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Hyperparameter Tuning with HyperDrive

file: hyperparameter_tuning.py

Let's see whether we can improve the performance of our model by tuning the hyperparameters.

This requires the following steps:

  1. Configure AML workspace
  2. Upload the data to the default datastore of your workspace
  3. Define a remote AMLCompute compute target
  4. Prepare scripts for uploading to compute target
  5. Define Tensorflow estimator
  6. Configure HyperDrive run
  7. Submit job for execution.

Configure AML workspace

First step is to attach to an AML workspace.

If you don't have one yet, you can create one using this documentation.

We recommend storing the information in a config.json file in the root directory of this repository.

Upload the data to the default datastore of your workspace

We upload the training data so that it can be mounted as remote drives in the aml compute targets.

Define a remote AMLCompute compute target

We are using a Standard_NC6 virtual machine. It is inexpensive and includes a GPU powerful enough for this purpose.

Prepare scripts for uploading to compute target

The training script and dependencies have to be available to the job running on the compute target.

Define Tensorflow estimator

HyperDrive works best if we use an Estimator specifically defined for tensorflow models.

Configure HyperDrive run

Next, we define which hyperparameters to search over, and which strategy to use for searching. Here, we are using RandomParameterSampling and a BanditPolicy.

Submit job for execution.

Now everything is good to go.