SINGA is a distributed deep learning library.
This release includes following changes:
Enhance distributed training
- Add support for configuration of number of GPUs to be used.
- Increase max epoch for better convergence.
- Print intermediate mini-batch information.
- Add support for switching between CPU and GPU devices.
Enhance example code
- Update the args of normalize forward function in the transforms of the BloodMnist example.
- Update the xceptionnet in the cnn example.
- Add arguments for weight decay, momentum and learning rates in the cnn example.
- Add training scripts for more datasets and model types in the cnn example.
- Add resnet dist version for the large dataset cnn example.
- Add cifar 10 multi process for the large dataset cnn example.
- Add sparsification implementation for mnist in the large dataset cnn example.
- Update the cifar datasets downloading to local directories.
- Extend the cifar datasets load function for customized directorires.
Enhance the webpage
- Update online documentation for distributed training.
Promote code quality
- Update inline comments for prepreocessing and data loading.
Update the PIL image module
Update the runtime Dockerfile
Update the conda files