Launching workloads with GPU fractions¶

Browse to the provided 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  

Log in using the following command. You will be prompted to enter your username and password:

runai login

To use the API, you will need to obtain a token. Please follow the API authentication article.

1. Go to the Workload manager → Workloads

2. Click +NEW WORKLOAD and select Workspace

   Within the New workspace form:

3. Select under which cluster to create the workspace

4. Select the project in which your workspace will run
5. Select a preconfigured template or select the Start from scratch to launch a new workspace quickly
6. Enter a name for the workspace (if the name already exists in the project, you will be requested to submit a different name)

7. Click CONTINUE

   In the next step:

8. 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 step-by-step guide:
   Create a jupyter-lab environment

   1. Click +NEW ENVIRONMENT
   2. Enter a name for the environment. The name must be unique.
   3. Enter the jupyter-lab Image URL - jupyter/scipy-notebook

   4. Tools - Set the connection for your tool

      • Click +TOOL
      • Select Jupyter tool from the list

   5. 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 argument --NotebookApp.token='' only.

   6. Click CREATE ENVIRONMENT

   • The newly created jupyter-lab will be selected automatically

9. Select the ‘small-fraction’ compute resource for your workspace (GPU % of devices: 10)

   • If ‘small-fraction’ is not displayed in the gallery, follow the step-by-step guide:
   Create a small-fraction compute resource

   1. Click +NEW COMPUTE RESOURCE
   2. Enter a name for the compute resource. The name must be unique.
   3. Set GPU devices per pod - 1
   4. 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)
   5. Optional: set the CPU compute per pod - 0.1 cores (default)
   6. Optional: set the CPU memory per pod - 100 MB (default)
   7. Click CREATE COMPUTE RESOURCE
   • The newly created small-fraction compute resource will be selected automatically

10. Click CREATE WORKSPACE

    After the workspace is created, it is added to the workloads table

Copy the following command to your terminal. Make sure to update the below with the name of your project and workload:

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=

This would start a workspace with a pre-configured Jupyter image with 10% of the GPU memory allocated.

Copy the following command to your terminal. Make sure to update the below with the name of your project and workload:

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

This would start a workspace with a pre-configured Jupyter image with 10% of the GPU memory allocated.

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

curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \ #<COMPANY-URL> is the link to the Run:ai user interface.
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer <TOKEN>' \ #<TOKEN> is the API access token obtained in Step 1. 
-d '{ 
    "name": "workload-name", 
    "projectId": "<PROJECT-ID>", '\ #The ID of the Project the workspace is running on. You can get the Project ID via the Get Projects API. 
    "clusterId": "<CLUSTER-UUID>", \ #<CLUSTER-UUID> is the unique identifier of the Cluster. You can get the Cluster UUID by adding the "Cluster ID" column to the Clusters view. 
    "spec": {
        "command" : "start-notebook.sh",
        "args" : "--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=''",
        "image": "jupyter/base-notebook",
        "exposedUrls": [
          {
            "container": 8888,
            "toolType": "jupyter-notebook",
            "toolName": "Jupyter"
          }
        ],
        "compute": {
          "gpuDevicesRequest": 1,
          "gpuRequestType": "portion",
          "gpuPortionRequest": 0.1

        }
    }
}'

This would start a workspace with a pre-configured Jupyter image with 10% of the GPU memory allocated.

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

To connect to the Jupyter Notebook, browse directly to https://<COMPANY-URL>/<PROJECT-NAME>/<WORKLOAD_NAME>

To connect to the Jupyter Notebook, browse directly to https://<COMPANY-URL>/<PROJECT-NAME>/<WORKLOAD_NAME>

To connect to the Jupyter Notebook, browse directly to https://<COMPANY-URL>/<PROJECT-NAME>/<WORKLOAD_NAME>