As a data scientist or DevOps engineer, you can deploy a deep learning VM from Automation Service Broker by using the AI workstation self-service catalog item.

When you request a deep learning VM in the Automation Service Broker catalog, you provision a GPU-enabled deep learning VM that can be configured with the desired vCPU, vGPU, Memory, and AI/ML NGC containers from NVIDIA. The vGPU guest driver and the specified deep learning workload are installed when you start the deep learning VM for the first time.

For a deep learning VM with an NVIDIA RAG workload, use the AI RAG Workstation catalog item. See Deploy a Deep Learning VM with a RAG Workload Using a Self-Service Catalog Item in VMware Aria Automation.

Prerequisites

  • Verify that your cloud administrator has configured Private AI Automation Services for your project.
  • Verify that you have permissions to request AI catalog items.

Deploy a Deep Learning VM by Using a Self-Service Catalog Item in VMware Aria Automation

As a data scientist, you can deploy a single GPU software-defined development environment from the self-service Automation Service Broker catalog.

You can customize the GPU-enabled virtual machine with machine parameters to model development requirements, pre-install AI/ML frameworks to meet training and inference requirements, and specify the AI/ML packages from the NVIDIA NGC registry via a portal access key.

Procedure

  1. On the Catalog page in Automation Service Broker, locate the AI Workstation card and click Request.
  2. Select a project.
  3. Enter a name and description for your deployment.
  4. Configure the AI workstation parameters.
    Setting Sample value
    VM class A100 Small - 1 vGPU (16 GB), 8 CPUs and 16 GB Memory
    Data disk size 32 GB
    User password Enter a password for the default user. You might be prompted to reset your password when you first log in.
    SSH public key This setting is optional.
  5. Select a software bundle to install on your workstation.
    Supported deep learning workloads include PyTorch, TensorFlow, and CUDA samples. For morе information, see Deep Learning Workloads in VMware Private AI Foundation with NVIDIA.
  6. (Optional) Enter a custom cloud-init that you want to install in addition to the cloud-init defined for the software bundle.
    VMware Aria Automation merges the cloud-init from the software bundle and the custom cloud-init.
  7. Provide your NVIDIA NGC API key.
  8. (Optional) Expose NVIDIA Data Center GPU Manager (DCGM) metrics via a load balancer.
    NVIDIA DCGM manages and monitors GPUs in data center environments.
  9. (Optional) If you are installing the PyTorch or the TensorFlow NGC software bundle, activate JupyterLab authentication.
  10. Click Submit.

Results

The deployment Overview tab contains a summary of the software that was installed, along with instructions on how to access the application, services, and the deep learning VM.

Deploy a Deep Learning VM with NVIDIA Triton Inference Server by Using a Self-Service Catalog Item in VMware Aria Automation

As a data scientist, you can deploy a GPU-enabled deep learning with NVIDIA Triton Inference Server from the self-service Automation Service Broker catalog.

The deployed workstation includes Ubuntu 22.04, an NVIDIA vGPU driver, Docker Engine, NVIDIA Container Toolkit, and NVIDIA Triton Inference Server.

Procedure

  1. On the Catalog page in Automation Service Broker, locate the Triton Inferencing Server card and click Request.
  2. Select a project.
  3. Enter a name and description for your deployment.
  4. Configure the AI workstation parameters.
    Setting Sample value
    VM class A100 Small - 1 vGPU (16 GB), 8 CPUs and 16 GB Memory

    VM classes with Unified Virtual Memory (UVM) support are required for running Triton Inferencing Server.
    Data disk size 32 GB
    User password Enter a password for the default user. You might be prompted to reset your password when you first log in.
    SSH public key This setting is optional.
  5. (Optional) Enter a custom cloud-init that you want to install in addition to the cloud-init defined for the software bundle.
    VMware Aria Automation merges the cloud-init from the software bundle and the custom cloud-init.
  6. (Optional) Expose NVIDIA Data Center GPU Manager (DCGM) metrics via a load balancer.
    NVIDIA DCGM manages and monitors GPUs in data center environments.
  7. Click Submit.