FedML

Source#

• Repository: FedML-AI/FedML
• Last source update: 2026-04-04
• Last verified: 2026-04-05

Tags#

Integration notes#

Platform-level abstraction; use worker wrappers for strict I/O schemas and auditable retries.

worker.md example#

Starter worker.md contract mapped from this registry entry. Copy this file and adapt schemas, constraints, and statuses for your task.

---
id: fedml-repo-derived-worker
name: FedML Repo-Derived Worker
version: 1.0.0
source_registry_url: https://worker.md/registry/fedml/
source_repository: https://github.com/FedML-AI/FedML
repository_default_branch: master
repository_language: Python
repository_license: Apache-2.0
repository_updated_at: 2026-04-04
worker_mode: agent-orchestration-worker
derivation_method: github_repository_metadata_plus_raw_readme
derivation_confidence: 0.75
derived_on: 2026-04-05
tags:
  - ai-agent
  - deep-learning
  - distributed-training
  - edge-ai
  - federated-learning
  - inference-engine
---

# FedML Repo-Derived Worker

## Repo-derived summary
- Registry summary: FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs o...
- Repository description: FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
- Stars (snapshot): 4,029
- Primary language: Python
- Worker mode classification: agent-orchestration-worker

## Extracted from
- https://github.com/FedML-AI/FedML
- https://github.com/FedML-AI/FedML/blob/master/README.md
- https://github.com/FedML-AI/FedML/blob/master/CODE_OF_CONDUCT.md

## Evidence notes (from repository text)
- README summary paragraph: # FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale

## Installation hints found in README
- No explicit package installation command detected in README text.

## worker.md contract (derived starter)
Purpose: Execute one orchestrated agent task as a bounded worker step.

### Input schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "task",
    "context"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "task": {
      "type": "string"
    },
    "context": {
      "type": "object"
    }
  }
}
```

### Output schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "status",
    "result"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "status": {
      "type": "string",
      "enum": [
        "ok",
        "retryable_error",
        "invalid_request",
        "invalid_output"
      ]
    },
    "result": {
      "type": "object"
    }
  }
}
```

### Constraints
- timeout_seconds: 30
- max_attempts: 2
- idempotency_key: run_id
- status_enum: [ok, retryable_error, invalid_request, invalid_output]
- notes: adapt to concrete APIs/classes documented in this repository before production use

## How this should be used
1. Treat this file as a repo-derived starter profile, not a claim of an official repository API contract.
2. Replace schemas with exact interfaces from code/docs you adopt.
3. Keep execution bounded and auditable using worker protocol constraints.

How to use#

• Save this as a worker spec file (for example: fedml-my-task.worker.md).
• Replace the input/output schemas and purpose with your real bounded task.
• Enforce schema validation + timeout + retry policy in your runtime before production use.

Citation#

Reference URL: https://worker.md/registry/fedml/

Source URL: https://github.com/FedML-AI/FedML