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update bug_report model links and notebook #10665

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Merged
merged 4 commits into from
Nov 22, 2021
Merged

update bug_report model links and notebook #10665

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8744b05
remove outputs from notebook
rohitgr7 Nov 21, 2021
8b2b985
update links
rohitgr7 Nov 21, 2021
dcb307e
update issue_template
rohitgr7 Nov 21, 2021
68f586a
remove deprecated
rohitgr7 Nov 22, 2021
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10 changes: 5 additions & 5 deletions .github/ISSUE_TEMPLATE/bug_report.md
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Expand Up @@ -16,11 +16,11 @@ assignees: ''
Please reproduce using the BoringModel!

You can use the following Colab link:
https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report/The_BoringModel.ipynb
https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report/bug_report_model.ipynb
IMPORTANT: has to be public.

or this simple template:
https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report_model.py
https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report/bug_report_model.py

If you could not reproduce using the BoringModel and still think there's a bug, please post here
but remember, bugs with code are fixed faster!
Expand All @@ -46,9 +46,9 @@ python collect_env_details.py
You can also fill out the list below manually.
-->

- PyTorch Lightning Version (e.g., 1.3.0):
- PyTorch Version (e.g., 1.8)
- Python version:
- PyTorch Lightning Version (e.g., 1.5.0):
- PyTorch Version (e.g., 1.10):
- Python version (e.g., 3.9):
- OS (e.g., Linux):
- CUDA/cuDNN version:
- GPU models and configuration:
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2 changes: 1 addition & 1 deletion docs/source/advanced/fault_tolerant_training.rst
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Expand Up @@ -134,7 +134,7 @@ Performance Impacts
-------------------

Fault-tolerant Training was tested on common and worst-case scenarios in order to measure the impact of the internal state tracking on the total training time.
On tiny models like the `BoringModel and RandomDataset <https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report_model.py>`_
On tiny models like the `BoringModel and RandomDataset <https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/bug_report/bug_report_model.py>`_
which has virtually no data loading and processing overhead, we noticed up to 50% longer training time with fault tolerance enabled.
In this worst-case scenario, fault-tolerant adds an overhead that is noticeable in comparison to the compute time for dataloading itself.
However, for more realistic training workloads where data loading and preprocessing is more expensive, the constant overhead that fault tolerance adds becomes less noticeable or not noticeable at all.
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