Launching workloads with GPU fractions

This quick start provides a step-by-step walkthrough for running a Jupyter Notebook workspace using GPU fractions.

NVIDIA Run:ai’s GPU fractions provides an agile and easy-to-use method to share a GPU or multiple GPUs across workloads. With GPU fractions, you can divide the GPU/s memory into smaller chunks and share the GPU/s compute resources between different workloads and users, resulting in higher GPU utilization and more efficient resource allocation.

Note

If enabled by your Administrator, the NVIDIA Run:ai UI allows you to create a new workload using either the Flexible or Original submission form. The steps in this quick start guide reflect the Original form only.

Prerequisites

Before you start, make sure:

You have created a project or have one created for you.
The project has an assigned quota of at least 0.5 GPU.

Step 1: Logging in

Browse to the provided NVIDIA Run:ai user interface and log in with your credentials.

Run the below --help command to obtain the login options and log in according to your setup:

runai login --help

runai login

Step 2: Submitting a workspace

Go to the Workload manager → Workloads
Click +NEW WORKLOAD and select Workspace
Select under which cluster to create the workload
Select the project in which your workspace will run
Select a preconfigured template or select the Start from scratch to launch a new workspace quickly
Enter a name for the workspace (if the name already exists in the project, you will be requested to submit a different name)
Click CONTINUE
In the next step:
Select the ‘jupyter-lab’ environment for your workspace (Image URL: jupyter/scipy-notebook)
- If the ‘jupyter-lab’ is not displayed in the gallery, follow the below steps:
  - Click +NEW ENVIRONMENT
  - Enter a name for the environment. The name must be unique.
  - Enter the jupyter-lab Image URL - jupyter/scipy-notebook
  - Tools - Set the connection for your tool
    Click +TOOL
    Select Jupyter tool from the list
  - Set the runtime settings for the environment
    Click +COMMAND
    Enter command - start-notebook.sh
    Enter arguments - --NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=''
    Note: If host-based routing is enabled on the cluster, enter the --NotebookApp.token='' only.
  - Click CREATE ENVIRONMENT
The newly created environment will be selected automatically
Select the ‘small-fraction’ compute resource for your workspace (GPU % of devices: 10)
- If ‘small-fraction’ is not displayed in the gallery, follow the below steps:
  - Click +NEW COMPUTE RESOURCE
    Enter a name for the compute resource. The name must be unique.
    Set GPU devices per pod - 1
    Set GPU memory per device
    Select % (of device) - Fraction of a GPU device’s memory
    Set the memory Request - 10 (the workload will allocate 10% of the GPU memory)
    Optional: set the CPU compute per pod - 0.1 cores (default)
    Optional: set the CPU memory per pod - 100 MB (default)
  - Click CREATE COMPUTE RESOURCE
The newly created compute resource will be selected automatically
Click CREATE WORKSPACE

Copy the following command to your terminal. Make sure to update the below with the name of your project and workload. For more details, see CLI reference:

runai project set "project-name"
runai workspace submit "workload-name" --image jupyter/scipy-notebook \
--gpu-devices-request 0.1 --command --external-url container=8888 \
--name-prefix jupyter --command -- start-notebook.sh \
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=

Copy the following command to your terminal. Make sure to update the below with the name of your project and workload. For more details, see CLI reference:

runai config project "project-name"
runai submit "workload-name" --jupyter -g 0.1

Copy the following command to your terminal. Make sure to update the below parameters. For more details, see Workspaces API:

curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \ 
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer <TOKEN>' \ 
-d '{ 
    "name": "workload-name", 
    "projectId": "<PROJECT-ID>",  
    "clusterId": "<CLUSTER-UUID>", 
    "spec": {
        "command" : "start-notebook.sh",
        "args" : "--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=''",
        "image": "jupyter/scipy-notebook",
        "compute": {
            "gpuDevicesRequest": 1,
            "gpuRequestType": "portion",
            "gpuPortionRequest": 0.1

        },
        "exposedUrls" : [
            { 
                "container" : 8888,
                "toolType": "jupyter-notebook", 
                "toolName": "Jupyter" 
            }
        ]
    }
}

<COMPANY-URL> - The link to the NVIDIA Run:ai user interface
<TOKEN> - The API access token obtained in Step 1
<PROJECT-ID> - The ID of the Project the workload is running on. You can get the Project ID via the Get Projects API.
<CLUSTER-UUID> - The unique identifier of the Cluster. You can get the Cluster UUID via the Get Clusters API.
toolType will show the Jupyter icon when connecting to the Jupyter tool via the user interface.
toolName will show when connecting to the Jupyter tool via the user interface.

Note

The above API snippet runs with NVIDIA Run:ai clusters of 2.18 and above only.

Step 3: Connecting to the Jupyter Notebook

Select the newly created workspace with the Jupyter application that you want to connect to
Click CONNECT
Select the Jupyter tool. The selected tool is opened in a new tab on your browser.

Next steps

Manage and monitor your newly created workload using the Workloads table.

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Last updated 5 days ago