Launching workloads with dynamic GPU fractions

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

NVIDIA Run:ai’s dynamic GPU fractions optimizes GPU utilization by enabling workloads to dynamically adjust their resource usage. It allows users to specify a guaranteed fraction of GPU memory and compute resources with a higher limit that can be dynamically utilized when additional resources are requested.

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.

• Dynamic GPU fractions is enabled.

Note

Dynamic GPU fractions is disabled by default in the NVIDIA Run:ai UI. To use dynamic GPU fractions, it must be enabled by your Administrator, under General Settings → Resources → GPU resource optimization.

Step 1: Logging in

UI

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

CLI v2

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

runai login --help

API

To use the API, you will need to obtain a token as shown in API authentication.

Step 2: Submitting the first workspace

UI

1. Go to the Workload manager → Workloads

2. Click +NEW WORKLOAD and select Workspace

3. Select under which cluster to create the workload

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. Create an environment for your workspace

   • Click +NEW ENVIRONMENT

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

   • Enter the Image URL - gcr.io/run-ai-lab/pytorch-example-jupyter

   • 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

9. Create a new “request-limit” compute resource

   • 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 GB - Fraction of a GPU device’s memory

     • Set the memory Request - 4GB (the workload will allocate 4GB of the GPU memory)

     • Toggle Limit and set to 12

   • Optional: set the CPU compute per pod - 0.1 cores (default)

   • Optional: set the CPU memory per pod - 100 MB (default)

   • Select More settings and toggle Increase shared memory size

   • Click CREATE COMPUTE RESOURCE

   The newly created compute resource will be selected automatically

10. Click CREATE WORKSPACE

CLI v2

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 gcr.io/run-ai-lab/pytorch-example-jupyter \
--gpu-memory-request 4G --gpu-memory-limit 12G --large-shm \
--external-url container=8888 --name-prefix jupyter  \
--command -- start-notebook.sh \
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=

API

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": "gcr.io/run-ai-lab/pytorch-example-jupyter",
        "compute": {
            "gpuDevicesRequest": 1,
            "gpuMemoryRequest": "4G",
            "gpuMemoryLimit": "12G",
            "largeShmRequest": true

        },
        "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: Submitting the second workspace

UI

1. Go to the Workload manager → Workloads

2. Click +NEW WORKLOAD and select Workspace

3. Select the cluster where the previous workspace was created

4. Select the project where the previous workspace was created

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 environment created in Step 2

9. Select the compute resource created in Step 2

10. Click CREATE WORKSPACE

CLI v2

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 gcr.io/run-ai-lab/pytorch-example-jupyter --gpu-memory-request 4G \
--gpu-memory-limit 12G --large-shm --external-url container=8888 \
--name-prefix jupyter --command -- start-notebook.sh \
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=

API

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": "gcr.io/run-ai-lab/pytorch-example-jupyter",
        "compute": {
            "gpuDevicesRequest": 1,
            "gpuMemoryRequest": "4G",
            "gpuMemoryLimit": "12G",
            "largeShmRequest": true

        },
        "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 4: Connecting to the Jupyter Notebook

UI

1. Select the newly created workspace with the Jupyter application that you want to connect to

2. Click CONNECT

3. Select the Jupyter tool. The selected tool is opened in a new tab on your browser.

4. Open a terminal and use the watch nvidia-smi to get a constant reading of the memory consumed by the pod. Note that the number shown in the memory box is the Limit and not the Request or Guarantee.

5. Open the file Untitled.ipynb and move the frame so you can see both tabs

6. Execute both cells in Untitled.ipynb. This will consume about 3 GB of GPU memory and be well below the 4GB of the GPU Memory Request value.

7. In the second cell, edit the value after --image-size from 100 to 200 and run the cell. This will increase the GPU memory utilization to about 11.5 GB which is above the Request value.

CLI v2

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

2. Open a terminal and use the watch nvidia-smi to get a constant reading of the memory consumed by the pod. Note that the number shown in the memory box is the Limit and not the Request or Guarantee.

3. Open the file Untitled.ipynb and move the frame so you can see both tabs

4. Execute both cells in Untitled.ipynb. This will consume about 3 GB of GPU memory and be well below the 4GB of the GPU Memory Request value.

5. In the second cell, edit the value after --image-size from 100 to 200 and run the cell. This will increase the GPU memory utilization to about 11.5 GB which is above the Request value.

API

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

2. Open a terminal and use the watch nvidia-smi to get a constant reading of the memory consumed by the pod. Note that the number shown in the memory box is the Limit and not the Request or Guarantee.

3. Open the file Untitled.ipynb and move the frame so you can see both tabs

4. Execute both cells in Untitled.ipynb. This will consume about 3 GB of GPU memory and be well below the 4GB of the GPU Memory Request value.

5. In the second cell, edit the value after --image-size from 100 to 200 and run the cell. This will increase the GPU memory utilization to about 11.5 GB which is above the Request value.

Next steps

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