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.

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

Flexible workload submission is disabled by default. If unavailable, your administrator must enable it under General Settings → Workloads → Flexible workload submission.
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

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

Step 2: Submitting the First 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 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:
Click the load icon. A side pane appears, displaying a list of available environments. To add a new environment:
- Click the + icon to create a new environment
- Enter quick-start as the 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 & ARGUMENTS and add the following:
  - Enter the command - start-notebook.sh
  - Enter the 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
- Select the newly created environment from the side pane
Click the load icon. A side pane appears, displaying a list of available compute resources. To add a new compute resource:
- Click the + icon to create a new compute resource
- Enter request-limit as the name for the compute resource. The name must be unique.
- Set GPU devices per pod - 1
- Enable GPU fractioning to set the 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)
  - Set the memory Limit - 12GB
- 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
- Select the newly created compute resource from the side pane
Click CREATE 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 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:
Create an environment for your workspace
- Click +NEW ENVIRONMENT
- Enter quick-start as the 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 & ARGUMENTS and add the following:
  - 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
Create a new “request-limit” compute resource for your workspace
- Click +NEW COMPUTE RESOURCE
- Enter request-limit as the name for the compute resource. The name must be unique.
- Set GPU devices per pod - 1
- Enable GPU fractioning to set the 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)
  - Set the memory Limit - 12GB
- 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
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 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=

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

Go to the Workload manager → Workloads
Click +NEW WORKLOAD and select Workspace
Select the cluster where the previous workspace was created
Select the project where the previous workspace was created
Select 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:
Click the load icon. A side pane appears, displaying a list of available environments. Select the environment created in Step 2.
Click the load icon. A side pane appears, displaying a list of available compute resources. Select the compute resources created in Step 2.
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 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=

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

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

To connect to the Jupyter Notebook, browse directly to https://<COMPANY-URL>/<PROJECT-NAME>/<WORKLOAD-NAME>
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.
Open the file Untitled.ipynb and move the frame so you can see both tabs
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.
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.

To connect to the Jupyter Notebook, browse directly to https://<COMPANY-URL>/<PROJECT-NAME>/<WORKLOAD-NAME>
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.
Open the file Untitled.ipynb and move the frame so you can see both tabs
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.
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.

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