They say: “Beauty requires sacrifice”.
Nonsense. Beauty requires TensorBoard!
TensorBoard is an interactive visualization toolkit for machine learning experiments. It generates beautiful and powerful plots that can help you understand your model’s training run and graphs.
So we are happy to announce that with the newest Deephaven Docker images (Python with PyTorch and Python with TensorFlow), we added support for TensorBoard.
If you want to be able to take advantage of TensorBoard, Deephaven makes it very easy – just check out our “How-to” guides:
You should also check out the Deephaven Learn library, an easy-to-use bridge between Deephaven tables and your machine learning models. This tool will work regardless of whether the Deephaven table is static or dynamic. Neither your mental model nor your code needs to change as you move between static and real-time use cases.
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