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The SummaryWriter class provides a high-level API to create an event file in a given directory and add summaries and events to it. The class updates the file contents asynchronously. This allows a training program to call methods to add data to the file directly from the training loop, without slowing down training. TensorboardX now supports logging directly to Comet. Comet is a free cloud based solution that allows you to automatically track, compare and explain your experiments. It adds a lot of functionality on top of tensorboard such as dataset management, diffing experiments, seeing the code that generated the results and more. Create special chart by collecting charts tags in ‘scalars’. Note that this function can only be called once for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called before or after the training loop.
Features
- A web server to serve visualizations of the training progress of a neural network
- Visualizes scalar values, images, text, etc.
- The purpose of this package is to let researchers use a simple interface to log events within PyTorch
- This package currently supports logging scalar, image, audio, histogram, text, embedding, and the route of back-propagation
- Each subfolder will be treated as different experiments in tensorboard
- Scalar value is the most simple data type to deal with
Project Samples
