Some GPU-enabled instance types are in Beta and are marked as such in the drop-down list when you select the driver and worker types during cluster creation.
Azure Databricks supports clusters accelerated with graphics processing units (GPUs). This topic describes how to create clusters with GPU-enabled instances and describes the GPU drivers and libraries installed on those instances.
To learn more about deep learning on GPU-enabled clusters, see Deep Learning.
Creating a GPU cluster is similar to creating any Spark cluster (See Clusters). You should keep in mind the following:
- The Databricks Runtime Version must be a GPU-enabled version, such as 4.1 (includes Apache Spark 2.3.0, GPU, Scala 2.11).
- The Worker Type and Driver Type must be GPU instance types.
- For single-machine workflows without Spark, you can set the number of workers to zero.
- In order to avoid conflicts among multiple Spark tasks trying to use the same GPU, Azure Databricks automatically configures Spark to use one executor thread per worker machine. This is generally optimal for libraries written for GPUs.
Azure Databricks supports the NC instance type series: NC12 and NC24 and the NCv3 instance type series: NC6s_v3, NC12s_v3, and NC24s_v3. See Azure Databricks Pricing for an up-to-date list of supported GPU instance types and their availability regions. Your Azure Databricks deployment must reside in a supported region to launch GPU-enabled clusters.
Azure Databricks installs the NVIDIA software required to use GPUs on Spark driver and worker instances. This software includes:
- Tesla driver for Linux x64.
- CUDA Toolkit, installed under
- cuDNN: NVIDIA CUDA Deep Neural Network Library.
For the versions of the software included, see the release notes for the Databricks Runtime version you are using.
This software contains source code provided by NVIDIA Corporation. Specifically, to support GPUs, Azure Databricks includes code from CUDA Samples.
When you select a GPU-enabled “Databricks Runtime Version” in Azure Databricks, you implicitly agree to the NVIDIA EULA.