Advanced Workload Orchestration With NVIDIA Run:ai

Environments

Exercise guide — refer to the official documentation for full details.

Overview

Environment assets define the container image and runtime configuration for your workloads.

Create an Environment for the Interactive Workspace

  1. Navigate to Workload Manager > Assets > Environments
  2. Click + New Environment

  3. Fill in the following:

    Field Value
    Scope alpha-project-1-gpu
    Name mnist-jupyter-lab-dev
    Image URL nvcr.io/nvidian/demo-pytorch-jp-example:25.01-py3

  4. Select Standard and Workspace as the workload architecture and type

  5. Click on Tools
  6. Click on +Tool
  7. Select Jupyter
  8. Click on Runtime Settings
  9. Click on + Command and Arguments

  10. Fill in:

    Field Value
    Command jupyter-lab
    Arguments --NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token='' --ServerApp.allow_remote_access=true --allow-root --port=8888 --no-browser

  11. Click on Create Environment

Create an Environment for Standard Training

  1. Navigate to Workload Manager > Assets > Environments
  2. Click + New Environment

  3. Fill in the following:

    Field Value
    Scope alpha-project-1-gpu
    Name mnist-standard-training
    Image URL nvcr.io/nvidian/demo-pytorch-standard-example:26.01-py3

  4. Select Standard and Training as the workload architecture and type

  5. Click on Runtime Settings
  6. Click on + Command and Arguments

  7. Fill in:

    Field Value
    Command ./run.sh

  8. Click on Create Environment

Create an Environment for Distributed Training

  1. Navigate to Workload Manager > Assets > Environments
  2. Click + New Environment

  3. Fill in the following:

    Field Value
    Scope omega-project-4-gpus
    Name pytorch-distributed-training-example
    Image URL nvcr.io/nvidian/demo-pytorch-ddp-example:24.07-py3

  4. Select Distributed as workload architecture

  5. Select PyTorch as the framework for the distributed workload
  6. Select Training as the workload type
  7. Click on Runtime Settings
  8. Click on + Command and Arguments

  9. Fill in:

    Field Value
    Command ./run.sh

  10. Click on Create Environment

Verify

Check the Workload Manager > Assets > Environments page — all environments should be listed and available for workload creation.

Tip: Pin image tags to specific versions (e.g., 2.0.1) rather than using latest for reproducibility.