Scalable Training Jobs for Deep Learning Models

Question

Pulumi · Accepted Answer

In order to create scalable training jobs for deep learning models in the cloud, you will typically need to use the services provided by cloud providers that cater to such tasks. These services generally offer a set of machine learning tools and compute resources that can dynamically scale according to the training job's requirements, which is essential for handling the often intensive workloads of deep learning model training.

In this program, we will be using the Google Cloud Platform's (GCP) AI Platform Training Jobs. It is a managed service that allows you to run your training jobs on various machine learning (ML) frameworks like TensorFlow, Keras, and PyTorch with the ability to scale resources as needed.

Using Pulumi, you can define and manage these resources as infrastructure code written in Python. I'll guide you through a program that creates a training job for a deep learning model on GCP using the `google-native.ml/v1.Job` resource from Pulumi's Google Native provider. This resource allows you to define a job with various settings, including the training code, input data, compute resources, and hyperparameters for tuning.

Here's what the program will do:
1. Define a training job with necessary parameters including the training script package, hyperparameter tuning specs, and compute resource specifications.
2. Set the appropriate access and permissions for the job.
3. Deploy this infrastructure by creating a stack with Pulumi and running `pulumi up`.

Below is a Pulumi Python program that creates a scalable training job on GCP:

```python
import pulumi
import pulumi_google_native as google_native

# Configuration for the Google Cloud Platform project
project = 'my-gcp-project'
location = 'us-central1'

# Define a Google AI Platform Training Job
job = google_native.ml.v1.Job(
    # Provide a unique name for the Job
    "deepLearningTrainingJob",
    args=google_native.ml.v1.JobArgs(
        project=project,
        location=location,
        # Job configuration
        training_input=google_native.ml.v1.JobArgsTrainingInputArgs(
            # Specify the Docker image if using a custom image or specify a runtime version
            runtime_version='2.3',  # Example runtime version
            python_version='3.7',    # The Python version to be used
            scale_tier='BASIC_GPU',  # The tier of machine to use
            # The Python module name to run after installing the packages
            python_module='trainer.task',
            # Specify Google Cloud Storage paths for the training package
            package_uris=[
                'gs://my-bucket/trainer-0.1.tar.gz',
            ],
            # Command line arguments to pass to the module
            args=[
                '--train-files=gs://my-bucket/data/train-data.csv',
                '--eval-files=gs://my-bucket/data/eval-data.csv',
                '--job-dir=gs://my-bucket/model/',
            ],
            # Location of the job staging directory on Google Cloud Storage
            job_dir='gs://my-bucket/model/',
            # Hyperparameter tuning configuration (if needed)
            hyperparameters=google_native.ml.v1.JobArgsTrainingInputArgsHyperparametersArgs(
                goal='MAXIMIZE',
                params=[
                    google_native.ml.v1.JobArgsTrainingInputArgsHyperparametersArgsParamsArgs(
                        parameter_name='learning_rate',
                        type='DOUBLE',
                        min_value=0.001,
                        max_value=0.1,
                        scale_type='UNIT_LINEAR_SCALE',
                    ),
                ],
                max_trials=10,
                max_parallel_trials=1,
            ),
            # Specifications for the worker nodes (if needed)
            worker_type='cloud_tpu',  # For example, to use TPUs
            worker_count='3',
            # ... other configuration ...
        ),
        # Training output settings
        training_output=google_native.ml.v1.JobArgsTrainingOutputArgs(
            # Output settings such as the path for storing model checkpoints
            # ... outputs configuration ...
        ),
    )
)

# Export the training job name
pulumi.export('job_name', job.name)
```

This program defines a training job that points to a training script package stored in a Google Cloud Storage bucket. It includes the setup for hyperparameter tuning, a common practice in training deep learning models where you search over a range of hyperparameters to find the best performing model.

After saving this program in a Python file, you should initialize a Pulumi project and then run `pulumi up` to create and configure the training job resources on Google Cloud Platform. Once the `pulumi up` command completes, it will output the training job name. Remember, for this Pulumi program to work, you must be authenticated with GCP with the appropriate permissions to create and manage these resources.

If you are new to Pulumi, you can start by installing the Pulumi CLI, setting up a Pulumi account, and configuring GCP access credentials. Then, you can create a new Pulumi project with `pulumi new gcp-python`, replace the generated `__main__.py` content with the program above, and finally, run `pulumi up` as instructed.

Make sure to replace placeholders like 'my-gcp-project', 'my-bucket', and the actual paths to your training data and scripts with your specific information. The values for `runtime_version`, `python_module`, `scale_tier`, `python_version`, `hyperparameters`, and other settings should be adjusted to match the specifics of your deep learning training job.