Comments (5)
rolled back the badge until the other one works.
I don't want to add unnecessary friction or mistrust at this stage in the project. If the other badge works, then great. Otherwise we'll keep doing it manually.
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Then I would move the badge image to the docs folder...
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Pls have look at the parameters codecov --help
I guess that you need to specify the commit or something like this... unfortunately I was using automatic mode for most of my project so this is a bit new for me... Also, we can ask Codecov support for advice...
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ok. in the meantime let’s roll back to the original badge. this is critical during the early adoption period.
once the auto thing is figured out we can roll back
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i’ll try one more time from master. maybe i have to run codecov on master and push directly. if that fails, we use the old static badge.
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Related Issues (20)
- Construct objects from yaml by classmethod
- FSDP Strategy checkpoint loading
- Current FSDPPrecision does not support custom scaler for 16-mixed precision
- Differentiate testing multiple sets/models when logging
- Issue in Manual optimisation, during self.manual_backward call HOT 1
- Existing metric keys not moved to device after LearningRateFinder
- Checkpoint every_n_steps reruns epoch on restore HOT 3
- Metrics logged by self.log and metric.compute() are different HOT 1
- Multi-node Training with DDP stuck at "Initialize distributed..." on SLURM cluster HOT 3
- Full validation after first microbatch when training after LearningRateFinder
- Add a warning when some of the modules are in eval mode before the training stage
- why pytorch-lightning doc say "Model-parallel training (FSDP and DeepSpeed)". I think there is something wrong. HOT 1
- AWS Trainium fails number of device validation when using more than 1 accelerator on the instances
- OnExceptionCheckpoint: training resumes if ckpt found, even if no ckpt_path provided
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- How to incorporate vLLM in Lightning for LLM inference?
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- Loading large models with fabric, FSDP and empty_init=True does not work
- Unable to extract confusion matrix as a metric from trainer HOT 1
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