Release v0.0.7

Release v0.0.6

v0.0.5

Featured:
   @skippable for efficient skip connections. With this interface, GPipe
   copies skip tensors directly to the destination device.

Improvements:
   - Checkpointing deterministically handles randomness managed by PyTorch.
   - balance_by_size() analyzes parameters as well.

Breaking Changes:
   - Moved torchgpipe_balancing module to torchgpipe.balance.
   - Redesigned interface of balance_by_time() and balance_by_size().

v0.0.4

- Reduced GPU memory fragmentation by caching CUDA streams for copy.
- Fixed potential GPU memory violation on tuple of multiple tensors.
- Fixed potential GPU memory violation on shifted view tensors.

v0.0.3

Released on September 30, 2019.

Featured:
   torchgpipe now overlaps copy and computation using the separate CUDA
   streams. Previously, GPU could not compute a partition while copying
   micro-batches across different GPUs because they all happened on the
   same default CUDA stream.

Other Improvements:
   - Added support for PyTorch 1.2.
   - Redesigned the internal pipeline parallelism to represent
     dependencies transparently.
   - Fixed the hanging issue when an exception is raised in a partition.
   - Fixed the unintended size accumulation (issue kakaobrain#3 by Shiyan Deng) of
     balance_by_size.

Breaking Changes:
   - No more support for PyTorch 1.0.
   - Changed type of GPipe.devices from tuple to list.
   - Removed current_microbatch. This approach turned out to be
     incompatible with checkpointing.

v0.0.2

Released on June 26, 2019.

- Added support for PyTorch 1.1.
- Refined public APIs.
- Detailed documentation.
- Proper exceptions for invalid usage.
- Provided automatic balancing.
- Provided inspecting utilities: current_microbatch() and is_recomputing()
- Reimplemented deferred batch normalization by subclassing.

v0.0.1

Released on May 14, 2019 to evaluate usability and efficiency
internally.

Provided a functional GPipe implementation, including pipeline
parallelism, checkpointing, and deferred batch normalization.

Supported Python 3.6+ and PyTorch 1.0.