Merge branch 'master' into bugfix/20852_add-support-for-xla2.7-which-deprecated-methods

Author: AlexandrByzov

.azure/gpu-tests-fabric.yml

@@ -130,7 +130,7 @@ jobs:
       - bash: |
           set -e
           extra=$(python -c "print({'lightning': 'fabric-'}.get('$(PACKAGE_NAME)', ''))")
-          pip install -e ".[${extra}dev]" -U --extra-index-url="${TORCH_URL}"
+          pip install -e ".[${extra}dev]" -U --upgrade-strategy=eager --extra-index-url="${TORCH_URL}"
         displayName: "Install package & dependencies"
 
       - bash: |

.azure/gpu-tests-pytorch.yml

@@ -134,7 +134,7 @@ jobs:
       - bash: |
           set -e
           extra=$(python -c "print({'lightning': 'pytorch-'}.get('$(PACKAGE_NAME)', ''))")
-          pip install -e ".[${extra}dev]" -U --extra-index-url="${TORCH_URL}"
+          pip install -e ".[${extra}dev]" -U --upgrade-strategy=eager --extra-index-url="${TORCH_URL}"
         displayName: "Install package & dependencies"
 
       - bash: pip uninstall -y lightning

.github/CONTRIBUTING.md

@@ -109,6 +109,36 @@ ______________________________________________________________________
 
 ## Guidelines
 
+### Development environment
+
+To set up a local development environment, we recommend using `uv`, which can be installed following their [instructions](https://docs.astral.sh/uv/getting-started/installation/).
+
+Once `uv` has been installed, begin by cloning the repository:
+
+```bash
+git clone https://github.com/Lightning-AI/lightning.git
+cd lightning
+```
+
+Once in root level of the repository, create a new virtual environment and install the project dependencies.
+
+```bash
+uv venv
+# uv venv --python 3.11 # use this instead if you need a specific python version
+
+source .venv/bin/activate # command may differ based on your shell
+uv pip install ".[dev, examples]"
+```
+
+Once the dependencies have been installed, install pre-commit and set up the git hook scripts:
+
+```bash
+uv pip install pre-commit
+pre-commit install
+```
+
+If you would like more information regarding the uv commands, please refer to uv's documentation for more information on their [pip interface](https://docs.astral.sh/uv/pip/).
+
 ### Developments scripts
 
 To build the documentation locally, simply execute the following commands from project root (only for Unix):

.github/markdown-links-config.json

@@ -22,5 +22,9 @@
         "Accept-Encoding": "zstd, br, gzip, deflate"
       }
     }
-  ]
+  ],
+  "timeout": "20s",
+  "retryOn429": true,
+  "retryCount": 5,
+  "fallbackRetryDelay": "20s"
 }

.github/workflows/ci-tests-fabric.yml

@@ -138,7 +138,8 @@ jobs:
       - name: Install package & dependencies
         timeout-minutes: 20
         run: |
-          pip install -e ".[${EXTRA_PREFIX}test,${EXTRA_PREFIX}strategies]" -U --prefer-binary \
+          pip install -e ".[${EXTRA_PREFIX}test,${EXTRA_PREFIX}strategies]" \
+            -U --upgrade-strategy=eager --prefer-binary \
             --extra-index-url="${TORCH_URL}" --find-links="${PYPI_CACHE_DIR}"
           pip list
       - name: Dump handy wheels

.github/workflows/ci-tests-pytorch.yml

@@ -136,7 +136,8 @@ jobs:
       - name: Install package & dependencies
         timeout-minutes: 20
         run: |
-          pip install ".[${EXTRA_PREFIX}extra,${EXTRA_PREFIX}test,${EXTRA_PREFIX}strategies]" -U --prefer-binary \
+          pip install ".[${EXTRA_PREFIX}extra,${EXTRA_PREFIX}test,${EXTRA_PREFIX}strategies]" \
+            -U --upgrade-strategy=eager --prefer-binary \
             -r requirements/_integrations/accelerators.txt \
             --extra-index-url="${TORCH_URL}" --find-links="${PYPI_CACHE_DIR}"
           pip list

.github/workflows/probot-check-group.yml

@@ -14,7 +14,7 @@ jobs:
     if: github.event.pull_request.draft == false
     timeout-minutes: 61 # in case something is wrong with the internal timeout
     steps:
-      - uses: Lightning-AI/probot@v5.4
+      - uses: Lightning-AI/probot@v5.5
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:

README.md

@@ -55,6 +55,12 @@ ______________________________________________________________________
 
  
 
+# Why PyTorch Lightning?   
+
+Training models in plain PyTorch is tedious and error-prone - you have to manually handle things like backprop, mixed precision, multi-GPU, and distributed training, often rewriting code for every new project. PyTorch Lightning organizes PyTorch code to automate those complexities so you can focus on your model and data, while keeping full control and scaling from CPU to multi-node without changing your core code. But if you want control of those things, you can still opt into more DIY.   
+
+Fun analogy: If PyTorch is Javascript, PyTorch Lightning is ReactJS or NextJS.
+
 # Lightning has 2 core packages
 
 [PyTorch Lightning: Train and deploy PyTorch at scale](#why-pytorch-lightning).

_notebooks

@@ -32,7 +32,7 @@ Let's use the `AutoEncoder` as a feature extractor in a separate model.
     class CIFAR10Classifier(LightningModule):
         def __init__(self):
             # init the pretrained LightningModule
-            self.feature_extractor = AutoEncoder.load_from_checkpoint(PATH)
+            self.feature_extractor = AutoEncoder.load_from_checkpoint(PATH).encoder
             self.feature_extractor.freeze()
 
             # the autoencoder outputs a 100-dim representation and CIFAR-10 has 10 classes