Distributed training

When possible, Azure Databricks recommends that you train neural networks on a single machine; distributed code for training and inference is more complex than single-machine code and slower due to communication overhead. However, you should consider distributed training and inference if your model or your data are too large to fit in memory on a single machine. For these workloads, Databricks Runtime ML includes the TorchDistributor, DeepSpeed distributor and Ray packages.

Azure Databricks also offers distributed training for Spark ML models with the pyspark.ml.connect module, see Train Spark ML models on Databricks Connect with pyspark.ml.connect.

Note

Databricks does not recommend running multi-node distributed training using NC-series VMs due to low inter-node network performance. Instead, use one multi-GPU node, or use a different GPU VM size such as the NCasT4_v3-series, which supports accelerated networking.

DeepSpeed distributor

The DeepSpeed distributor is built on top of TorchDistributor and is a recommended solution for customers with models that require higher compute power, but are limited by memory constraints. DeepSpeed is an open-source library developed by Microsoft and offers optimized memory usage, reduced communication overhead, and advanced pipeline parallelism. Learn more about Distributed training with DeepSpeed distributor

TorchDistributor

TorchDistributor is an open-source module in PySpark that helps users do distributed training with PyTorch on their Spark clusters, so it lets you launch PyTorch training jobs as Spark jobs. Under-the-hood, it initializes the environment and the communication channels between the workers and utilizes the CLI command torch.distributed.run to run distributed training across the worker nodes. Learn more about Distributed training with TorchDistributor.

Ray

Ray is an open-source framework that specializes in parallel compute processing for scaling ML workflows and AI applications. See What is Ray on Azure Databricks?.