tensorflow / tensorflow/tensorboard
Scaling to very large numbers of tags within the scalar dashboard
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Description
TensorBoard is capable of presenting runs with 10k+ tags, but is very slow when doing so.
I created a logdir with a single run with 12,345 tags. Each tag contains a single scalar. Loading the frame of the page was quick, but there it hung until the list of scalar tags was loaded. This took 1.6 minutes
I've uploaded the logdir to 12345tags.zip and attached to this github issue.
12345tags.zip
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Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Reproduce the issue with the attached 12,345-tag logdir and inspect the scalar dashboard path that loads the list of scalar tags. Measure the loading step that takes 1.6 minutes and trace it to the relevant frontend entry point. Done means the scalar dashboard loads the large tag set substantially faster without regressing normal tag lists.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- typescript
- Domain
- frontend, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100