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Lightning-AI · Aug 2, 2021 · 960646c · 960646c
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diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -4,8 +4,8 @@ about: Create a report to help us improve
 title: ''
 labels: bug / fix, help wanted
 assignees: ''
-
 ---
+
 ## 🐛 Bug
 
 <!-- A clear and concise description of what the bug is. -->
@@ -46,15 +46,15 @@ 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:
- - OS (e.g., Linux):
- - CUDA/cuDNN version:
- - GPU models and configuration:
- - How you installed PyTorch (`conda`, `pip`, source):
- - If compiling from source, the output of `torch.__config__.show()`:
- - Any other relevant information:
+- PyTorch Lightning Version (e.g., 1.3.0):
+- PyTorch Version (e.g., 1.8)
+- Python version:
+- OS (e.g., Linux):
+- CUDA/cuDNN version:
+- GPU models and configuration:
+- How you installed PyTorch (`conda`, `pip`, source):
+- If compiling from source, the output of `torch.__config__.show()`:
+- Any other relevant information:
 
 ### Additional context
 

diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -4,7 +4,6 @@ about: Suggest an idea for this project
 title: ''
 labels: enhancement, help wanted
 assignees: ''
-
 ---
 
 ## 🚀 Feature
@@ -27,7 +26,7 @@ assignees: ''
 
 <!-- Add any other context or screenshots about the feature request here. -->
 
----
+______________________________________________________________________
 
 #### If you enjoy Lightning, check out our other projects! ⚡
 

diff --git a/CHANGELOG.md b/CHANGELOG.md
diff --git a/dockers/README.md b/dockers/README.md
@@ -20,7 +20,9 @@ docker image build \
     --build-arg PYTORCH_VERSION=1.8 \
     .
 ```
+
 or nightly version from Conda
+
 ```bash
 git clone <git-repository>
 docker image build \
@@ -48,6 +50,7 @@ docker image rm pytorch-lightning:latest
 ## Run docker image with GPUs
 
 To run docker image with access to you GPUs you need to install
+
 ```bash
 # Add the package repositories
 distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
@@ -69,17 +72,17 @@ docker run --rm -it --gpus all pytorchlightning/pytorch_lightning:base-cuda-py3.
 Inspiration comes from https://u.group/thinking/how-to-put-jupyter-notebooks-in-a-dockerfile
 
 1. Build the docker image:
-    ```bash
-    docker image build \
-        -t pytorch-lightning:v1.3.1 \
-        -f dockers/nvidia/Dockerfile \
-        --build-arg LIGHTNING_VERSION=1.3.1 \
-        .
-    ```
-2. start the server and map ports:
-    ```bash
-    docker run --rm -it --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -p 8888:8888 pytorch-lightning:v1.3.1
-    ```
-3. Connect in local browser:
-    - copy the generated path e.g. `http://hostname:8888/?token=0719fa7e1729778b0cec363541a608d5003e26d4910983c6`
-    - replace the `hostname` by `localhost`
+   ```bash
+   docker image build \
+       -t pytorch-lightning:v1.3.1 \
+       -f dockers/nvidia/Dockerfile \
+       --build-arg LIGHTNING_VERSION=1.3.1 \
+       .
+   ```
+1. start the server and map ports:
+   ```bash
+   docker run --rm -it --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=all -p 8888:8888 pytorch-lightning:v1.3.1
+   ```
+1. Connect in local browser:
+   - copy the generated path e.g. `http://hostname:8888/?token=0719fa7e1729778b0cec363541a608d5003e26d4910983c6`
+   - replace the `hostname` by `localhost`
diff --git a/pl_examples/README.md b/pl_examples/README.md
@@ -1,19 +1,22 @@
 # Examples
+
 Our most robust examples showing all sorts of implementations
 can be found in our sister library [lightning-bolts](https://lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2).
 
----
+______________________________________________________________________
 
 ## Basic examples
+
 In this folder we add 3 simple examples:
 
-* [MNIST Classifier](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/basic_examples/simple_image_classifier.py) (defines the model inside the `LightningModule`).
-* [Image Classifier](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/basic_examples/backbone_image_classifier.py) (trains arbitrary datasets with arbitrary backbones).
-* [Autoencoder](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/basic_examples/autoencoder.py) (shows how the `LightningModule` can be used as a system)
+- [MNIST Classifier](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/basic_examples/simple_image_classifier.py) (defines the model inside the `LightningModule`).
+- [Image Classifier](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/basic_examples/backbone_image_classifier.py) (trains arbitrary datasets with arbitrary backbones).
+- [Autoencoder](https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/basic_examples/autoencoder.py) (shows how the `LightningModule` can be used as a system)
 
----
+______________________________________________________________________
 
 ## Domain examples
+
 This folder contains older examples. You should instead use the examples
 in [lightning-bolts](https://lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2)
 for advanced use cases.
diff --git a/pl_examples/basic_examples/README.md b/pl_examples/basic_examples/README.md
@@ -1,8 +1,11 @@
 ## Basic Examples
+
 Use these examples to test how lightning works.
 
 #### MNIST
+
 Trains MNIST where the model is defined inside the `LightningModule`.
+
 ```bash
 # cpu
 python simple_image_classifier.py
@@ -14,17 +17,23 @@ python simple_image_classifier.py --trainer.gpus 2
 python simple_image_classifier.py --trainer.gpus 2 --trainer.accelerator 'dp'
 ```
 
----
+______________________________________________________________________
+
 #### MNIST with DALI
+
 The MNIST example above using [NVIDIA DALI](https://developer.nvidia.com/DALI).
 Requires NVIDIA DALI to be installed based on your CUDA version, see [here](https://docs.nvidia.com/deeplearning/dali/user-guide/docs/installation.html).
+
 ```bash
 python dali_image_classifier.py
 ```
 
----
+______________________________________________________________________
+
 #### Image classifier
+
 Generic image classifier with an arbitrary backbone (ie: a simple system)
+
 ```bash
 # cpu
 python backbone_image_classifier.py
@@ -36,9 +45,12 @@ python backbone_image_classifier.py --trainer.gpus 2
 python backbone_image_classifier.py --trainer.gpus 2 --trainer.accelerator 'dp'
 ```
 
----
+______________________________________________________________________
+
 #### Autoencoder
+
 Showing the power of a system... arbitrarily complex training loops
+
 ```bash
 # cpu
 python autoencoder.py
@@ -49,12 +61,14 @@ python autoencoder.py --trainer.gpus 2
 # dataparallel
 python autoencoder.py --trainer.gpus 2 --trainer.accelerator 'dp'
 ```
----
+
+______________________________________________________________________
+
 # Multi-node example
 
 This demo launches a job using 2 GPUs on 2 different nodes (4 GPUs total).
 To run this demo do the following:
 
 1. Log into the jumphost node of your SLURM-managed cluster.
-2. Create a conda environment with Lightning and a GPU PyTorch version.
-3. Choose a script to submit
+1. Create a conda environment with Lightning and a GPU PyTorch version.
+1. Choose a script to submit
diff --git a/tests/README.md b/tests/README.md
@@ -1,9 +1,11 @@
 # PyTorch-Lightning Tests
+
 Most PL tests train a full MNIST model under various trainer conditions (ddp, ddp2+amp, etc...).
 This provides testing for most combinations of important settings.
 The tests expect the model to perform to a reasonable degree of testing accuracy to pass.
 
 ## Running tests
+
 ```bash
 git clone https://github.com/PyTorchLightning/pytorch-lightning
 cd pytorch-lightning
@@ -23,6 +25,7 @@ such as Horovod, FairScale, NVIDIA/apex, NVIDIA/DALI, etc.
 You can rely on our CI to make sure all these tests pass.
 
 ## Running Coverage
+
 Make sure to run coverage on a GPU machine with at least 2 GPUs and NVIDIA apex installed.
 
 ```bash
@@ -41,11 +44,14 @@ coverage xml
 ## Building test image
 
 You can build it on your own, note it takes lots of time, be prepared.
+
 ```bash
 git clone <git-repository>
 docker image build -t pytorch_lightning:devel-torch1.9 -f dockers/cuda-extras/Dockerfile --build-arg TORCH_VERSION=1.9 .
 ```
+
 To build other versions, select different Dockerfile.
+
 ```bash
 docker image list
 docker run --rm -it pytorch_lightning:devel-torch1.9 bash