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Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
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.
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.
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.
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.
When teams consider FEDML, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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.
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.
Yes, the TensorOpera Launch scheduler can provision and orchestrate jobs on private or hybrid clusters without requiring cloud services.
FEDML is released under the Apache‑2.0 license, which permits commercial use without additional fees.
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
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