Environments

This section explains what environments are and how to create and use them.

Environments are one type of workload assets. An environment consists of a configuration that simplifies how workloads are submitted and can be used by AI practitioners when they submit their workloads.

An environment asset is a preconfigured building block that encapsulates aspects for the workload such as:

• Container image and container configuration

• Tools and connections

• The type of workload it serves

Environments table

The Environments table can be found under Workload manager in the NVIDIA Run:ai platform.

The Environment table provides a list of all the environment defined in the platform and allows you to manage them.

The Environments table consists of the following columns:

Column	Description
Environment	The name of the environment
Description	A description of the environment
Scope	The scope of this environment within the organizational tree. Click the name of the scope to view the organizational tree diagram
Image	The application or service to be run by the workload
Workload Architecture	This can be either standard for running workloads on a single node or distributed for running distributed workloads on multiple nodes
Tool(s)	The tools and connection types the environment exposes
Workload(s)	The list of existing workloads that use the environment
Workload types	The workload types that can use the environment (Workspace/ Training / Inference)
Template(s)	The list of workload templates that use this environment
Created by	The user who created the environment. By default NVIDIA Run:ai UI comes with preinstalled environments created by NVIDIA Run:ai
Creation time	The timestamp of when the environment was created
Last updated	The timestamp of when the environment was last updated
Cluster	The cluster with which the environment is associated

Tools associated with the environment

Click one of the values in the tools column to view the list of tools and their connection type.

Column	Description
Tool name	The name of the tool or application AI practitioner can set up within the environment. For more information, see Integrations.
Connection type	The method by which you can access and interact with the running workload. It's essentially the "doorway" through which you can reach and use the tools the workload provide. (E.g node port, external URL, etc)

Workloads associated with the environment

Click one of the values in the Workload(s) column to view the list of workloads and their parameters.

Column	Description
Workload	The workload that uses the environment
Type	The workload type (Workspace/Training/Inference)
Status	Represents the workload lifecycle. See the full list of workload status)

Customizing the table view

• Filter - Click ADD FILTER, select the column to filter by, and enter the filter values

• Search - Click SEARCH and type the value to search by

• Sort - Click each column header to sort by

• Column selection - Click COLUMNS and select the columns to display in the table

• Download table - Click MORE and then Click Download as CSV. Export to CSV is limited to 20,000 rows.

Environments created by NVIDIA Run:ai

When installing NVIDIA Run:ai, you automatically get the environments created by NVIDIA Run:ai to ease up the onboarding process and support different use cases out of the box. These environments are created at the scope of the account.

Note

The environments listed below are available based on your cluster settings. Some environments, such as vscode and rstudio, are only available in clusters with host-based routing.

Environment	Image	Description
jupyter-lab / jupyter-scipy	jupyter/scipy-notebook	An interactive development environment for Jupyter notebooks, code, and data visualization
jupyter-tensorboard	gcr.io/run-ai-demo/jupyter-tensorboard	An integrated combination of the interactive Jupyter development environment and TensorFlow's visualization toolkit for monitoring and analyzing ML models
tensorboard / tensorboad-tensorflow	tensorflow/tensorflow:latest	A visualization toolkit for TensorFlow that helps users monitor and analyze ML models, displaying various metrics and model architecture
llm-server	runai.jfrog.io/core-llm/runai-vllm:v0.6.4-0.10.0	A vLLM-based server that hosts and serves large language models for inference, enabling API-based access to AI models
chatbot-ui	runai.jfrog.io/core-llm/llm-app	A user interface for interacting with chat-based AI models, often used for testing and deploying chatbot applications
rstudio	rocker/rstudio:4	An integrated development environment (IDE) for R, commonly used for statistical computing and data analysis
vscode	ghcr.io/coder/code-server	A fast, lightweight code editor with powerful features like intelligent code completion, debugging, Git integration, and extensions, ideal for web development, data science, and more
gpt2	runai.jfrog.io/core-llm/quickstart-inference:gpt2-cpu	A package containing an inference server, GPT2 model and chat UI often used for quick demos

Adding a new environment

Environment creation is limited to specific roles

To add a new environment:

1. Go to the Environments table

2. Click +NEW ENVIRONMENT

3. Select under which cluster to create the environment

4. Select a scope

5. Enter a name for the environment. The name must be unique.

6. Optional: Provide a description of the essence of the environment

7. Enter the Image URL If a token or secret is required to pull the image, it is possible to create it via credentials of type docker registry. These credentials are automatically used once the image is pulled (which happens when the workload is submitted)

8. Set the image pull policy - the condition for when to pull the image from the registry

9. Set the workload architecture:

   • Standard Only standard workloads can use the environment. A standard workload consists of a single process.

   • Distributed Only distributed workloads can use the environment. A distributed workload consists of multiple processes working together. These processes can run on different nodes.

     • Select a framework from the list.

10. Set the workload type:

    • Workspace

    • Training

    • Inference

      • When inference is selected, define the endpoint of the model by providing both the protocol and the container’s serving port

11. Optional: Set the connection for your tool(s). The tools must be configured in the image. When submitting a workload using the environment, it is possible to connect to these tools

    • Select the tool from the list (the available tools varies from IDE, experiment tracking, and more, including a custom tool for your choice)

    • Select the connection type

      • External URL

        • Auto generate A unique URL is automatically created for each workload using the environment

        • Custom URL The URL is set manually

      • Node port

        • Auto generate A unique port is automatically exposed for each workload using the environment

        • Custom URL Set the port manually

      • Set the container port

12. Optional: Set a command and arguments for the container running the pod

    • When no command is added, the default command of the image is used (the image entrypoint)

    • The command can be modified while submitting a workload using the environment

    • The argument(s) can be modified while submitting a workload using the environment

13. Optional: Set the environment variable(s)

    • Click +ENVIRONMENT VARIABLE

    • Enter a name

    • Select the source for the environment variable

      • Custom

        • Enter a value

        • Leave empty

        • Add instructions for the expected value if any

      • Credentials - Select an existing credential as the environment variable

        • Select a credential name To add new credentials to the credentials list, and for additional information, see Credentials.

        • Select a secret key

      • ConfigMap - Select a predefined ConfigMap

        • Select a ConfigMap name To create a ConfigMap in your cluster, see Creating ConfigMaps in advance.

        • Enter a ConfigMap key

    • The environment variables can be modified and new variables can be added while submitting a workload using the environment

14. Optional: Set the container’s working directory to define where the container’s process starts running. When left empty, the default directory is used.

15. Optional: Set where the UID, GID and supplementary groups are taken from, this can be:

    • From the image

    • From the IdP token (only available in an SSO installations)

    • Custom (manually set) - decide whether the submitter can modify these value upon submission.

      • Set the User ID (UID), Group ID (GID) and the supplementary groups that can run commands in the container

        • Enter UID

        • Enter GID

        • Add Supplementary groups (multiple groups can be added, separated by commas)

        • Disable Allow the values above to be modified within the workload if you want the above values to be used as the default

16. Optional: Select Linux capabilities - Grant certain privileges to a container without granting all the privileges of the root user.

17. Click CREATE ENVIRONMENT

Note

It is also possible to add data sources directly when creating a specific workspace, training or inference workload.

Editing an environment

To edit an existing environment:

1. Select the environment you want to edit

2. Click Edit

3. Update the environment and click SAVE ENVIRONMENT

Note

• The already bound workload that is using this asset will not be affected.

• llm-server and chatbot-ui environments cannot be edited.

Copying an environment

To copy an existing environment:

1. Select the environment you want to copy

2. Click MAKE A COPY

3. Enter a name for the environment. The name must be unique.

4. Update the environment and click CREATE ENVIRONMENT

Deleting an environment

To delete an environment:

1. Select the environment you want to delete

2. Click DELETE

3. On the dialog, click DELETE to confirm

Note

The already bound workload that is using this asset will not be affected.

Using API

Go to the Environment API reference to view the available actions