update samples - test

README.md

@@ -2,8 +2,7 @@
 
 This repository contains example notebooks demonstrating the [Azure Machine Learning](https://azure.microsoft.com/en-us/services/machine-learning-service/) Python SDK which allows you to build, train, deploy and manage machine learning solutions using Azure.  The AML SDK allows you the choice of using local or cloud compute resources, while managing and maintaining the complete data science workflow from the cloud.
 
-![Azure ML Workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/concept-azure-machine-learning-architecture/workflow.png)
+![Azure ML workflow](https://raw.githubusercontent.com/MicrosoftDocs/azure-docs/master/articles/machine-learning/service/media/overview-what-is-azure-ml/aml.png)
-
 
 ## Quick installation
 ```sh
@@ -39,7 +38,6 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 - [Machine Learning Pipelines](./how-to-use-azureml/machine-learning-pipelines) - Examples showing how to create and use reusable pipelines for training and batch scoring
 - [Deployment](./how-to-use-azureml/deployment) - Examples showing how to deploy and manage machine learning models and solutions
 - [Azure Databricks](./how-to-use-azureml/azure-databricks) - Examples showing how to use Azure ML with Azure Databricks
-- [Monitor Models](./how-to-use-azureml/monitor-models) - Examples showing how to enable model monitoring services such as DataDrift
 
 ---
 ## Documentation
@@ -50,18 +48,12 @@ The [How to use Azure ML](./how-to-use-azureml) folder contains specific example
 
 ---
 
-
-## Community Repository 
-Visit this [community repository](https://github.com/microsoft/MLOps/tree/master/examples) to find useful end-to-end sample notebooks. Also, please follow these [contribution guidelines](https://github.com/microsoft/MLOps/blob/master/contributing.md) when contributing to this repository.   
-
 ## Projects using Azure Machine Learning
 
 Visit following repos to see projects contributed by Azure ML users:
- - [AMLSamples](https://github.com/Azure/AMLSamples) Number of end-to-end examples, including face recognition, predictive maintenance, customer churn and sentiment analysis.
+
- - [Learn about Natural Language Processing best practices using Azure Machine Learning service](https://github.com/microsoft/nlp)
+ - [Fine tune natural language processing models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
- - [Pre-Train BERT models using Azure Machine Learning service](https://github.com/Microsoft/AzureML-BERT)
  - [Fashion MNIST with Azure ML SDK](https://github.com/amynic/azureml-sdk-fashion)
- - [UMass Amherst Student Samples](https://github.com/katiehouse3/microsoft-azure-ml-notebooks) - A number of end-to-end machine learning notebooks, including machine translation, image classification, and customer churn, created by students in the 696DS course at UMass Amherst.
  
 ## Data/Telemetry 
 This repository collects usage data and sends it to Mircosoft to help improve our products and services. Read Microsoft's [privacy statement to learn more](https://privacy.microsoft.com/en-US/privacystatement)

configuration.ipynb

@@ -58,7 +58,7 @@
         "\n",
         "### What is an Azure Machine Learning workspace\n",
         "\n",
-        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inference, and the monitoring of deployed models."
+        "An Azure ML Workspace is an Azure resource that organizes and coordinates the actions of many other Azure resources to assist in executing and sharing machine learning workflows.  In particular, an Azure ML Workspace coordinates storage, databases, and compute resources providing added functionality for machine learning experimentation, deployment, inferencing, and the monitoring of deployed models."
       ]
     },
     {
@@ -103,7 +103,7 @@
       "source": [
         "import azureml.core\n",
         "\n",
-        "print(\"This notebook was created using version 1.0.69 of the Azure ML SDK\")\n",
+        "print(\"This notebook was created using version  of the Azure ML SDK\")\n",
         "print(\"You are currently using version\", azureml.core.VERSION, \"of the Azure ML SDK\")"
       ]
     },

contrib/RAPIDS/README.md

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

contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb

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

contrib/RAPIDS/process_data.py

@@ -15,6 +15,21 @@ from glob import glob
 import os
 import argparse
 
+def initialize_rmm_pool():
+    from librmm_cffi import librmm_config as rmm_cfg
+
+    rmm_cfg.use_pool_allocator = True
+    #rmm_cfg.initial_pool_size = 2<<30 # set to 2GiB. Default is 1/2 total GPU memory
+    import cudf
+    return cudf._gdf.rmm_initialize()
+
+def initialize_rmm_no_pool():
+    from librmm_cffi import librmm_config as rmm_cfg
+    
+    rmm_cfg.use_pool_allocator = False
+    import cudf
+    return cudf._gdf.rmm_initialize()
+
 def run_dask_task(func, **kwargs):
     task = func(**kwargs)
     return task
@@ -192,26 +207,26 @@ def gpu_load_names(col_path):
 
 def create_ever_features(gdf, **kwargs):
     everdf = gdf[['loan_id', 'current_loan_delinquency_status']]
-    everdf = everdf.groupby('loan_id', method='hash').max().reset_index()
+    everdf = everdf.groupby('loan_id', method='hash').max()
     del(gdf)
-    everdf['ever_30'] = (everdf['current_loan_delinquency_status'] >= 1).astype('int8')
+    everdf['ever_30'] = (everdf['max_current_loan_delinquency_status'] >= 1).astype('int8')
-    everdf['ever_90'] = (everdf['current_loan_delinquency_status'] >= 3).astype('int8')
+    everdf['ever_90'] = (everdf['max_current_loan_delinquency_status'] >= 3).astype('int8')
-    everdf['ever_180'] = (everdf['current_loan_delinquency_status'] >= 6).astype('int8')
+    everdf['ever_180'] = (everdf['max_current_loan_delinquency_status'] >= 6).astype('int8')
-    everdf.drop_column('current_loan_delinquency_status')
+    everdf.drop_column('max_current_loan_delinquency_status')
     return everdf
 
 def create_delinq_features(gdf, **kwargs):
     delinq_gdf = gdf[['loan_id', 'monthly_reporting_period', 'current_loan_delinquency_status']]
     del(gdf)
-    delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
+    delinq_30 = delinq_gdf.query('current_loan_delinquency_status >= 1')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
-    delinq_30['delinquency_30'] = delinq_30['monthly_reporting_period']
+    delinq_30['delinquency_30'] = delinq_30['min_monthly_reporting_period']
-    delinq_30.drop_column('monthly_reporting_period')
+    delinq_30.drop_column('min_monthly_reporting_period')
-    delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
+    delinq_90 = delinq_gdf.query('current_loan_delinquency_status >= 3')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
-    delinq_90['delinquency_90'] = delinq_90['monthly_reporting_period']
+    delinq_90['delinquency_90'] = delinq_90['min_monthly_reporting_period']
-    delinq_90.drop_column('monthly_reporting_period')
+    delinq_90.drop_column('min_monthly_reporting_period')
-    delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min().reset_index()
+    delinq_180 = delinq_gdf.query('current_loan_delinquency_status >= 6')[['loan_id', 'monthly_reporting_period']].groupby('loan_id', method='hash').min()
-    delinq_180['delinquency_180'] = delinq_180['monthly_reporting_period']
+    delinq_180['delinquency_180'] = delinq_180['min_monthly_reporting_period']
-    delinq_180.drop_column('monthly_reporting_period')
+    delinq_180.drop_column('min_monthly_reporting_period')
     del(delinq_gdf)
     delinq_merge = delinq_30.merge(delinq_90, how='left', on=['loan_id'], type='hash')
     delinq_merge['delinquency_90'] = delinq_merge['delinquency_90'].fillna(np.dtype('datetime64[ms]').type('1970-01-01').astype('datetime64[ms]'))
@@ -264,15 +279,16 @@ def create_joined_df(gdf, everdf, **kwargs):
 def create_12_mon_features(joined_df, **kwargs):
     testdfs = []
     n_months = 12
-
     for y in range(1, n_months + 1):
         tmpdf = joined_df[['loan_id', 'timestamp_year', 'timestamp_month', 'delinquency_12', 'upb_12']]
         tmpdf['josh_months'] = tmpdf['timestamp_year'] * 12 + tmpdf['timestamp_month']
         tmpdf['josh_mody_n'] = ((tmpdf['josh_months'].astype('float64') - 24000 - y) / 12).floor()
-        tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'}).reset_index()
+        tmpdf = tmpdf.groupby(['loan_id', 'josh_mody_n'], method='hash').agg({'delinquency_12': 'max','upb_12': 'min'})
-        tmpdf['delinquency_12'] = (tmpdf['delinquency_12']>3).astype('int32')
+        tmpdf['delinquency_12'] = (tmpdf['max_delinquency_12']>3).astype('int32')
-        tmpdf['delinquency_12'] +=(tmpdf['upb_12']==0).astype('int32')
+        tmpdf['delinquency_12'] +=(tmpdf['min_upb_12']==0).astype('int32')
-        tmpdf['upb_12'] = tmpdf['upb_12']
+        tmpdf.drop_column('max_delinquency_12')
+        tmpdf['upb_12'] = tmpdf['min_upb_12']
+        tmpdf.drop_column('min_upb_12')
         tmpdf['timestamp_year'] = (((tmpdf['josh_mody_n'] * n_months) + 24000 + (y - 1)) / 12).floor().astype('int16')
         tmpdf['timestamp_month'] = np.int8(y)
         tmpdf.drop_column('josh_mody_n')
@@ -313,7 +329,6 @@ def last_mile_cleaning(df, **kwargs):
         'delinquency_30', 'delinquency_90', 'delinquency_180', 'upb_12',
         'zero_balance_effective_date','foreclosed_after', 'disposition_date','timestamp'
     ]
-
     for column in drop_list:
         df.drop_column(column)
     for col, dtype in df.dtypes.iteritems():
@@ -327,6 +342,7 @@ def last_mile_cleaning(df, **kwargs):
     return df.to_arrow(preserve_index=False)
 
 def main():
+    #print('XGBOOST_BUILD_DOC is ' + os.environ['XGBOOST_BUILD_DOC'])
     parser = argparse.ArgumentParser("rapidssample")
     parser.add_argument("--data_dir", type=str, help="location of data")
     parser.add_argument("--num_gpu", type=int, help="Number of GPUs to use", default=1)
@@ -348,6 +364,7 @@ def main():
     print('data_dir = {0}'.format(data_dir))
     print('num_gpu = {0}'.format(num_gpu))
     print('part_count = {0}'.format(part_count))
+    #part_count = part_count + 1 # adding one because the usage below is not inclusive
     print('end_year = {0}'.format(end_year))
     print('cpu_predictor = {0}'.format(cpu_predictor))
     
@@ -363,17 +380,19 @@ def main():
     client
     print(client.ncores())
 
-    # to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
+# to download data for this notebook, visit https://rapidsai.github.io/demos/datasets/mortgage-data and update the following paths accordingly
     acq_data_path = "{0}/acq".format(data_dir) #"/rapids/data/mortgage/acq"
     perf_data_path = "{0}/perf".format(data_dir) #"/rapids/data/mortgage/perf"
     col_names_path = "{0}/names.csv".format(data_dir) # "/rapids/data/mortgage/names.csv"
     start_year = 2000
+#end_year = 2000 # end_year is inclusive -- converted to parameter
+#part_count = 2 # the number of data files to train against -- converted to parameter
 
+    client.run(initialize_rmm_pool)
     client
-    print('--->>> Workers used: {0}'.format(client.ncores()))
+    print(client.ncores())
-
+# NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
-    # NOTE: The ETL calculates additional features which are then dropped before creating the XGBoost DMatrix.
+# This can be optimized to avoid calculating the dropped features.
-    # This can be optimized to avoid calculating the dropped features.
     print("Reading ...")
     t1 = datetime.datetime.now()
     gpu_dfs = []
@@ -395,9 +414,14 @@ def main():
             
     wait(gpu_dfs)
     t2 = datetime.datetime.now()
-    print("Reading time: {0}".format(str(t2-t1)))
+    print("Reading time ...")
-    print('--->>> Number of data parts: {0}'.format(len(gpu_dfs)))
+    print(t2-t1)
-
+    print('len(gpu_dfs) is {0}'.format(len(gpu_dfs)))
+    
+    client.run(cudf._gdf.rmm_finalize)
+    client.run(initialize_rmm_no_pool)
+    client
+    print(client.ncores())
     dxgb_gpu_params = {
         'nround':            100,
         'max_depth':         8,
@@ -414,7 +438,7 @@ def main():
         'n_gpus':            1, 
         'distributed_dask':  True,
         'loss':              'ls',
-        'objective':         'reg:squarederror',
+        'objective':         'gpu:reg:linear',
         'max_features':      'auto',
         'criterion':         'friedman_mse',
         'grow_policy':       'lossguide',
@@ -422,13 +446,13 @@ def main():
     }
       
     if cpu_predictor:
-        print('\n---->>>> Training using CPUs <<<<----\n')
+        print('Training using CPUs')
         dxgb_gpu_params['predictor'] = 'cpu_predictor'
         dxgb_gpu_params['tree_method'] = 'hist'
         dxgb_gpu_params['objective'] = 'reg:linear'
         
     else:
-        print('\n---->>>> Training using GPUs <<<<----\n')
+        print('Training using GPUs')
     
     print('Training parameters are {0}'.format(dxgb_gpu_params))
     
@@ -457,13 +481,14 @@ def main():
     gpu_dfs = [gpu_df.persist() for gpu_df in gpu_dfs]
     gc.collect()
     wait(gpu_dfs)
-
+    
-    # TRAIN THE MODEL
     labels = None
     t1 = datetime.datetime.now()
     bst = dxgb_gpu.train(client, dxgb_gpu_params, gpu_dfs, labels, num_boost_round=dxgb_gpu_params['nround'])
     t2 = datetime.datetime.now()
-    print('\n---->>>> Training time: {0} <<<<----\n'.format(str(t2-t1)))
+    print("Training time ...")
+    print(t2-t1)
+    print('str(bst) is {0}'.format(str(bst)))
     print('Exiting script')
 
 if __name__ == '__main__':

contrib/RAPIDS/rapids.yml

@@ -0,0 +1,35 @@
+name: rapids
+channels:
+- nvidia
+- numba
+- conda-forge
+- rapidsai
+- defaults
+- pytorch
+
+dependencies:
+- arrow-cpp=0.12.0
+- bokeh
+- cffi=1.11.5
+- cmake=3.12
+- cuda92
+- cython==0.29
+- dask=1.1.1
+- distributed=1.25.3
+- faiss-gpu=1.5.0
+- numba=0.42
+- numpy=1.15.4
+- nvstrings
+- pandas=0.23.4
+- pyarrow=0.12.0
+- scikit-learn
+- scipy
+- cudf
+- cuml
+- python=3.6.2
+- jupyterlab
+- pip:
+  - file:/rapids/xgboost/python-package/dist/xgboost-0.81-py3-none-any.whl
+  - git+https://github.com/rapidsai/dask-xgboost@dask-cudf
+  - git+https://github.com/rapidsai/dask-cudf@master
+  - git+https://github.com/rapidsai/dask-cuda@master

contrib/datadrift/azure-ml-datadrift.ipynb

@@ -17,7 +17,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/monitor-models/data-drift/azureml-datadrift.png)"
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/contrib/datadrift/azureml-datadrift.png)"
       ]
     },
     {
@@ -33,9 +33,10 @@
       "source": [
         "## Install the DataDrift package\n",
         "\n",
-        "Install the azureml-datadrift, azureml-opendatasets and lightgbm packages before running this notebook.\n",
+        "Install the azureml-contrib-datadrift, azureml-contrib-opendatasets and lightgbm packages before running this notebook.\n",
         "```\n",
-        "pip install azureml-datadrift\n",
+        "pip install azureml-contrib-datadrift\n",
+        "pip install azureml-contrib-datasets\n",
         "pip install lightgbm\n",
         "```"
       ]
@@ -61,14 +62,15 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "import requests\n",
-        "from azureml.core import Dataset, Workspace\n",
+        "from azureml.contrib.datadrift import DataDriftDetector, AlertConfiguration\n",
+        "from azureml.contrib.opendatasets import NoaaIsdWeather\n",
+        "from azureml.core import Dataset, Workspace, Run\n",
         "from azureml.core.compute import AksCompute, ComputeTarget\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "from azureml.core.experiment import Experiment\n",
         "from azureml.core.image import ContainerImage\n",
         "from azureml.core.model import Model\n",
         "from azureml.core.webservice import Webservice, AksWebservice\n",
-        "from azureml.datadrift import DataDriftDetector, AlertConfiguration\n",
-        "from azureml.opendatasets import NoaaIsdWeather\n",
         "from azureml.widgets import RunDetails\n",
         "from sklearn.externals import joblib\n",
         "from sklearn.model_selection import train_test_split\n"
@@ -136,7 +138,7 @@
         "             '722071', '720326', '725415', '724504', '725665', '725424',\n",
         "             '725066']\n",
         "\n",
-        "columns = ['wban', 'datetime', 'latitude', 'longitude', 'elevation', 'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']\n",
+        "columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation', 'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']\n",
         "\n",
         "\n",
         "def enrich_weather_noaa_data(noaa_df):\n",
@@ -247,16 +249,19 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dataset_name = \"training_dataset\"\n",
+        "dataset_name = \"dataset\"\n",
+        "name_suffix = datetime.utcnow().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
+        "snapshot_name = \"snapshot-{}\".format(name_suffix)\n",
+        "\n",
         "dstore = ws.get_default_datastore()\n",
         "dstore.upload(training_dir, \"data/training\", show_progress=True)\n",
+        "dpath = dstore.path(\"data/training/training.csv\")\n",
+        "trainingDataset = Dataset.auto_read_files(dpath, include_path=True)\n",
+        "trainingDataset = trainingDataset.register(workspace=ws, name=dataset_name, description=\"dset\", exist_ok=True)\n",
         "\n",
-        "datastore_path = [(dstore, 'data/training/training.csv')]\n",
+        "trainingDataSnapshot = trainingDataset.create_snapshot(snapshot_name=snapshot_name, compute_target=None, create_data_snapshot=True)\n",
-        "trainingDataset = Dataset.Tabular.from_delimited_files(path=datastore_path)\n",
+        "datasets = [(Dataset.Scenario.TRAINING, trainingDataSnapshot)]\n",
-        "trainingDataset = trainingDataset.register(workspace=ws, name=dataset_name, description=\"training\", create_new_version=True)\n",
+        "print(\"dataset registration done.\\n\")\n",
-        "\n",
-        "datasets = [(Dataset.Scenario.TRAINING, trainingDataset)]\n",
-        "print(\"Dataset registration done.\\n\")\n",
         "datasets"
       ]
     },
@@ -502,9 +507,11 @@
       "outputs": [],
       "source": [
         "# One Hot Encode the scoring dataset to match the training dataset schema\n",
-        "columns = list(model.datasets[\"training\"][0].to_pandas_dataframe().columns)\n",
+        "columns_dict = model.datasets[\"training\"][0].get_profile().columns\n",
-        "extra_cols = ['Path', 'Column1']\n",
+        "extra_cols = ('Path', 'Column1')\n",
-        "training_columns = [c for c in columns if c not in extra_cols]\n",
+        "for k in extra_cols:\n",
+        "    columns_dict.pop(k, None)\n",
+        "training_columns = list(columns_dict.keys())\n",
         "\n",
         "categorical_columns = scoring_df.dtypes == object\n",
         "categorical_columns = categorical_columns[categorical_columns == True]\n",
@@ -567,22 +574,6 @@
         "        time.sleep(3)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "We need to wait up to 10 minutes for the Model Data Collector to dump the model input and inference data to storage in the Workspace, where it's used by the DataDriftDetector job."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "time.sleep(600)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -599,14 +590,12 @@
         "services = [service_name]\n",
         "start = datetime.now() - timedelta(days=2)\n",
         "end = datetime(year=2020, month=1, day=22, hour=15, minute=16)\n",
-        "feature_list = ['latitude', 'longitude', 'sine_hourofday', 'cosine_hourofday', 'temperature-7']\n",
+        "feature_list = ['usaf', 'wban', 'latitude', 'longitude', 'station_name', 'p_k',  'sine_hourofday', 'cosine_hourofday', 'temperature-7']\n",
         "alert_config = AlertConfiguration([email_address]) if email_address else None\n",
         "\n",
         "# there will be an exception indicating using get() method if DataDrift object already exist\n",
         "try:\n",
-        "    # With consideration for data latency, by default the scheduled jobs will process previous day's data. \n",
+        "    datadrift = DataDriftDetector.create(ws, model.name, model.version, services, frequency=\"Day\", alert_config=alert_config)\n",
-        "    # In this demo, scoring data will be generated from current day, therefore set schedule start time to next day to process current day's data.\n",
-        "    datadrift = DataDriftDetector.create(ws, model.name, model.version, services, frequency=\"Day\", schedule_start=datetime.utcnow() + timedelta(days=1), alert_config=alert_config)\n",
         "except KeyError:\n",
         "    datadrift = DataDriftDetector.get(ws, model.name, model.version)\n",
         "    \n",
@@ -636,8 +625,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "child_run = list(run.get_children())[0]\n",
+        "exp = Experiment(ws, datadrift._id)\n",
-        "RunDetails(child_run).show()"
+        "dd_run = Run(experiment=exp, run_id=run)\n",
+        "RunDetails(dd_run).show()"
       ]
     },
     {
@@ -653,7 +643,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "child_run.wait_for_completion(wait_post_processing=True)\n",
+        "children = list(dd_run.get_children())\n",
+        "for child in children:\n",
+        "    child.wait_for_completion()\n",
         "\n",
         "drift_metrics = datadrift.get_output(start_time=start, end_time=end)\n",
         "drift_metrics"
@@ -710,8 +702,7 @@
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
       "version": "3.6.6"
-    },
+    }
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License."
   },
   "nbformat": 4,
   "nbformat_minor": 2

contrib/datadrift/azure-ml-datadrift.yml

@@ -2,7 +2,7 @@ name: azure-ml-datadrift
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-datadrift
+  - azureml-contrib-datadrift
-  - azureml-opendatasets
+  - azureml-contrib-opendatasets
   - lightgbm
   - azureml-widgets

how-to-use-azureml/README.md

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

how-to-use-azureml/automated-machine-learning/README.md

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

how-to-use-azureml/automated-machine-learning/automl_env.yml

@@ -2,26 +2,20 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0
-- numpy>=1.16.0,<=1.16.2
+- numpy>=1.11.0,<=1.16.2
 - cython
 - urllib3<1.24
 - scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<=0.23.4
 - py-xgboost<=0.80
-- pyarrow>=0.11.0
-- conda-forge::fbprophet==0.5
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-defaults
+  - azureml-sdk[automl,explain]
-  - azureml-train-automl
   - azureml-widgets
-  - azureml-explain-model
-  - azureml-contrib-interpret
   - pandas_ml
   

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

@@ -2,27 +2,21 @@ name: azure_automl
 dependencies:
   # The python interpreter version.
   # Currently Azure ML only supports 3.5.2 and later.
-- pip
 - nomkl
 - python>=3.5.2,<3.6.8
 - nb_conda
 - matplotlib==2.1.0
-- numpy>=1.16.0,<=1.16.2
+- numpy>=1.11.0,<=1.16.2
 - cython
 - urllib3<1.24
 - scipy>=1.0.0,<=1.1.0
 - scikit-learn>=0.19.0,<=0.20.3
 - pandas>=0.22.0,<0.23.0
 - py-xgboost<=0.80
-- pyarrow>=0.11.0
-- conda-forge::fbprophet==0.5
 
 - pip:
   # Required packages for AzureML execution, history, and data preparation.
-  - azureml-defaults
+  - azureml-sdk[automl,explain]
-  - azureml-train-automl
   - azureml-widgets
-  - azureml-explain-model
-  - azureml-contrib-interpret
   - pandas_ml
   

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

@@ -69,17 +69,22 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "import json\n",
         "import logging\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
+        "import numpy as np\n",
         "import pandas as pd\n",
         "import os\n",
+        "from sklearn import datasets\n",
+        "import azureml.dataprep as dprep\n",
+        "from sklearn.model_selection import train_test_split\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl import AutoMLConfig"
+        "from azureml.train.automl.run import AutoMLRun"
       ]
     },
     {
@@ -92,6 +97,8 @@
         "\n",
         "# choose a name for experiment\n",
         "experiment_name = 'automl-classification-bmarketing'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-classification-bankmarketing'\n",
         "\n",
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
@@ -101,6 +108,7 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -147,12 +155,11 @@
         "    # Create the cluster.\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
-        "print('Checking cluster status...')\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -161,7 +168,20 @@
       "source": [
         "# Data\n",
         "\n",
-        "Create a run configuration for the remote run."
+        "Here load the data in the get_data() script to be utilized in azure compute. To do this  first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_Run_config."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')\n",
+        "    \n",
+        "if not os.path.exists(project_folder):\n",
+        "    os.makedirs(project_folder)"
       ]
     },
     {
@@ -172,7 +192,6 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -180,8 +199,10 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -191,7 +212,7 @@
       "source": [
         "### Load Data\n",
         "\n",
-        "Load the bank marketing dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model."
+        "Here we create the script to be run in azure comput for loading the data, we load the bank marketing dataset into X_train and y_train. Next X_train and y_train is returned for training the model."
       ]
     },
     {
@@ -201,8 +222,11 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
-        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "dflow = dprep.auto_read_file(data)\n",
-        "dataset.take(5).to_pandas_dataframe()"
+        "dflow.get_profile()\n",
+        "X_train = dflow.drop_columns(columns=['y'])\n",
+        "y_train = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
+        "dflow.head()"
       ]
     },
     {
@@ -220,8 +244,9 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**training_data**|Input dataset, containing both features and label column.|\n",
+        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**label_column_name**|The name of the label column.|\n",
+        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
+        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -244,9 +269,10 @@
         "\n",
         "automl_config = AutoMLConfig(task = 'classification',\n",
         "                             debug_log = 'automl_errors.log',\n",
+        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
-        "                             training_data = dataset,\n",
+        "                             X = X_train,\n",
-        "                             label_column_name = 'y',\n",
+        "                             y = y_train,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -378,7 +404,7 @@
         "def run(rawdata):\n",
         "    try:\n",
         "        data = json.loads(rawdata)['data']\n",
-        "        data = np.array(data)\n",
+        "        data = numpy.array(data)\n",
         "        result = model.predict(data)\n",
         "    except Exception as e:\n",
         "        result = str(e)\n",
@@ -415,7 +441,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -425,8 +451,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-        "                                 pip_packages=['azureml-defaults','azureml-train-automl'])\n",
+        "                                 pip_packages=['azureml-sdk[automl]'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -446,7 +474,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
@@ -463,7 +491,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploy the model as a Web Service on Azure Container Instance"
+        "### Create a Container Image\n",
+        "\n",
+        "Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
+        "or when testing a model that is under development."
       ]
     },
     {
@@ -472,23 +503,63 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.image import Image, ContainerImage\n",
-        "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
+        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                   entry_script = script_file_name,\n",
+        "                                 execution_script = script_file_name,\n",
-        "                                   conda_file = conda_env_file_name)\n",
+        "                                 conda_file = conda_env_file_name,\n",
+        "                                 tags = {'area': \"bmData\", 'type': \"automl_classification\"},\n",
+        "                                 description = \"Image for automl classification sample\")\n",
+        "\n",
+        "image = Image.create(name = \"automlsampleimage\",\n",
+        "                     # this is the model object \n",
+        "                     models = [model],\n",
+        "                     image_config = image_config, \n",
+        "                     workspace = ws)\n",
+        "\n",
+        "image.wait_for_creation(show_output = True)\n",
+        "\n",
+        "if image.creation_state == 'Failed':\n",
+        "    print(\"Image build log at: \" + image.image_build_log_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy the Image as a Web Service on Azure Container Instance\n",
+        "\n",
+        "Deploy an image that contains the model and other assets needed by the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",
         "                                               tags = {'area': \"bmData\", 'type': \"automl_classification\"}, \n",
-        "                                               description = 'sample service for Automl Classification')\n",
+        "                                               description = 'sample service for Automl Classification')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
         "\n",
         "aci_service_name = 'automl-sample-bankmarketing'\n",
         "print(aci_service_name)\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+        "                                           image = image,\n",
+        "                                           name = aci_service_name,\n",
+        "                                           workspace = ws)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
@@ -545,6 +616,8 @@
       "outputs": [],
       "source": [
         "# Load the bank marketing datasets.\n",
+        "from sklearn.datasets import load_diabetes\n",
+        "from sklearn.model_selection import train_test_split\n",
         "from numpy import array"
       ]
     },
@@ -555,10 +628,11 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_validate.csv\"\n",
-        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "dflow = dprep.auto_read_file(data)\n",
-        "X_test = dataset.drop_columns(columns=['y'])\n",
+        "dflow.get_profile()\n",
-        "y_test = dataset.keep_columns(columns=['y'], validate=True)\n",
+        "X_test = dflow.drop_columns(columns=['y'])\n",
-        "dataset.take(5).to_pandas_dataframe()"
+        "y_test = dflow.keep_columns(columns=['y'], validate_column_exists=True)\n",
+        "dflow.head()"
       ]
     },
     {

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

@@ -2,9 +2,6 @@ name: auto-ml-classification-bank-marketing
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -74,12 +74,14 @@
         "from matplotlib import pyplot as plt\n",
         "import pandas as pd\n",
         "import os\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import azureml.dataprep as dprep\n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
+        "from azureml.train.automl import AutoMLConfig\n",
-        "from azureml.train.automl import AutoMLConfig"
+        "from azureml.train.automl.run import AutoMLRun"
       ]
     },
     {
@@ -92,6 +94,8 @@
         "\n",
         "# choose a name for experiment\n",
         "experiment_name = 'automl-classification-ccard'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-classification-creditcard'\n",
         "\n",
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
@@ -101,6 +105,7 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -147,12 +152,11 @@
         "    # Create the cluster.\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
-        "print('Checking cluster status...')\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
+        "    \n",
-        "\n",
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
-        "# For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -161,7 +165,20 @@
       "source": [
         "# Data\n",
         "\n",
-        "Create a run configuration for the remote run."
+        "Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')\n",
+        "    \n",
+        "if not os.path.exists(project_folder):\n",
+        "    os.makedirs(project_folder)"
       ]
     },
     {
@@ -172,7 +189,6 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -180,8 +196,10 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -191,7 +209,7 @@
       "source": [
         "### Load Data\n",
         "\n",
-        "Load the credit card dataset from a csv file containing both training features and labels. The features are inputs to the model, while the training labels represent the expected output of the model. Next, we'll split the data using random_split and extract the training data for the model."
+        "Here create the script to be run in azure compute for loading the data, load the credit card dataset into cards and store the Class column (y) in the y variable and store the remaining data in the x variable. Next split the data  using train_test_split and return X_train and y_train for training the model."
       ]
     },
     {
@@ -201,11 +219,12 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/creditcard.csv\"\n",
-        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "dflow = dprep.auto_read_file(data)\n",
-        "training_data, validation_data = dataset.random_split(percentage=0.8, seed=223)\n",
+        "dflow.get_profile()\n",
-        "label_column_name = 'Class'\n",
+        "X = dflow.drop_columns(columns=['Class'])\n",
-        "X_test = validation_data.drop_columns(columns=[label_column_name])\n",
+        "y = dflow.keep_columns(columns=['Class'], validate_column_exists=True)\n",
-        "y_test = validation_data.keep_columns(columns=[label_column_name], validate=True)\n"
+        "X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
+        "y_train, y_test = y.random_split(percentage=0.8, seed=223)"
       ]
     },
     {
@@ -223,8 +242,9 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**training_data**|Input dataset, containing both features and label column.|\n",
+        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**label_column_name**|The name of the label column.|\n",
+        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
+        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -253,10 +273,11 @@
         "}\n",
         "\n",
         "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
+        "                             debug_log = 'automl_errors_20190417.log',\n",
+        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
-        "                             training_data = training_data,\n",
+        "                             X = X_train,\n",
-        "                             label_column_name = label_column_name,\n",
+        "                             y = y_train,\n",
         "                             **automl_settings\n",
         "                            )"
       ]
@@ -424,7 +445,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -435,7 +456,7 @@
       "outputs": [],
       "source": [
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-        "                                 pip_packages=['azureml-defaults','azureml-train-automl'])\n",
+        "                                 pip_packages=['azureml-sdk[automl]'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -455,7 +476,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
@@ -472,7 +493,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploy the model as a Web Service on Azure Container Instance"
+        "### Create a Container Image\n",
+        "\n",
+        "Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
+        "or when testing a model that is under development."
       ]
     },
     {
@@ -481,23 +505,63 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.image import Image, ContainerImage\n",
-        "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
+        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                   entry_script = script_file_name,\n",
+        "                                 execution_script = script_file_name,\n",
-        "                                   conda_file = conda_env_file_name)\n",
+        "                                 conda_file = conda_env_file_name,\n",
+        "                                 tags = {'area': \"cards\", 'type': \"automl_classification\"},\n",
+        "                                 description = \"Image for automl classification sample\")\n",
+        "\n",
+        "image = Image.create(name = \"automlsampleimage\",\n",
+        "                     # this is the model object \n",
+        "                     models = [model],\n",
+        "                     image_config = image_config, \n",
+        "                     workspace = ws)\n",
+        "\n",
+        "image.wait_for_creation(show_output = True)\n",
+        "\n",
+        "if image.creation_state == 'Failed':\n",
+        "    print(\"Image build log at: \" + image.image_build_log_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy the Image as a Web Service on Azure Container Instance\n",
+        "\n",
+        "Deploy an image that contains the model and other assets needed by the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",
         "                                               tags = {'area': \"cards\", 'type': \"automl_classification\"}, \n",
-        "                                               description = 'sample service for Automl Classification')\n",
+        "                                               description = 'sample service for Automl Classification')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
         "\n",
         "aci_service_name = 'automl-sample-creditcard'\n",
         "print(aci_service_name)\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+        "                                           image = image,\n",
+        "                                           name = aci_service_name,\n",
+        "                                           workspace = ws)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]

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

@@ -2,9 +2,6 @@ name: auto-ml-classification-credit-card-fraud
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -92,6 +92,8 @@
         "\n",
         "# choose a name for experiment\n",
         "experiment_name = 'automl-classification-deployment'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-classification-deployment'\n",
         "\n",
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
@@ -101,6 +103,7 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -123,7 +126,8 @@
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
+        "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
+        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
       ]
     },
     {
@@ -144,7 +148,8 @@
         "                             iterations = 10,\n",
         "                             verbosity = logging.INFO,\n",
         "                             X = X_train, \n",
-        "                             y = y_train)"
+        "                             y = y_train,\n",
+        "                             path = project_folder)"
       ]
     },
     {
@@ -292,7 +297,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -305,7 +310,7 @@
         "from azureml.core.conda_dependencies import CondaDependencies\n",
         "\n",
         "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost<=0.80'],\n",
-        "                                 pip_packages=['azureml-defaults','azureml-train-automl'])\n",
+        "                                 pip_packages=['azureml-sdk[automl]'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -325,7 +330,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
@@ -342,9 +347,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploy the model as a Web Service on Azure Container Instance\n",
+        "### Create a Container Image"
-        "\n",
-        "Create the configuration needed for deploying the model as a web service service."
       ]
     },
     {
@@ -353,12 +356,40 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.image import Image, ContainerImage\n",
-        "from azureml.core.webservice import AciWebservice\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
+        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                   entry_script = script_file_name,\n",
+        "                                 execution_script = script_file_name,\n",
-        "                                   conda_file = conda_env_file_name)\n",
+        "                                 conda_file = conda_env_file_name,\n",
+        "                                 tags = {'area': \"digits\", 'type': \"automl_classification\"},\n",
+        "                                 description = \"Image for automl classification sample\")\n",
+        "\n",
+        "image = Image.create(name = \"automlsampleimage\",\n",
+        "                     # this is the model object \n",
+        "                     models = [model],\n",
+        "                     image_config = image_config, \n",
+        "                     workspace = ws)\n",
+        "\n",
+        "image.wait_for_creation(show_output = True)\n",
+        "\n",
+        "if image.creation_state == 'Failed':\n",
+        "    print(\"Image build log at: \" + image.image_build_log_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy the Image as a Web Service on Azure Container Instance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",
@@ -373,33 +404,17 @@
       "outputs": [],
       "source": [
         "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import Model\n",
         "\n",
         "aci_service_name = 'automl-sample-01'\n",
         "print(aci_service_name)\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+        "                                           image = image,\n",
+        "                                           name = aci_service_name,\n",
+        "                                           workspace = ws)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Get the logs from service deployment"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if aci_service.state != 'Healthy':\n",
-        "    # run this command for debugging.\n",
-        "    print(aci_service.get_logs())"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -416,6 +431,22 @@
         "#aci_service.delete()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Get Logs from a Deployed Web Service"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#aci_service.get_logs()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},

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

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

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

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

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

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

how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.ipynb

@@ -21,7 +21,7 @@
       "metadata": {},
       "source": [
         "# Automated Machine Learning\n",
-        "_**Load Data using `TabularDataset` for Remote Execution (AmlCompute)**_\n",
+        "_**Prepare Data using `azureml.dataprep` for Remote Execution (AmlCompute)**_\n",
         "\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
@@ -37,20 +37,23 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "In this example we showcase how you can use AzureML Dataset to load data for AutoML.\n",
+        "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
-        "1. Create a `TabularDataset` pointing to the training data.\n",
+        "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
-        "2. Pass the `TabularDataset` to AutoML for a remote run."
+        "2. Pass the `Dataflow` to AutoML for a local run.\n",
+        "3. Pass the `Dataflow` to AutoML for a remote run."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup"
+        "## Setup\n",
+        "\n",
+        "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
       ]
     },
     {
@@ -67,13 +70,15 @@
       "outputs": [],
       "source": [
         "import logging\n",
+        "import time\n",
         "\n",
         "import pandas as pd\n",
         "\n",
         "import azureml.core\n",
+        "from azureml.core.compute import DsvmCompute\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
+        "import azureml.dataprep as dprep\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -84,9 +89,11 @@
       "outputs": [],
       "source": [
         "ws = Workspace.from_config()\n",
-        "\n",
+        " \n",
         "# choose a name for experiment\n",
-        "experiment_name = 'automl-dataset-remote-bai'\n",
+        "experiment_name = 'automl-dataprep-remote-dsvm'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-dataprep-remote-dsvm'\n",
         " \n",
         "experiment = Experiment(ws, experiment_name)\n",
         " \n",
@@ -96,6 +103,7 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -115,21 +123,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
         "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
+        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+        "# and convert column types manually.\n",
         "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
+        "dflow = dprep.auto_read_file(example_data).skip(1)  # Remove the header row.\n",
-        "dataset.take(5).to_pandas_dataframe()"
+        "dflow.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Review the data\n",
-        "\n",
-        "You can peek the result of a `TabularDataset` at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records, which makes it fast even against large datasets.\n",
-        "\n",
-        "`TabularDataset` objects are immutable and are composed of a list of subsetting transformations (optional)."
       ]
     },
     {
@@ -138,8 +138,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "training_data = dataset.drop_columns(columns=['FBI Code'])\n",
+        "# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
-        "label_column_name = 'Primary Type'"
+        "dflow = dflow.drop_nulls('Primary Type')\n",
+        "dflow.head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Review the Data Preparation Result\n",
+        "\n",
+        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets.\n",
+        "\n",
+        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
+        "y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
       ]
     },
     {
@@ -183,7 +205,7 @@
         "from azureml.core.compute import ComputeTarget\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"automlc2\"\n",
+        "amlcompute_cluster_name = \"cpu-cluster\"\n",
         "\n",
         "found = False\n",
         "\n",
@@ -204,12 +226,11 @@
         "    # Create the cluster.\\n\",\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "\n",
-        "print('Checking cluster status...')\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -220,7 +241,6 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -228,8 +248,9 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -237,9 +258,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Pass Data with `TabularDataset` Objects\n",
+        "### Pass Data with `Dataflow` Objects\n",
         "\n",
-        "The `TabularDataset` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `TabularDataset` object and send it to the remote compute target. The `TabularDataset` will not be evaluated locally."
+        "The `Dataflow` objects captured above can also be passed to the `submit` method for a remote run. AutoML will serialize the `Dataflow` object and send it to the remote compute target. The `Dataflow` will not be evaluated locally."
       ]
     },
     {
@@ -250,9 +271,10 @@
       "source": [
         "automl_config = AutoMLConfig(task = 'classification',\n",
         "                             debug_log = 'automl_errors.log',\n",
+        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
-        "                             training_data = training_data,\n",
+        "                             X = X,\n",
-        "                             label_column_name = label_column_name,\n",
+        "                             y = y,\n",
         "                             **automl_settings)"
       ]
     },
@@ -441,13 +463,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
+        "dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
-        "\n",
+        "dflow_test = dflow_test.drop_nulls('Primary Type')"
-        "df_test = dataset_test.to_pandas_dataframe()\n",
-        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
-        "\n",
-        "y_test = df_test[['Primary Type']]\n",
-        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
       ]
     },
     {
@@ -466,6 +483,10 @@
       "source": [
         "from pandas_ml import ConfusionMatrix\n",
         "\n",
+        "y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
+        "X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
+        "\n",
+        "\n",
         "ypred = fitted_model.predict(X_test)\n",
         "\n",
         "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",

how-to-use-azureml/automated-machine-learning/dataprep-remote-execution/auto-ml-dataprep-remote-execution.yml

@@ -1,9 +1,8 @@
-name: automl-forecasting-function
+name: auto-ml-dataprep-remote-execution
 dependencies:
 - pip:
   - azureml-sdk
   - azureml-train-automl
   - azureml-widgets
-  - pandas_ml
-  - statsmodels
   - matplotlib
+  - pandas_ml

how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.ipynb

@@ -1,12 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.png)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -16,12 +9,19 @@
         "Licensed under the MIT License."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.png)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
         "# Automated Machine Learning\n",
-        "_**Load Data using `TabularDataset` for Local Execution**_\n",
+        "_**Prepare Data using `azureml.dataprep` for Local Execution**_\n",
         "\n",
         "## Contents\n",
         "1. [Introduction](#Introduction)\n",
@@ -37,20 +37,23 @@
       "metadata": {},
       "source": [
         "## Introduction\n",
-        "In this example we showcase how you can use AzureML Dataset to load data for AutoML.\n",
+        "In this example we showcase how you can use the `azureml.dataprep` SDK to load and prepare data for AutoML. `azureml.dataprep` can also be used standalone; full documentation can be found [here](https://github.com/Microsoft/PendletonDocs).\n",
         "\n",
         "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
         "\n",
         "In this notebook you will learn how to:\n",
-        "1. Create a `TabularDataset` pointing to the training data.\n",
+        "1. Define data loading and preparation steps in a `Dataflow` using `azureml.dataprep`.\n",
-        "2. Pass the `TabularDataset` to AutoML for a local run."
+        "2. Pass the `Dataflow` to AutoML for a local run.\n",
+        "3. Pass the `Dataflow` to AutoML for a remote run."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Setup"
+        "## Setup\n",
+        "\n",
+        "Currently, Data Prep only supports __Ubuntu 16__ and __Red Hat Enterprise Linux 7__. We are working on supporting more linux distros."
       ]
     },
     {
@@ -73,7 +76,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
+        "import azureml.dataprep as dprep\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -86,7 +89,9 @@
         "ws = Workspace.from_config()\n",
         " \n",
         "# choose a name for experiment\n",
-        "experiment_name = 'automl-dataset-local'\n",
+        "experiment_name = 'automl-dataprep-local'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-dataprep-local'\n",
         " \n",
         "experiment = Experiment(ws, experiment_name)\n",
         " \n",
@@ -96,6 +101,7 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -115,21 +121,13 @@
       "metadata": {},
       "outputs": [],
       "source": [
+        "# You can use `auto_read_file` which intelligently figures out delimiters and datatypes of a file.\n",
         "# The data referenced here was a 1MB simple random sample of the Chicago Crime data into a local temporary directory.\n",
+        "# You can also use `read_csv` and `to_*` transformations to read (with overridable delimiter)\n",
+        "# and convert column types manually.\n",
         "example_data = 'https://dprepdata.blob.core.windows.net/demo/crime0-random.csv'\n",
-        "dataset = Dataset.Tabular.from_delimited_files(example_data)\n",
+        "dflow = dprep.auto_read_file(example_data).skip(1)  # Remove the header row.\n",
-        "dataset.take(5).to_pandas_dataframe()"
+        "dflow.get_profile()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Review the data\n",
-        "\n",
-        "You can peek the result of a `TabularDataset` at any range using `skip(i)` and `take(j).to_pandas_dataframe()`. Doing so evaluates only `j` records, which makes it fast even against large datasets.\n",
-        "\n",
-        "`TabularDataset` objects are immutable and are composed of a list of subsetting transformations (optional)."
       ]
     },
     {
@@ -138,8 +136,30 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "training_data = dataset.drop_columns(columns=['FBI Code'])\n",
+        "# As `Primary Type` is our y data, we need to drop the values those are null in this column.\n",
-        "label_column_name = 'Primary Type'"
+        "dflow = dflow.drop_nulls('Primary Type')\n",
+        "dflow.head(5)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Review the Data Preparation Result\n",
+        "\n",
+        "You can peek the result of a Dataflow at any range using `skip(i)` and `head(j)`. Doing so evaluates only `j` records for all the steps in the Dataflow, which makes it fast even against large datasets.\n",
+        "\n",
+        "`Dataflow` objects are immutable and are composed of a list of data preparation steps. A `Dataflow` object can be branched at any point for further usage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "X = dflow.drop_columns(columns=['Primary Type', 'FBI Code'])\n",
+        "y = dflow.keep_columns(columns=['Primary Type'], validate_column_exists=True)"
       ]
     },
     {
@@ -170,9 +190,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Pass Data with `TabularDataset` Objects\n",
+        "### Pass Data with `Dataflow` Objects\n",
         "\n",
-        "The `TabularDataset` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `TabularDataset` for model training."
+        "The `Dataflow` objects captured above can be passed to the `submit` method for a local run. AutoML will retrieve the results from the `Dataflow` for model training."
       ]
     },
     {
@@ -183,8 +203,8 @@
       "source": [
         "automl_config = AutoMLConfig(task = 'classification',\n",
         "                             debug_log = 'automl_errors.log',\n",
-        "                             training_data = training_data,\n",
+        "                             X = X,\n",
-        "                             label_column_name = label_column_name,\n",
+        "                             y = y,\n",
         "                             **automl_settings)"
       ]
     },
@@ -335,13 +355,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "dataset_test = Dataset.Tabular.from_delimited_files(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv')\n",
+        "dflow_test = dprep.auto_read_file(path='https://dprepdata.blob.core.windows.net/demo/crime0-test.csv').skip(1)\n",
-        "\n",
+        "dflow_test = dflow_test.drop_nulls('Primary Type')"
-        "df_test = dataset_test.to_pandas_dataframe()\n",
-        "df_test = df_test[pd.notnull(df_test['Primary Type'])]\n",
-        "\n",
-        "y_test = df_test[['Primary Type']]\n",
-        "X_test = df_test.drop(['Primary Type', 'FBI Code'], axis=1)"
       ]
     },
     {
@@ -360,6 +375,9 @@
       "source": [
         "from pandas_ml import ConfusionMatrix\n",
         "\n",
+        "y_test = dflow_test.keep_columns(columns=['Primary Type']).to_pandas_dataframe()\n",
+        "X_test = dflow_test.drop_columns(columns=['Primary Type', 'FBI Code']).to_pandas_dataframe()\n",
+        "\n",
         "ypred = fitted_model.predict(X_test)\n",
         "\n",
         "cm = ConfusionMatrix(y_test['Primary Type'], ypred)\n",

how-to-use-azureml/automated-machine-learning/dataprep/auto-ml-dataprep.yml

@@ -1,4 +1,4 @@
-name: auto-ml-dataset
+name: auto-ml-dataprep
 dependencies:
 - pip:
   - azureml-sdk
@@ -6,4 +6,3 @@ dependencies:
   - azureml-widgets
   - matplotlib
   - pandas_ml
-  - azureml-dataprep[pandas]

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

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

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

@@ -97,6 +97,8 @@
         "\n",
         "# choose a name for the run history container in the workspace\n",
         "experiment_name = 'automl-bikeshareforecasting'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-local-bikeshareforecasting'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -106,6 +108,7 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Run History Name'] = experiment_name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -218,12 +221,11 @@
         "|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>\n",
         "|**iterations**|Number of iterations. In each iteration, Auto ML trains a specific pipeline on the given data|\n",
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**training_data**|Input dataset, containing both features and label column.|\n",
+        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**label_column_name**|The name of the label column.|\n",
+        "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**country_or_region**|The country/region used to generate holiday features. These should be ISO 3166 two-letter country/region codes (i.e. 'US', 'GB').|\n",
-        "\n",
+        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset. You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
       ]
     },
     {
@@ -244,12 +246,12 @@
         "\n",
         "automl_config = AutoMLConfig(task='forecasting',                             \n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             blacklist_models = ['ExtremeRandomTrees'],\n",
         "                             iterations=10,\n",
         "                             iteration_timeout_minutes=5,\n",
-        "                             training_data=train,\n",
+        "                             X=X_train,\n",
-        "                             label_column_name=target_column_name,\n",
+        "                             y=y_train,\n",
         "                             n_cross_validations=3,                             \n",
+        "                             path=project_folder,\n",
         "                             verbosity=logging.INFO,\n",
         "                            **automl_settings)"
       ]
@@ -343,10 +345,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "# Get the featurization summary as a list of JSON\n",
+        "fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()"
-        "featurization_summary = fitted_model.named_steps['timeseriestransformer'].get_featurization_summary()\n",
-        "# View the featurization summary as a pandas dataframe\n",
-        "pd.DataFrame.from_records(featurization_summary)"
       ]
     },
     {
@@ -523,7 +522,7 @@
         "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib inline\n",
+        "%matplotlib notebook\n",
         "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
         "test_test = plt.scatter(y_test, y_test, color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
@@ -565,7 +564,7 @@
         "df_all_APE = df_all.assign(APE=APE(df_all[target_column_name], df_all['predicted']))\n",
         "APEs = [df_all_APE[df_all['horizon_origin'] == h].APE.values for h in range(1, max_horizon + 1)]\n",
         "\n",
-        "%matplotlib inline\n",
+        "%matplotlib notebook\n",
         "plt.boxplot(APEs)\n",
         "plt.yscale('log')\n",
         "plt.xlabel('horizon')\n",
@@ -579,7 +578,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "xiaga@microsoft.com, tosingli@microsoft.com, erwright@microsoft.com"
       }
     ],
     "kernelspec": {

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

@@ -93,6 +93,8 @@
         "\n",
         "# choose a name for the run history container in the workspace\n",
         "experiment_name = 'automl-energydemandforecasting'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-local-energydemandforecasting'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -102,6 +104,7 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Run History Name'] = experiment_name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -210,7 +213,8 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|"
+        "|**n_cross_validations**|Number of cross validation splits. Rolling Origin Validation is used to split time-series in a temporally consistent way.|\n",
+        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. "
       ]
     },
     {
@@ -227,12 +231,12 @@
         "automl_config = AutoMLConfig(task='forecasting',\n",
         "                             debug_log='automl_nyc_energy_errors.log',\n",
         "                             primary_metric='normalized_root_mean_squared_error',\n",
-        "                             blacklist_models = ['ExtremeRandomTrees', 'AutoArima'],\n",
         "                             iterations=10,\n",
         "                             iteration_timeout_minutes=5,\n",
         "                             X=X_train,\n",
         "                             y=y_train,\n",
         "                             n_cross_validations=3,\n",
+        "                             path=project_folder,\n",
         "                             verbosity = logging.INFO,\n",
         "                             **time_series_settings)"
       ]
@@ -428,7 +432,7 @@
         "print('MAPE: %.2f' % MAPE(df_all[target_column_name], df_all['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib inline\n",
+        "%matplotlib notebook\n",
         "pred, = plt.plot(df_all[time_column_name], df_all['predicted'], color='b')\n",
         "actual, = plt.plot(df_all[time_column_name], df_all[target_column_name], color='g')\n",
         "plt.xticks(fontsize=8)\n",
@@ -458,9 +462,7 @@
       "source": [
         "We did not use lags in the previous model specification. In effect, the prediction was the result of a simple regression on date, grain and any additional features. This is often a very good prediction as common time series patterns like seasonality and trends can be captured in this manner. Such simple regression is horizon-less: it doesn't matter how far into the future we are predicting, because we are not using past data. In the previous example, the horizon was only used to split the data for cross-validation.\n",
         "\n",
-        "Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features.\n",
+        "Now that we configured target lags, that is the previous values of the target variables, and the prediction is no longer horizon-less. We therefore must still specify the `max_horizon` that the model will learn to forecast. The `target_lags` keyword specifies how far back we will construct the lags of the target variable, and the `target_rolling_window_size` specifies the size of the rolling window over which we will generate the `max`, `min` and `sum` features."
-        "\n",
-        "This notebook uses the blacklist_models parameter to exclude some models that take a longer time to train on this dataset.  You can choose to remove models from the blacklist_models list but you may need to increase the iteration_timeout_minutes parameter value to get results."
       ]
     },
     {
@@ -479,12 +481,13 @@
         "automl_config_lags = AutoMLConfig(task='forecasting',\n",
         "                                  debug_log='automl_nyc_energy_errors.log',\n",
         "                                  primary_metric='normalized_root_mean_squared_error',\n",
-        "                                  blacklist_models=['ElasticNet','ExtremeRandomTrees','GradientBoosting','XGBoostRegressor'],\n",
+        "                                  blacklist_models=['ElasticNet'],\n",
         "                                  iterations=10,\n",
         "                                  iteration_timeout_minutes=10,\n",
         "                                  X=X_train,\n",
         "                                  y=y_train,\n",
         "                                  n_cross_validations=3,\n",
+        "                                  path=project_folder,\n",
         "                                  verbosity=logging.INFO,\n",
         "                                  **time_series_settings_with_lags)"
       ]
@@ -540,7 +543,7 @@
         "print('MAPE: %.2f' % MAPE(df_lags[target_column_name], df_lags['predicted']))\n",
         "\n",
         "# Plot outputs\n",
-        "%matplotlib inline\n",
+        "%matplotlib notebook\n",
         "pred, = plt.plot(df_lags[time_column_name], df_lags['predicted'], color='b')\n",
         "actual, = plt.plot(df_lags[time_column_name], df_lags[target_column_name], color='g')\n",
         "plt.xticks(fontsize=8)\n",
@@ -552,97 +555,23 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### What features matter for the forecast?\n",
+        "### What features matter for the forecast?"
-        "The following steps will allow you to compute and visualize engineered feature importance based on your test data for forecasting. "
       ]
     },
     {
-      "cell_type": "markdown",
+      "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
-        "#### Setup the model explanations for AutoML models\n",
+        "from azureml.train.automl.automlexplainer import explain_model\n",
-        "The *fitted_model* can generate the following which will be used for getting the engineered and raw feature explanations using *automl_setup_model_explanations*:-\n",
-        "1. Featurized data from train samples/test samples \n",
-        "2. Gather engineered and raw feature name lists\n",
-        "3. Find the classes in your labeled column in classification scenarios\n",
         "\n",
-        "The *automl_explainer_setup_obj* contains all the structures from above list. "
+        "# feature names are everything in the transformed data except the target\n",
-      ]
+        "features = X_trans_lags.columns[:-1]\n",
-    },
+        "expl = explain_model(fitted_model_lags, X_train.copy(), X_test.copy(), features=features, best_run=best_run_lags, y_train=y_train)\n",
-    {
+        "# unpack the tuple\n",
-      "cell_type": "code",
+        "shap_values, expected_values, feat_overall_imp, feat_names, per_class_summary, per_class_imp = expl\n",
-      "execution_count": null,
+        "best_run_lags"
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
-        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train.copy(), \n",
-        "                                                             X_test=X_test.copy(), y=y_train, \n",
-        "                                                             task='forecasting')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Initialize the Mimic Explainer for feature importance\n",
-        "For explaining the AutoML models, use the *MimicWrapper* from *azureml.explain.model* package. The *MimicWrapper* can be initialized with fields in *automl_explainer_setup_obj*, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (*fitted_model* here). The *MimicWrapper* also takes the *best_run* object where the raw and engineered explanations will be uploaded."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
-        "from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
-        "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
-        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=best_run,\n",
-        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
-        "                         feature_maps=[automl_explainer_setup_obj.feature_map])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
-        "The *explain()* method in *MimicWrapper* can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the generated engineered features by AutoML featurizers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
-        "print(engineered_explanations.get_feature_importance_dict())\n",
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
-        "ExplanationDashboard(engineered_explanations, automl_explainer_setup_obj.automl_estimator, automl_explainer_setup_obj.X_test_transform)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
-        "The *explain()* method in *MimicWrapper* can be again called with the transformed test samples and setting *get_raw* to *True* to get the feature importance for the raw features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the raw features."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "raw_explanations = explainer.explain(['local', 'global'], get_raw=True, \n",
-        "                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
-        "                                     eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
-        "print(raw_explanations.get_feature_importance_dict())\n",
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
-        "ExplanationDashboard(raw_explanations, automl_explainer_setup_obj.automl_pipeline, automl_explainer_setup_obj.X_test_raw)"
       ]
     },
     {
@@ -658,7 +587,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "xiaga, tosingli, erwright"
       }
     ],
     "kernelspec": {

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

@@ -2,11 +2,9 @@ name: auto-ml-forecasting-energy-demand
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
   - pandas_ml
   - statsmodels
   - azureml-explain-model
-  - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/forecasting-high-frequency/automl-forecasting-function.ipynb

@@ -1,615 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "\n",
-        "## Forecasting away from training data\n",
-        "\n",
-        "This notebook demonstrates the full interface to the `forecast()` function. \n",
-        "\n",
-        "The best known and most frequent usage of `forecast` enables forecasting on test sets that immediately follows training data. \n",
-        "\n",
-        "However, in many use cases it is necessary to continue using the model for some time before retraining it. This happens especially in **high frequency forecasting** when forecasts need to be made more frequently than the model can be retrained. Examples are in Internet of Things and predictive cloud resource scaling.\n",
-        "\n",
-        "Here we show how to use the `forecast()` function when a time gap exists between training data and prediction period.\n",
-        "\n",
-        "Terminology:\n",
-        "* forecast origin: the last period when the target value is known\n",
-        "* forecast periods(s): the period(s) for which the value of the target is desired.\n",
-        "* forecast horizon: the number of forecast periods\n",
-        "* lookback: how many past periods (before forecast origin) the model function depends on. The larger of number of lags and length of rolling window.\n",
-        "* prediction context: `lookback` periods immediately preceding the forecast origin\n",
-        "\n",
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/automated-machine-learning/automl-forecasting-function.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Please make sure you have followed the `configuration.ipynb` notebook so that your ML workspace information is saved in the config file."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import pandas as pd\n",
-        "import numpy as np\n",
-        "import logging\n",
-        "import warnings\n",
-        "\n",
-        "from pandas.tseries.frequencies import to_offset\n",
-        "\n",
-        "# Squash warning messages for cleaner output in the notebook\n",
-        "warnings.showwarning = lambda *args, **kwargs: None\n",
-        "\n",
-        "np.set_printoptions(precision=4, suppress=True, linewidth=120)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import azureml.core\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for the run history container in the workspace\n",
-        "experiment_name = 'automl-forecast-function-demo'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Run History 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": [
-        "## Data\n",
-        "For the demonstration purposes we will generate the data artificially and use them for the forecasting."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "TIME_COLUMN_NAME = 'date'\n",
-        "GRAIN_COLUMN_NAME = 'grain'\n",
-        "TARGET_COLUMN_NAME = 'y'\n",
-        "\n",
-        "def get_timeseries(train_len: int,\n",
-        "                   test_len: int,\n",
-        "                   time_column_name: str,\n",
-        "                   target_column_name: str,\n",
-        "                   grain_column_name: str,\n",
-        "                   grains: int = 1,\n",
-        "                   freq: str = 'H'):\n",
-        "    \"\"\"\n",
-        "    Return the time series of designed length.\n",
-        "\n",
-        "    :param train_len: The length of training data (one series).\n",
-        "    :type train_len: int\n",
-        "    :param test_len: The length of testing data (one series).\n",
-        "    :type test_len: int\n",
-        "    :param time_column_name: The desired name of a time column.\n",
-        "    :type time_column_name: str\n",
-        "    :param\n",
-        "    :param grains: The number of grains.\n",
-        "    :type grains: int\n",
-        "    :param freq: The frequency string representing pandas offset.\n",
-        "                 see https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html\n",
-        "    :type freq: str\n",
-        "    :returns: the tuple of train and test data sets.\n",
-        "    :rtype: tuple\n",
-        "\n",
-        "    \"\"\"\n",
-        "    data_train = []  # type: List[pd.DataFrame]\n",
-        "    data_test = []  # type: List[pd.DataFrame]\n",
-        "    data_length = train_len + test_len\n",
-        "    for i in range(grains):\n",
-        "        X = pd.DataFrame({\n",
-        "            time_column_name: pd.date_range(start='2000-01-01',\n",
-        "                                            periods=data_length,\n",
-        "                                            freq=freq),\n",
-        "            target_column_name: np.arange(data_length).astype(float) + np.random.rand(data_length) + i*5,\n",
-        "            'ext_predictor': np.asarray(range(42, 42 + data_length)),\n",
-        "            grain_column_name: np.repeat('g{}'.format(i), data_length)\n",
-        "        })\n",
-        "        data_train.append(X[:train_len])\n",
-        "        data_test.append(X[train_len:])\n",
-        "    X_train = pd.concat(data_train)\n",
-        "    y_train = X_train.pop(target_column_name).values\n",
-        "    X_test = pd.concat(data_test)\n",
-        "    y_test = X_test.pop(target_column_name).values\n",
-        "    return X_train, y_train, X_test, y_test\n",
-        "\n",
-        "n_test_periods = 6\n",
-        "n_train_periods = 30\n",
-        "X_train, y_train, X_test, y_test = get_timeseries(train_len=n_train_periods,\n",
-        "                                                  test_len=n_test_periods,\n",
-        "                                                  time_column_name=TIME_COLUMN_NAME,\n",
-        "                                                  target_column_name=TARGET_COLUMN_NAME,\n",
-        "                                                  grain_column_name=GRAIN_COLUMN_NAME,\n",
-        "                                                  grains=2)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's see what the training data looks like."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X_train.tail()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# plot the example time series\n",
-        "import matplotlib.pyplot as plt\n",
-        "whole_data = X_train.copy()\n",
-        "whole_data['y'] = y_train\n",
-        "for g in whole_data.groupby('grain'):    \n",
-        "    plt.plot(g[1]['date'].values, g[1]['y'].values, label=g[0])\n",
-        "plt.legend()\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create the configuration and train a forecaster\n",
-        "First generate the configuration, in which we:\n",
-        "* Set metadata columns: target, time column and grain column names.\n",
-        "* Ask for 10 iterations through models, last of which will represent the Ensemble of previous ones.\n",
-        "* Validate our data using cross validation with rolling window method.\n",
-        "* Set normalized root mean squared error as a metric to select the best model.\n",
-        "\n",
-        "* Finally, we set the task to be forecasting.\n",
-        "* By default, we apply the lag lead operator and rolling window to the target value i.e. we use the previous values as a predictor for the future ones."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "lags = [1,2,3]\n",
-        "rolling_window_length = 0 # don't do rolling windows\n",
-        "max_horizon = n_test_periods\n",
-        "time_series_settings = {    \n",
-        "    'time_column_name': TIME_COLUMN_NAME,\n",
-        "    'grain_column_names': [ GRAIN_COLUMN_NAME ],\n",
-        "    'max_horizon': max_horizon,\n",
-        "    'target_lags': lags\n",
-        "}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Run the model selection and training process."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.train.automl import AutoMLConfig\n",
-        "\n",
-        "\n",
-        "automl_config = AutoMLConfig(task='forecasting',\n",
-        "                             debug_log='automl_forecasting_function.log',\n",
-        "                             primary_metric='normalized_root_mean_squared_error',                             \n",
-        "                             iterations=10,                             \n",
-        "                             X=X_train,\n",
-        "                             y=y_train,\n",
-        "                             n_cross_validations=3,\n",
-        "                             verbosity = logging.INFO,\n",
-        "                             **time_series_settings)\n",
-        "\n",
-        "local_run = experiment.submit(automl_config, show_output=True)\n",
-        "\n",
-        "# Retrieve the best model to use it further.\n",
-        "_, fitted_model = local_run.get_output()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Forecasting from the trained model"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In this section we will review the `forecast` interface for two main scenarios: forecasting right after the training data, and the more complex interface for forecasting when there is a gap (in the time sense) between training and testing data."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### X_train is directly followed by the X_test\n",
-        "\n",
-        "Let's first consider the case when the prediction period immediately follows the training data. This is typical in scenarios where we have the time to retrain the model every time we wish to forecast. Forecasts that are made on daily and slower cadence typically fall into this category. Retraining the model every time benefits the accuracy because the most recent data is often the most informative.\n",
-        "\n",
-        "![Forecasting after training](forecast_function_at_train.png)\n",
-        "\n",
-        "The `X_test` and `y_query` below, taken together, form the **forecast request**. The two are interpreted as aligned - `y_query` could actally be a column in `X_test`. `NaN`s in `y_query` are the question marks. These will be filled with the forecasts.\n",
-        "\n",
-        "When the forecast period immediately follows the training period, the models retain the last few points of data. You can simply fill `y_query` filled with question marks - the model has the data for the lookback already.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Typical path: X_test is known, forecast all upcoming periods"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# The data set contains hourly data, the training set ends at 01/02/2000 at 05:00\n",
-        "\n",
-        "# These are predictions we are asking the model to make (does not contain thet target column y),\n",
-        "# for 6 periods beginning with 2000-01-02 06:00, which immediately follows the training data\n",
-        "X_test"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "y_query = np.repeat(np.NaN, X_test.shape[0])\n",
-        "y_pred_no_gap, xy_nogap =  fitted_model.forecast(X_test, y_query)\n",
-        "\n",
-        "# xy_nogap contains the predictions in the _automl_target_col column.\n",
-        "# Those same numbers are output in y_pred_no_gap\n",
-        "xy_nogap"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Distribution forecasts\n",
-        "\n",
-        "Often the figure of interest is not just the point prediction, but the prediction at some quantile of the distribution. \n",
-        "This arises when the forecast is used to control some kind of inventory, for example of grocery items of virtual machines for a cloud service. In such case, the control point is usually something like \"we want the item to be in stock and not run out 99% of the time\". This is called a \"service level\". Here is how you get quantile forecasts."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# specify which quantiles you would like \n",
-        "fitted_model.quantiles = [0.01, 0.5, 0.95]\n",
-        "# use forecast_quantiles function, not the forecast() one\n",
-        "y_pred_quantiles =  fitted_model.forecast_quantiles(X_test, y_query)\n",
-        "\n",
-        "# it all nicely aligns column-wise\n",
-        "pd.concat([X_test.reset_index(), pd.DataFrame({'query' : y_query}), y_pred_quantiles], axis=1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Destination-date forecast: \"just do something\"\n",
-        "\n",
-        "In some scenarios, the X_test is not known. The forecast is likely to be weak, becaus eit is missing contemporaneous predictors, which we will need to impute. If you still wish to predict forward under the assumption that the last known values will be carried forward, you can forecast out to \"destination date\". The destination date still needs to fit within the maximum horizon from training."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# We will take the destination date as a last date in the test set.\n",
-        "dest = max(X_test[TIME_COLUMN_NAME])\n",
-        "y_pred_dest, xy_dest = fitted_model.forecast(forecast_destination=dest)\n",
-        "\n",
-        "# This form also shows how we imputed the predictors which were not given. (Not so well! Use with caution!)\n",
-        "xy_dest"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Forecasting away from training data\n",
-        "\n",
-        "Suppose we trained a model, some time passed, and now we want to apply the model without re-training. If the model \"looks back\" -- uses previous values of the target -- then we somehow need to provide those values to the model.\n",
-        "\n",
-        "![Forecasting after training](forecast_function_away_from_train.png)\n",
-        "\n",
-        "The notion of forecast origin comes into play: the forecast origin is **the last period for which we have seen the target value**. This applies per grain, so each grain can have a different forecast origin. \n",
-        "\n",
-        "The part of data before the forecast origin is the **prediction context**. To provide the context values the model needs when it looks back, we pass definite values in `y_test` (aligned with corresponding times in `X_test`)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# generate the same kind of test data we trained on, \n",
-        "# but now make the train set much longer, so that the test set will be in the future\n",
-        "X_context, y_context, X_away, y_away = get_timeseries(train_len=42, # train data was 30 steps long\n",
-        "                                      test_len=4,\n",
-        "                                      time_column_name=TIME_COLUMN_NAME,\n",
-        "                                      target_column_name=TARGET_COLUMN_NAME,\n",
-        "                                      grain_column_name=GRAIN_COLUMN_NAME,\n",
-        "                                      grains=2)\n",
-        "\n",
-        "# end of the data we trained on\n",
-        "print(X_train.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].max())\n",
-        "# start of the data we want to predict on\n",
-        "print(X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].min())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There is a gap of 12 hours between end of training and beginning of `X_away`. (It looks like 13 because all timestamps point to the start of the one hour periods.) Using only `X_away` will fail without adding context data for the model to consume."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "try: \n",
-        "    y_query = y_away.copy()\n",
-        "    y_query.fill(np.NaN)\n",
-        "    y_pred_away, xy_away = fitted_model.forecast(X_away, y_query)\n",
-        "    xy_away\n",
-        "except Exception as e:\n",
-        "    print(e)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "How should we read that eror message? The forecast origin is at the last time themodel saw an actual values of `y` (the target). That was at the end of the training data! Because the model received all `NaN` (and not an actual target value), it is attempting to forecast from the end of training data. But the requested forecast periods are past the maximum horizon. We need to provide a define `y` value to establish the forecast origin.\n",
-        "\n",
-        "We will use this helper function to take the required amount of context from the data preceding the testing data. It's definition is intentionally simplified to keep the idea in the clear."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "def make_forecasting_query(fulldata, time_column_name, target_column_name, forecast_origin, horizon, lookback):\n",
-        "\n",
-        "    \"\"\"\n",
-        "    This function will take the full dataset, and create the query\n",
-        "    to predict all values of the grain from the `forecast_origin`\n",
-        "    forward for the next `horizon` horizons. Context from previous\n",
-        "    `lookback` periods will be included.\n",
-        "\n",
-        "    \n",
-        "\n",
-        "    fulldata: pandas.DataFrame           a time series dataset. Needs to contain X and y.\n",
-        "    time_column_name: string             which column (must be in fulldata) is the time axis\n",
-        "    target_column_name: string           which column (must be in fulldata) is to be forecast\n",
-        "    forecast_origin: datetime type       the last time we (pretend to) have target values \n",
-        "    horizon: timedelta                   how far forward, in time units (not periods)\n",
-        "    lookback: timedelta                  how far back does the model look?\n",
-        "\n",
-        "    Example:\n",
-        "\n",
-        "\n",
-        "    ```\n",
-        "\n",
-        "    forecast_origin = pd.to_datetime('2012-09-01') + pd.DateOffset(days=5) # forecast 5 days after end of training\n",
-        "    print(forecast_origin)\n",
-        "\n",
-        "    X_query, y_query = make_forecasting_query(data, \n",
-        "                       forecast_origin = forecast_origin,\n",
-        "                       horizon = pd.DateOffset(days=7), # 7 days into the future\n",
-        "                       lookback = pd.DateOffset(days=1), # model has lag 1 period (day)\n",
-        "                      )\n",
-        "\n",
-        "    ```\n",
-        "    \"\"\"\n",
-        "\n",
-        "    X_past = fulldata[ (fulldata[ time_column_name ] > forecast_origin - lookback) &\n",
-        "                       (fulldata[ time_column_name ] <= forecast_origin)\n",
-        "                     ]\n",
-        "\n",
-        "    X_future = fulldata[ (fulldata[ time_column_name ] > forecast_origin) &\n",
-        "                         (fulldata[ time_column_name ] <= forecast_origin + horizon)\n",
-        "                       ]\n",
-        "\n",
-        "    y_past = X_past.pop(target_column_name).values.astype(np.float)\n",
-        "    y_future = X_future.pop(target_column_name).values.astype(np.float)\n",
-        "\n",
-        "    # Now take y_future and turn it into question marks\n",
-        "    y_query = y_future.copy().astype(np.float)  # because sometimes life hands you an int\n",
-        "    y_query.fill(np.NaN)\n",
-        "\n",
-        "\n",
-        "    print(\"X_past is \" + str(X_past.shape) + \" - shaped\")\n",
-        "    print(\"X_future is \" + str(X_future.shape) + \" - shaped\")\n",
-        "    print(\"y_past is \" + str(y_past.shape) + \" - shaped\")\n",
-        "    print(\"y_query is \" + str(y_query.shape) + \" - shaped\")\n",
-        "\n",
-        "\n",
-        "    X_pred = pd.concat([X_past, X_future])\n",
-        "    y_pred = np.concatenate([y_past, y_query])\n",
-        "    return X_pred, y_pred"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Let's see where the context data ends - it ends, by construction, just before the testing data starts."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(X_context.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
-        "print(   X_away.groupby(GRAIN_COLUMN_NAME)[TIME_COLUMN_NAME].agg(['min','max','count']))\n",
-        "X_context.tail(5)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Since the length of the lookback is 3, \n",
-        "# we need to add 3 periods from the context to the request\n",
-        "# so that the model has the data it needs\n",
-        "\n",
-        "# Put the X and y back together for a while. \n",
-        "# They like each other and it makes them happy.\n",
-        "X_context[TARGET_COLUMN_NAME] = y_context\n",
-        "X_away[TARGET_COLUMN_NAME] = y_away\n",
-        "fulldata = pd.concat([X_context, X_away])\n",
-        "\n",
-        "# forecast origin is the last point of data, which is one 1-hr period before test\n",
-        "forecast_origin = X_away[TIME_COLUMN_NAME].min() - pd.DateOffset(hours=1)\n",
-        "# it is indeed the last point of the context\n",
-        "assert forecast_origin == X_context[TIME_COLUMN_NAME].max()\n",
-        "print(\"Forecast origin: \" + str(forecast_origin))\n",
-        "      \n",
-        "# the model uses lags and rolling windows to look back in time\n",
-        "n_lookback_periods = max(max(lags), rolling_window_length)\n",
-        "lookback = pd.DateOffset(hours=n_lookback_periods)\n",
-        "\n",
-        "horizon = pd.DateOffset(hours=max_horizon)\n",
-        "\n",
-        "# now make the forecast query from context (refer to figure)\n",
-        "X_pred, y_pred = make_forecasting_query(fulldata, TIME_COLUMN_NAME, TARGET_COLUMN_NAME,\n",
-        "                                        forecast_origin, horizon, lookback)\n",
-        "\n",
-        "# show the forecast request aligned\n",
-        "X_show = X_pred.copy()\n",
-        "X_show[TARGET_COLUMN_NAME] = y_pred\n",
-        "X_show"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Note that the forecast origin is at 17:00 for both grains, and periods from 18:00 are to be forecast."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Now everything works\n",
-        "y_pred_away, xy_away = fitted_model.forecast(X_pred, y_pred)\n",
-        "\n",
-        "# show the forecast aligned\n",
-        "X_show = xy_away.reset_index()\n",
-        "# without the generated features\n",
-        "X_show[['date', 'grain', 'ext_predictor', '_automl_target_col']]\n",
-        "# prediction is in _automl_target_col"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "erwright, nirovins"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

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

@@ -89,6 +89,8 @@
         "\n",
         "# choose a name for the run history container in the workspace\n",
         "experiment_name = 'automl-ojforecasting'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-local-ojforecasting'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -98,6 +100,7 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Run History Name'] = experiment_name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -241,9 +244,9 @@
         "|**X**|Training matrix of features as a pandas DataFrame, shape = [n_training_samples, n_features]|\n",
         "|**y**|Target values as a numpy.ndarray, shape = [n_training_samples, ]|\n",
         "|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection|\n",
-        "|**enable_voting_ensemble**|Allow AutoML to create a Voting ensemble of the best performing models\n",
+        "|**enable_ensembling**|Allow AutoML to create ensembles of the best performing models\n",
-        "|**enable_stack_ensemble**|Allow AutoML to create a Stack ensemble of the best performing models\n",
         "|**debug_log**|Log file path for writing debugging information\n",
+        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "|**time_column_name**|Name of the datetime column in the input data|\n",
         "|**grain_column_names**|Name(s) of the columns defining individual series in the input data|\n",
         "|**drop_column_names**|Name(s) of columns to drop prior to modeling|\n",
@@ -270,8 +273,8 @@
         "                             X=X_train,\n",
         "                             y=y_train,\n",
         "                             n_cross_validations=3,\n",
-        "                             enable_voting_ensemble=False,\n",
+        "                             enable_ensembling=False,\n",
-        "                             enable_stack_ensemble=False,\n",
+        "                             path=project_folder,\n",
         "                             verbosity=logging.INFO,\n",
         "                             **time_series_settings)"
       ]
@@ -460,7 +463,7 @@
         "# Plot outputs\n",
         "import matplotlib.pyplot as plt\n",
         "\n",
-        "%matplotlib inline\n",
+        "%matplotlib notebook\n",
         "test_pred = plt.scatter(df_all[target_column_name], df_all['predicted'], color='b')\n",
         "test_test = plt.scatter(y_test, y_test, color='g')\n",
         "plt.legend((test_pred, test_test), ('prediction', 'truth'), loc='upper left', fontsize=8)\n",
@@ -660,10 +663,10 @@
         "conda_env_file_name = 'fcast_env.yml'\n",
         "\n",
         "dependencies = ml_run.get_run_sdk_dependencies(iteration = best_iteration)\n",
-        "for p in ['azureml-train-automl', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))\n",
         "\n",
-        "myenv = CondaDependencies.create(conda_packages=['numpy>=1.16.0,<=1.16.2','scikit-learn','fbprophet==0.5'], pip_packages=['azureml-defaults','azureml-train-automl'])\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
         "\n",
         "myenv.save_to_file('.', conda_env_file_name)"
       ]
@@ -685,7 +688,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
@@ -700,7 +703,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploy the model as a Web Service on Azure Container Instance"
+        "### Create a Container Image"
       ]
     },
     {
@@ -709,23 +712,62 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.image import Image, ContainerImage\n",
-        "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
+        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                   entry_script = script_file_name,\n",
+        "                                 execution_script = script_file_name,\n",
-        "                                   conda_file = conda_env_file_name)\n",
+        "                                 conda_file = conda_env_file_name,\n",
+        "                                 tags = {'type': \"automl-forecasting\"},\n",
+        "                                 description = \"Image for automl forecasting sample\")\n",
+        "\n",
+        "image = Image.create(name = \"automl-fcast-image\",\n",
+        "                     # this is the model object \n",
+        "                     models = [model],\n",
+        "                     image_config = image_config, \n",
+        "                     workspace = ws)\n",
+        "\n",
+        "image.wait_for_creation(show_output = True)\n",
+        "\n",
+        "if image.creation_state == 'Failed':\n",
+        "    print(\"Image build log at: \" + image.image_build_log_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy the Image as a Web Service on Azure Container Instance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 2, \n",
         "                                               tags = {'type': \"automl-forecasting\"},\n",
-        "                                               description = \"Automl forecasting sample service\")\n",
+        "                                               description = \"Automl forecasting sample service\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
         "\n",
         "aci_service_name = 'automl-forecast-01'\n",
         "print(aci_service_name)\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "\n",
+        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+        "                                           image = image,\n",
+        "                                           name = aci_service_name,\n",
+        "                                           workspace = ws)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]
@@ -787,7 +829,7 @@
   "metadata": {
     "authors": [
       {
-        "name": "erwright"
+        "name": "erwright, tosingli"
       }
     ],
     "kernelspec": {
@@ -805,7 +847,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.8"
     }
   },
   "nbformat": 4,

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

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

how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.ipynb

@@ -1,593 +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/automated-machine-learning/classification-bank-marketing/auto-ml-classification-bank-marketing.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Regression on remote compute using Computer Hardware dataset with model explanations**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n",
-        "1. [Explanations](#Explanations)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "\n",
-        "In this example we use the Hardware Performance Dataset to showcase how you can use AutoML for a simple regression problem. After training AutoML models for this regression data set, we show how you can compute model explanations on your remote compute using a sample explainer script.\n",
-        "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set.  Otherwise, go through the [configuration](../../../configuration.ipynb)  notebook first if you haven't already to establish your connection to the AzureML Workspace. \n",
-        "\n",
-        "In this notebook you will learn how to:\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Configure AutoML using `AutoMLConfig`.\n",
-        "3. Train the model using remote compute.\n",
-        "4. Explore the results.\n",
-        "5. Setup remote compute for computing the model explanations for a given AutoML model.\n",
-        "6. Start an AzureML experiment on your remote compute to compute explanations for an AutoML model.\n",
-        "7. Download the feature importance for engineered features and visualize the explanations for engineered features. \n",
-        "8. Download the feature importance for raw features and visualize the explanations for raw features. \n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "\n",
-        "from matplotlib import pyplot as plt\n",
-        "import pandas as pd\n",
-        "import os\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
-        "from azureml.train.automl import AutoMLConfig"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# choose a name for experiment\n",
-        "experiment_name = 'automl-regression-computer-hardware'\n",
-        "\n",
-        "experiment=Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create or Attach existing AmlCompute\n",
-        "You will need to create a compute target for your AutoML run. In this tutorial, you create AmlCompute as your training compute resource.\n",
-        "#### Creation of AmlCompute takes approximately 5 minutes. \n",
-        "If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read this article on the default limits and how to request more quota."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
-        "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"automlcl\"\n",
-        "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "    \n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
-        "\n",
-        "    # Create the cluster.\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "    \n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Conda Dependecies for AutoML training experiment\n",
-        "\n",
-        "Create the conda dependencies for running AutoML experiment on remote compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute\n",
-        "conda_run_config.target = compute_target\n",
-        "conda_run_config.environment.docker.enabled = True\n",
-        "\n",
-        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
-        "conda_run_config.environment.python.conda_dependencies = cd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Setup Training and Test Data for AutoML experiment\n",
-        "\n",
-        "Here we create the train and test datasets for hardware performance dataset. We also register the datasets in your workspace using a name so that these datasets may be accessed from the remote compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Data source\n",
-        "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
-        "\n",
-        "# Create dataset from the url\n",
-        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
-        "\n",
-        "# Split the dataset into train and test datasets\n",
-        "train_dataset, test_dataset = dataset.random_split(percentage=0.8, seed=223)\n",
-        "\n",
-        "# Register the train dataset with your workspace\n",
-        "train_dataset.register(workspace = ws, name = 'hardware_performance_train_dataset',\n",
-        "                       description = 'hardware performance training data',\n",
-        "                       create_new_version=True)\n",
-        "\n",
-        "# Register the test dataset with your workspace\n",
-        "test_dataset.register(workspace = ws, name = 'hardware_performance_test_dataset',\n",
-        "                      description = 'hardware performance test data',\n",
-        "                      create_new_version=True)\n",
-        "\n",
-        "# Drop the labeled column from the train dataset\n",
-        "X_train = train_dataset.drop_columns(columns=['ERP'])\n",
-        "y_train = train_dataset.keep_columns(columns=['ERP'], validate=True)\n",
-        "\n",
-        "# Drop the labeled column from the test dataset\n",
-        "X_test = test_dataset.drop_columns(columns=['ERP'])  \n",
-        "\n",
-        "# Display the top rows in the train dataset\n",
-        "X_train.take(5).to_pandas_dataframe()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "Instantiate an `AutoMLConfig` object to specify the settings and data used to run the experiment.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**task**|classification or regression|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Regression supports the following primary metrics: <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
-        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
-        "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
-        "\n",
-        "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\": 5,\n",
-        "    \"iterations\": 10,\n",
-        "    \"n_cross_validations\": 2,\n",
-        "    \"primary_metric\": 'spearman_correlation',\n",
-        "    \"preprocess\": True,\n",
-        "    \"max_concurrent_iterations\": 1,\n",
-        "    \"verbosity\": logging.INFO,\n",
-        "}\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             debug_log = 'automl_errors_model_exp.log',\n",
-        "                             run_configuration=conda_run_config,\n",
-        "                             X = X_train,\n",
-        "                             y = y_train,\n",
-        "                             **automl_settings\n",
-        "                            )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. Execution of local runs is synchronous. Depending on the data and the number of iterations this can run for a while.\n",
-        "In this example, we specify `show_output = True` to print currently running iterations to the console."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Results"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explanations\n",
-        "This section will walk you through the workflow to compute model explanations for an AutoML model on your remote compute.\n",
-        "\n",
-        "### Retrieve any AutoML Model for explanations\n",
-        "\n",
-        "Below we select the some AutoML pipeline from our iterations. The `get_output` method returns the a AutoML run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_run, fitted_model = remote_run.get_output(iteration=5)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Setup model explanation run on the remote compute\n",
-        "The following section provides details on how to setup an AzureML experiment to run model explanations for an AutoML model on your remote compute."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Sample script used for computing explanations\n",
-        "View the sample script for computing the model explanations for your AutoML model on remote compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "with open('train_explainer.py', 'r') as cefr:\n",
-        "    print(cefr.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Substitute values in your sample script\n",
-        "The following cell shows how you change the values in the sample script so that you can change the sample script according to your experiment and dataset."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "\n",
-        "# create script folder\n",
-        "script_folder = './sample_projects/automl-regression-computer-hardware'\n",
-        "if not os.path.exists(script_folder):\n",
-        "    os.makedirs(script_folder)\n",
-        "\n",
-        "# Copy the sample script to script folder.\n",
-        "shutil.copy('train_explainer.py', script_folder)\n",
-        "\n",
-        "# Create the explainer script that will run on the remote compute.\n",
-        "script_file_name = script_folder + '/train_explainer.py'\n",
-        "\n",
-        "# Open the sample script for modification\n",
-        "with open(script_file_name, 'r') as cefr:\n",
-        "    content = cefr.read()\n",
-        "\n",
-        "# Replace the values in train_explainer.py file with the appropriate values\n",
-        "content = content.replace('<<experimnet_name>>', automl_run.experiment.name) # your experiment name.\n",
-        "content = content.replace('<<run_id>>', automl_run.id) # Run-id of the AutoML run for which you want to explain the model.\n",
-        "content = content.replace('<<target_column_name>>', 'ERP') # Your target column name\n",
-        "content = content.replace('<<task>>', 'regression') # Training task type\n",
-        "# Name of your training dataset register with your workspace\n",
-        "content = content.replace('<<train_dataset_name>>', 'hardware_performance_train_dataset') \n",
-        "# Name of your test dataset register with your workspace\n",
-        "content = content.replace('<<test_dataset_name>>', 'hardware_performance_test_dataset')\n",
-        "\n",
-        "# Write sample file into your script folder.\n",
-        "with open(script_file_name, 'w') as cefw:\n",
-        "    cefw.write(content)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create conda configuration for model explanations experiment\n",
-        "We need `azureml-explain-model`, `azureml-train-automl` and `azureml-core` packages for computing model explanations for your AutoML model on remote compute."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute\n",
-        "conda_run_config.target = compute_target\n",
-        "conda_run_config.environment.docker.enabled = True\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-train-automl', 'azureml-core', 'azureml-explain-model'\n",
-        "]\n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "conda_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
-        "    conda_packages=['scikit-learn', 'numpy','py-xgboost<=0.80'],\n",
-        "    pip_packages=azureml_pip_packages)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Submit the experiment for model explanations\n",
-        "Submit the experiment with the above `run_config` and the sample script for computing explanations."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Now submit a run on AmlCompute for model explanations\n",
-        "from azureml.core.script_run_config import ScriptRunConfig\n",
-        "\n",
-        "script_run_config = ScriptRunConfig(source_directory=script_folder,\n",
-        "                                    script='train_explainer.py',\n",
-        "                                    run_config=conda_run_config)\n",
-        "\n",
-        "run = experiment.submit(script_run_config)\n",
-        "\n",
-        "# Show run details\n",
-        "run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "# Shows output of the run on stdout.\n",
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Feature importance  and  explanation dashboard\n",
-        "In this section we describe how you can download the explanation results from the explanations experiment and visualize the feature importance for your AutoML model. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Setup for visualizing the model explanation results\n",
-        "For visualizing the explanation results for the *fitted_model* we need to perform the following steps:-\n",
-        "1. Featurize test data samples.\n",
-        "\n",
-        "The *automl_explainer_setup_obj* contains all the structures from above list. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
-        "explainer_setup_class = automl_setup_model_explanations(fitted_model, 'regression', X_test=X_test)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Download engineered feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the engineered features."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient\n",
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
-        "client = ExplanationClient.from_run(automl_run)\n",
-        "engineered_explanations = client.download_model_explanation(raw=False)\n",
-        "print(engineered_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(engineered_explanations, explainer_setup_class.automl_estimator, explainer_setup_class.X_test_transform)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Download raw feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *automl_run*. You can also use ExplanationDashboard to view the dash board visualization of the feature importance values of the raw features."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "raw_explanations = client.download_model_explanation(raw=True)\n",
-        "print(raw_explanations.get_feature_importance_dict())\n",
-        "ExplanationDashboard(raw_explanations, explainer_setup_class.automl_pipeline, explainer_setup_class.X_test_raw)"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "v-rasav"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.7"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/auto-ml-model-explanations-remote-compute.yml

@@ -1,11 +0,0 @@
-name: auto-ml-model-explanations-remote-compute
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - azureml-explain-model
-  - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/model-explanation-remote-amlcompute/train_explainer.py

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

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

@@ -21,16 +21,14 @@
       "metadata": {},
       "source": [
         "# Automated Machine Learning\n",
-        "_**Explain classification model, visualize the explanation and operationalize the explainer along with AutoML model**_\n",
+        "_**Explain classification model and visualize the explanation**_\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. [Results](#Results)"
-        "1. [Explanations](#Explanations)\n",
-        "1. [Operationailze](#Operationailze)"
       ]
     },
     {
@@ -47,8 +45,7 @@
         "2. Instantiating AutoMLConfig\n",
         "3. Training the Model using local compute and explain the model\n",
         "4. Visualization model's feature importance in widget\n",
-        "5. Explore any model's explanation\n",
+        "5. Explore best model's explanation"
-        "6. Operationalize the AutoML model and the explaination model"
       ]
     },
     {
@@ -72,9 +69,7 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.train.automl import AutoMLConfig\n",
+        "from azureml.train.automl import AutoMLConfig"
-        "from azureml.core.dataset import Dataset\n",
-        "from azureml.explain.model._internal.explanation_client import ExplanationClient"
       ]
     },
     {
@@ -87,6 +82,8 @@
         "\n",
         "# choose a name for experiment\n",
         "experiment_name = 'automl-model-explanation'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/automl-model-explanation'\n",
         "\n",
         "experiment=Experiment(ws, experiment_name)\n",
         "\n",
@@ -96,6 +93,7 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -109,42 +107,29 @@
         "## Data"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Training Data"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "train_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_train.csv\"\n",
+        "from sklearn import datasets\n",
-        "train_dataset = Dataset.Tabular.from_delimited_files(train_data)\n",
+        "\n",
-        "X_train = train_dataset.drop_columns(columns=['y']).to_pandas_dataframe()\n",
+        "iris = datasets.load_iris()\n",
-        "y_train = train_dataset.keep_columns(columns=['y'], validate=True).to_pandas_dataframe()"
+        "y = iris.target\n",
-      ]
+        "X = iris.data\n",
-    },
+        "\n",
-    {
+        "features = iris.feature_names\n",
-      "cell_type": "markdown",
+        "\n",
-      "metadata": {},
+        "from sklearn.model_selection import train_test_split\n",
-      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
-        "### Test Data"
+        "                                                    y,\n",
-      ]
+        "                                                    test_size=0.1,\n",
-    },
+        "                                                    random_state=100,\n",
-    {
+        "                                                    stratify=y)\n",
-      "cell_type": "code",
+        "\n",
-      "execution_count": null,
+        "X_train = pd.DataFrame(X_train, columns=features)\n",
-      "metadata": {},
+        "X_test = pd.DataFrame(X_test, columns=features)"
-      "outputs": [],
-      "source": [
-        "test_data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/bankmarketing_test.csv\"\n",
-        "test_dataset = Dataset.Tabular.from_delimited_files(test_data)\n",
-        "X_test = test_dataset.drop_columns(columns=['y']).to_pandas_dataframe()\n",
-        "y_test = test_dataset.keep_columns(columns=['y'], validate=True).to_pandas_dataframe()"
       ]
     },
     {
@@ -163,7 +148,10 @@
         "|**iterations**|Number of iterations. In each iteration Auto ML trains the data with a specific pipeline|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
-        "|**model_explainability**|Indicate to explain each trained pipeline or not |"
+        "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features]|\n",
+        "|**y_valid**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
+        "|**model_explainability**|Indicate to explain each trained pipeline or not |\n",
+        "|**path**|Relative path to the project folder.  AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder. |"
       ]
     },
     {
@@ -178,11 +166,12 @@
         "                             iteration_timeout_minutes = 200,\n",
         "                             iterations = 10,\n",
         "                             verbosity = logging.INFO,\n",
-        "                             preprocess = True,\n",
         "                             X = X_train, \n",
         "                             y = y_train,\n",
-        "                             n_cross_validations = 5,\n",
+        "                             X_valid = X_test,\n",
-        "                             model_explainability=True)"
+        "                             y_valid = y_test,\n",
+        "                             model_explainability=True,\n",
+        "                             path=project_folder)"
       ]
     },
     {
@@ -265,15 +254,16 @@
       "source": [
         "### Best Model 's explanation\n",
         "\n",
-        "Retrieve the explanation from the *best_run* which includes explanations for engineered features and raw features."
+        "Retrieve the explanation from the best_run. And explanation information includes:\n",
-      ]
+        "\n",
-    },
+        "1.\tshap_values: The explanation information generated by shap lib\n",
-    {
+        "2.\texpected_values: The expected value of the model applied to set of X_train data.\n",
-      "cell_type": "markdown",
+        "3.\toverall_summary: The model level feature importance values sorted in descending order\n",
-      "metadata": {},
+        "4.\toverall_imp: The feature names sorted in the same order as in overall_summary\n",
-      "source": [
+        "5.\tper_class_summary: The class level feature importance values sorted in descending order. Only available for the classification case\n",
-        "#### Download engineered feature importance from artifact store\n",
+        "6.\tper_class_imp: The feature names sorted in the same order as in per_class_summary. Only available for the classification case\n",
-        "You can use *ExplanationClient* to download the engineered feature explanations from the artifact store of the *best_run*."
+        "\n",
+        "Note:- The **retrieve_model_explanation()** API only works in case AutoML has been configured with **'model_explainability'** flag set to **True**. "
       ]
     },
     {
@@ -282,17 +272,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "client = ExplanationClient.from_run(best_run)\n",
+        "from azureml.train.automl.automlexplainer import retrieve_model_explanation\n",
-        "engineered_explanations = client.download_model_explanation(raw=False)\n",
+        "\n",
-        "print(engineered_explanations.get_feature_importance_dict())"
+        "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
-      ]
+        "    retrieve_model_explanation(best_run)"
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Download raw feature importance from artifact store\n",
-        "You can use *ExplanationClient* to download the raw feature explanations from the artifact store of the *best_run*."
       ]
     },
     {
@@ -301,24 +284,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "client = ExplanationClient.from_run(best_run)\n",
+        "print(overall_summary)\n",
-        "raw_explanations = client.download_model_explanation(raw=True)\n",
+        "print(overall_imp)"
-        "print(raw_explanations.get_feature_importance_dict())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Explanations\n",
-        "In this section, we will show how to compute model explanations and visualize the explanations using azureml-explain-model package. Besides retrieving an existing model explanation for an AutoML model, you can also explain your AutoML model with different test data. The following steps will allow you to compute and visualize engineered feature importance and raw feature importance based on your test data. "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Retrieve any other AutoML model from training"
       ]
     },
     {
@@ -327,20 +294,15 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "automl_run, fitted_model = local_run.get_output(iteration=0)"
+        "print(per_class_summary)\n",
+        "print(per_class_imp)"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "#### Setup the model explanations for AutoML models\n",
+        "Beside retrieve the existed model explanation information, explain the model with different train/test data"
-        "The *fitted_model* can generate the following which will be used for getting the engineered and raw feature explanations using *automl_setup_model_explanations*:-\n",
-        "1. Featurized data from train samples/test samples \n",
-        "2. Gather engineered and raw feature name lists\n",
-        "3. Find the classes in your labeled column in classification scenarios\n",
-        "\n",
-        "The *automl_explainer_setup_obj* contains all the structures from above list. "
       ]
     },
     {
@@ -349,19 +311,10 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.train.automl.automl_explain_utilities import AutoMLExplainerSetupClass, automl_setup_model_explanations\n",
+        "from azureml.train.automl.automlexplainer import explain_model\n",
         "\n",
-        "automl_explainer_setup_obj = automl_setup_model_explanations(fitted_model, X=X_train, \n",
+        "shap_values, expected_values, overall_summary, overall_imp, per_class_summary, per_class_imp = \\\n",
-        "                                                             X_test=X_test, y=y_train, \n",
+        "    explain_model(fitted_model, X_train, X_test, features=features)"
-        "                                                             task='classification')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Initialize the Mimic Explainer for feature importance\n",
-        "For explaining the AutoML models, use the *MimicWrapper* from *azureml.explain.model* package. The *MimicWrapper* can be initialized with fields in *automl_explainer_setup_obj*, your workspace and a LightGBM model which acts as a surrogate model to explain the AutoML model (*fitted_model* here). The *MimicWrapper* also takes the *automl_run* object where the raw and engineered explanations will be uploaded."
       ]
     },
     {
@@ -370,236 +323,8 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.explain.model.mimic.models.lightgbm_model import LGBMExplainableModel\n",
+        "print(overall_summary)\n",
-        "from azureml.explain.model.mimic_wrapper import MimicWrapper\n",
+        "print(overall_imp)"
-        "explainer = MimicWrapper(ws, automl_explainer_setup_obj.automl_estimator, LGBMExplainableModel, \n",
-        "                         init_dataset=automl_explainer_setup_obj.X_transform, run=automl_run,\n",
-        "                         features=automl_explainer_setup_obj.engineered_feature_names, \n",
-        "                         feature_maps=[automl_explainer_setup_obj.feature_map],\n",
-        "                         classes=automl_explainer_setup_obj.classes)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Use Mimic Explainer for computing and visualizing engineered feature importance\n",
-        "The *explain()* method in *MimicWrapper* can be called with the transformed test samples to get the feature importance for the generated engineered features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the generated engineered features by AutoML featurizers."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "engineered_explanations = explainer.explain(['local', 'global'], eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
-        "print(engineered_explanations.get_feature_importance_dict())\n",
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
-        "ExplanationDashboard(engineered_explanations, automl_explainer_setup_obj.automl_estimator, automl_explainer_setup_obj.X_test_transform)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Use Mimic Explainer for computing and visualizing raw feature importance\n",
-        "The *explain()* method in *MimicWrapper* can be again called with the transformed test samples and setting *get_raw* to *True* to get the feature importance for the raw features. You can also use *ExplanationDashboard* to view the dash board visualization of the feature importance values of the raw features."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "raw_explanations = explainer.explain(['local', 'global'], get_raw=True, \n",
-        "                                     raw_feature_names=automl_explainer_setup_obj.raw_feature_names,\n",
-        "                                     eval_dataset=automl_explainer_setup_obj.X_test_transform)\n",
-        "print(raw_explanations.get_feature_importance_dict())\n",
-        "from azureml.contrib.interpret.visualize import ExplanationDashboard\n",
-        "ExplanationDashboard(raw_explanations, automl_explainer_setup_obj.automl_pipeline, automl_explainer_setup_obj.X_test_raw)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Operationailze\n",
-        "In this section we will show how you can operationalize an AutoML model and the explainer which was used to compute the explanations in the previous section.\n",
-        "\n",
-        "#### Register the AutoML model and the scoring explainer\n",
-        "We use the *TreeScoringExplainer* from *azureml.explain.model* package to create the scoring explainer which will be used to compute the raw and engineered feature importances at the inference time. Note that, we initialize the scoring explainer with the *feature_map* that was computed previously. The *feature_map* will be used by the scoring explainer to return the raw feature importance.\n",
-        "\n",
-        "In the cell below, we pickle the scoring explainer and register the AutoML model and the scoring explainer with the Model Management Service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.explain.model.scoring.scoring_explainer import TreeScoringExplainer, save\n",
-        "\n",
-        "# Initialize the ScoringExplainer\n",
-        "scoring_explainer = TreeScoringExplainer(explainer.explainer, feature_maps=[automl_explainer_setup_obj.feature_map])\n",
-        "\n",
-        "# Pickle scoring explainer locally\n",
-        "save(scoring_explainer, exist_ok=True)\n",
-        "\n",
-        "# Register trained automl model present in the 'outputs' folder in the artifacts\n",
-        "original_model = automl_run.register_model(model_name='automl_model', \n",
-        "                                           model_path='outputs/model.pkl')\n",
-        "\n",
-        "# Register scoring explainer\n",
-        "automl_run.upload_file('scoring_explainer.pkl', 'scoring_explainer.pkl')\n",
-        "scoring_explainer_model = automl_run.register_model(model_name='scoring_explainer', model_path='scoring_explainer.pkl')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Create the conda dependencies for setting up the service\n",
-        "We need to create the conda dependencies comprising of the *azureml-explain-model*, *azureml-train-automl* and *azureml-defaults* packages. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "azureml_pip_packages = [\n",
-        "    'azureml-explain-model', 'azureml-train-automl', 'azureml-defaults'\n",
-        "]\n",
-        " \n",
-        "\n",
-        "# specify CondaDependencies obj\n",
-        "myenv = CondaDependencies.create(conda_packages=['scikit-learn', 'pandas', 'numpy', 'py-xgboost<=0.80'],\n",
-        "                                 pip_packages=azureml_pip_packages,\n",
-        "                                 pin_sdk_version=True)\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())\n",
-        "\n",
-        "with open(\"myenv.yml\",\"r\") as f:\n",
-        "    print(f.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View your scoring file"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "with open(\"score_local_explain.py\",\"r\") as f:\n",
-        "    print(f.read())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Deploy the service\n",
-        "In the cell below, we deploy the service using the conda file and the scoring file from the previous steps. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import InferenceConfig\n",
-        "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
-        "                                               memory_gb=1, \n",
-        "                                               tags={\"data\": \"Bank Marketing\",  \n",
-        "                                                     \"method\" : \"local_explanation\"}, \n",
-        "                                               description='Get local explanations for Bank marketing test data')\n",
-        "\n",
-        "inference_config = InferenceConfig(runtime= \"python\", \n",
-        "                                   entry_script=\"score_local_explain.py\",\n",
-        "                                   conda_file=\"myenv.yml\")\n",
-        "\n",
-        "# Use configs and models generated above\n",
-        "service = Model.deploy(ws, 'model-scoring', [scoring_explainer_model, original_model], inference_config, aciconfig)\n",
-        "service.wait_for_deployment(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### View the service logs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "service.get_logs()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Inference using some test data\n",
-        "Inference using some test data to see the predicted value from autml model, view the engineered feature importance for the predicted value and raw feature importance for the predicted value."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if service.state == 'Healthy':\n",
-        "    # Serialize the first row of the test data into json\n",
-        "    X_test_json = X_test[:1].to_json(orient='records')\n",
-        "    print(X_test_json)\n",
-        "    # Call the service to get the predictions and the engineered and raw explanations\n",
-        "    output = service.run(X_test_json)\n",
-        "    # Print the predicted value\n",
-        "    print(output['predictions'])\n",
-        "    # Print the engineered feature importances for the predicted value\n",
-        "    print(output['engineered_local_importance_values'])\n",
-        "    # Print the raw feature importances for the predicted value\n",
-        "    print(output['raw_local_importance_values'])"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Delete the service\n",
-        "Delete the service once you have finished inferencing."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "service.delete()"
       ]
     }
   ],
@@ -624,7 +349,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.6.7"
+      "version": "3.6.6"
     }
   },
   "nbformat": 4,

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

@@ -2,10 +2,8 @@ name: auto-ml-model-explanation
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
   - pandas_ml
   - azureml-explain-model
-  - azureml-contrib-interpret

how-to-use-azureml/automated-machine-learning/model-explanation/score_local_explain.py

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

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

@@ -70,12 +70,13 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "import os\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import azureml.dataprep as dprep\n",
         " \n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -87,8 +88,9 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose a name for the experiment.\n",
+        "# Choose a name for the experiment and specify the project folder.\n",
         "experiment_name = 'automl-regression-concrete'\n",
+        "project_folder = './sample_projects/automl-regression-concrete'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -98,6 +100,7 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -144,12 +147,11 @@
         "    # Create the cluster.\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
-        "print('Checking cluster status...')\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -158,7 +160,20 @@
       "source": [
         "# Data\n",
         "\n",
-        "Create a run configuration for the remote run."
+        "Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')\n",
+        "    \n",
+        "if not os.path.exists(project_folder):\n",
+        "    os.makedirs(project_folder)"
       ]
     },
     {
@@ -169,7 +184,6 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -177,8 +191,10 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -188,7 +204,7 @@
       "source": [
         "### Load Data\n",
         "\n",
-        "Load the concrete strength dataset into X and y.  X contains the training features, which are inputs to the model.  y contains the training labels, which are the expected output of the model."
+        "Here create the script to be run in azure compute for loading the data, load the concrete strength dataset into the X and y  variables. Next, split the data  using train_test_split and return X_train and y_train for training the model. Finally, return X_train and y_train for training the model."
       ]
     },
     {
@@ -198,12 +214,13 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/compresive_strength_concrete.csv\"\n",
-        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "dflow = dprep.auto_read_file(data)\n",
-        "X = dataset.drop_columns(columns=['CONCRETE'])\n",
+        "dflow.get_profile()\n",
-        "y = dataset.keep_columns(columns=['CONCRETE'], validate=True)\n",
+        "X = dflow.drop_columns(columns=['CONCRETE'])\n",
+        "y = dflow.keep_columns(columns=['CONCRETE'], validate_column_exists=True)\n",
         "X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
         "y_train, y_test = y.random_split(percentage=0.8, seed=223) \n",
-        "dataset.take(5).to_pandas_dataframe()"
+        "dflow.head()"
       ]
     },
     {
@@ -223,6 +240,7 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
+        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -252,6 +270,7 @@
         "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
         "                             debug_log = 'automl.log',\n",
+        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
         "                             X = X_train,\n",
         "                             y = y_train,\n",
@@ -463,7 +482,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -473,7 +492,9 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-defaults','azureml-train-automl'])\n",
+        "from azureml.core.conda_dependencies import CondaDependencies\n",
+        "\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -493,7 +514,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
@@ -510,7 +531,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploy the model as a Web Service on Azure Container Instance"
+        "### Create a Container Image\n",
+        "\n",
+        "Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
+        "or when testing a model that is under development."
       ]
     },
     {
@@ -519,23 +543,63 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.image import Image, ContainerImage\n",
-        "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
+        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                   entry_script = script_file_name,\n",
+        "                                 execution_script = script_file_name,\n",
-        "                                   conda_file = conda_env_file_name)\n",
+        "                                 conda_file = conda_env_file_name,\n",
+        "                                 tags = {'area': \"digits\", 'type': \"automl_regression\"},\n",
+        "                                 description = \"Image for automl regression sample\")\n",
+        "\n",
+        "image = Image.create(name = \"automlsampleimage\",\n",
+        "                     # this is the model object \n",
+        "                     models = [model],\n",
+        "                     image_config = image_config, \n",
+        "                     workspace = ws)\n",
+        "\n",
+        "image.wait_for_creation(show_output = True)\n",
+        "\n",
+        "if image.creation_state == 'Failed':\n",
+        "    print(\"Image build log at: \" + image.image_build_log_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy the Image as a Web Service on Azure Container Instance\n",
+        "\n",
+        "Deploy an image that contains the model and other assets needed by the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",
         "                                               tags = {'area': \"digits\", 'type': \"automl_regression\"}, \n",
-        "                                               description = 'sample service for Automl Regression')\n",
+        "                                               description = 'sample service for Automl Regression')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
         "\n",
         "aci_service_name = 'automl-sample-concrete'\n",
         "print(aci_service_name)\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+        "                                           image = image,\n",
+        "                                           name = aci_service_name,\n",
+        "                                           workspace = ws)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]

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

@@ -2,11 +2,7 @@ name: auto-ml-regression-concrete-strength
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
   - pandas_ml
-  - azureml-dataprep[pandas]

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

@@ -70,12 +70,13 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "import os\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import azureml.dataprep as dprep\n",
         " \n",
         "\n",
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -87,8 +88,9 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose a name for the experiment.\n",
+        "# Choose a name for the experiment and specify the project folder.\n",
         "experiment_name = 'automl-regression-hardware'\n",
+        "project_folder = './sample_projects/automl-remote-regression'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -98,6 +100,7 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -144,12 +147,11 @@
         "    # Create the cluster.\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "    \n",
-        "print('Checking cluster status...')\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "    \n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -158,7 +160,20 @@
       "source": [
         "# Data\n",
         "\n",
-        "Create a run configuration for the remote run."
+        "Here load the data in the get_data script to be utilized in azure compute. To do this, first load all the necessary libraries and dependencies to set up paths for the data and to create the conda_run_config."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "if not os.path.isdir('data'):\n",
+        "    os.mkdir('data')\n",
+        "    \n",
+        "if not os.path.exists(project_folder):\n",
+        "    os.makedirs(project_folder)"
       ]
     },
     {
@@ -169,7 +184,6 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -177,8 +191,10 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(conda_packages=['numpy', 'py-xgboost<=0.80'])\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
@@ -188,7 +204,7 @@
       "source": [
         "### Load Data\n",
         "\n",
-        "Load the hardware performance dataset into X and y. X contains the training features, which are inputs to the model. y contains the training labels, which are the expected output of the model."
+        "Here create the script to be run in azure compute for loading the data, load the hardware dataset into the X and y variables. Next split the data  using train_test_split and return X_train and y_train for training the model."
       ]
     },
     {
@@ -198,12 +214,13 @@
       "outputs": [],
       "source": [
         "data = \"https://automlsamplenotebookdata.blob.core.windows.net/automl-sample-notebook-data/machineData.csv\"\n",
-        "dataset = Dataset.Tabular.from_delimited_files(data)\n",
+        "dflow = dprep.auto_read_file(data)\n",
-        "X = dataset.drop_columns(columns=['ERP'])\n",
+        "dflow.get_profile()\n",
-        "y = dataset.keep_columns(columns=['ERP'], validate=True)\n",
+        "X = dflow.drop_columns(columns=['ERP'])\n",
+        "y = dflow.keep_columns(columns=['ERP'], validate_column_exists=True)\n",
         "X_train, X_test = X.random_split(percentage=0.8, seed=223)\n",
-        "y_train, y_test = y.random_split(percentage=0.8, seed=223)\n",
+        "y_train, y_test = y.random_split(percentage=0.8, seed=223) \n",
-        "dataset.take(5).to_pandas_dataframe()"
+        "dflow.head()"
       ]
     },
     {
@@ -224,6 +241,7 @@
         "|**n_cross_validations**|Number of cross validation splits.|\n",
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], targets values.|\n",
+        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|\n",
         "\n",
         "**_You can find more information about primary metrics_** [here](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-auto-train#primary-metric)"
       ]
@@ -252,7 +270,8 @@
         "}\n",
         "\n",
         "automl_config = AutoMLConfig(task = 'regression',\n",
-        "                             debug_log = 'automl_errors.log',\n",
+        "                             debug_log = 'automl_errors_20190417.log',\n",
+        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
         "                             X = X_train,\n",
         "                             y = y_train,\n",
@@ -481,7 +500,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "for p in ['azureml-train-automl', 'azureml-core']:\n",
+        "for p in ['azureml-train-automl', 'azureml-sdk', 'azureml-core']:\n",
         "    print('{}\\t{}'.format(p, dependencies[p]))"
       ]
     },
@@ -491,7 +510,7 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn','py-xgboost==0.80'], pip_packages=['azureml-defaults','azureml-train-automl'])\n",
+        "myenv = CondaDependencies.create(conda_packages=['numpy','scikit-learn'], pip_packages=['azureml-sdk[automl]'])\n",
         "\n",
         "conda_env_file_name = 'myenv.yml'\n",
         "myenv.save_to_file('.', conda_env_file_name)"
@@ -511,7 +530,7 @@
         "    content = cefr.read()\n",
         "\n",
         "with open(conda_env_file_name, 'w') as cefw:\n",
-        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-train-automl']))\n",
+        "    cefw.write(content.replace(azureml.core.VERSION, dependencies['azureml-sdk']))\n",
         "\n",
         "# Substitute the actual model id in the script file.\n",
         "\n",
@@ -528,7 +547,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Deploy the model as a Web Service on Azure Container Instance"
+        "### Create a Container Image\n",
+        "\n",
+        "Next use Azure Container Instances for deploying models as a web service for quickly deploying and validating your model\n",
+        "or when testing a model that is under development."
       ]
     },
     {
@@ -537,23 +559,63 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from azureml.core.model import InferenceConfig\n",
+        "from azureml.core.image import Image, ContainerImage\n",
-        "from azureml.core.webservice import AciWebservice\n",
-        "from azureml.core.webservice import Webservice\n",
-        "from azureml.core.model import Model\n",
         "\n",
-        "inference_config = InferenceConfig(runtime = \"python\", \n",
+        "image_config = ContainerImage.image_configuration(runtime= \"python\",\n",
-        "                                   entry_script = script_file_name,\n",
+        "                                 execution_script = script_file_name,\n",
-        "                                   conda_file = conda_env_file_name)\n",
+        "                                 conda_file = conda_env_file_name,\n",
+        "                                 tags = {'area': \"digits\", 'type': \"automl_regression\"},\n",
+        "                                 description = \"Image for automl regression sample\")\n",
+        "\n",
+        "image = Image.create(name = \"automlsampleimage\",\n",
+        "                     # this is the model object \n",
+        "                     models = [model],\n",
+        "                     image_config = image_config, \n",
+        "                     workspace = ws)\n",
+        "\n",
+        "image.wait_for_creation(show_output = True)\n",
+        "\n",
+        "if image.creation_state == 'Failed':\n",
+        "    print(\"Image build log at: \" + image.image_build_log_uri)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "### Deploy the Image as a Web Service on Azure Container Instance\n",
+        "\n",
+        "Deploy an image that contains the model and other assets needed by the service."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import AciWebservice\n",
         "\n",
         "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
         "                                               memory_gb = 1, \n",
         "                                               tags = {'area': \"digits\", 'type': \"automl_regression\"}, \n",
-        "                                               description = 'sample service for Automl Regression')\n",
+        "                                               description = 'sample service for Automl Regression')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from azureml.core.webservice import Webservice\n",
         "\n",
         "aci_service_name = 'automl-sample-hardware'\n",
         "print(aci_service_name)\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+        "aci_service = Webservice.deploy_from_image(deployment_config = aciconfig,\n",
+        "                                           image = image,\n",
+        "                                           name = aci_service_name,\n",
+        "                                           workspace = ws)\n",
         "aci_service.wait_for_deployment(True)\n",
         "print(aci_service.state)"
       ]

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

@@ -2,11 +2,7 @@ name: auto-ml-regression-hardware-performance
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib
   - pandas_ml
-  - azureml-dataprep[pandas]

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

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

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

@@ -1,542 +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/automated-machine-learning/remote-amlcompute/auto-ml-remote-amlcompute.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Automated Machine Learning\n",
-        "_**Remote Execution using AmlCompute**_\n",
-        "\n",
-        "## Contents\n",
-        "1. [Introduction](#Introduction)\n",
-        "1. [Setup](#Setup)\n",
-        "1. [Data](#Data)\n",
-        "1. [Train](#Train)\n",
-        "1. [Results](#Results)\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Introduction\n",
-        "In this example we use the scikit-learn's [iris dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) to showcase how you can use AutoML for a simple classification problem.\n",
-        "\n",
-        "Make sure you have executed the [configuration](../../../configuration.ipynb) before running this notebook.\n",
-        "\n",
-        "In this notebook you would see\n",
-        "1. Create an `Experiment` in an existing `Workspace`.\n",
-        "2. Create or Attach existing AmlCompute to a workspace.\n",
-        "3. Configure AutoML using `AutoMLConfig`.\n",
-        "4. Train the model using AmlCompute with ONNX compatible config on.\n",
-        "5. Explore the results and save the ONNX model.\n",
-        "6. Inference with the ONNX model.\n",
-        "\n",
-        "In addition this notebook showcases the following features\n",
-        "- **Parallel** executions for iterations\n",
-        "- **Asynchronous** tracking of progress\n",
-        "- **Cancellation** of individual iterations or the entire run\n",
-        "- Retrieving models for any iteration or logged metric\n",
-        "- Specifying AutoML settings as `**kwargs`"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Setup\n",
-        "\n",
-        "As part of the setup you have already created an Azure ML `Workspace` object. For AutoML you will need to create an `Experiment` object, which is a named object in a `Workspace` used to run experiments."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import logging\n",
-        "import os\n",
-        "\n",
-        "import pandas as pd\n",
-        "from sklearn import datasets\n",
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "import azureml.core\n",
-        "from azureml.core.experiment import Experiment\n",
-        "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
-        "from azureml.train.automl import AutoMLConfig"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# Choose an experiment name.\n",
-        "experiment_name = 'automl-remote-amlcompute-with-onnx'\n",
-        "\n",
-        "experiment = Experiment(ws, experiment_name)\n",
-        "\n",
-        "output = {}\n",
-        "output['SDK version'] = azureml.core.VERSION\n",
-        "output['Subscription ID'] = ws.subscription_id\n",
-        "output['Workspace Name'] = ws.name\n",
-        "output['Resource Group'] = ws.resource_group\n",
-        "output['Location'] = ws.location\n",
-        "output['Experiment Name'] = experiment.name\n",
-        "pd.set_option('display.max_colwidth', -1)\n",
-        "outputDf = pd.DataFrame(data = output, index = [''])\n",
-        "outputDf.T"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create or Attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for your AutoML run. In this tutorial, you create `AmlCompute` as your training compute resource.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace this code will skip the creation process.\n",
-        "\n",
-        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import AmlCompute\n",
-        "from azureml.core.compute import ComputeTarget\n",
-        "\n",
-        "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"automlc2\"\n",
-        "\n",
-        "found = False\n",
-        "# Check if this compute target already exists in the workspace.\n",
-        "cts = ws.compute_targets\n",
-        "if amlcompute_cluster_name in cts and cts[amlcompute_cluster_name].type == 'AmlCompute':\n",
-        "    found = True\n",
-        "    print('Found existing compute target.')\n",
-        "    compute_target = cts[amlcompute_cluster_name]\n",
-        "\n",
-        "if not found:\n",
-        "    print('Creating a new compute target...')\n",
-        "    provisioning_config = AmlCompute.provisioning_configuration(vm_size = \"STANDARD_D2_V2\", # for GPU, use \"STANDARD_NC6\"\n",
-        "                                                                #vm_priority = 'lowpriority', # optional\n",
-        "                                                                max_nodes = 6)\n",
-        "\n",
-        "    # Create the cluster.\\n\",\n",
-        "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
-        "\n",
-        "print('Checking cluster status...')\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Data\n",
-        "For remote executions, you need to make the data accessible from the remote compute.\n",
-        "This can be done by uploading the data to DataStore.\n",
-        "In this example, we upload scikit-learn's [load_iris](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) data."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "iris = datasets.load_iris()\n",
-        "\n",
-        "X_train, X_test, y_train, y_test = train_test_split(iris.data, \n",
-        "                                                    iris.target, \n",
-        "                                                    test_size=0.2, \n",
-        "                                                    random_state=0)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Ensure the x_train and x_test are pandas DataFrame."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Convert the X_train and X_test to pandas DataFrame and set column names,\n",
-        "# This is needed for initializing the input variable names of ONNX model, \n",
-        "# and the prediction with the ONNX model using the inference helper.\n",
-        "X_train = pd.DataFrame(X_train, columns=['c1', 'c2', 'c3', 'c4'])\n",
-        "X_test = pd.DataFrame(X_test, columns=['c1', 'c2', 'c3', 'c4'])\n",
-        "y_train = pd.DataFrame(y_train, columns=['label'])\n",
-        "\n",
-        "if not os.path.isdir('data'):\n",
-        "    os.mkdir('data')\n",
-        "\n",
-        "X_train.to_csv(\"data/X_train.csv\", index=False)\n",
-        "y_train.to_csv(\"data/y_train.csv\", index=False)\n",
-        "\n",
-        "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path='irisdata', overwrite=True, show_progress=True)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.runconfig import RunConfiguration\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
-        "\n",
-        "# create a new RunConfig object\n",
-        "conda_run_config = RunConfiguration(framework=\"python\")\n",
-        "\n",
-        "# Set compute target to AmlCompute\n",
-        "conda_run_config.target = compute_target\n",
-        "conda_run_config.environment.docker.enabled = True\n",
-        "\n",
-        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
-        "conda_run_config.environment.python.conda_dependencies = cd"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "###  Creating a TabularDataset\n",
-        "\n",
-        "Defined X and y as `TabularDataset`s, which are passed to automated machine learning in the AutoMLConfig."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "X = Dataset.Tabular.from_delimited_files(path=ds.path('irisdata/X_train.csv'))\n",
-        "y = Dataset.Tabular.from_delimited_files(path=ds.path('irisdata/y_train.csv'))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train\n",
-        "\n",
-        "You can specify `automl_settings` as `**kwargs` as well. \n",
-        "\n",
-        "**Note:** Set the parameter enable_onnx_compatible_models=True, if you also want to generate the ONNX compatible models. Please note, the forecasting task and TensorFlow models are not ONNX compatible yet.\n",
-        "\n",
-        "**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
-        "\n",
-        "|Property|Description|\n",
-        "|-|-|\n",
-        "|**primary_metric**|This is the metric that you want to optimize. Classification supports the following primary metrics: <br><i>accuracy</i><br><i>AUC_weighted</i><br><i>average_precision_score_weighted</i><br><i>norm_macro_recall</i><br><i>precision_score_weighted</i>|\n",
-        "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
-        "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
-        "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of nodes in the AmlCompute cluster.|\n",
-        "|**enable_onnx_compatible_models**|Enable the ONNX compatible models in the experiment.|"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Set the preprocess=True,  currently the InferenceHelper only supports this mode."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "automl_settings = {\n",
-        "    \"iteration_timeout_minutes\": 10,\n",
-        "    \"iterations\": 10,\n",
-        "    \"n_cross_validations\": 5,\n",
-        "    \"primary_metric\": 'AUC_weighted',\n",
-        "    \"preprocess\": True,\n",
-        "    \"max_concurrent_iterations\": 5,\n",
-        "    \"verbosity\": logging.INFO\n",
-        "}\n",
-        "\n",
-        "automl_config = AutoMLConfig(task = 'classification',\n",
-        "                             debug_log = 'automl_errors.log',\n",
-        "                             run_configuration=conda_run_config,\n",
-        "                             X = X,\n",
-        "                             y = y,\n",
-        "                             enable_onnx_compatible_models=True, # This will generate ONNX compatible models.\n",
-        "                             **automl_settings\n",
-        "                            )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
-        "In this example, we specify `show_output = False` to suppress console output while the run is in progress."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run = experiment.submit(automl_config, show_output = False)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Results\n",
-        "\n",
-        "#### Loading executed runs\n",
-        "In case you need to load a previously executed run, enable the cell below and replace the `run_id` value."
-      ]
-    },
-    {
-      "cell_type": "raw",
-      "metadata": {},
-      "source": [
-        "remote_run = AutoMLRun(experiment = experiment, run_id = 'AutoML_5db13491-c92a-4f1d-b622-8ab8d973a058')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Widget for Monitoring Runs\n",
-        "\n",
-        "The widget will first report a \"loading\" status while running the first iteration. After completing the first iteration, an auto-updating graph and table will be shown. The widget will refresh once per minute, so you should see the graph update as child runs complete.\n",
-        "\n",
-        "You can click on a pipeline to see run properties and output logs.  Logs are also available on the DSVM under `/tmp/azureml_run/{iterationid}/azureml-logs`\n",
-        "\n",
-        "**Note:** The widget displays a link at the bottom. Use this link to open a web interface to explore the individual run details."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "remote_run"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(remote_run).show() "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Wait until the run finishes.\n",
-        "remote_run.wait_for_completion(show_output = True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Cancelling Runs\n",
-        "\n",
-        "You can cancel ongoing remote runs using the `cancel` and `cancel_iteration` functions."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Cancel the ongoing experiment and stop scheduling new iterations.\n",
-        "# remote_run.cancel()\n",
-        "\n",
-        "# Cancel iteration 1 and move onto iteration 2.\n",
-        "# remote_run.cancel_iteration(1)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Retrieve the Best ONNX Model\n",
-        "\n",
-        "Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing.  Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*.\n",
-        "\n",
-        "Set the parameter return_onnx_model=True to retrieve the best ONNX model, instead of the Python model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "best_run, onnx_mdl = remote_run.get_output(return_onnx_model=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Save the best ONNX model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.automl.core.onnx_convert import OnnxConverter\n",
-        "onnx_fl_path = \"./best_model.onnx\"\n",
-        "OnnxConverter.save_onnx_model(onnx_mdl, onnx_fl_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Predict with the ONNX model, using onnxruntime package"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import sys\n",
-        "import json\n",
-        "from azureml.automl.core.onnx_convert import OnnxConvertConstants\n",
-        "from azureml.train.automl import constants\n",
-        "\n",
-        "if sys.version_info < OnnxConvertConstants.OnnxIncompatiblePythonVersion:\n",
-        "    python_version_compatible = True\n",
-        "else:\n",
-        "    python_version_compatible = False\n",
-        "\n",
-        "try:\n",
-        "    import onnxruntime\n",
-        "    from azureml.automl.core.onnx_convert import OnnxInferenceHelper    \n",
-        "    onnxrt_present = True\n",
-        "except ImportError:\n",
-        "    onnxrt_present = False\n",
-        "\n",
-        "def get_onnx_res(run):\n",
-        "    res_path = 'onnx_resource.json'\n",
-        "    run.download_file(name=constants.MODEL_RESOURCE_PATH_ONNX, output_file_path=res_path)\n",
-        "    with open(res_path) as f:\n",
-        "        return json.load(f)\n",
-        "\n",
-        "if onnxrt_present and python_version_compatible:    \n",
-        "    mdl_bytes = onnx_mdl.SerializeToString()\n",
-        "    onnx_res = get_onnx_res(best_run)\n",
-        "\n",
-        "    onnxrt_helper = OnnxInferenceHelper(mdl_bytes, onnx_res)\n",
-        "    pred_onnx, pred_prob_onnx = onnxrt_helper.predict(X_test)\n",
-        "\n",
-        "    print(pred_onnx)\n",
-        "    print(pred_prob_onnx)\n",
-        "else:\n",
-        "    if not python_version_compatible:\n",
-        "        print('Please use Python version 3.6 or 3.7 to run the inference helper.')    \n",
-        "    if not onnxrt_present:\n",
-        "        print('Please install the onnxruntime package to do the prediction with ONNX model.')"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": []
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "savitam"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.6",
-      "language": "python",
-      "name": "python36"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.6"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}

how-to-use-azureml/automated-machine-learning/remote-amlcompute-with-onnx/auto-ml-remote-amlcompute-with-onnx.yml

@@ -1,12 +0,0 @@
-name: auto-ml-remote-amlcompute-with-onnx
-dependencies:
-- pip:
-  - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml
-  - onnxruntime

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

@@ -74,6 +74,7 @@
       "source": [
         "import logging\n",
         "import os\n",
+        "import csv\n",
         "\n",
         "from matplotlib import pyplot as plt\n",
         "import numpy as np\n",
@@ -83,7 +84,6 @@
         "import azureml.core\n",
         "from azureml.core.experiment import Experiment\n",
         "from azureml.core.workspace import Workspace\n",
-        "from azureml.core.dataset import Dataset\n",
         "from azureml.train.automl import AutoMLConfig"
       ]
     },
@@ -95,8 +95,9 @@
       "source": [
         "ws = Workspace.from_config()\n",
         "\n",
-        "# Choose an experiment name.\n",
+        "# Choose a name for the run history container in the workspace.\n",
         "experiment_name = 'automl-remote-amlcompute'\n",
+        "project_folder = './project'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -106,6 +107,7 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -134,7 +136,7 @@
         "from azureml.core.compute import ComputeTarget\n",
         "\n",
         "# Choose a name for your cluster.\n",
-        "amlcompute_cluster_name = \"automlc2\"\n",
+        "amlcompute_cluster_name = \"cpu-cluster\"\n",
         "\n",
         "found = False\n",
         "# Check if this compute target already exists in the workspace.\n",
@@ -153,12 +155,11 @@
         "    # Create the cluster.\\n\",\n",
         "    compute_target = ComputeTarget.create(ws, amlcompute_cluster_name, provisioning_config)\n",
         "\n",
-        "print('Checking cluster status...')\n",
+        "    # Can poll for a minimum number of nodes and for a specific timeout.\n",
-        "# Can poll for a minimum number of nodes and for a specific timeout.\n",
+        "    # If no min_node_count is provided, it will use the scale settings for the cluster.\n",
-        "# If no min_node_count is provided, it will use the scale settings for the cluster.\n",
+        "    compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
-        "compute_target.wait_for_completion(show_output = True, min_node_count = None, timeout_in_minutes = 20)\n",
         "\n",
-        "# For a more detailed view of current AmlCompute status, use get_status()."
+        "     # For a more detailed view of current AmlCompute status, use get_status()."
       ]
     },
     {
@@ -181,12 +182,22 @@
         "\n",
         "if not os.path.isdir('data'):\n",
         "    os.mkdir('data')\n",
-        "\n",
+        "    \n",
-        "pd.DataFrame(data_train.data[100:,:]).to_csv(\"data/X_train.csv\", index=False)\n",
+        "if not os.path.exists(project_folder):\n",
-        "pd.DataFrame(data_train.target[100:]).to_csv(\"data/y_train.csv\", index=False)\n",
+        "    os.makedirs(project_folder)\n",
+        "    \n",
+        "pd.DataFrame(data_train.data).to_csv(\"data/X_train.tsv\", index=False, header=False, quoting=csv.QUOTE_ALL, sep=\"\\t\")\n",
+        "pd.DataFrame(data_train.target).to_csv(\"data/y_train.tsv\", index=False, header=False, sep=\"\\t\")\n",
         "\n",
         "ds = ws.get_default_datastore()\n",
-        "ds.upload(src_dir='./data', target_path='digitsdata', overwrite=True, show_progress=True)"
+        "ds.upload(src_dir='./data', target_path='bai_data', overwrite=True, show_progress=True)\n",
+        "\n",
+        "from azureml.core.runconfig import DataReferenceConfiguration\n",
+        "dr = DataReferenceConfiguration(datastore_name=ds.name, \n",
+        "                   path_on_datastore='bai_data', \n",
+        "                   path_on_compute='/tmp/azureml_runs',\n",
+        "                   mode='download', # download files from datastore to compute target\n",
+        "                   overwrite=False)"
       ]
     },
     {
@@ -197,7 +208,6 @@
       "source": [
         "from azureml.core.runconfig import RunConfiguration\n",
         "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "import pkg_resources\n",
         "\n",
         "# create a new RunConfig object\n",
         "conda_run_config = RunConfiguration(framework=\"python\")\n",
@@ -205,28 +215,30 @@
         "# Set compute target to AmlCompute\n",
         "conda_run_config.target = compute_target\n",
         "conda_run_config.environment.docker.enabled = True\n",
+        "conda_run_config.environment.docker.base_image = azureml.core.runconfig.DEFAULT_CPU_IMAGE\n",
         "\n",
-        "cd = CondaDependencies.create(conda_packages=['numpy','py-xgboost<=0.80'])\n",
+        "# set the data reference of the run coonfiguration\n",
+        "conda_run_config.data_references = {ds.name: dr}\n",
+        "\n",
+        "cd = CondaDependencies.create(pip_packages=['azureml-sdk[automl]'], conda_packages=['numpy','py-xgboost<=0.80'])\n",
         "conda_run_config.environment.python.conda_dependencies = cd"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "###  Creating TabularDataset\n",
-        "\n",
-        "Defined X and y as `TabularDataset`s, which are passed to Automated ML in the AutoMLConfig. `from_delimited_files` by default sets the `infer_column_types` to true, which will infer the columns type automatically. If you do wish to manually set the column types, you can set the `set_column_types` argument to manually set the type of each columns."
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {},
       "outputs": [],
       "source": [
-        "X = Dataset.Tabular.from_delimited_files(path=ds.path('digitsdata/X_train.csv'))\n",
+        "%%writefile $project_folder/get_data.py\n",
-        "y = Dataset.Tabular.from_delimited_files(path=ds.path('digitsdata/y_train.csv'))"
+        "\n",
+        "import pandas as pd\n",
+        "\n",
+        "def get_data():\n",
+        "    X_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/X_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
+        "    y_train = pd.read_csv(\"/tmp/azureml_runs/bai_data/y_train.tsv\", delimiter=\"\\t\", header=None, quotechar='\"')\n",
+        "\n",
+        "    return { \"X\" : X_train.values, \"y\" : y_train[0].values }\n"
       ]
     },
     {
@@ -235,7 +247,7 @@
       "source": [
         "## Train\n",
         "\n",
-        "You can specify `automl_settings` as `**kwargs` as well.\n",
+        "You can specify `automl_settings` as `**kwargs` as well. Also note that you can use a `get_data()` function for local excutions too.\n",
         "\n",
         "**Note:** When using AmlCompute, you can't pass Numpy arrays directly to the fit method.\n",
         "\n",
@@ -245,7 +257,7 @@
         "|**iteration_timeout_minutes**|Time limit in minutes for each iteration.|\n",
         "|**iterations**|Number of iterations. In each iteration AutoML trains a specific pipeline with the data.|\n",
         "|**n_cross_validations**|Number of cross validation splits.|\n",
-        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of nodes in the AmlCompute cluster.|"
+        "|**max_concurrent_iterations**|Maximum number of iterations that would be executed in parallel. This should be less than the number of cores on the DSVM.|"
       ]
     },
     {
@@ -256,7 +268,7 @@
       "source": [
         "automl_settings = {\n",
         "    \"iteration_timeout_minutes\": 10,\n",
-        "    \"iterations\": 10,\n",
+        "    \"iterations\": 20,\n",
         "    \"n_cross_validations\": 5,\n",
         "    \"primary_metric\": 'AUC_weighted',\n",
         "    \"preprocess\": False,\n",
@@ -266,9 +278,9 @@
         "\n",
         "automl_config = AutoMLConfig(task = 'classification',\n",
         "                             debug_log = 'automl_errors.log',\n",
+        "                             path = project_folder,\n",
         "                             run_configuration=conda_run_config,\n",
-        "                             X = X,\n",
+        "                             data_script = project_folder + \"/get_data.py\",\n",
-        "                             y = y,\n",
         "                             **automl_settings\n",
         "                            )\n"
       ]

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

@@ -2,9 +2,6 @@ name: auto-ml-remote-amlcompute
 dependencies:
 - pip:
   - azureml-sdk
-  - interpret
-  - azureml-defaults
-  - azureml-explain-model
   - azureml-train-automl
   - azureml-widgets
   - matplotlib

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

@@ -82,6 +82,8 @@
         "experiment_name = 'non_sample_weight_experiment'\n",
         "sample_weight_experiment_name = 'sample_weight_experiment'\n",
         "\n",
+        "project_folder = './sample_projects/sample_weight'\n",
+        "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "sample_weight_experiment=Experiment(ws, sample_weight_experiment_name)\n",
         "\n",
@@ -91,6 +93,7 @@
         "output['Workspace Name'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -128,7 +131,8 @@
         "                                 n_cross_validations = 2,\n",
         "                                 verbosity = logging.INFO,\n",
         "                                 X = X_train, \n",
-        "                                 y = y_train)\n",
+        "                                 y = y_train,\n",
+        "                                 path = project_folder)\n",
         "\n",
         "automl_sample_weight = AutoMLConfig(task = 'classification',\n",
         "                                    debug_log = 'automl_errors.log',\n",
@@ -139,7 +143,8 @@
         "                                    verbosity = logging.INFO,\n",
         "                                    X = X_train, \n",
         "                                    y = y_train,\n",
-        "                                    sample_weight = sample_weight)"
+        "                                    sample_weight = sample_weight,\n",
+        "                                    path = project_folder)"
       ]
     },
     {

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

@@ -87,6 +87,8 @@
         "\n",
         "# choose a name for the experiment\n",
         "experiment_name = 'sparse-data-train-test-split'\n",
+        "# project folder\n",
+        "project_folder = './sample_projects/sparse-data-train-test-split'\n",
         "\n",
         "experiment = Experiment(ws, experiment_name)\n",
         "\n",
@@ -96,6 +98,7 @@
         "output['Workspace'] = ws.name\n",
         "output['Resource Group'] = ws.resource_group\n",
         "output['Location'] = ws.location\n",
+        "output['Project Directory'] = project_folder\n",
         "output['Experiment Name'] = experiment.name\n",
         "pd.set_option('display.max_colwidth', -1)\n",
         "outputDf = pd.DataFrame(data = output, index = [''])\n",
@@ -162,7 +165,8 @@
         "|**X**|(sparse) array-like, shape = [n_samples, n_features]|\n",
         "|**y**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
         "|**X_valid**|(sparse) array-like, shape = [n_samples, n_features] for the custom validation set.|\n",
-        "|**y_valid**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|"
+        "|**y_valid**|(sparse) array-like, shape = [n_samples, ], Multi-class targets.|\n",
+        "|**path**|Relative path to the project folder. AutoML stores configuration files for the experiment under this folder. You can specify a new empty folder.|"
       ]
     },
     {
@@ -181,7 +185,8 @@
         "                             X = X_train, \n",
         "                             y = y_train,\n",
         "                             X_valid = X_valid, \n",
-        "                             y_valid = y_valid)"
+        "                             y_valid = y_valid, \n",
+        "                             path = project_folder)"
       ]
     },
     {

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

@@ -1,113 +0,0 @@
-# Table of Contents
-1. [Introduction](#introduction)
-1. [Setup using Azure Data Studio](#azuredatastudiosetup)
-1. [Energy demand example using Azure Data Studio](#azuredatastudioenergydemand)
-1. [Set using SQL Server Management Studio for SQL Server 2017 on Windows](#ssms2017)
-1. [Set using SQL Server Management Studio for SQL Server 2019 on Linux](#ssms2019)
-1. [Energy demand example using SQL Server Management Studio](#ssmsenergydemand)
-
-
-<a name="introduction"></a>
-# Introduction
-SQL Server 2017 or 2019 can call Azure ML automated machine learning to create models trained on data from SQL Server.
-This uses the sp_execute_external_script stored procedure, which can call Python scripts.
-SQL Server 2017 and SQL Server 2019 can both run on Windows or Linux.
-However, this integration is not available for SQL Server 2017 on Linux. 
-
-This folder shows how to setup the integration and has a sample that uses the integration to train and predict based on an energy demand dataset.
-
-This integration is part of SQL Server and so can be used from any SQL client. 
-These instructions show using it from Azure Data Studio or SQL Server Managment Studio.
-
-<a name="azuredatastudiosetup"></a>
-## Setup using Azure Data Studio
-
-These step show setting up the integration using Azure Data Studio.
-
-1. If you don't already have SQL Server, you can install it from [https://www.microsoft.com/en-us/sql-server/sql-server-downloads](https://www.microsoft.com/en-us/sql-server/sql-server-downloads)
-1. Install Azure Data Studio from [https://docs.microsoft.com/en-us/sql/azure-data-studio/download?view=sql-server-2017](https://docs.microsoft.com/en-us/sql/azure-data-studio/download?view=sql-server-2017)
-1. Start Azure Data Studio and connect to SQL Server. [https://docs.microsoft.com/en-us/sql/azure-data-studio/sql-notebooks?view=sql-server-2017](https://docs.microsoft.com/en-us/sql/azure-data-studio/sql-notebooks?view=sql-server-2017)
-1. Create a database named "automl".
-1. Open the notebook how-to-use-azureml\automated-machine-learning\sql-server\setup\auto-ml-sql-setup.ipynb and follow the instructions in it.
-
- <a name="azuredatastudioenergydemand"></a>
-## Energy demand example using Azure Data Studio
-
-Once you have completed the setup, you can try the energy demand sample in the notebook energy-demand\auto-ml-sql-energy-demand.ipynb.
-This has cells to train a model, predict based on the model and show metrics for each pipeline run in training the model.
-
-<a name="ssms2017"></a>
-## Setup using SQL Server Management Studio for SQL Server 2017 on Windows
-
-These instruction setup the integration for SQL Server 2017 on Windows.
-
-1. If you don't already have SQL Server, you can install it from [https://www.microsoft.com/en-us/sql-server/sql-server-downloads](https://www.microsoft.com/en-us/sql-server/sql-server-downloads)
-2. Enable external scripts with the following commands: 
-```sh
-   sp_configure 'external scripts enabled',1 
-   reconfigure with override
-```
-3. Stop SQL Server. 
-4. Install the automated machine learning libraries using the following commands from Administrator command prompt (If you are using a non-default SQL Server instance name, replace MSSQLSERVER in the second command with the instance name)
-```sh
-   cd "C:\Program Files\Microsoft SQL Server"
-   cd "MSSQL14.MSSQLSERVER\PYTHON_SERVICES"
-   python.exe -m pip install azureml-sdk[automl]
-   python.exe -m pip install --upgrade numpy
-   python.exe -m pip install --upgrade sklearn
-```
-5. Start SQL Server and the service "SQL Server Launchpad service". 
-6. In Windows Firewall, click on advanced settings and in Outbound Rules, disable "Block network access for R local user accounts in SQL Server instance xxxx". 
-7. Execute the files in the setup folder in SQL Server Management Studio: aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql and AutoMLTrain.sql 
-8. Create an Azure Machine Learning Workspace.  You can use the instructions at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace ](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace)
-9. Create a config.json file file using the subscription id, resource group name and workspace name that you used to create the workspace.  The file is described at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace)
-10. Create an Azure service principal.  You can do this with the commands: 
-```sh
-   az login 
-   az account set --subscription subscriptionid 
-   az ad sp create-for-rbac --name principlename --password password 
-```
-11. Insert the values \<tenant\>, \<AppId\> and \<password\> returned by create-for-rbac above into the aml_connection table.  Set \<path\> as the absolute path to your config.json file. Set the name to <20>Default<6C>. 
- 
-<a name="ssms2019"></a>
-## Setup using SQL Server Management Studio for SQL Server 2019 on Linux
-1. Install SQL Server 2019 from: [https://www.microsoft.com/en-us/sql-server/sql-server-downloads](https://www.microsoft.com/en-us/sql-server/sql-server-downloads)
-2. Install machine learning support from: [https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-machine-learning?view=sqlallproducts-allversions#ubuntu](https://docs.microsoft.com/en-us/sql/linux/sql-server-linux-setup-machine-learning?view=sqlallproducts-allversions#ubuntu)
-3. Then install SQL Server management Studio from [https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017](https://docs.microsoft.com/en-us/sql/ssms/download-sql-server-management-studio-ssms?view=sql-server-2017)
-4. Enable external scripts with the following commands: 
-```sh
-   sp_configure 'external scripts enabled',1 
-   reconfigure with override 
-```
-5. Stop SQL Server. 
-6. Install the automated machine learning libraries using the following commands from Administrator command (If you are using a non-default SQL Server instance name, replace MSSQLSERVER in the second command with the instance name): 
-```sh
-   sudo /opt/mssql/mlservices/bin/python/python -m pip install azureml-sdk[automl] 
-   sudo /opt/mssql/mlservices/bin/python/python -m pip install --upgrade numpy 
-   sudo /opt/mssql/mlservices/bin/python/python -m pip install --upgrade sklearn
-```
-7. Start SQL Server. 
-8. Execute the files aml_model.sql, aml_connection.sql, AutoMLGetMetrics.sql, AutoMLPredict.sql, AutoMLForecast.sql and AutoMLTrain.sql in SQL Server Management Studio. 
-9. Create an Azure Machine Learning Workspace.  You can use the instructions at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-workspace)
-10. Create a config.json file file using the subscription id, resource group name and workspace name that you use to create the workspace.  The file is described at: [https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-configure-environment#workspace)
-11. Create an Azure service principal.  You can do this with the commands: 
-```sh
-   az login 
-   az account set --subscription subscriptionid 
-   az ad sp create-for-rbac --name principlename --password password 
-``` 
-12. Insert the values \<tenant\>, \<AppId\> and \<password\> returned by create-for-rbac above into the aml_connection table.  Set \<path\> as the absolute path to your config.json file. Set the name to <20>Default<6C>. 
- 
-<a name="ssmsenergydemand"></a>
-## Energy demand example using SQL Server Management Studio
-
-Once you have completed the setup, you can try the energy demand sample queries.
-First you need to load the sample data in the database.
-1. In SQL Server Management Studio, you can right-click the database, select Tasks, then Import Flat file. 
-1. Select the file MachineLearningNotebooks\notebooks\how-to-use-azureml\automated-machine-learning\forecasting-energy-demand\nyc_energy.csv. 
-1. When you get to the column definition page, allow nulls for all columns. 
-
-You can then run the queries in the energy-demand folder:
-* TrainEnergyDemand.sql runs AutoML, trains multiple models on data and selects the best model.
-* ForecastEnergyDemand.sql forecasts based on the most recent training run.
-* GetMetrics.sql returns all the metrics for each model in the most recent training run.

how-to-use-azureml/automated-machine-learning/sql-server/energy-demand/ForecastEnergyDemand.sql

@@ -1,23 +0,0 @@
--- This shows using the AutoMLForecast stored procedure to predict using a forecasting model for the nyc_energy dataset.
-
-DECLARE @Model NVARCHAR(MAX) = (SELECT TOP 1 Model FROM dbo.aml_model
-                                WHERE ExperimentName = 'automl-sql-forecast'
-								ORDER BY CreatedDate DESC)
-
-DECLARE @max_horizon INT = 48
-DECLARE @split_time NVARCHAR(22) = (SELECT DATEADD(hour, -@max_horizon, MAX(timeStamp)) FROM nyc_energy WHERE demand IS NOT NULL)
-
-DECLARE @TestDataQuery NVARCHAR(MAX) = '
-SELECT CAST(timeStamp AS NVARCHAR(30)) AS timeStamp,
-       demand,
-	   precip,
-	   temp
-FROM nyc_energy
-WHERE demand IS NOT NULL AND precip IS NOT NULL AND temp IS NOT NULL
-AND timeStamp > ''' + @split_time + ''''
-
-EXEC dbo.AutoMLForecast @input_query=@TestDataQuery,
-@label_column='demand',
-@time_column_name='timeStamp',
-@model=@model
-WITH RESULT SETS ((timeStamp DATETIME, grain NVARCHAR(255), predicted_demand FLOAT, precip FLOAT, temp FLOAT, actual_demand FLOAT))