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name: mars description: "Routes Mars and pymars users to the right local compute, remote execution, learning, and deployment workflows with verified install, import, and backend guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Mars

Use this skill for the mars / pymars package when a user asks about Mars tensor, DataFrame, remote functions, Mars Learn, local sessions, cluster/back-end startup, or optional Ray / GPU / Kubernetes / YARN paths.

Mars is a tensor-based unified framework for large-scale data computation that scales NumPy-, pandas-, and scikit-learn-style workflows. The package exposes mars, mars.tensor as mt, mars.dataframe as md, mars.remote as mr, and mars.config.options as the main public entry points.

Start here

• Install the public package with pip install pymars.
• If you are working from a local checkout, use NO_WEB_UI=1 python -m pip install . to avoid the optional web UI build.
• Minimal import check: python -I -c "import mars; print(mars.__version__)".
• Use python -m pip check after installation.

Read references/repo-provenance.md if you need to know whether this skill is current for the active checkout, or before refreshing it against a newer commit.

Route map

sub-skills/tensor-dataframe-core/

Use this for local Mars sessions, lazy/eager execution, tensor and DataFrame creation, execute/fetch patterns, and small file-backed IO workflows. Typical signals: new_session, execute, fetch, stop_server, mt.*, md.*, option_context, eager_mode, read_csv, to_pandas, to_numpy.

sub-skills/remote-and-scripts/

Use this for mars.remote.spawn, ExecutableTuple, nested remote DAGs, fetch_log, and script-run workflows. Typical signals: remote fan-out/fan-in, dependency passing, log retrieval, or run_script.

sub-skills/learn-and-integrations/

Use this for Mars Learn estimators and optional integrations such as Dask, PyTorch, TensorFlow, XGBoost, LightGBM, Statsmodels, Joblib, and Proxima. Typical signals: KMeans, PCA, NearestNeighbors, make_blobs, fit, predict, mars_scheduler, or integration import questions.

sub-skills/deployment-and-backends/

Use this for mars-supervisor, mars-worker, Ray sessions, CUDA/GPU notes, mars.deploy.kubernetes, and mars.deploy.yarn. Typical signals: CLI flags, cluster endpoints, backend='ray', gpu=True, to_gpu, to_cpu, Kubernetes, YARN, or service prerequisites.

Shared references

• references/workflows.md for the high-level workflow map.
• references/troubleshooting.md for cross-cutting install/import/build issues.
• scripts/check_mars_install.py for a safe import and smoke helper.

Quick usage rules

• Prefer CPU/local guidance unless the user explicitly asks for Ray, GPU, Kubernetes, YARN, or an external ML framework.
• Do not tell future agents to run original repo tests, notebooks, or scripts that are not bundled in this skill tree.
• Keep path references inside this skill tree only.
• Optional backends may be documented even when uninstalled, but do not claim them as verified unless a backend-specific smoke has passed.

Common failures

• Editable installs may fail on newer packaging backends because the project does not expose a PEP 660 build_editable hook. Use a normal install from a checkout instead.
• mars.dataframe import can trip over a shadowed or partial ray module; run from a neutral directory and check the optional Ray path if that happens.
• Set NO_WEB_UI=1 when the checkout does not have the web UI toolchain.

For deeper guidance, open the sub-skill that matches the user’s request.