Pull Prebuilt Containers

Prebuilt containers are provided in:

  • Intel Gaudi vault

  • Amazon ECR Public Library

  • AWS Deep Learning Containers (DLC)

Pull and Launch Docker Image - Intel Gaudi Vault


Before running Docker, make sure to map the dataset as detailed in Map Dataset to Docker.

Use the below commands to pull and run Dockers. Make sure to update the below command with the required operating system. See the Support Matrix for a list of supported operating systems:

docker pull vault.habana.ai/gaudi-docker/1.16.2/{$OS}/habanalabs/pytorch-installer-2.2.2:latest
docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.16.2/{$OS}/habanalabs/pytorch-installer-2.2.2:latest


  • Include –ipc=host in the Docker run command for the Docker images. This is required for distributed training using the Habana Collective Communication Library (HCCL); allowing re-use of host shared memory for best performance.

  • To run the Docker image with a partial number of the supplied Gaudi devices, make sure to set the Device to module mapping correctly. See Multiple Dockers Each with a Single Workload for further details.

AWS Deep Learning Containers

To set up and use AWS Deep Learning containers, follow the instructions detailed in AWS Available Deep Learning Containers Images.