Releases: aws/sagemaker-pytorch-training-toolkit
Releases · aws/sagemaker-pytorch-training-toolkit
v2.1.0
Features
- add Dockerfiles for PyTorch 1.5.0
v2.0.0
Breaking Changes
- Replace sagemaker-containers with sagemaker-training
v1.3.3
Bug Fixes and Other Changes
- change miniconda installation in 1.4.0 Dockerfiles
Testing and Release Infrastructure
- parallelize SageMaker integ test runs
- remove (unused) model_fn from training scripts
v1.3.2
Bug Fixes and Other Changes
- bump smdebug version
Testing and Release Infrastructure
- add requirements.txt integ test
v1.3.1
Bug Fixes and Other Changes
- upgrade pillow etc. to fix safety issues
- Upgrade sagemaker-containers and test with more than 1 epoch
v1.3.0
Features
- Install toolkit from PyPI.
Bug Fixes and Other Changes
- upgrade sagemaker-containers to 2.8.2
- Install jupyter_client 5.3.4 in advanced for py2 gpu image
- update smdebug
Testing and Release Infrastructure
- run test-toolkit unit tests for release
- run build steps only when necessary.
- refactor toolkit tests.
v1.2.4
Bug Fixes and Other Changes
- install sm experiments always when python 3.6 or greater
v1.2.3
Bug Fixes and Other Changes
- Update smdebug to 0.7.0
- install sagemaker-experiments package only for 3.6
v1.2.2
Bug Fixes and Other Changes
- upgrade to latest sagemaker-experiments
- install SageMaker Python SDK into Python 3 images
v1.2.1
Bug Fixes and Other Changes
- Install awscli from pypi instead of conda for PyTorch containers