Load data

This section covers information about loading data specifically for ML and DL applications. For general information about loading data, see Ingest data into the Databricks Lakehouse.

Store files for data loading and model checkpointing

Machine learning applications may need to use shared storage for data loading and model checkpointing. This is particularly important for distributed deep learning. Databricks provides What is the Databricks File System (DBFS)? for accessing data on a cluster using both Spark and local file APIs.

Databricks provides a high performance FUSE mount.

Load tabular data

You can load tabular machine learning data from tables or files (for example, see CSV file). You can convert Apache Spark DataFrames into pandas DataFrames using the PySpark method toPandas(), and then optionally convert to NumPy format using the pandas method to_numpy().

Prepare data for distributed training

This section covers two methods for preparing data for distributed training: Petastorm and TFRecords.