prob-ml / bliss

Bayesian Light Source Separator
MIT License
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torchdata #1026

Closed jeff-regier closed 4 months ago

jeff-regier commented 4 months ago

This PR simplifies our code for distributed training by using the torchdata package. The data loading workers seem to be more efficient now.

codecov[bot] commented 4 months ago

Codecov Report

Attention: Patch coverage is 94.92754% with 7 lines in your changes missing coverage. Please review.

Project coverage is 92.54%. Comparing base (06014a3) to head (3487354).

Files Patch % Lines
bliss/cached_dataset.py 94.73% 7 Missing :warning:
Additional details and impacted files ```diff @@ Coverage Diff @@ ## master #1026 +/- ## ========================================== + Coverage 92.52% 92.54% +0.01% ========================================== Files 25 23 -2 Lines 3132 3071 -61 ========================================== - Hits 2898 2842 -56 + Misses 234 229 -5 ``` | [Flag](https://app.codecov.io/gh/prob-ml/bliss/pull/1026/flags?src=pr&el=flags&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=prob-ml) | Coverage Δ | | |---|---|---| | [unittests](https://app.codecov.io/gh/prob-ml/bliss/pull/1026/flags?src=pr&el=flag&utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=prob-ml) | `92.54% <94.92%> (+0.01%)` | :arrow_up: | Flags with carried forward coverage won't be shown. [Click here](https://docs.codecov.io/docs/carryforward-flags?utm_medium=referral&utm_source=github&utm_content=comment&utm_campaign=pr+comments&utm_term=prob-ml#carryforward-flags-in-the-pull-request-comment) to find out more.

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