Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference. For more details of Chainer, see the documents and resources listed above and join the community in Forum, Slack, and Twitter.
ChainerMN is an additional package for Chainer, a flexible deep learning framework. ChainerMN enables multi-node distributed deep learning.
See official GitHub pages for Chainer (https://github.com/chainer/chainer) and ChainerMN (https://github.com/chainer/chainermn)
This Chainer-GPU-Distributed recipe contains information on how to run distributed Chainer training job across multiple GPU nodes with BatchAI.
This Chainer-GPU-Distributed-Infiniband recipe contains information on how to run distributed Chainer training job across multiple GPU nodes with Infiniband enabled.
If you have any problems or questions, you can reach the Batch AI team at [email protected] or you can create an issue on GitHub.
We also welcome your contributions of additional sample notebooks, scripts, or other examples of working with Batch AI.