Launching a ML JupyterLab Job

ML JupyterLab is an app in the AI/ML Accelerator package. A license is required in order to use the AI/ML Accelerator package. For more information, please contact DNAnexus Sales via [email protected].

ML JupyterLab is essentially a purpose-built JupyterLab instance on DNAnexus Platform. It inherits all capabilities of a standard JupyterLab, plus specialized features for AI/ML development. This section gives you a quick start on how to launch ML JupyterLab.

To Launch with GUI

1. Find ML JupyterLab in your Tools Library: Simply open your Tools Library and search for “AI/ML Accelerator - ML JupyterLab”.

2. Set the Essential Inputs:

When starting an ML JupyterLab job, you need to pay attention to the two essential inputs: Instance Type and Initial Instance Count. To find this input, click on the instance icon in the top right corner of the input panel which is automatically open when you try to launch ML JupyterLab.

• Instance Type: Choose either a CPU or GPU instance. This will determine the computational environment (CPU-based or GPU-based), which will be described in detail later.

• Initial Instance Count: Defines the number of nodes in the cluster.

By default, ML JupyterLab launches with one mem2_ssd1_v2_x8 instance (Initial Instance Count: 1, Instance Type: mem2_ssd1_v2_x8). This means your job will run in a CPU environment, where the head node (master node) can also perform computations.

3. Set the Optional Inputs

In addition to the required settings, you may configure the following optional inputs:

• Input Files: Files specified here will be downloaded once the job is ready.

• Additional Requirements: Provide a plain text file listing extra Python libraries to install (one per line, with optional versioning, e.g., numpy==1.21.0). Dependencies are resolved automatically. Format follows the PIP v24.2 standard.

• Wheel Files: Specify an array of .whl files to install. Wheel files are pre-built distributions, offering faster and more reliable setup.

• Snapshot Image: Upload a tarball capturing the exact state of a previous ML JupyterLab session (packages, configs, and files). Useful for pausing, sharing, or restoring environments.

• Command Line: Enter a custom command to run the job in non-interactive mode.

4. Opening the Worker URL: Once your ML JupyterLab is launched, you will be redirected to the Monitor screen. From there, click on the Open button.

Use the Open button in the Worker URL to use ML JupyterLab

Even when the Job State is “Running”, it might take a few more minutes for the Platform to set up ML JupyterLab. When the job is not ready, you will see the below screen. In such cases, simply reload your browser after a few minutes.

The waiting screen of ML JupyterLab when the instance is not ready

To Launch with CLI

You can also launch your job with dxtoolkit:

dx run app-ml_jupyterlab_ray_cluster \
  --name='My first ML JupyterLab' \
    --instance-type mem2_ssd1_v2_x8 --instance-count 1 -y

Once the Job State is at Running, you can get the Worker URL with:

dx describe job-xxxx --json | jq -r .httpsApp.dns.url

Resources

Full Documentation

To create a support ticket if there are technical issues:

1. Go to the Help header (same section where Projects and Tools are) inside the platform

2. Select “Contact Support”

3. Fill in the Subject and Message to submit a support ticket.