This tutorial demonstrates how run a TensorFlow job at scale using Azure ML. You will train a TensorFlow model to classify handwritten digits (MNIST) using a deep neural network (DNN) and log your results to the Azure ML service.
If you don’t have access to an Azure ML workspace, follow the setup tutorial to configure and create a workspace.
The setup for your development work in this tutorial includes the following actions:
Instantiate a workspace object from your existing workspace. The following code will load the workspace details from a config.json file if you previously wrote one out with
Or, you can retrieve a workspace by directly specifying your workspace details:
An Azure ML experiment tracks a grouping of runs, typically from the same training script. Create an experiment to track the runs for training the TensorFlow model on the MNIST data.
If you would like to track your runs in an existing experiment, simply specify that experiment’s name to the
name parameter of
By using Azure Machine Learning Compute (AmlCompute), a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. In this tutorial, you create a GPU-enabled cluster as your training environment. The code below creates the compute cluster for you if it doesn’t already exist in your workspace.
You may need to wait a few minutes for your compute cluster to be provisioned if it doesn’t already exist.
cluster_name <- "gpucluster"
compute_target <- get_compute(ws, cluster_name = cluster_name)
vm_size <- "STANDARD_NC6"
compute_target <- create_aml_compute(workspace = ws,
cluster_name = cluster_name,
vm_size = vm_size,
max_nodes = 4)
wait_for_provisioning_completion(compute_target, show_output = TRUE)
A training script called
tf_mnist.R has been provided for you in the
train-with-tensorflow/ subfolder of this vignette. The Azure ML SDK provides a set of logging APIs for logging various metrics during training runs. These metrics are recorded and persisted in the experiment run record, and can be be accessed at any time or viewed in the run details page in Azure Machine Learning studio.
In order to collect and upload run metrics, you need to do the following inside the training script:
log_metric_to_run() function to track our primary metric, “accuracy”, for this experiment. If you have your own training script with several important metrics, simply create a logging call for each one within the script.
feed_dict = dict(x = mnist$test$images, y_ = mnist$test$labels)))
See the reference for the full set of logging methods
log_*() available from the R SDK.
An Azure ML estimator encapsulates the run configuration information needed for executing a training script on the compute target. Azure ML runs are run as containerized jobs on the specified compute target.
To create the estimator, define the following:
source_directory). All the files in this directory are uploaded to the cluster node(s) for execution. The directory must contain your training script and any additional scripts required.
compute_target), in this case the AmlCompute cluster you created earlier.
train-with-tensorflow/ folder for reference. See the
r_environment() reference for the full set of configurable options. Pass the environment object to the environment parameter in estimator.
Finally submit the job to run on your cluster.
submit_experiment() returns a
Run object that you can then use to interface with the run.
You can view the run’s details as a table. Clicking the “Web View” link provided will bring you to Azure Machine Learning studio, where you can monitor the run in the UI.
Model training happens in the background. Wait until the model has finished training before you run more code.
Once your job has finished, you can view the metrics collected during your TensorFlow run.