FedML
Unified and scalable ML library for distributed training, model serving, federated learning
GraphCanon updated Oct 3, 2026 · GitHub synced Oct 3, 2026
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Decision brief
FedML supports comprehensive AI workflows including generative AI services through integration with TensorOpera AI. It excels in large-scale distributed training and federated learning across multiple environments.
Good fit when
- When you need to run ML tasks on decentralized GPUs or multi-clouds without complex setup
- For projects requiring scalable model deployment and serving with low latency
Avoid when
- Avoid if you require a standalone solution that does not integrate with TensorOpera AI services
- Not recommended for small-scale projects where resource flexibility is less critical
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (339d since push)
- As of Oct 3, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 3, 2026
- Security (OSV)
- 88 low (88 low)
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install FedML PyPISimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
FEDML is a universal machine learning library supporting large-scale distributed training, model deployment, and federated learning. It integrates TensorOpera AI for comprehensive generative AI services.
Capability facts
- Languages
- python
Source: github.language · Oct 3, 2026
Categories
Tags
README
FEDML Open Source: A Unified and Scalable Machine Learning Library for Running Training and Deployment Anywhere at Any Scale Backed by TensorOpera AI: Your Generative AI Platform at Scale (https://TensorOpera.ai) <div align="center" <img src="docs/images/TensorOpera arch.png" wid...
For agents
This page has a .md twin and JSON over the API.