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Serverless GPU compute | Databricks Documentation

Serverless GPU compute

Beta

This feature is in Beta.

This article describes serverless GPU compute on Databricks and provides recommended use cases, guidance for how to set up GPU compute resources, and feature limitations.

What is serverless GPU compute?​

Serverless GPU compute is part of the Serverless compute offering. Serverless GPU compute is specialized for custom single and multi-node deep learning workloads. You can use serverless GPU compute to train and fine-tune custom models using your favorite frameworks and get state-of-the-art efficiency, performance, and quality.

Serverless GPU compute includes:

The pre-installed packages on serverless GPU compute are not a replacement for Databricks Runtime ML. While there are common packages, not all Databricks Runtime ML dependencies and libraries are reflected in the serverless GPU compute environment.

Recommended use cases​

Databricks recommends serverless GPU compute for any model training use case that requires training customizations and GPUs.

For example:

Requirements​ What's installed​

Serverless GPU compute for notebooks uses environment versions, which provide a stable client API to ensure application compatibility. This allows Databricks to upgrade the server independently, delivering performance improvements, security enhancements, and bug fixes without requiring any code changes to workloads.

Serverless GPU compute uses environment version 3 in addition to the following packages:

See Serverless environment version 3 for the packages included in system environment version 3.

Also see the Serverless GPU Python API documentation.

Add libraries to the environment​

You can install additional libraries to the serverless GPU compute environment. See Add dependencies to the notebook.

Set up serverless GPU compute​

You can select to use a serverless GPU compute from the notebook environment in your workspace.

After you open your notebook:

  1. Select the to open the Environment side panel.
  2. Select A10 from the Accelerator field.
  3. Select 3 as the Environment version.
  4. Select Apply and then Confirm that you want to apply the serverless GPU compute to your notebook environment. After connecting to a resource, notebooks immediately begin using the available compute.

note

Connection to your compute auto-terminates after 60 minutes of inactivity.

Create and schedule a job​

The following steps show how to create and schedule jobs for your serverless GPU compute workloads. See Create and manage scheduled notebook jobs for more details.

After you open the notebook you want to use:

  1. Select the Schedule button on the top right.
  2. Select Add schedule.
  3. Populate the New schedule form with the Job name, Schedule _and _Compute.
  4. Select Create.

You can also create and schedule jobs from the Jobs and pipelines UI. See Create a new job for step-by-step guidance.

Limitations​ Notebook examples​

The notebooks in this section are examples to help demonstrate how to use Serverless GPU compute for different scenarios.

Deep learning with PyTorch​

The following notebook provides a simple example of how to run deep learning training using PyTorch and serverless GPU compute.

Deep learning training using PyTorch notebook Distributed training and hyperparameter sweeps​

The following notebook provides an example of distributed training and hyperparameter sweeps fine-tuning using the Serverless GPU Python API.

Serverless GPU compute sweeps and distributed training notebook Fine-tune Qwen2-0.5B model​

The following notebook provides an example of how to efficiently fine-tune the Qwen2-0.5B model using:

Fine-tune the Qwen2-0.5B model notebook Fine-tune an embedding model​

The following notebook provides an example of how to fine-tune an embedding model. This example uses contrastive learning to fine-tune an embedding model, gte-large-en-v1.5 on a single A10G.

Fine-tune an embedding model notebook Fine-tune Llama-3.2-3B with Unsloth​

This notebook demonstrates how to fine-tune Llama-3.2-3B using the Unsloth library.

Fine-tune Llama model with Unsloth notebook Object detection custom fine-tuning​

This notebook demonstrates how to train an object detection model using a Hugging Face example on one A10 GPU.

Object detection custom fine-tuning notebook XGBoost model training​

This notebook demonstrates how to train an XGBoost regression model on a single GPU.

XGBoost model training notebook Two-tower recommendation model​

These notebooks demonstrate how to convert your recommendation data into MDS format and then use that data to create a two-tower recommendation model.

Distributed supervised fine-tuning using TRL​

This notebook demonstrates how to use Databricks Serverless GPU to run supervised fine-tuning (SFT) using the TRL library with DeepSpeed ZeRO Stage 3 optimization on a single node A10 GPU.

Distributed TRL SFT Training notebook Time series forecasting with GluonTS​

This notebook demonstrates an end-to-end workflow for probabilistic time-series forecasting of electricity-consumption data with GluonTS’s DeepAR model on a serverless GPU cluster, covering data ingestion, resampling, model training, prediction, visualization, and evaluation.

Time series forecasting with GluonTS notebook

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