file_name.endswith("pt"): + # Load the fc-tcn torch model. + assert _torch_present, "Loading DNN models needs torch to be presented." + if torch.cuda.is_available(): + map_location = map_location_cuda + else: + map_location = "cpu" + with open(model_full_path, "rb") as fh: + fitted_model = torch.load(fh, map_location=map_location) + else: + # Load the sklearn pipeline. + fitted_model = joblib.load(model_full_path) + return fitted_model + + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "--model_name", + type=str, + dest="model_name", + help="Model to be loaded", + ) + + parser.add_argument( + "--ouput_dataset_name", + type=str, + dest="ouput_dataset_name", + default="results", + help="Dataset name of the final output", + ) + parser.add_argument( + "--target_column_name", + type=str, + dest="target_column_name", + help="The target column name.", + ) + parser.add_argument( + "--test_dataset_name", + type=str, + dest="test_dataset_name", + default="results", + help="Dataset name of the final output", + ) + parser.add_argument( + "--output_path", + type=str, + dest="output_path", + default="results", + help="The output path", + ) + args = parser.parse_args() + return args + + +def get_data( + run, + fitted_model, + target_column_name, + test_dataset_name, +): + + # get input dataset by name + test_dataset = Dataset.get_by_name(run.experiment.workspace, test_dataset_name) + test_df = test_dataset.to_pandas_dataframe() + if target_column_name in test_df: + y_test = test_df.pop(target_column_name) + else: + y_test = np.full(test_df.shape[0], np.nan) + + return test_df, y_test + + +def get_model_filename(run, model_name, model_path): + model = Model(run.experiment.workspace, model_name) + if "model_file_name" in model.tags: + return model.tags["model_file_name"] + is_pkl = True + if model.tags.get("algorithm") == "TCNForecaster" or os.path.exists( + os.path.join(model_path, "model.pt") + ): + is_pkl = False + return "model.pkl" if is_pkl else "model.pt" + + +if __name__ == "__main__": + run = Run.get_context() + + args = get_args() + model_name = args.model_name + ouput_dataset_name = args.ouput_dataset_name + test_dataset_name = args.test_dataset_name + target_column_name = args.target_column_name + print("args passed are: ") + + print(model_name) + print(test_dataset_name) + print(ouput_dataset_name) + print(target_column_name) + + model_path = Model.get_model_path(model_name) + model_file_name = get_model_filename(run, model_name, model_path) + print(model_file_name) + fitted_model = get_model(model_path, model_file_name) + + X_test_df, y_test = get_data( + run, + fitted_model, + target_column_name, + test_dataset_name, + ) + + infer_forecasting_dataset_tcn( + X_test_df, y_test, fitted_model, args.output_path, ouput_dataset_name + ) diff --git a/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/register_model.py b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/register_model.py new file mode 100644 index 00000000..6f3089c2 --- /dev/null +++ b/how-to-use-azureml/automated-machine-learning/forecasting-pipelines/scripts/register_model.py @@ -0,0 +1,64 @@ +import argparse +import os +import uuid +import shutil +from azureml.core.model import Model, Dataset +from azureml.core.run import Run, _OfflineRun +from azureml.core import Workspace +import azureml.automl.core.shared.constants as constants +from azureml.train.automl.run import AutoMLRun + + +def get_best_automl_run(pipeline_run): + all_children = [c for c in pipeline_run.get_children()] + automl_step = [ + c for c in all_children if c.properties.get("runTemplate") == "AutoML" + ] + for c in all_children: + print(c, c.properties) + automlrun = AutoMLRun(pipeline_run.experiment, automl_step[0].id) + best = automlrun.get_best_child() + return best + + +def get_model_path(model_artifact_path): + return model_artifact_path.split("/")[1] + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--model_name") + parser.add_argument("--model_path") + parser.add_argument("--ds_name") + args = parser.parse_args() + + print("Argument 1(model_name): %s" % args.model_name) + print("Argument 2(model_path): %s" % args.model_path) + print("Argument 3(ds_name): %s" % args.ds_name) + + run = Run.get_context() + ws = None + if type(run) == _OfflineRun: + ws = Workspace.from_config() + else: + ws = run.experiment.workspace + + train_ds = Dataset.get_by_name(ws, args.ds_name) + datasets = [(Dataset.Scenario.TRAINING, train_ds)] + new_dir = str(uuid.uuid4()) + os.makedirs(new_dir) + + # Register model with training dataset + best_run = get_best_automl_run(run.parent) + model_artifact_path = best_run.properties[constants.PROPERTY_KEY_OF_MODEL_PATH] + algo = best_run.properties.get("run_algorithm") + model_artifact_dir = model_artifact_path.split("/")[0] + model_file_name = model_artifact_path.split("/")[1] + model = best_run.register_model( + args.model_name, + model_path=model_artifact_dir, + datasets=datasets, + tags={"algorithm": algo, "model_file_name": model_file_name}, + ) + + print("Registered version {0} of model {1}".format(model.version, model.name)) diff --git a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb index 8a0f4575..eb343b5c 100644 --- a/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb +++ b/how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb @@ -515,6 +515,10 @@ "# specify CondaDependencies obj\n", "conda_run_config.environment.python.conda_dependencies = (\n", " automl_run.get_environment().python.conda_dependencies\n", + ")\n", + "\n", + "conda_run_config.environment.python.conda_dependencies.add_pip_package(\n", + " \"dotnetcore2==2.1.23\"\n", ")" ] }, diff --git a/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb b/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb index 49b39085..5b1e4763 100644 --- a/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb +++ b/how-to-use-azureml/deployment/deploy-with-controlled-rollout/deploy-aks-with-controlled-rollout.ipynb @@ -111,7 +111,7 @@ " 'azureml-defaults',\n", " 'inference-schema[numpy-support]',\n", " 'numpy',\n", - " 'scikit-learn==0.19.1',\n", + " 'scikit-learn==0.22.1',\n", " 'scipy'\n", "])" ] diff --git a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb index 9a06114f..466da238 100644 --- a/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb +++ b/how-to-use-azureml/deployment/enable-app-insights-in-production-service/enable-app-insights-in-production-service.ipynb @@ -172,7 +172,7 @@ "source": [ "from azureml.core.conda_dependencies import CondaDependencies\n", "\n", - "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.20.3'],\n", + "myenv = CondaDependencies.create(conda_packages=['numpy==1.19.5','scikit-learn==0.22.1'],\n", " pip_packages=['azureml-defaults'])\n", "\n", "with open(\"myenv.yml\",\"w\") as f:\n", diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb index d78603b2..838aa996 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks-gpu/production-deploy-to-aks-gpu.ipynb @@ -240,8 +240,9 @@ "# Please see [Azure ML Containers repository](https://github.com/Azure/AzureML-Containers#featured-tags)\n", "# for open-sourced GPU base images.\n", "env.docker.base_image = DEFAULT_GPU_IMAGE\n", - "env.python.conda_dependencies = CondaDependencies.create(conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n", - " pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n", + "env.python.conda_dependencies = CondaDependencies.create(python_version=\"3.6.2\", \n", + " conda_packages=['tensorflow-gpu==1.12.0','numpy'],\n", + " pip_packages=['azureml-contrib-services', 'azureml-defaults'])\n", "\n", "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n", "aks_config = AksWebservice.deploy_configuration()\n", diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb index 99a4ecd9..baec6484 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks-ssl.ipynb @@ -109,7 +109,7 @@ "from azureml.core import Environment\n", "from azureml.core.conda_dependencies import CondaDependencies \n", "\n", - "conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.19.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", + "conda_deps = CondaDependencies.create(conda_packages=['numpy', 'scikit-learn==0.22.1', 'scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", "myenv = Environment(name='myenv')\n", "myenv.python.conda_dependencies = conda_deps" ] diff --git a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb index 05580b7d..59270d81 100644 --- a/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb +++ b/how-to-use-azureml/deployment/production-deploy-to-aks/production-deploy-to-aks.ipynb @@ -109,7 +109,7 @@ "from azureml.core import Environment\n", "from azureml.core.conda_dependencies import CondaDependencies \n", "\n", - "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.19.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", + "conda_deps = CondaDependencies.create(conda_packages=['numpy','scikit-learn==0.22.1','scipy'], pip_packages=['azureml-defaults', 'inference-schema'])\n", "myenv = Environment(name='myenv')\n", "myenv.python.conda_dependencies = conda_deps" ] @@ -295,12 +295,14 @@ "\n", "\n", "environment = Environment('my-sklearn-environment')\n", - "environment.python.conda_dependencies = CondaDependencies.create(pip_packages=[\n", + "environment.python.conda_dependencies = CondaDependencies.create(conda_packages=[\n", + " 'pip==20.2.4'],\n", + " pip_packages=[\n", " 'azureml-defaults',\n", " 'inference-schema[numpy-support]',\n", " 'joblib',\n", " 'numpy',\n", - " 'scikit-learn==0.19.1',\n", + " 'scikit-learn==0.22.1',\n", " 'scipy'\n", "])\n", "inference_config = InferenceConfig(entry_script='score.py', environment=environment)\n", diff --git a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb index a36762e7..7b35faee 100644 --- a/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb +++ b/how-to-use-azureml/explain-model/azure-integration/gpu-explanation/train-explain-model-gpu-tree-explainer.ipynb @@ -106,7 +106,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(\"This notebook was created using version 1.41.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml index 54423168..4f986413 100644 --- a/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml +++ b/how-to-use-azureml/explain-model/azure-integration/remote-explanation/explain-model-on-amlcompute.yml @@ -11,6 +11,6 @@ dependencies: - matplotlib - azureml-dataset-runtime - ipywidgets - - raiwidgets~=0.17.0 + - raiwidgets~=0.18.1 - itsdangerous==2.0.1 - markupsafe<2.1.0 diff --git a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml index 84a84e24..577828b4 100644 --- a/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml +++ b/how-to-use-azureml/explain-model/azure-integration/run-history/save-retrieve-explanations-run-history.yml @@ -10,7 +10,7 @@ dependencies: - ipython - matplotlib - ipywidgets - - raiwidgets~=0.17.0 + - raiwidgets~=0.18.1 - packaging>=20.9 - itsdangerous==2.0.1 - markupsafe<2.1.0 diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml index debb1c17..e1193a53 100644 --- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml +++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-locally-and-deploy.yml @@ -10,8 +10,7 @@ dependencies: - ipython - matplotlib - ipywidgets - - raiwidgets~=0.17.0 + - raiwidgets~=0.18.1 - packaging>=20.9 - itsdangerous==2.0.1 - markupsafe<2.1.0 - - raiutils diff --git a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml index bf03522b..050f7180 100644 --- a/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml +++ b/how-to-use-azureml/explain-model/azure-integration/scoring-time/train-explain-model-on-amlcompute-and-deploy.yml @@ -12,7 +12,6 @@ dependencies: - azureml-dataset-runtime - azureml-core - ipywidgets - - raiwidgets~=0.17.0 + - raiwidgets~=0.18.1 - itsdangerous==2.0.1 - markupsafe<2.1.0 - - raiutils diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb index 50736a54..9b1600b5 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/file-dataset-image-inference-mnist.ipynb @@ -359,7 +359,9 @@ "from azureml.core import Environment\n", "from azureml.core.runconfig import CondaDependencies, DEFAULT_CPU_IMAGE\n", "\n", - "batch_conda_deps = CondaDependencies.create(pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \n", + "batch_conda_deps = CondaDependencies.create(python_version=\"3.7\",\n", + " conda_packages=['pip==20.2.4'],\n", + " pip_packages=[\"tensorflow==1.15.2\", \"pillow\", \n", " \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n", "batch_env = Environment(name=\"batch_environment\")\n", "batch_env.python.conda_dependencies = batch_conda_deps\n", diff --git a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb index 205d2fbd..d60ed533 100644 --- a/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/parallel-run/tabular-dataset-inference-iris.ipynb @@ -308,7 +308,9 @@ "from azureml.core import Environment\n", "from azureml.core.runconfig import CondaDependencies\n", "\n", - "predict_conda_deps = CondaDependencies.create(pip_packages=[\"scikit-learn==0.20.3\",\n", + "predict_conda_deps = CondaDependencies.create(python_version=\"3.7\", \n", + " conda_packages=['pip==20.2.4'],\n", + " pip_packages=[\"scikit-learn==0.20.3\",\n", " \"azureml-core\", \"azureml-dataset-runtime[pandas,fuse]\"])\n", "\n", "predict_env = Environment(name=\"predict_environment\")\n", diff --git a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb index 32000b07..293b994b 100644 --- a/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb +++ b/how-to-use-azureml/machine-learning-pipelines/pipeline-style-transfer/pipeline-style-transfer-parallel-run.ipynb @@ -308,7 +308,7 @@ "metadata": {}, "outputs": [], "source": [ - "cd = CondaDependencies()\n", + "cd = CondaDependencies.create(python_version=\"3.7\", conda_packages=['pip==20.2.4'])\n", "\n", "cd.add_channel(\"conda-forge\")\n", "cd.add_conda_package(\"ffmpeg==4.0.2\")\n", @@ -401,13 +401,12 @@ "from azureml.core import Environment\n", "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n", "\n", - "parallel_cd = CondaDependencies()\n", + "parallel_cd = CondaDependencies.create(python_version=\"3.7\", conda_packages=['pip==20.2.4', 'numpy==1.19'])\n", "\n", "parallel_cd.add_channel(\"pytorch\")\n", "parallel_cd.add_conda_package(\"pytorch\")\n", "parallel_cd.add_conda_package(\"torchvision\")\n", "parallel_cd.add_conda_package(\"pillow<7\") # needed for torchvision==0.4.0\n", - "parallel_cd.add_pip_package(\"azureml-core\")\n", "\n", "styleenvironment = Environment(name=\"styleenvironment\")\n", "styleenvironment.python.conda_dependencies=parallel_cd\n", diff --git a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb index 3da5e83a..65df19a1 100644 --- a/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb +++ b/how-to-use-azureml/ml-frameworks/keras/train-hyperparameter-tune-deploy-with-keras/train-hyperparameter-tune-deploy-with-keras.ipynb @@ -430,7 +430,7 @@ "channels:\n", "- conda-forge\n", "dependencies:\n", - "- python=3.6.2\n", + "- python=3.7\n", "- pip=21.3.1\n", "- pip:\n", " - h5py<=2.10.0\n", @@ -984,7 +984,7 @@ "source": [ "from azureml.core.conda_dependencies import CondaDependencies\n", "\n", - "cd = CondaDependencies.create()\n", + "cd = CondaDependencies.create(python_version=\"3.7\")\n", "cd.add_tensorflow_conda_package()\n", "cd.add_conda_package('h5py<=2.10.0')\n", "cd.add_conda_package('keras<=2.3.1')\n", diff --git a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml index a1368b4d..99612082 100644 --- a/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml +++ b/how-to-use-azureml/ml-frameworks/pytorch/train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.yml @@ -6,5 +6,5 @@ dependencies: - pillow==5.4.1 - matplotlib - numpy==1.19.3 - - https://download.pytorch.org/whl/cpu/torch-1.6.0%2Bcpu-cp38-cp38-win_amd64.whl - - https://download.pytorch.org/whl/cpu/torchvision-0.7.0%2Bcpu-cp38-cp38-win_amd64.whl + - pytorch==1.8.1 + - torchvision==0.9.1 diff --git a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb index aca27bb7..855869d8 100644 --- a/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb +++ b/how-to-use-azureml/ml-frameworks/tensorflow/train-hyperparameter-tune-deploy-with-tensorflow/train-hyperparameter-tune-deploy-with-tensorflow.ipynb @@ -941,7 +941,7 @@ "\n", "cd = CondaDependencies.create()\n", "cd.add_conda_package('numpy')\n", - "cd.add_pip_package('tensorflow==2.0.0')\n", + "cd.add_pip_package('tensorflow==2.2.0')\n", "cd.add_pip_package(\"azureml-defaults\")\n", "cd.save_to_file(base_directory='./', conda_file_path='myenv.yml')\n", "\n", diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu index c4e0e378..24af8505 100644 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu +++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-cpu @@ -1,5 +1,8 @@ FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04 +USER root +RUN conda install -c anaconda python=3.7 + RUN pip install ray-on-aml==0.1.6 RUN pip install gym[atari]==0.19.0 RUN pip install gym[accept-rom-license]==0.19.0 @@ -9,8 +12,6 @@ RUN pip install ray==0.8.7 RUN pip install ray[rllib,tune,serve]==0.8.7 RUN pip install tensorflow==1.14.0 -USER root - RUN apt-get update RUN apt-get install -y jq RUN apt-get install -y rsync diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu index 09fd5549..d16adb56 100644 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu +++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/docker/Dockerfile-gpu @@ -1,4 +1,7 @@ -FROM mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.0.3-cudnn8-ubuntu18.04:20211111.v1 +FROM mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.0.3-cudnn8-ubuntu18.04 + +USER root +RUN conda install -c anaconda python=3.7 # CUDA repository key rotation: https://forums.developer.nvidia.com/t/notice-cuda-linux-repository-key-rotation/212771 RUN apt-key del 7fa2af80 diff --git a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb index 6453d3ac..2b453175 100644 --- a/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb +++ b/how-to-use-azureml/reinforcement-learning/atari-on-distributed-compute/pong_rllib.ipynb @@ -93,7 +93,7 @@ "source": [ "%matplotlib inline\n", "\n", - "# Azure Machine Learning core imports\n", + "# Azure Machine Learning Core imports\n", "import azureml.core\n", "\n", "# Check core SDK version number\n", diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb index 8eea14f6..63734d11 100644 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb +++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/cartpole_ci.ipynb @@ -90,7 +90,7 @@ "outputs": [], "source": [ "import azureml.core\n", - "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)" + "print(\"Azure Machine Learning SDK version:\", azureml.core.VERSION)" ] }, { diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile index a4bfb39e..897dff47 100644 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile +++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-compute-instance/files/docker/Dockerfile @@ -1,5 +1,8 @@ FROM mcr.microsoft.com/azureml/openmpi3.1.2-ubuntu18.04 +USER root +RUN conda install -c anaconda python=3.7 + RUN pip install ray-on-aml==0.1.6 RUN pip install gym[atari]==0.19.0 RUN pip install gym[accept-rom-license]==0.19.0 @@ -10,8 +13,6 @@ RUN pip install ray==0.8.7 RUN pip install ray[rllib,tune,serve]==0.8.7 RUN pip install tensorflow==1.14.0 -USER root - RUN apt-get update RUN apt-get install -y jq RUN apt-get install -y rsync diff --git a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb index cf7a11b8..c1b5cd9a 100644 --- a/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb +++ b/how-to-use-azureml/reinforcement-learning/cartpole-on-single-compute/cartpole_sc.ipynb @@ -91,7 +91,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"Azure Machine Learning SDK Version:\", azureml.core.VERSION)" + "print(\"Azure Machine Learning SDK version:\", azureml.core.VERSION)" ] }, { diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile index 31353f76..81b54f1d 100644 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile +++ b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/docker/cpu/Dockerfile @@ -1,5 +1,7 @@ FROM akdmsft/particle-cpu +RUN conda install -c anaconda python=3.7 + # Install required pip packages RUN pip3 install --upgrade pip setuptools && pip3 install --upgrade \ pandas \ diff --git a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb index 5e99a9b5..97ea9a75 100644 --- a/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb +++ b/how-to-use-azureml/reinforcement-learning/multiagent-particle-envs/particle.ipynb @@ -85,7 +85,7 @@ "outputs": [], "source": [ "import azureml.core\n", - "print('Azure Machine Learning SDK Version: ', azureml.core.VERSION)" + "print('Azure Machine Learning SDK version: ', azureml.core.VERSION)" ] }, { diff --git a/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb b/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb deleted file mode 100644 index a2c29c21..00000000 --- a/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb +++ /dev/null @@ -1,699 +0,0 @@ -{ - "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/responsible-ai/auto-ml-regresion-responsibleai/auto-ml-regresion-responsibleai.png)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Automated Machine Learning\n", - "_**Regression with Aml Compute**_\n", - "\n", - "## Contents\n", - "1. [Introduction](#Introduction)\n", - "1. [Setup](#Setup)\n", - "1. [Data](#Data)\n", - "1. [Train](#Train)\n", - "1. [Results](#Results)\n", - "1. [Test](#Test)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction\n", - "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. The regression goal is to predict the performance of certain combinations of hardware parts.\n", - "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 Compute Instance, 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. Instantiate AutoMLConfig with FeaturizationConfig for customization.\n", - "3. Train the model using remote compute.\n", - "4. Explore the results and featurization transparency options.\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.\n", - "7. Submit model analysis, explain runs and counterfactual runs for a specific AutoML model.\n", - "8. Download the feature importance for raw features and visualize the explanations for raw features on azure portal. \n", - "10. Download counterfactual examples and view them in the notebook.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "As part of the setup you have already created an Azure ML `Workspace` object. For Automated ML 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", - "import pandas as pd\n", - "\n", - "import azureml.core\n", - "from azureml.core.experiment import Experiment\n", - "from azureml.core.workspace import Workspace\n", - "import azureml.dataprep as dprep\n", - "from azureml.automl.core.featurization import FeaturizationConfig\n", - "from azureml.train.automl import AutoMLConfig\n", - "from azureml.core.dataset import Dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This sample notebook may use features that are not available in previous versions of the Azure ML SDK." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(\"This notebook was created using version 1.41.0 of the Azure ML SDK\")\n", - "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ws = Workspace.from_config()\n", - "\n", - "# Choose a name for the experiment.\n", - "experiment_name = 'automl-regression-rai'\n", - "experiment = Experiment(ws, experiment_name)\n", - "\n", - "output = {}\n", - "output['Subscription ID'] = ws.subscription_id\n", - "output['Workspace Name'] = ws.name\n", - "output['Resource Group'] = ws.resource_group\n", - "output['Location'] = ws.location\n", - "output['Experiment Name'] = experiment.name\n", - "pd.set_option('display.max_colwidth', -1)\n", - "outputDf = pd.DataFrame(data = output, index = [''])\n", - "outputDf.T" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create or Attach existing AmlCompute\n", - "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n", - "\n", - "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n", - "\n", - "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n", - "\n", - "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.compute import ComputeTarget, AmlCompute\n", - "from azureml.core.compute_target import ComputeTargetException\n", - "\n", - "# Choose a name for your cluster.\n", - "amlcompute_cluster_name = \"hardware-rai\"\n", - "\n", - "# Verify that cluster does not exist already\n", - "try:\n", - " compute_target = ComputeTarget(workspace=ws, name=amlcompute_cluster_name)\n", - " print('Found existing cluster, use it.')\n", - "except ComputeTargetException:\n", - " compute_config = AmlCompute.provisioning_configuration(vm_size='STANDARD_D2_V2',\n", - " max_nodes=4)\n", - " compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, compute_config)\n", - "\n", - "compute_target.wait_for_completion(show_output=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup Training and Test Data for AutoML experiment\n", - "\n", - "Load the hardware 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. 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 = 'https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv'\n", - "\n", - "dataset = Dataset.Tabular.from_delimited_files(data)\n", - "\n", - "# Split the dataset into train and test datasets\n", - "train_data, test_data = dataset.random_split(percentage=0.8, seed=223)\n", - "\n", - "# Drop ModelName\n", - "train_data = train_data.drop_columns(['ModelName', 'VendorName'])\n", - "test_data = test_data.drop_columns(['ModelName', 'VendorName'])\n", - "\n", - "# Register the train dataset with your workspace\n", - "train_data.register(workspace = ws, name = 'rai_machine_train_dataset',\n", - " description = 'hardware performance training data',\n", - " create_new_version=True)\n", - "\n", - "# Register the test dataset with your workspace\n", - "test_data.register(workspace = ws, name = 'rai_machine_test_dataset', description = 'hardware performance test data', create_new_version=True)\n", - "\n", - "label =\"ERP\"\n", - "\n", - "train_data.to_pandas_dataframe().head()" - ] - }, - { - "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, regression or forecasting|\n", - "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics:
spearman_correlation
normalized_root_mean_squared_error
r2_score
normalized_mean_absolute_error|\n", - "|**experiment_timeout_hours**| Maximum amount of time in hours that all iterations combined can take before the experiment terminates.|\n", - "|**enable_early_stopping**| Flag to enble early termination if the score is not improving in the short term.|\n", - "|**featurization**| 'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used. Setting this enables AutoML to perform featurization on the input to handle *missing data*, and to perform some common *feature extraction*. Note: If the input data is sparse, featurization cannot be turned on.|\n", - "|**n_cross_validations**|Number of cross validation splits.|\n", - "|**training_data**|(sparse) array-like, shape = [n_samples, n_features]|\n", - "|**label_column_name**|(sparse) array-like, shape = [n_samples, ], targets values.|" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Customization\n", - "\n", - "Supported customization includes:\n", - "\n", - "1. Column purpose update: Override feature type for the specified column.\n", - "2. Transformer parameter update: Update parameters for the specified transformer. Currently supports Imputer and HashOneHotEncoder.\n", - "3. Drop columns: Columns to drop from being featurized.\n", - "4. Block transformers: Allow/Block transformers to be used on featurization process." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create FeaturizationConfig object using API calls" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-featurizationconfig-remarks2" - ] - }, - "outputs": [], - "source": [ - "featurization_config = FeaturizationConfig()\n", - "featurization_config.blocked_transformers = ['LabelEncoder']\n", - "#featurization_config.drop_columns = ['MMIN']\n", - "featurization_config.add_column_purpose('MYCT', 'Numeric')\n", - "#default strategy mean, add transformer param for for 3 columns\n", - "featurization_config.add_transformer_params('Imputer', ['CACH'], {\"strategy\": \"median\"})\n", - "featurization_config.add_transformer_params('Imputer', ['CHMIN'], {\"strategy\": \"median\"})\n", - "featurization_config.add_transformer_params('Imputer', ['PRP'], {\"strategy\": \"most_frequent\"})\n", - "#featurization_config.add_transformer_params('HashOneHotEncoder', [], {\"number_of_bits\": 3})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "sample-featurizationconfig-remarks3" - ] - }, - "outputs": [], - "source": [ - "automl_settings = {\n", - " \"enable_early_stopping\": True, \n", - " \"experiment_timeout_hours\" : 0.25,\n", - " \"max_concurrent_iterations\": 4,\n", - " \"max_cores_per_iteration\": -1,\n", - " \"n_cross_validations\": 5,\n", - " \"primary_metric\": 'normalized_root_mean_squared_error',\n", - " \"verbosity\": logging.INFO\n", - "}\n", - "\n", - "automl_config = AutoMLConfig(task = 'regression',\n", - " debug_log = 'automl_errors.log',\n", - " compute_target=compute_target,\n", - " featurization=featurization_config,\n", - " training_data = train_data,\n", - " label_column_name = label,\n", - " **automl_settings\n", - " )" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the `submit` method on the experiment object and pass the `AutoMLConfig`. 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=False` to suppress output for each iteration. You can monitor the run by clicking on the link in the output." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run = experiment.submit(automl_config, show_output=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run the following cell to access previous runs. Uncomment the cell below and update the run_id." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#from azureml.train.automl.run import AutoMLRun\n", - "#remote_run = AutoMLRun(experiment=experiment, run_id='AutoML_1723d4fe-c33d-41f7-83ad-c010215583b0')\n", - "#remote_run" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "remote_run.wait_for_completion(wait_post_processing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generating Responsible AI insights for AutoML model\n", - "This section will walk you through the workflow to compute Responsible AI insights like model explanations and counterfactual examples using model analysis workflow for an AutoML model on your remote compute.\n", - "\n", - "### Retrieve any AutoML Model for explanations\n", - "\n", - "Below we select an AutoML pipeline from our iterations. The `get_best_child` method returns the a AutoML run with the best score for the specified metric" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "automl_run = remote_run.get_best_child(metric='mean_absolute_error')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Setup model analysis on the remote compute\n", - "The following section provides details on how to setup an AzureML experiment to run model analysis for an AutoML model on your remote compute." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Create conda configuration for model analysis and explanations runs from automl_run object." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core.runconfig import RunConfiguration\n", - "from azureml.core.conda_dependencies import CondaDependencies\n", - "\n", - "# create a new RunConfiguration object\n", - "conda_run_config = RunConfiguration(framework=\"python\")\n", - "\n", - "# Set compute target to AmlCompute\n", - "conda_run_config.target = compute_target\n", - "\n", - "# specify CondaDependencies obj\n", - "conda_run_config.environment = automl_run.get_environment()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Register the AutoML model and create a `PickleModelLoader` for the model analysis so that the model analysis can instantiate the model downloaded from AzureML." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.core import Model\n", - "from azureml.responsibleai.common.pickle_model_loader import PickleModelLoader\n", - "from azureml.responsibleai.tools.model_analysis.model_analysis_config import ModelAnalysisConfig\n", - "from azureml.responsibleai.tools.model_analysis.explain_config import ExplainConfig\n", - "from azureml.automl.core.shared.constants import MODEL_PATH\n", - "\n", - "automl_run.download_file(name=MODEL_PATH, output_file_path='model.pkl')\n", - "\n", - "model = automl_run.register_model(model_name='automl_rai', \n", - " model_path='outputs/model.pkl')\n", - "\n", - "model_loader = PickleModelLoader('model.pkl')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Construct the list of the feature column names by dropping the name of the label column from the list of all column names." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "X_column_names = train_data.to_pandas_dataframe().columns.values\n", - "X_column_names = X_column_names[X_column_names!=label]\n", - "X_column_names" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the train and test dataset for the model analysis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "train_dataset = Dataset.get_by_name(workspace=ws, name='rai_machine_train_dataset')\n", - "test_dataset = Dataset.get_by_name(workspace=ws, name='rai_machine_test_dataset')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the `ModelAnalysisConfig` below, `confidential_datastore_name` is the name of the datastore where the analyses will be uploaded. This example uses the default data store because the dataset is also in the default datastore. If you have confidential data in the dataset, you should specify a different data store as the `confidential_datastore_name` because analysis makes a copy of the data in this data store." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_analysis_config = ModelAnalysisConfig(\n", - " title=\"Model analysis\",\n", - " model=model,\n", - " model_type='regression',\n", - " model_loader=model_loader,\n", - " train_dataset=train_dataset,\n", - " test_dataset=test_dataset,\n", - " X_column_names=X_column_names,\n", - " target_column_name=label,\n", - " confidential_datastore_name=ws.get_default_datastore().name,\n", - " run_configuration=conda_run_config,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Run model analysis\n", - "\n", - "The model analysis run takes a snapshot of the data in preparation for model explanation, error analysis, causal and counterfactual.\n", - "The model analysis run is the parent run for the model explanation, error analysis, causal and counterfactual runs.\n", - "In this example we will just generate an explanation and counterfactuals, but causal and error analyses may be performed as well." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model_analysis_run = experiment.submit(model_analysis_config)\n", - "model_analysis_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Compute explanations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run model explanation based on the model analysis.\n", - "The explanation run is a child run of the model analysis run.\n", - "In the future, the `add_request` method will allow extra parameters to configure the explanation generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "explain_config = ExplainConfig(model_analysis_run, conda_run_config)\n", - "explain_config.add_request()\n", - "explain_run = model_analysis_run.submit_child(explain_config)\n", - "explain_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `explanation_manager.list` method below returns a list of metadata dictionaries for each explain run. In this case, there is a single explain run. So, the list contains a single dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "explanations = model_analysis_run.explanation_manager.list()\n", - "explanation = explanations[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Feature importance and visualizing 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 on the azure portal." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "feature_explanations = model_analysis_run.explanation_manager.download_by_id(explanation['id'])\n", - "print(feature_explanations.get_feature_importance_dict())\n", - "print(\"You can visualize the explanations for your features under the 'Explanations (preview)' tab in the explain run at:-\\n\" + explain_run.get_portal_url())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Generate counterfactual examples\n", - "\n", - "Generate counterfactuals for all the samples in the `test_dataset` based on the model analysis.\n", - "The counterfactual run is a child run of the model analysis run.\n", - "In the future, the `add_request` method will allow extra parameters to configure the counterfactuals generated." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from azureml.responsibleai.tools.model_analysis.counterfactual_config import CounterfactualConfig\n", - "\n", - "cf_config = CounterfactualConfig(model_analysis_run, conda_run_config)\n", - "cf_config.add_request(total_CFs=10, desired_range=[10, 300])\n", - "cf_run = model_analysis_run.submit_child(cf_config)\n", - "cf_run.wait_for_completion(raise_on_error=True, wait_post_processing=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Downloading counterfactual examples\n", - "The `counterfactual_manager.list` method below returns a list of metadata dictionaries for each counterfactual run. In this case, there is a single counterfactual run. So, the list contains a single dictionary.\n", - "\n", - "The `download_by_id()` method available in the `counterfactual_manager` can be used to download the counterfactual examples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cf_meta = model_analysis_run.counterfactual_manager.list()\n", - "counterfactual_object = model_analysis_run.counterfactual_manager.download_by_id(cf_meta[0]['id'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing the generated counterfactuals\n", - "You can use `visualize_as_dataframe()` method to view the generated counterfactual examples for the samples in `test_dataset`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "counterfactual_object.visualize_as_dataframe(show_only_changes=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualize counterfactual feature importance" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "counterfactual_object.summary_importance" - ] - } - ], - "metadata": { - "authors": [ - { - "name": "jeffshep" - } - ], - "categories": [ - "how-to-use-azureml", - "automated-machine-learning" - ], - "category": "tutorial", - "compute": [ - "AML" - ], - "datasets": [ - "MachineData" - ], - "deployment": [ - "ACI" - ], - "exclude_from_index": false, - "framework": [ - "None" - ], - "friendly_name": "Automated ML run with featurization and model explainability.", - "index_order": 5, - "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": "3.6.12" - }, - "tags": [ - "featurization", - "explainability", - "remote_run", - "AutomatedML" - ], - "task": "Regression" - }, - "nbformat": 4, - "nbformat_minor": 2 -} \ No newline at end of file diff --git a/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.yml b/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.yml deleted file mode 100644 index f20b7571..00000000 --- a/how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.yml +++ /dev/null @@ -1,5 +0,0 @@ -name: auto-ml-regression-responsibleai -dependencies: -- pip: - - azureml-sdk - - azureml-responsibleai diff --git a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml index 4958d0bf..cafe4ef9 100644 --- a/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml +++ b/how-to-use-azureml/responsible-ai/visualize-upload-loan-decision/rai-loan-decision.yml @@ -8,7 +8,7 @@ dependencies: - matplotlib - azureml-dataset-runtime - ipywidgets - - raiwidgets~=0.17.0 + - raiwidgets~=0.18.1 - liac-arff - packaging>=20.9 - itsdangerous==2.0.1 diff --git a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb index 44f02711..7ab49ed0 100644 --- a/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb +++ b/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb @@ -100,7 +100,7 @@ "\n", "# Check core SDK version number\n", "\n", - "print(\"This notebook was created using SDK version 1.41.0, you are currently running version\", azureml.core.VERSION)" + "print(\"This notebook was created using SDK version 1.42.0, you are currently running version\", azureml.core.VERSION)" ] }, { diff --git a/how-to-use-azureml/training/using-environments/using-environments.ipynb b/how-to-use-azureml/training/using-environments/using-environments.ipynb index 7064480d..bdfe91e6 100644 --- a/how-to-use-azureml/training/using-environments/using-environments.ipynb +++ b/how-to-use-azureml/training/using-environments/using-environments.ipynb @@ -160,7 +160,7 @@ "\n", "myenv = Environment(name=\"myenv\")\n", "conda_dep = CondaDependencies()\n", - "conda_dep.add_conda_package(\"scikit-learn\")" + "conda_dep.add_conda_package(\"scikit-learn==0.22.1\")" ] }, { @@ -180,7 +180,7 @@ }, "outputs": [], "source": [ - "conda_dep.add_pip_package(\"pillow==5.4.1\")\n", + "conda_dep.add_pip_package(\"pillow==6.2.1\")\n", "myenv.python.conda_dependencies=conda_dep" ] }, diff --git a/index.md b/index.md index a5fa0428..9bfec903 100644 --- a/index.md +++ b/index.md @@ -17,6 +17,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:| | [Forecasting BikeShare Demand](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-bike-share/auto-ml-forecasting-bike-share.ipynb) | Forecasting | BikeShare | Remote | None | Azure ML AutoML | Forecasting | | [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None | +| [Forecasting orange juice sales with deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-pipelines/auto-ml-forecasting-pipelines.ipynb) | Forecasting | Orange Juice Sales | Remote | Azure Container Instance | Azure ML AutoML | None | | [Register a model and deploy locally](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/deployment/deploy-to-local/register-model-deploy-local.ipynb) | Deployment | None | Local | Local | None | None | | :star:[Data drift quickdemo](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datadrift-tutorial/datadrift-tutorial.ipynb) | Filtering | NOAA | Remote | None | Azure ML | Dataset, Timeseries, Drift | | :star:[Datasets with ML Pipeline](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/work-with-data/datasets-tutorial/pipeline-with-datasets/pipeline-for-image-classification.ipynb) | Train | Fashion MNIST | Remote | None | Azure ML | Dataset, Pipeline, Estimator, ScriptRun | @@ -27,7 +28,6 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb) | Classification | Creditcard | AML Compute | None | None | remote_run, AutomatedML | | [Classification of credit card fraudulent transactions using Automated ML](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/experimental/classification-credit-card-fraud-local-managed/auto-ml-classification-credit-card-fraud-local-managed.ipynb) | Classification | Creditcard | AML Compute | None | None | AutomatedML | | [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/regression-explanation-featurization/auto-ml-regression-explanation-featurization.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | -| [Automated ML run with featurization and model explainability.](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/responsible-ai/auto-ml-regression-responsibleai/auto-ml-regression-responsibleai.ipynb) | Regression | MachineData | AML | ACI | None | featurization, explainability, remote_run, AutomatedML | | [auto-ml-forecasting-backtest-single-model](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/forecasting-backtest-single-model/auto-ml-forecasting-backtest-single-model.ipynb) | | None | Remote | None | Azure ML AutoML | | | :star:[Azure Machine Learning Pipeline with DataTranferStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) | Demonstrates the use of DataTranferStep | Custom | ADF | None | Azure ML | None | | [Getting Started with Azure Machine Learning Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb) | Getting Started notebook for ANML Pipelines | Custom | AML Compute | None | Azure ML | None | diff --git a/setup-environment/configuration.ipynb b/setup-environment/configuration.ipynb index 2180db45..e099b6f9 100644 --- a/setup-environment/configuration.ipynb +++ b/setup-environment/configuration.ipynb @@ -102,7 +102,7 @@ "source": [ "import azureml.core\n", "\n", - "print(\"This notebook was created using version 1.41.0 of the Azure ML SDK\")\n", + "print(\"This notebook was created using version 1.42.0 of the Azure ML SDK\")\n", "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")" ] }, diff --git a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb b/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb index ca6739f2..999b9d3c 100644 --- a/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb +++ b/tutorials/machine-learning-pipelines-advanced/tutorial-pipeline-batch-scoring-classification.ipynb @@ -305,7 +305,9 @@ "from azureml.core.conda_dependencies import CondaDependencies\n", "from azureml.core.runconfig import DEFAULT_GPU_IMAGE\n", "\n", - "cd = CondaDependencies.create(pip_packages=[\"tensorflow-gpu==1.15.2\",\n", + "cd = CondaDependencies.create(python_version=\"3.7\",\n", + " conda_packages=['pip==20.2.4'],\n", + " pip_packages=[\"tensorflow-gpu==1.15.2\",\n", " \"azureml-core\", \"azureml-dataset-runtime[fuse]\"])\n", "\n", "env = Environment(name=\"parallelenv\")\n",