How to compose TensorBoard into pipeline during training
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- Dominant language
- Python
- Stars
- 124
- Forks
- 33
- PR merge metrics
- No merged PRs in 30d
Description
I have tried MLBlocks, MLPrimitives, Orion to train neural network models, but it seems like there is no way to add some custom operations, such as TensorBoard.
BTW, I'm curious about roadmap of MLBlocks. Once I tried this, it didn't save my time actually, due to lots of annotation work. But I was wondering it's good for AutoML. TF/Pytorch do have good AutoML tools, so I'm not sure for now.
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.
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Research direction
No files, tests, or entry points are named. First clarify whether this requests TensorBoard integration, support for custom training operations, or roadmap information, then identify the MLBlocks pipeline and primitive execution paths involved and define the expected training-time behavior before implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100