TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs.
This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard, see the tutorial: TensorBoard: Visualizing Learning. For in-depth information on the Graph Visualizer, see this tutorial: TensorBoard: Graph Visualization.
You may also want to watch this video tutorial that walks through setting up and using TensorBoard. There's an associated tutorial with an end-to-end example of training TensorFlow and using TensorBoard.
Before running TensorBoard, make sure you have generated summary data in a log directory by creating a summary writer:
In order to run TensorBoard, you must install the following package:
torch >= 1.0.1
future
tensorflow >=1.12
onnx
requests
- GraphEdit: A web-based graph editor for different deep learning framework, including caffe/caffe2, pytorch, onnx and tensorflow
- Convert: A tool for transformation among different deep learning frameworks
- DebugDB: A grapg debug tool for tracing the training process of different deep learning frameworks
- Inference: A tool for checking a pretrained deep learning model, including features, data and model comparison