1. Distributed Deep Learning Training with Databricks Clusters


    Distributed deep learning training involves utilizing a group of interconnected systems to execute machine learning models on large data sets more quickly than a single machine could. Databricks is a managed platform that simplifies big data processing and machine learning. It allows you to quickly launch clusters and integrate them with other data services to train machine learning models, especially deep learning models that can benefit from distributed training.

    To set up distributed deep learning training with Databricks clusters using Pulumi, you'll need to:

    1. Provision a Databricks Workspace: This workspace serves as an environment to manage your Databricks resources. It can be done using Pulumi with the appropriate cloud provider resources or by selecting an existing Databricks workspace if you already have one.

    2. Create a Databricks Cluster: This involves defining and provisioning a cluster where your deep learning model will be trained. You'll determine the size, type of machines, and autoscaling properties of the cluster.

    3. Install Necessary Libraries: You'll need to install the libraries and dependencies required for deep learning. This could include TensorFlow, PyTorch, Keras, or other machine learning frameworks and their dependencies.

    4. Launch the Training Job: Once the cluster is up and running, and the necessary libraries are installed, you can launch a training job. This could be a Databricks notebook, a Python script, or a JAR that includes your deep learning model training code.

    Below is a Pulumi Python program that sets up a Databricks cluster configured for distributing deep learning training. Assume that the Databricks workspace and other necessary permissions are already set up:

    import pulumi import pulumi_databricks as databricks # Create a Databricks cluster configured for deep learning training. cluster = databricks.Cluster("deep-learning-cluster", # General cluster configuration cluster_name="deep-learning-cluster", spark_version="7.3.x-gpu-ml-scala2.12", # Select a Databricks runtime that includes GPU support for deep learning node_type_id="Standard_DS3_v2", # Choose a VM size. This selection may vary based on your training needs autotermination_minutes=120, # Automatically terminate the cluster after 2 hours of inactivity autoscale=databricks.ClusterAutoscaleArgs( min_workers=1, max_workers=4, # Autoscale the cluster depending on the workload ), # Ensure you have GPU-enabled nodes for deep learning training jobs aws_attributes=databricks.ClusterAwsAttributesArgs( instance_profile_arn="arn:aws:iam::123456789012:instance-profile/DatabricksEC2Role", # You should replace this with your own ARN zone_id="us-west-2a", # Select the appropriate zone ), # Library configuration - you can install Python packages, Maven packages, JARs, etc. libraries=[ databricks.ClusterLibraryArgs( pypi=databricks.LibraryPypiArgs( package="tensorflow" # Replace with the desired version of TensorFlow or other libraries as needed ) ), databricks.ClusterLibraryArgs( pypi=databricks.LibraryPypiArgs( package="torch" # Replace with the desired version of PyTorch or other libraries as needed ) ), # You can add more libraries as needed ], # Deep learning specific configurations spark_conf={ "spark.databricks.delta.preview.enabled": "true", "spark.databricks.mlflow.trackMLlib.enabled": "true", # Enable MLflow for experiment tracking }, ) # Export the cluster URL for direct access from the UI or API. pulumi.export("cluster_url", cluster.cluster_log_conf.apply(lambda conf: conf.dbfs.destination if conf else None))

    In this program:

    • We create a Databricks cluster with GPU support needed for deep learning tasks.
    • We configure autoscaling for our cluster to optimize resource utilization.
    • We specify AWS attributes including the instance profile ARN and the zone where our nodes will launch.
    • We install dependencies like TensorFlow and PyTorch that are essential for deep learning training.
    • We enable MLflow integration for tracking experiments, which is a superior practice during model training.
    • Finally, we export cluster_url so you can access the cluster logs directly for debugging and tracking the progress of your training job.

    Remember to replace placeholders such as the instance profile ARN with your actual information before running the program.

    You can run this Pulumi program by saving it to a file (e.g., deep_learning_cluster.py) and executing it with the Pulumi CLI. Be sure that your Pulumi stack is appropriately configured with the necessary cloud provider credentials and settings.