DJL is a Java framework for running deep learning models across different engines.
Open source
24 alternativesOneflow
Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.
About Oneflow
OneFlow is an open-source deep learning framework for training and running models. It provides a PyTorch-like API for programming models, Global Tensor for n-dimensional-parallel execution, and a Graph Compiler for accelerating and deploying models.
It runs on Linux with Python 3.7 through 3.11. CUDA use requires CUDA arch 60 or above, CUDA Toolkit 10.0 or above, and Nvidia driver 440.33 or above. It can be installed via pip, Docker image, or built from source.
Key features
- PyTorch-like API for programming models
- Global Tensor for n-dimensional-parallel execution
- Graph Compiler to accelerate and deploy models
- CPU-only and CUDA pip installs including nightly builds
- Docker images for installation and source builds
- Libai toolbox for parallel training of large Transformer models
Best for: Developers building distributed deep learning models on Linux with CPU or CUDA systems.
Good for
- Open-source codebase
- Installable via pip, Docker image, or source build
- Includes examples for BERT-large, GPT, T5, and vision transformers
Watch out for
- Requires Linux
- Requires Python 3.7 to 3.11
- CUDA use requires specific architecture, toolkit, and driver versions
Oneflow at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Oneflow FAQ
What are the system requirements for OneFlow?
Linux and Python 3.7, 3.8, 3.9, 3.10, or 3.11. For CUDA, it needs CUDA arch 60 or above, CUDA Toolkit 10.0 or above, and Nvidia driver 440.33 or above.
How can OneFlow be installed?
With pip using `python3 -m pip install oneflow` for CUDA stable release, nightly CPU or CUDA pip options, with Docker image `docker pull oneflowinc/oneflow:nightly-cuda11.8`, or by building from cloned source.
How can an install be checked?
Run `python3 -m oneflow --doctor` for simple validation.
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