tensorboard-pytorch
A library that enables PyTorch, Chainer, MXNet, and NumPy users to write TensorBoard events with simple function calls.
What is tensorboard-pytorch?
TensorboardX is a Python library that enables PyTorch, Chainer, MXNet, and NumPy users to write TensorBoard events with simple function calls. It solves the problem of visualizing training metrics, model graphs, and embeddings for non-TensorFlow frameworks by providing a compatible interface to TensorBoard's logging system.
Deep learning researchers and engineers using PyTorch, Chainer, MXNet, or NumPy who need experiment visualization and tracking without switching to TensorFlow.
Developers choose TensorboardX because it provides full TensorBoard compatibility with minimal code changes, supports all major visualization types, and works seamlessly with their existing PyTorch/MXNet/Chainer workflows.
Overview
tensorboard for pytorch (and chainer, mxnet, numpy, ...)
Use Cases
Best For
- Visualizing PyTorch training metrics like loss and accuracy curves
- Debugging model architectures by exporting computational graphs
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