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Using Azure Machine Learning Pipelines for Batch Inference

In this notebook, we will demonstrate how to make predictions on large quantities of data asynchronously using the ML pipelines with Azure Machine Learning. Batch inference (or batch scoring) provides cost-effective inference, with unparalleled throughput for asynchronous applications. Batch prediction pipelines can scale to perform inference on terabytes of production data. Batch prediction is optimized for high throughput, fire-and-forget predictions for a large collection of data.

Tip If your system requires low-latency processing (to process a single document or small set of documents quickly), use real-time scoring instead of batch prediction.

In this example will be take a digit identification model already-trained on MNIST dataset using the AzureML training with deep learning example notebook, and run that trained model on some of the MNIST test images in batch.

The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from this repository.

The outline of this notebook is as follows:

• Create a DataStore referencing MNIST images stored in a blob container.
• Register the pretrained MNIST model into the model registry.
• Use the registered model to do batch inference on the images in the data blob container.

Prerequisites

If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you go through the configuration Notebook located at https://github.com/Azure/MachineLearningNotebooks first. This sets you up with a working config file that has information on your workspace, subscription id, etc.

Connect to workspace

Create a workspace object from the existing workspace. Workspace.from_config() reads the file config.json and loads the details into an object named ws.

Create or Attach existing compute resource

By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.

Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.

Creation of compute takes approximately 5 minutes. If the AmlCompute with that name is already in your workspace the code will skip the creation process.

Create a datastore containing sample images

The input dataset used for this notebook differs from a standard MNIST dataset in that it has been converted to PNG images to demonstrate use of files as inputs to Batch Inference. A sample of PNG-converted images of the MNIST dataset were take from this repository.

We have created a public blob container sampledata on an account named pipelinedata, containing these images from the MNIST dataset. In the next step, we create a datastore with the name images_datastore, which points to this blob container. In the call to register_azure_blob_container below, setting the overwrite flag to True overwrites any datastore that was created previously with that name.

This step can be changed to point to your blob container by providing your own datastore_name, container_name, and account_name.

Next, let's specify the default datastore for the outputs.

Create a FileDataset

A FileDataset references single or multiple files in your datastores or public urls. The files can be of any format. FileDataset provides you with the ability to download or mount the files to your compute. By creating a dataset, you create a reference to the data source location. If you applied any subsetting transformations to the dataset, they will be stored in the dataset as well. The data remains in its existing location, so no extra storage cost is incurred. You can use dataset objects as inputs. Register the datasets to the workspace if you want to reuse them later.

The input dataset can be specified as a pipeline parameter, so that you can pass in new data when rerun the PRS pipeline.

Intermediate/Output Data

Intermediate data (or output of a Step) is represented by PipelineData object. PipelineData can be produced by one step and consumed in another step by providing the PipelineData object as an output of one step and the input of one or more steps.

Download the Model

Download and extract the model from https://pipelinedata.blob.core.windows.net/mnist-model/mnist-tf.tar.gz to "models" directory

Register the model with Workspace

A registered model is a logical container for one or more files that make up your model. For example, if you have a model that's stored in multiple files, you can register them as a single model in the workspace. After you register the files, you can then download or deploy the registered model and receive all the files that you registered.

Using tags, you can track useful information such as the name and version of the machine learning library used to train the model. Note that tags must be alphanumeric. Learn more about registering models here

Using your model to make batch predictions

To use the model to make batch predictions, you need an entry script and a list of dependencies:

An entry script

This script accepts requests, scores the requests by using the model, and returns the results.

• init() - Typically this function loads the model into a global object. This function is run only once at the start of batch processing per worker node/process. Init method can make use of following environment variables (ParallelRunStep input):
  1. AZUREML_BI_OUTPUT_PATH - output folder path
• run(mini_batch) - The method to be parallelized. Each invocation will have one minibatch.
  mini_batch: Batch inference will invoke run method and pass either a list or Pandas DataFrame as an argument to the method. Each entry in min_batch will be - a filepath if input is a FileDataset, a Pandas DataFrame if input is a TabularDataset.
  run method response: run() method should return a Pandas DataFrame or an array. For append_row output_action, these returned elements are appended into the common output file. For summary_only, the contents of the elements are ignored. For all output actions, each returned output element indicates one successful inference of input element in the input mini-batch. User should make sure that enough data is included in inference result to map input to inference. Inference output will be written in output file and not guaranteed to be in order, user should use some key in the output to map it to input.

Dependencies

Helper scripts or Python/Conda packages required to run the entry script.

Build and run the batch inference pipeline

The data, models, and compute resource are now available. Let's put all these together in a pipeline.

Specify the environment to run the script

Specify the conda dependencies for your script. This will allow us to install pip packages as well as configure the inference environment.

• Always include azureml-core and azureml-dataset-runtime[fuse] in the pip package list to make ParallelRunStep run properly.

If you're using custom image (batch_env.python.user_managed_dependencies = True), you need to install the package to your image.

Create the configuration to wrap the inference script

Create the pipeline step

Create the pipeline step using the script, environment configuration, and parameters. Specify the compute target you already attached to your workspace as the target of execution of the script. We will use ParallelRunStep to create the pipeline step.

Run the pipeline

At this point you can run the pipeline and examine the output it produced. The Experiment object is used to track the run of the pipeline

Monitor the run

The pipeline run status could be checked in Azure Machine Learning portal (https://ml.azure.com). The link to the pipeline run could be retrieved by inspecting the pipeline_run object.

Optional: View detailed logs (streaming)

View the prediction results per input image

In the digit_identification.py file above you can see that the ResultList with the filename and the prediction result gets returned. These are written to the DataStore specified in the PipelineData object as the output data, which in this case is called inferences. This containers the outputs from all of the worker nodes used in the compute cluster. You can download this data to view the results ... below just filters to the first 10 rows

Resubmit a with different dataset

Since we made the input a PipelineParameter, we can resubmit with a different dataset without having to create an entirely new experiment. We'll use the same datastore but use only a single image.

Cleanup Compute resources

For re-occurring jobs, it may be wise to keep compute the compute resources and allow compute nodes to scale down to 0. However, since this is just a single-run job, we are free to release the allocated compute resources.