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  • ๐ŸชŸIntroduction
    • ๐ŸงฒWhat is Distri.AI
    • ๐Ÿ—ž๏ธWhitepaper
  • ๐Ÿ•น๏ธGetting Started
    • ๐Ÿ›’User
    • ๐Ÿ› ๏ธCompute Node
    • ๐ŸšฐFaucet
  • ๐Ÿ”ฑDistri.AI Aggregator
    • ๐Ÿ๏ธGPU Market
    • ๐Ÿ›ซModel Hub
    • ๐Ÿ“‘Dataset Repository
  • ๐Ÿ“ฆML Workspace
    • ๐ŸคนJupyter
    • ๐Ÿคนโ€โ™€๏ธDesktop GUI
    • ๐Ÿคนโ€โ™€๏ธVisual Studio Code
    • ๐Ÿคนโ€โ™‚๏ธJupyterLab
    • ๐ŸคนGit Integration
    • ๐Ÿคนโ€โ™€๏ธFile Sharing
    • ๐Ÿคนโ€โ™‚๏ธAccess Ports
    • ๐ŸคนTensorboard
    • ๐Ÿคนโ€โ™€๏ธExtensibility
    • ๐Ÿคนโ€โ™‚๏ธHardware Monitoring
    • ๐ŸคนSSH Access
    • ๐Ÿคนโ€โ™€๏ธRemote Development
    • ๐Ÿคนโ€โ™‚๏ธRun as a job
    • ๐Ÿ“ฌFAQ
  • ๐Ÿ“žContact & Social Media
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  1. ML Workspace

Remote Development

PreviousSSH AccessNextRun as a job

Last updated 1 year ago

The workspace can be integrated and used as a remote runtime (also known as remote kernel/machine/interpreter) for a variety of popular development tools and IDEs, such as Jupyter, VS Code, PyCharm, Colab, or Atom Hydrogen. Thereby, you can connect your favorite development tool running on your local machine to a remote machine for code execution. This enables a local-quality development experience with remote-hosted compute resources.

These integrations usually require a passwordless SSH connection from the local machine to the workspace. To set up an SSH connection, please follow the steps explained in the SSH Access section.

Jupyter - Remote Kernel (click to expand...)

The workspace can be added to a Jupyter instance as a remote kernel by using the tool. If you have installed remote_ikernel (pip install remote_ikernel) on your local machine, the SSH setup script of the workspace will automatically offer you the option to setup a remote kernel connection.

When running kernels on remote machines, the notebooks themselves will be saved onto the local filesystem, but the kernel will only have access to the filesystem of the remote machine running the kernel. If you need to sync data, you can make use of rsync, scp, or sshfs as explained in the SSH Access section.

In case you want to manually setup and manage remote kernels, use the command-line tool, as shown below:

# Change my-workspace with the name of a workspace SSH connection
remote_ikernel manage --add \
    --interface=ssh \
    --kernel_cmd="ipython kernel -f {connection_file}" \
    --name="ml-server (Python)" \
    --host="my-workspace"

You can use the remote_ikernel command line functionality to list (remote_ikernel manage --show) or delete (remote_ikernel manage --delete <REMOTE_KERNEL_NAME>) remote kernel connections.

VS Code - Remote Machine (click to expand...)

The Visual Studio Code extension allows you to open a remote folder on any remote machine with SSH access and work with it just as you would if the folder were on your own machine. Once connected to a remote machine, you can interact with files and folders anywhere on the remote filesystem and take full advantage of VS Code's feature set (IntelliSense, debugging, and extension support). The discovers and works out-of-the-box with passwordless SSH connections as configured by the workspace SSH setup script. To enable your local VS Code application to connect to a workspace:

  1. Install extension inside your local VS Code.

  2. Run the SSH setup script of a selected workspace as explained in the SSH Access section.

  3. Open the Remote-SSH panel in your local VS Code. All configured SSH connections should be automatically discovered. Just select any configured workspace connection you like to connect to as shown below:

๐Ÿ“– You can find additional features and information about the Remote SSH extension in .

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remote_ikernel
remote_ikernel
Remote - SSH
Remote - SSH
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