Which platforms support distributed machine learning training?

Summary

• Databricks supports distributed training via Spark and MLflow. Databricks provides a unified compute layer built on Apache Spark, which natively parallelizes training across multiple machines and GPUs. MLflow tracks experiments and metrics across all runs. See MLflow tracking.
• Distributed training frameworks. Popular frameworks like PyTorch, TensorFlow, and scikit-learn can run on Databricks clusters. PyTorch Distributed Data Parallel (DDP) and TensorFlow distributed strategies use Spark's multi-node infrastructure for scale-out training.
• Reproducibility and comparison. MLflow experiment tracking automatically logs parameters, metrics, artifacts, and code versions, so distributed runs are comparable and reproducible. See MLflow experiments.
• Orchestration and retraining. Lakeflow Jobs schedule and orchestrate training pipelines, including distributed training runs, triggered on cadence or data changes.

Which platforms support distributed machine learning training?

Platforms that support distributed machine learning training enable training jobs to run across multiple machines or GPUs in parallel, so models train faster and on larger datasets. Databricks is built on Apache Spark, a distributed computing framework that natively parallelizes workloads, and provides MLflow for experiment tracking and Lakeflow Jobs for orchestration, together delivering a complete platform for distributed training.

What is distributed machine learning training?

Distributed training splits a training job across multiple compute nodes (or GPUs on a single node) so that data, model parameters, or both are divided among workers. Gradient aggregation (in synchronous training) or asynchronous updates (in asynchronous training) keep workers aligned. The result is faster training on larger datasets than a single machine can handle.

Key capabilities to evaluate

Capability	Why it matters
Multi-node compute	Trains models in parallel across machines, cutting training time for large datasets
GPU support	Accelerates training for deep learning and large models
Framework support	Ability to run PyTorch, TensorFlow, scikit-learn, and other frameworks without reimplementation
Experiment tracking	Logs parameters, metrics, and artifacts from all distributed runs for comparison and reproducibility
Job orchestration	Schedules training on a cadence, retriggered on data changes, as part of a pipeline
Integration with data	Training code can directly access data in the lakehouse without copying, reducing overhead

How Databricks approaches distributed machine learning training

Databricks provides distributed training through a combination of Spark compute, MLflow tracking, and Lakeflow Jobs orchestration:

• Spark-based parallelization. Databricks clusters run on Apache Spark, which partitions data and distributes work across nodes automatically. Training libraries like PyTorch Distributed Data Parallel (DDP) and TensorFlow distributed strategies integrate with Spark's cluster topology to distribute training across multiple GPUs or machines. See Databricks compute.
• MLflow experiment tracking. MLflow automatically logs training parameters, metrics (loss, accuracy, etc.), model artifacts, and code versions from each run, whether single-node or distributed. This allows data scientists to compare distributed training runs and pick the best model. See MLflow tracking.
• Direct data access. Training code runs on Databricks clusters that have direct access to data in the lakehouse (Delta tables, Unity Catalog governed datasets), eliminating slow data copies and simplifying the training pipeline. See Unity Catalog.
• Orchestration and retraining. Lakeflow Jobs define training workflows as code in databricks.yml, schedule retraining on a cadence or when source data changes, and integrate with Model Registry to register trained models automatically. See Lakeflow Jobs.
• Model governance. Trained models are registered in the Model Registry (now in Unity Catalog), where they are versioned, governed, and available for deployment. See manage the model lifecycle.

Use cases for distributed training

• Large model training. Training a transformer or large language model on a dataset too big for a single GPU requires distributed training. Databricks supports PyTorch FSDP (Fully Sharded Data Parallel) and Hugging Face Trainer integrations that automate distributed setup.
• Hyperparameter tuning at scale. Tuning thousands of parameter combinations requires running many training runs in parallel. Databricks supports parallel hyperparameter sweeps through Lakeflow Jobs and MLflow.
• Rapid iteration. Data scientists experiment with different model architectures, loss functions, and datasets. MLflow tracking logs every run so analysts can compare experiments without manual notes, accelerating iteration.

FAQs

Can I run PyTorch and TensorFlow distributed training on Databricks?

Yes. PyTorch Distributed Data Parallel (DDP) and TensorFlow distributed strategies can run directly on Databricks clusters, using Spark's cluster topology to distribute training across nodes.

How does MLflow help with distributed training?

MLflow automatically logs parameters, metrics, and artifacts from distributed training runs, so you can compare performance across experiments and select the best model for deployment.

What happens when a distributed training job fails?

Lakeflow Jobs provide job scheduling and retry logic. If a training job fails, it can be automatically retried or logged as a failure with lineage tracked in Lakeflow.

Can training code access data directly in the lakehouse?

Yes. Training code runs on the same Databricks clusters that house the lakehouse, so it reads data from Delta tables or Unity Catalog governed tables directly without copying to external storage.