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FEDML
Unified ML library for scalable training, serving, and federated learning.
FEDML provides a unified, scalable Python library and cross‑cloud scheduler to run distributed training, model serving, and federated learning on any GPU resources, from clouds to edge devices.
Overview
Overview
FEDML is a Python‑centric machine‑learning library that unifies distributed training, model serving, and federated learning under a single API. It targets data scientists, MLOps engineers, and AI researchers who need to move workloads seamlessly across heterogeneous GPU environments.
Core Capabilities
The library ships with a cross‑cloud scheduler (TensorOpera Launch) that automatically matches AI jobs to the most cost‑effective GPU resources, whether in public clouds, private data centers, or edge devices. Integrated MLOps tools—Studio for fine‑tuning foundation models and Job Store for reusable job templates—streamline the end‑to‑end workflow. The compute layer includes dedicated modules for high‑performance training, low‑latency serving, and on‑device federated learning.
Deployment Flexibility
FEDML can be deployed on single‑GPU machines, large multi‑cloud clusters, or hybrid on‑premise setups. Its federated learning component enables secure on‑device training for smartphones and edge servers, while the serving stack scales to handle high request volumes with minimal latency.
Highlights
Cross‑cloud scheduler automatically matches jobs with the most cost‑effective GPU resources.
Unified API covers distributed training, model serving, and federated learning in a single codebase.
Supports on‑prem, hybrid, and multi‑cloud clusters, including edge and smartphone devices.
Integrated MLOps tools (Studio, Job Store) streamline data preparation, fine‑tuning, and deployment.
Pros
- Highly extensible Python library with broad AI ecosystem support.
- Seamless scaling from a single GPU to large multi‑cloud clusters.
- Built‑in federated learning ops enable on‑device training.
- Apache‑2.0 license encourages commercial and research use.
Considerations
- Steep learning curve for advanced distributed configurations.
- Primary language Python may limit integration with non‑Python stacks.
- Scheduler relies on external GPU marketplaces, which may incur additional costs.
- Documentation may be fragmented across TensorOpera resources.
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Fit guide
Great for
- Teams needing to train large foundation models across heterogeneous GPU resources.
- Enterprises deploying AI services on edge devices or private clouds.
- Researchers experimenting with federated learning on smartphones.
- MLOps engineers looking for a unified pipeline from training to serving.
Not ideal when
- Small projects that only require single‑node training.
- Organizations without access to GPU resources or cloud credits.
- Developers preferring a pure‑C++ or Java ML framework.
- Use cases demanding real‑time inference on CPU‑only environments.
How teams use it
Large‑scale LLM fine‑tuning on multi‑cloud GPUs
Accelerated training time and reduced cost by auto‑selecting the cheapest GPU instances across clouds.
Edge AI model serving for mobile apps
Low‑latency inference on smartphones using TensorOpera Deploy, with automatic model conversion and scaling.
Federated health data analysis across hospitals
Secure on‑device model updates via TensorOpera Federate, preserving patient privacy while improving model accuracy.
Continuous integration pipeline for AI models
Studio and Job Store automate dataset ingestion, model versioning, and deployment, enabling rapid iteration.
Tech snapshot
Python79%
Jupyter Notebook14%
Java3%
Shell2%
C++1%
Dockerfile1%
Frequently asked questions
What programming languages are supported?
The core library is written in Python and provides bindings for C/C++ extensions; you can call it from any language that can interface with Python.
Can FEDML run on on‑premise GPU clusters?
Yes, the TensorOpera Launch scheduler can provision and orchestrate jobs on private or hybrid clusters without requiring cloud services.
Is there a commercial license?
FEDML is released under the Apache‑2.0 license, which permits commercial use without additional fees.
How does federated learning handle data privacy?
Federated learning runs training locally on devices, sending only model updates to a central server, ensuring raw data never leaves the device.
Project at a glance
Active- Stars
- 3,993
- Watchers
- 3,993
- Forks
- 762
LicenseApache-2.0
Repo age5 years old
Last commit3 months ago
Primary languagePython
Last synced 3 hours ago