tensorflow / tensorflow/datasets
API docs for `tf.data.AUTOTUNE`
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 4.6k
- Forks
- 1.6k
- Avg merge
- 3h 54m
- Merged PRs (30d)
- 1
Description
Description of issue
API docs for tf.data.AUTOTUNE (e.g. here https://www.tensorflow.org/guide/data_performance) lead to https://www.tensorflow.org/api_docs/python/tf/data#AUTOTUNE which doesn't currently have a description (e.g. "prompts the tf.data runtime to tune the value dynamically at runtime"). (UPDATE: the API appears to be out of experimental and is used in a lot of examples).
Submit a pull request?
Can submit a PR. UPDATE: Similar to https://www.tensorflow.org/api_docs/python/tf/data/experimental/CsvDataset, we could document it as e.g. "For num_parallel_calls, use tf.data.AUTOTUNE to set the number of parallel calls dynamically based on available CPU."
cc @MarkDaoust @lamberta @wolffg
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
Start with the tf.data API documentation at https://www.tensorflow.org/api_docs/python/tf/data#AUTOTUNE and compare it with the data performance guide and the experimental CsvDataset documentation linked in the issue. Done means the AUTOTUNE entry explains its dynamic runtime tuning and its use for num_parallel_calls.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 1/5
- Estimated time
- Under an hour
- Activity status
- Stale
- Clarity
- Clearly specified
- Newbie friendliness
- 35/100