1. Persisting ML Model Checkpoints in Databricks DBFS

    Python

    Persisting machine learning model checkpoints is a critical aspect of the model development lifecycle. It allows you to save intermediate versions of your model, which you can later use to recover from failures or analyze the progress of your training.

    In this Pulumi program, we'll use the Pulumi Databricks provider to persist ML model checkpoints in the Databricks File System (DBFS). The databricks.DbfsFile resource will be used to create a file in DBFS which can be the checkpoint or the serialized form of your ML model.

    We will assume that you have the model data to be persisted as a base64 encoded string. This technique is useful when dealing with binary data, as DBFS files expect their content to be in base64 when it's not in plaintext.

    Below is the Pulumi code written in Python which will:

    • Import the required Pulumi Databricks package.
    • Create a DbfsFile resource that places an ML model checkpoint file into DBFS.

    After presenting the code, I will explain each section in more detail.

    import pulumi import pulumi_databricks as databricks # Assume we have the model checkpoint data as a base64 encoded string. # In practice, you would likely generate this from a file on disk or directly from a machine learning library. model_checkpoint_base64 = 'base64-encoded-string-representing-your-model' # Create a DBFS file in Databricks to hold your ML model checkpoint. # Replace `your-file-path` with the desired location in DBFS where you want to store the model checkpoint. ml_model_checkpoint_file = databricks.DbfsFile("ml-model-checkpoint-file", content_base64=model_checkpoint_base64, path="/dbfs/your-file-path/checkpoint.bin", ) # Export the URL of the saved ML model checkpoint in DBFS. pulumi.export("model_checkpoint_dbfs_path", ml_model_checkpoint_file.path)

    Now let's break down what each part of this program does:

    1. Imports: The script starts by importing the needed Pulumi packages.

    2. Model Checkpoint Data: It's preparing the string model_checkpoint_base64, which represents your ML model checkpoint data encoded in base64. This is a placeholder for your actual model data.

    3. DBFS File Resource: It then defines a DbfsFile resource named ml-model-checkpoint-file. This resource tells Pulumi to create a file within the Databricks File System (DBFS) at the path you specify. The content_base64 attribute takes the base64 encoded model data to be saved in that file. You need to replace 'your-file-path' with the path where you wish the checkpoint to be stored.

    4. Export: Finally, the program exports the DBFS path of the ML model checkpoint. This output can be used to reference the checkpoint file in other parts of your Databricks workspace, or in other Pulumi stacks if needed.

    Remember to modify the model_checkpoint_base64 with the actual base64 encoded string of your model before running this Pulumi program. The file path in the path attribute should also be updated to reflect your desired location in DBFS for the checkpoint file.

    When you run this Pulumi program with the correct setup, it will provision the necessary resources in your Databricks workspace, resulting in the model checkpoint being saved in DBFS. This allows for both versioning and persistence of machine learning models, which is essential for robust ML workflows.


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