colablisted

# Google Colab CLI Use `colab` when a task needs an accelerator the workstation lacks. The official Colab skill documents every command; this skill owns authentication, session hygiene, and the spend boundary. ## Workflow 1. **Authenticate**: OAuth by default (`--auth oauth2`), or `--auth adc` to reuse the Application Default Credentials from [gcloud](../gcloud/SKILL.md); session state lives under `~/.config/colab-cli/`. 1. **Prefer ephemeral runs**: `colab run` rents a VM, runs the script, and releases it; a shebang `#!/usr/bin/env -S colab run --gpu T4` makes a single file self-contained per [python-script](../python-script/SKILL.md). ```bash colab run --gpu T4 --timeout 3600 train.py ``` 1. **Keep a session only while iterating**: `colab new -s <name> --gpu L4` (or `--tpu v6e1`), then `colab exec -s <name> -f snippet.py --timeout 600`, `colab upload`, `colab download`, and `colab ls`. 1. **Stop what you started**: `colab sessions` then `colab stop -s <name>`; an idle session keeps consuming compute units. Run `colab status` before claiming a job finished. 1. **Verify**: `colab log` shows the history; download the artifacts before stopping the session. ## Gotchas - **30-second default**: `colab run` and `colab exec` abort code execution after 30 seconds unless `--timeout <seconds>` covers the whole job. - **Pinned dependency**: mise installs `google-colab-cli` with `jupyter-kernel-client==0.15.0`; 1.0.0 renamed the client class and breaks every session. - **