Operate Iris jobs and tasks, from submission and monitoring to debugging and recovery. The skill also guides scheduling checks, coordination between clusters, controller deployments, development GPU or TPU reservations, and investigation of stuck CoreWeave pods.
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Use Iris to submit, inspect, debug, monitor, or recover jobs and tasks; diagnose scheduling and federation; deploy controllers; or reserve dev GPUs and TPUs. Use for ordinary Iris operations, including babysitting a job, controller rollouts, accelerator sessions, and stuck CoreWeave pods. Use a narrower production-run or Zephyr skill when that workflow is explicitly requested.
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Read only the material needed for the request:
lib/iris/OPS.md.lib/iris/docs/federation.md.lib/iris/docs/task-outputs.md.query-finelog.Resolve cluster facts from lib/iris/config/<cluster>.yaml; do not copy live coordinates from memory.
uv run iris --cluster=<cluster> job describe <job>
uv run iris --cluster=<cluster> task describe <task>
uv run iris --cluster=<cluster> task events <task>
uv run iris --cluster=<cluster> rpc controller list-backends
For a pending federated root, inspect all three parent-side views:
uv run iris --cluster=<parent> job list --prefix <root-job>
uv run iris --cluster=<parent> rpc controller list-peers
uv run iris --cluster=<parent> query \
"SELECT job_id, peer_id, handoff_state FROM federated_jobs WHERE job_id='<root-job>'"
Only root jobs federate; their whole tree stays on the peer. Parent job describe is the liveness source, while forwarded logs may lag. CoreWeave tasks normally read regional S3 and GCP tasks read GCS.
Write bounded diagnostics to $IRIS_OUTPUT_DIR. Iris preserves that directory as one outputs.tar.zst archive per attempt without changing the command outcome when capture fails. Find the archive URI and its uploaded, empty, failed, or unavailable state with:
uv run iris --cluster=<cluster> attempt describe <task>:<attempt>
Use direct object-storage writes for large or durable outputs. See lib/iris/docs/task-outputs.md for retention, limits, and data-access boundaries.
iris cluster restart without explicit approval for the named cluster; it kills all workers and jobs.kubectl describe pod on task pods because it can print environment values.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Compare Zephyr changes using the same prepared dataset and matching execution settings. Analyze Finelog metrics for each processing stage to distinguish CPU efficiency, elapsed time, and peak memory changes, then report improvements, regressions, or tradeoffs.
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Register Hugging Face datasets for Marin by inspecting their structure without downloading the full dataset. Identify text fields, configurations, and data splits, then add a dataset module and validate the mapping with a sample.
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Turn a specified GCP access request into a reviewable Pulumi change and pull request. Select the narrowest suitable role and encrypt personal email identities, covering resource permissions and Cloud Run viewer access through IAP, an access gateway.
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Add or update Pallas/Mosaic kernels, specialized computation routines for JAX. The skill covers reference implementations, output and gradient checks, public wrappers, fallback behavior, performance benchmarks, and hardware-specific tuning when requested.
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Preserve old experiment scripts with a dated Git tag, a named snapshot of the code, and add comments linking related GitHub issues to the archived files and cleanup pull request.
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Launch and continuously monitor a specified Zephyr pipeline on Iris when asked. It checks coordinator and worker health, tracks stage progress, identifies stalled or repeated failures, and requires explicit approval before stopping or restarting jobs.
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