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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.

Example usage

  - name: ide-notebook
    provider: notebook
      - path: output
      interruptible: true
      gpu: 1

Properties reference

The following properties are optional:

  • before_run - (Optional) The list of shell commands to run before running the Notebook application
  • requirements - (Optional) The path to the requirements.txt file
  • python - (Optional) The major version of Python. By default, it's 3.10.
  • environment - (Optional) The list of environment variables
  • artifacts - (Optional) The list of output artifacts
  • resources - (Optional) The hardware resources required by the workflow
  • working_dir - (Optional) The path to the working directory


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.


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 the workflow can run on interruptible instances. By default, it's false.


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.


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)