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name: cli-cloud description: "Routes AXLearn GCP CLI, bundling, launch, VM, bastion, Dataflow, logs, and auth workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

cli-cloud

Use this sub-skill for AXLearn's cloud command tree and GCP-facing operations.

Typical triggers:

• axlearn gcp config, activate, cleanup, or get.
• axlearn gcp bundle for tar/docker bundling.
• axlearn gcp launch, vm, bastion, dataflow, logs, or auth.
• Questions about the .axlearn/axlearn.default.config and .axlearn/.axlearn.config files.
• TPU/GKE/Dataflow launch workflows or Cloud Logging log retrieval.

If the task is only about trainer config mechanics or fake-data smoke checks, use ../training-core/ first. If the task is about the model that will run on the cloud, jump to the relevant domain sub-skill.

What to read

• references/cli-reference.md for the command tree and config-file behavior.
• references/gcp-config-template.md for the distilled TOML config shape.
• references/troubleshooting.md for GCP auth, config, and log-view failures.
• scripts/inspect_gcp_cli.py for a safe command-tree probe.

Core cloud workflows

1) Inspect the command tree

The CLI is self-documenting. Use the bundled helper or run axlearn gcp --help to inspect subcommands and flags.

2) Activate a GCP config

The GCP config namespace is stored in the repo or home config file. List available entries first, then activate one by label.

3) Bundle the local code

Use bundle when you need to package the current checkout into a tarball or Docker image for launch.

4) Launch or monitor jobs

Use launch, vm, or bastion to submit a job and to manage its runtime. Use dataflow for Dataflow jobs and logs for Cloud Logging log access.

Decision points

• Choose config when the task is about project activation or CLI defaults.
• Choose bundle when the task is about packaging the current checkout.
• Choose launch or vm when the task is about running a command remotely.
• Choose bastion when the task is about job scheduling and quota management.
• Choose dataflow when the task is about Beam/Dataflow runner setup.
• Choose logs when the task is about log inspection rather than job execution.
• Do not route trainer/model-specific questions here unless the question is really about how the cloud launcher wraps them.