Comments (1)
Hi Kapil, thanks for your kind words and your questions!
The goal of TNT is to provide a lightweight training framework as well as standalone utilities which are agnostic to any training framework. We are able to keep the framework minimal by putting as much as possible into the standalone utilities (torchtnt/utils
) rather than in the framework itself. This also allows users to benefit from the utils if they donβt wish to use the framework.
Compared to Lightning Fabric, TNT runs the training/evaluation/predict loop for the user.
Regarding level of commitment we have a dedicated team at Meta supporting this framework and evolving it to keep up with the latest advances in PyTorch and ML training.
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Related Issues (20)
- Confusion Meter with batch of 1.
- Visdom Image Overlapping HOT 1
- Pypi package stuck at 0.0.4
- supported dimension by TNT?
- APMeter trigger a bug with Pytorch 1.3.1
- Is APMeter working correct way?
- APMeter meets a bug in torch==1.1.0
- Bug using MAP Meter
- Installation error HOT 1
- Issue with torch 1.13 HOT 1
- Improve testing for composable FSDP
- Add conda recipe
- How to check `is_last_batch` in torchtnt==0.1.0? HOT 2
- get_module_summary assertion fail HOT 3
- Could not measure the FLOPs of LSTM HOT 2
- Use PEP692 for AutoUnit kwargs typing HOT 1
- AttributeError: module 'torch.amp' has no attribute 'GradScaler' HOT 4
- Filter app state keys in TorchSnapshotSaver
- Increment epoch should not reset num_steps_completed_in_epoch
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