Databricks Runtime 15.2 for Machine Learning (EoS)

Databricks Runtime 15.2 for Machine Learning provides a ready-to-go environment for machine learning and data science based on Databricks Runtime 15.2 (EoS). Databricks Runtime ML contains many popular machine learning libraries, including TensorFlow, PyTorch, and XGBoost. Databricks Runtime ML includes AutoML, a tool to automatically train machine learning pipelines. Databricks Runtime ML also supports distributed deep learning training using Horovod.

Databricks Runtime 15.2 ML is built on top of Databricks Runtime 15.2. For information on what's new in Databricks Runtime 15.2, including Apache Spark MLlib and SparkR, see the Databricks Runtime 15.2 (EoS) release notes.

StreamingDataset is used to make training on large datasets from cloud storage as fast, cheap, and scalable as possible. It is pre-installed in Databricks Runtime 15.2 ML.

Starting from Databricks Runtime 15.2 ML, you can accelerate your Spark SQL and Spark DataFrame workloads by enabling Photon on your CPU cluster.

For machine learning applications, Photon provides faster performance for use cases such as:

• Data preparation using SQL or DataFrame API.
• Feature engineering with point-in-time lookup.
• Graph analysis with GraphFrames.

Photon does not enhance the performance of Python libraries, such as TensorFlow, PyTorch, and XGBoost.

Spark RDD APIs and Spark MLlib have limited compatibility with Photon. When processing large datasets using Spark RDD or Spark MLlib, you may experience Spark memory issues. See Spark memory issues.

The system environment in Databricks Runtime 15.2 ML differs from Databricks Runtime 15.2 as follows:

• For GPU clusters, Databricks Runtime ML includes the following NVIDIA GPU libraries:
  • CUDA 12.1
  • cusolver 11.4.5.107-1
  • cupti 12.1
  • cuDNN 8.9.0.131-1
  • NCCL 2.17.1
  • TensorRT 8.6.1.6-1

The following sections list the libraries included in Databricks Runtime 15.2 ML that differ from those included in Databricks Runtime 15.2.

• Top-tier libraries
• Python libraries
• R libraries
• Java and Scala libraries (Scala 2.12 cluster)

Databricks Runtime 15.2 ML includes the following top-tier libraries:

• datasets
• GraphFrames
• MLflow
• PyTorch
• spark-tensorflow-connector
• Scikit-learn
• TensorFlow
• TensorBoard
• transformers

Databricks Runtime 15.2 ML uses virtualenv for Python package management and includes many popular ML packages.

In addition to the packages specified in the following sections, Databricks Runtime 15.2 ML also includes the following packages:

• hyperopt 0.2.7+db3
• sparkdl 3.0.0_db1
• automl 1.26.0

To reproduce the Databricks Runtime ML Python environment in your local Python virtual environment, download the requirements-15.2.txt file and run pip install -r requirements-15.2.txt. This command installs all of the open source libraries that Databricks Runtime ML uses, but does not install libraries developed by Databricks, such as databricks-automl, databricks-feature-engineering, or the Databricks fork of hyperopt.

The R libraries are identical to the R Libraries in Databricks Runtime 15.2.

In addition to Java and Scala libraries in Databricks Runtime 15.2, Databricks Runtime 15.2 ML contains the following JARs: