FedML Repo Skill

Use this skill when the task involves the fedml Python package, the fedml CLI, FedML/TensorOpera MLOps jobs, FedML training examples, federated-learning simulation/cross-silo workflows, model serving, or FedML workflow DAGs.

First checks

1. Read references/repo-provenance.md if the task depends on the current source checkout.

2. Read references/installation.md before installing or repairing the package.

3. Run a minimal check in the target environment when needed:

   python scripts/check_install.py
   

4. If a command/API may contact the platform, ask before creating, stopping, killing, deploying, uploading, or launching remote resources.

Scope

Covered:

• Python package install/import and CLI command routing.
• Account, device, cluster, run, storage, network, and environment diagnostics.
• Build/package and launch-job workflows.
• Centralized, cross-cloud, and LLM training recipes.
• Federated learning: single-process simulation, cross-silo/cross-cloud patterns, security/privacy/analytics references.
• Model cards, local serving, remote deployment, streaming inference, and inference requests.
• fedml.workflow DAGs and customized job wrappers.

Excluded from verified runtime coverage:

• Android, IoT, MNN/mobile client stacks.
• Maintainer release scripts and CI plumbing.
• Host-mutating MPI install scripts.
• Docker/AWS/PDSH cluster scripts unless the user explicitly asks for infrastructure setup.

Route by task

Task shape	Read next	Why
Install, import, CLI help, login/logout, device binding, run/cluster/storage/env/network diagnostics	sub-skills/setup-and-cli/SKILL.md	Owns package setup and platform CLI basics
Build local packages or launch YAML-defined jobs on FedML/TensorOpera	sub-skills/launch-and-packaging/SKILL.md	Owns fedml build, fedml launch, and launch APIs
Centralized training, cross-cloud training, LLM train scripts, FedMLRunner, data/model loading	sub-skills/distributed-training/SKILL.md	Owns non-FL training pipelines
Simulation, cross-silo/cross-device/cross-cloud FL, algorithm flow, privacy/security/analytics examples	sub-skills/federated-learning/SKILL.md	Owns FL training modes and optional MPI/NCCL routing
Model card lifecycle, local/on-prem/cloud serving, FedMLPredictor, FedMLInferenceRunner, streaming inference	sub-skills/model-serving/SKILL.md	Owns deployment and inference surfaces
Multi-job DAGs, Workflow, Job, TrainJob, ModelDeployJob, ModelInferenceJob	sub-skills/workflow-orchestration/SKILL.md	Owns workflow dependency graphs and job wrappers

Shared references

• references/cli-reference.md — verified CLI command names and groups.
• references/api-reference.md — public Python APIs and network-bound calls.
• references/workflows.md — workflow routing map and source-script policy.
• references/backend-matrix.md — CPU/CUDA/MPI/remote-backend requirements.
• references/troubleshooting.md — import, CLI, backend, launch, training, serving, and workflow failures.
• references/repo-routing-metadata.json — structured router import metadata.

Repo-specific cautions

• The current CLI command for connectivity diagnostics is fedml network, not fedml diagnosis.
• The current CLI root does not expose fedml jobs; use fedml launch, fedml run, or fedml.api.launch_job.
• MPI and NCCL examples are optional backend variants. Do not claim they are verified unless the target runtime has the needed host tools.
• fedml model deploy, fedml launch, remote fedml.api.* calls, storage operations, and workflow job wrappers can create or inspect remote state; require user approval and credentials.