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high prioritymodule: cudnnRelated to torch.backends.cudnn, and CuDNN supportRelated to torch.backends.cudnn, and CuDNN supportmodule: nnRelated to torch.nnRelated to torch.nnsmallWe think this is a small issue to fix. Consider knocking off high priority small issuesWe think this is a small issue to fix. Consider knocking off high priority small issuestriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module
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Description
🐛 Bug
To Reproduce
Steps to reproduce the behavior:
1.I i updated pytorch version and ctc,use pytorch_nightly, but in my train ,nn.CTCloss() is still zero,so,i would like to ask if the version pytorch(nightly) has been solved this problem
1.
1.
Expected behavior
Environment
Please copy and paste the output from our
environment collection script
(or fill out the checklist below manually).
You can get the script and run it with:
wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
# For security purposes, please check the contents of collect_env.py before running it.
python collect_env.py
- PyTorch Version (e.g., 1.0):
- OS (e.g., Linux):
- How you installed PyTorch (
conda,pip, source): - Build command you used (if compiling from source):
- Python version:
- CUDA/cuDNN version:
- GPU models and configuration:
- Any other relevant information:
Additional context
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high prioritymodule: cudnnRelated to torch.backends.cudnn, and CuDNN supportRelated to torch.backends.cudnn, and CuDNN supportmodule: nnRelated to torch.nnRelated to torch.nnsmallWe think this is a small issue to fix. Consider knocking off high priority small issuesWe think this is a small issue to fix. Consider knocking off high priority small issuestriagedThis issue has been looked at a team member, and triaged and prioritized into an appropriate moduleThis issue has been looked at a team member, and triaged and prioritized into an appropriate module