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notebook

The notebook provider launches a Jupyter notebook dev environment.

It comes with Python and Conda pre-installed, and allows to expose ports.

If GPU is requested, the provider pre-installs the CUDA driver too.

Usage example

workflows:
  - name: ide-notebook
    provider: notebook
    resources:
      interruptible: true
      gpu: 1

To run this workflow, use the following command:

$ dstack run ide-notebook

Properties reference

The following properties are optional:

  • setup - (Optional) The list of shell commands to run before running the Notebook application
  • python - (Optional) The major version of Python
  • environment - (Optional) The list of environment variables
  • artifacts - (Optional) The list of output artifacts
  • resources - (Optional) The hardware resources required by the workflow
  • ports - (Optional) The list of ports to expose
  • working_dir - (Optional) The path to the working directory
  • ssh - (Optional) Runs SSH server in the container if true (by default)
  • cache - (Optional) The list of directories to cache between runs

artifacts

The list of output artifacts

  • path – (Required) The relative path of the folder that must be saved as an output artifact
  • mount – (Optional) true if the artifact files must be saved in real-time. Must be used only when real-time access to the artifacts is important: for storing checkpoints (e.g. if interruptible instances are used) and event files (e.g. TensorBoard event files, etc.) By default, it's false.

resources

The hardware resources required by the workflow

  • cpu - (Optional) The number of CPU cores
  • memory (Optional) The size of RAM memory, e.g. "16GB"
  • gpu - (Optional) The number of GPUs, their model name and memory
  • shm_size - (Optional) The size of shared memory, e.g. "8GB"
  • interruptible - (Optional) true if you want the workflow to use interruptible instances. By default, it's false.

NOTE:

If your workflow is using parallel communicating processes (e.g. dataloaders in PyTorch), you may need to configure the size of the shared memory (/dev/shm filesystem) via the shm_size property.

gpu

The number of GPUs, their name and memory

  • count - (Optional) The number of GPUs
  • memory (Optional) The size of GPU memory, e.g. "16GB"
  • name (Optional) The name of the GPU model (e.g. "K80", "V100", etc)

cache

The list of directories to cache between runs

  • path – (Required) The relative path of the folder that must be cached

Ports

If you'd like your workflow to expose ports, you have to specify the ports property with the list of ports to expose. You could specify a mapping APP_PORT:LOCAL_PORT or just APP_PORT — in this case dstack will choose available LOCAL_PORT for you.

NOTE:

Ports range 10000-10999 is reserved for dstack needs. However, you could remap them to different LOCAL_PORTs.