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Summary: This PR adds TensorBoard logging support natively within PyTorch. It is based on the tensorboardX code developed by lanpa and relies on changes inside the tensorflow/tensorboard repo landing at https://github.com/tensorflow/tensorboard/pull/2065. With these changes users can simply `pip install tensorboard; pip install torch` and then log PyTorch data directly to the TensorBoard protobuf format using ``` import torch from torch.utils.tensorboard import SummaryWriter writer = SummaryWriter() s1 = torch.rand(1) writer.add_scalar('data/scalar1', s1[0], 0) writer.close() ``` Design: - `EventFileWriter` and `RecordWriter` from tensorboardX now live in tensorflow/tensorboard - `SummaryWriter` and PyTorch-specific conversion from tensors, nn modules, etc. now live in pytorch/pytorch. We also support Caffe2 blobs and nets. Action items: - [x] `from torch.utils.tensorboard import SummaryWriter` - [x] rename functions - [x] unittests - [x] move actual writing function to tensorflow/tensorboard in https://github.com/tensorflow/tensorboard/pull/2065 Review: - Please review for PyTorch standard formatting, code usage, etc. - Please verify unittest usage is correct and executing in CI Any significant changes made here will likely be synced back to github.com/lanpa/tensorboardX/ in the future. cc orionr, ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/16196 Differential Revision: D15062901 Pulled By: orionr fbshipit-source-id: 3812eb6aa07a2811979c5c7b70810261f9ea169e |
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| .. | ||
| __init__.py | ||
| _caffe2_graph.py | ||
| _convert_np.py | ||
| _embedding.py | ||
| _onnx_graph.py | ||
| _proto_graph.py | ||
| _pytorch_graph.py | ||
| _utils.py | ||
| summary.py | ||
| writer.py | ||