Update notebooks
This commit is contained in:
Roope Astala
@@ -98,7 +98,7 @@
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"source": [
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"### Create experiment\n",
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"\n",
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"Create an experiment to track the runs in your workspace. A workspace can have muliple experiments; an experiment must belongn to a workspace."
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"Create an experiment to track the runs in your workspace. A workspace can have muliple experiments. "
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]
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},
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{
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@@ -121,9 +121,7 @@
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"\n",
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"Azure Azure ML Managed Compute is a managed service that enables data scientists to train machine learning models on clusters of Azure virtual machines, including VMs with GPU support. In this tutorial, you create an Azure Managed Compute cluster as your training environment. This code creates a cluster for you if it does not already exist in your workspace. \n",
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"\n",
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" **Creation of the cluster takes approximately 5 minutes.** If the cluster is already in the workspace this code uses it and skips the creation process.\n",
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"\n",
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"**Note**: As with other Azure services, there are limits on certain resources (for eg. BatchAI cluster size) 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."
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" **Creation of the cluster takes approximately 5 minutes.** If the cluster is already in the workspace this code uses it and skips the creation process."
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]
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},
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{
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@@ -146,7 +144,7 @@
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"try:\n",
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" # look for the existing cluster by name\n",
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" compute_target = ComputeTarget(workspace=ws, name=batchai_cluster_name)\n",
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" if compute_target is BatchAiCompute:\n",
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" if type(compute_target) is BatchAiCompute:\n",
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" print('found compute target {}, just use it.'.format(batchai_cluster_name))\n",
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" else:\n",
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" print('{} exists but it is not a Batch AI cluster. Please choose a different name.'.format(batchai_cluster_name))\n",
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@@ -188,6 +186,13 @@
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"Download the MNIST dataset and save the files into a `data` directory locally. Images and labels for both training and testing are downloaded."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@@ -330,7 +335,7 @@
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"\n",
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"### Create a directory\n",
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"\n",
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"Create a directory to hold all script files are other assets."
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"Create a directory to deliver the necessary code from your computer to the remote resource."
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]
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},
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{
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@@ -434,7 +439,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Copy the utility library that loads the dataset into the script folder to be accessed by the training script."
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"The file `utils.py` is referenced from the training script to load the dataset correctly. Copy this script into the script folder so that it can be accessed along with the training script on the remote resource."
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]
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},
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{
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@@ -457,11 +462,12 @@
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"\n",
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"* The name of the estimator object, `est`\n",
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"* The directory that contains your scripts. All the files in this directory are uploaded into the cluster nodes for execution. \n",
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"* The compute target. In this case you will use the Managed Compute cluster you created\n",
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"* The compute target. In this case you will use the Batch AI cluster you created\n",
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"* The training script name, train.py\n",
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"* The `data-folder` parameter used by the training script to access the data\n",
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"* Any Python packages needed for training\n",
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"In this tutorial, this target is the Managed Compute cluster. All files in the script folder are uploaded into the cluster nodes for execution."
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"* Parameters required from the training script \n",
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"* Python packages needed for training\n",
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"\n",
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"In this tutorial, this target is the Batch AI cluster. All files in the script folder are uploaded into the cluster nodes for execution. The data_folder is set to use the datastore (`ds.as_mount()`)."
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]
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},
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{
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@@ -507,7 +513,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Since the call is asynchronous, it returns a **Preparing** or **running** state as soon as the job is started.\n",
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"Since the call is asynchronous, it returns a **Preparing** or **Running** state as soon as the job is started.\n",
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"\n",
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"## Monitor a remote run\n",
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"\n",
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@@ -595,7 +601,7 @@
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"\n",
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"## Register model\n",
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"\n",
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"The last step in the training script wrote the file `outputs/sklearn_mnist_model.pkl` in a folder named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special folder in that all content in the `outputs` directory is automatically uploaded as part of the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace. \n",
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"The last step in the training script wrote the file `outputs/sklearn_mnist_model.pkl` in a directory named `outputs` in the VM of the cluster where the job is executed. `outputs` is a special directory in that all content in this directory is automatically uploaded to your workspace. This content appears in the run record in the experiment under your workspace. Hence, the model file is now also available in your workspace.\n",
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"\n",
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"You can see files associated with that run."
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]
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@@ -15,7 +15,7 @@
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"source": [
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"# Tutorial #2: Deploy an image classification model in Azure Container Instance (ACI)\n",
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"\n",
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"This tutorial is **part two of a two-part tutorial series**. In the [previous tutorial](01.train-models.ipynb), you trained machine learning models and then registered the best one in your workspace on the cloud. \n",
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"This tutorial is **part two of a two-part tutorial series**. In the [previous tutorial](01.train-models.ipynb), you trained machine learning models and then registered a model in your workspace on the cloud. \n",
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"\n",
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"Now, you're ready to deploy the model as a web service in [Azure Container Instances](https://docs.microsoft.com/azure/container-instances/) (ACI). A web service is an image, in this case a Docker image, that encapsulates the scoring logic and the model itself. \n",
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"\n",
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@@ -33,8 +33,7 @@
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"## Prerequisites\n",
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"\n",
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"Complete the model training in the [Tutorial #1: Train an image classification model with Azure Machine Learning](01.train-models.ipynb) notebook. \n",
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"\n",
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"If you did NOT complete the tutorial, you can instead run this cell to create a model and download the data needed for this tutorial:"
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"\n"
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]
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},
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{
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@@ -43,6 +42,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# If you did NOT complete the tutorial, you can instead run this cell \n",
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"# This will register a model and download the data needed for this tutorial\n",
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"# These prerequisites are created in the training tutorial\n",
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"# Feel free to skip this cell if you completed the training tutorial \n",
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"\n",
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@@ -251,9 +252,9 @@
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"Create the scoring script, called score.py, used by the web service call to show how to use the model.\n",
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"\n",
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"You must include two required functions into the scoring script:\n",
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"* The `init()` function, which typically loads the model into a global object. This function is executed only once when the Docker container is started. \n",
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"* The `init()` function, which typically loads the model into a global object. This function is run only once when the Docker container is started. \n",
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"\n",
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"* The `run(input_data)` function uses the model to predict a value based on the input data. Inputs and outputs to the run typically use JSON for serialization and de-serialization, but other formats are supported."
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"* The `run(input_data)` function uses the model to predict a value based on the input data. Inputs and outputs to the run typically use JSON for serialization and de-serialization, but other formats are supported.\n"
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]
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},
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{
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@@ -332,7 +333,7 @@
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"source": [
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"### Create configuration file\n",
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"\n",
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"Create a deployment configuration file and specify the number of CPUs and gigabyte of RAM needed for your ACI container. While it depends on your model, the default of 1 core and 1 gigabyte of RAM is usually sufficient for many models. If you feel you need more later, you can always modify the configuration and redeploy the service."
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"Create a deployment configuration file and specify the number of CPUs and gigabyte of RAM needed for your ACI container. While it depends on your model, the default of 1 core and 1 gigabyte of RAM is usually sufficient for many models. If you feel you need more later, you would have to recreate the image and redeploy the service."
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]
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},
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{
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@@ -13,13 +13,13 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Tutorial: Automatically train a classification model with Azure Automated Machine Learning\n",
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"# Tutorial: Train a classification model with automated machine learning\n",
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"\n",
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"In this tutorial, you'll learn how to automatically generate a machine learning model. This model can then be deployed following the workflow in the [Deploy a model](02.deploy-models.ipynb) tutorial.\n",
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"In this tutorial, you'll learn how to generate a machine learning model using automated machine learning (automated ML). Azure Machine Learning can perform data preprocessing, algorithm selection and hyperparameter selection in an automated way for you. The final model can then be deployed following the workflow in the [Deploy a model](02.deploy-models.ipynb) tutorial.\n",
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"\n",
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"[flow diagram](./imgs/flow2.png)\n",
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"\n",
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"Similar to the [train models tutorial](01.train-models.ipynb), this tutorial classifies handwritten images of digits (0-9) from the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset.\n",
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"Similar to the [train models tutorial](01.train-models.ipynb), this tutorial classifies handwritten images of digits (0-9) from the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. But this time you don't to specify an algorithm or tune hyperparameters. The automated ML technique iterates over many combinations of algorithms and hyperparameters until it finds the best model based on your criterion.\n",
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"\n",
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"You'll learn how to:\n",
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"\n",
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@@ -190,11 +190,10 @@
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"|**primary_metric**|AUC Weighted | Metric that you want to optimize.|\n",
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"|**max_time_sec**|12,000|Time limit in seconds for each iteration|\n",
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"|**iterations**|20|Number of iterations. In each iteration, the model trains with the data with a specific pipeline|\n",
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"|**n_cross_validations**|5|Number of cross validation splits|\n",
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"|**n_cross_validations**|3|Number of cross validation splits|\n",
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"|**preprocess**|True| *True/False* Enables experiment to perform preprocessing on the input. Preprocessing handles *missing data*, and performs some common *feature extraction*|\n",
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"|**exit_score**|0.994|*double* value indicating the target for *primary_metric*. Once the target is surpassed the run terminates|\n",
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"|**blacklist_algos**|['kNN','LinearSVM']|*Array* of *strings* indicating algorithms to ignore.\n",
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"|**concurrent_iterations**|5|Max number of iterations that would be executed in parallel. This number should be less than the number of cores on the DSVM. Used in remote training.|"
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"|**exit_score**|0.995|*double* value indicating the target for *primary_metric*. Once the target is surpassed the run terminates|\n",
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"|**blacklist_algos**|['kNN','LinearSVM']|*Array* of *strings* indicating algorithms to ignore.\n"
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]
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},
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{
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@@ -211,6 +210,8 @@
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" max_time_sec = 12000,\n",
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" iterations = 20,\n",
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" n_cross_validations = 3,\n",
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" preprocess = True,\n",
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" exit_score = 0.995,\n",
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" blacklist_algos = ['kNN','LinearSVM'],\n",
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" X = X_digits,\n",
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" y = y_digits,\n",
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@@ -279,7 +280,7 @@
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"metricslist = {}\n",
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"for run in children:\n",
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" properties = run.get_properties()\n",
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" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)} \n",
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" metrics = {k: v for k, v in run.get_metrics().items() if isinstance(v, float)}\n",
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" metricslist[int(properties['iteration'])] = metrics\n",
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"\n",
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"import pandas as pd\n",
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@@ -287,6 +288,15 @@
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"rundata"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Register the best model \n",
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"\n",
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"Use the `local_run` object to get the best model and register it into the workspace. "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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