MetOffice / MetOffice/XBTs_classification
Investigate use of dask for improved performance
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- Dominant language
- Jupyter Notebook
- Stars
- 4
- Forks
- 2
- PR merge metrics
- No merged PRs in 30d
Description
Loading of data can be quite slow for what is not a very big dataset. Running the code on Azure Pangeo, we should be able to make use of dask to improve performance. This might require use of daskML
https://dask-ml.readthedocs.io/en/latest/
Contributor guide
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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 by profiling the current data-loading path on Azure Pangeo and read the linked dask-ml documentation. Compare the baseline with a Dask-based approach and document whether it improves loading for the described dataset; the issue names no files or tests.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook
- Domain
- machine-learning, performance
- Issue type
- Refactor
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100