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@@ -48,7 +48,7 @@ After downloading, copy the `code` executable to a location in your PATH, so tha
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export PATH=$HOME/.local/$(uname -m)/bin:$PATH
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```
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The `uname -m` command will print `aarch64` or `x86_64`, according to the microarchitecture of the node it is run on.
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Then create the path, and copy the `code` executable to the architecture-specific path:
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```
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mkdir -p $HOME/.local/$(uname -m)/bin
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Tunnels
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Sign in to tunnels registered with GitHub
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```
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If you have not signed in to GitHub with VS Code editor, you will be redirected to the browser to sign in.
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After signing in and authorizing VSCode, the open tunnel should be visible under REMOTES (TUNNELS/SSH) -> Tunnels.
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### Using with uenv
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If you plan to do any intensive work: repeated compilation of large projects or running python code in Jupyter, please see the guide to running on a compute node below.
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Running intensive workloads on login nodes, which are shared resources between all users, is against CSCS [fair usage][ref-policies-fair-use] of Shared Resources policy.
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### Using with containers
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!!! todo
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write a guide
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### Running on a compute node
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If you plan to do computation using your VSCode, then you should first allocate resources on a compute node and set up your environment there.
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* `-t120` requests a 2 hour (120 minute) reservation
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* `-n1` requests a single rank - only one rank/process is required for VSCode
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* `--pty` allows forwarding of terminal I/O, required to sign in to Github
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Once the job allocation is granted, you will be prompted to log into GitHub, the same as starting a session on the login node.
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If you don't want to use a uenv, the command is even simpler:
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```
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# start an interactive shell session
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srun -t120 -n1 --pty bash
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# set up the environment before starting the tunnel
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uenv start prgenv-gnu/24.11:v1 --view=default
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code tunnel --name=$CLUSTER_NAME-tunnel
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```
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* `-t120` requests a 2 hour (120 minute) reservation
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* `-n1` requests a single rank - only one rank/process is required for VSCode
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* `--pty` allows forwarding of terminal I/O, for bash to work interactively
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### Using with containers
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This will use CSCS's custom **Container Engine** which can easily pull a container from a registry like DockerHub. Same setup process as earlier with GitHub.
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#### TOML File with Image and Mount Paths
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```toml
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image = "nvcr.io#nvidia/pytorch:24.01-py3"# example of PyTorch NGC image
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