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Merge branch 'master' into fix/20972-mlflow-logger-windows-uri-from-master
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‎.azure/gpu-benchmarks.yml

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@@ -46,7 +46,7 @@ jobs:
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variables:
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DEVICES: $( python -c 'print("$(Agent.Name)".split("_")[-1])' )
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container:
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.5-cuda12.1.0"
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image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.8"
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options: "--gpus=all --shm-size=32g"
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strategy:
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matrix:

‎.azure/gpu-tests-fabric.yml

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strategy:
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matrix:
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"Fabric | oldest":
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.10-torch2.1-cuda12.1.1"
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image: "pytorchlightning/pytorch_lightning:base-cuda12.1.1-py3.10-torch2.1"
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PACKAGE_NAME: "fabric"
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"Fabric | latest":
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.8-cuda12.6.3"
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image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.8"
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PACKAGE_NAME: "fabric"
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#"Fabric | future":
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# image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.7-cuda12.6.3"
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# image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.7"
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# PACKAGE_NAME: "fabric"
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"Lightning | latest":
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.8-cuda12.6.3"
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image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.8"
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PACKAGE_NAME: "lightning"
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workspace:
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clean: all

‎.azure/gpu-tests-pytorch.yml

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strategy:
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matrix:
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"PyTorch | oldest":
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.10-torch2.1-cuda12.1.1"
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image: "pytorchlightning/pytorch_lightning:base-cuda12.1.1-py3.10-torch2.1"
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PACKAGE_NAME: "pytorch"
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"PyTorch | latest":
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.8-cuda12.6.3"
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image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.8"
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PACKAGE_NAME: "pytorch"
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#"PyTorch | future":
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# image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.7-cuda12.6.3"
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# image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.7"
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# PACKAGE_NAME: "pytorch"
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"Lightning | latest":
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image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.8-cuda12.6.3"
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image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.8"
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PACKAGE_NAME: "lightning"
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pool: lit-rtx-3090
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variables:

‎.lightning/workflows/fabric.yml

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matrix: {}
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include:
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# note that this is setting also all oldest requirements which is linked to Torch == 2.0
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- image: "pytorchlightning/pytorch_lightning:base-cuda-py3.10-torch2.1-cuda12.1.1"
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- image: "pytorchlightning/pytorch_lightning:base-cuda12.1.1-py3.10-torch2.1"
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PACKAGE_NAME: "fabric"
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- image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.7-cuda12.6.3"
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- image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.7"
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PACKAGE_NAME: "fabric"
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# - image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.7-cuda12.6.3"
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# - image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.7"
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# PACKAGE_NAME: "fabric"
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- image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.7-cuda12.6.3"
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- image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.7"
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PACKAGE_NAME: "lightning"
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exclude: []
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‎.lightning/workflows/pytorch.yml

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matrix: {}
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include:
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# note that this is setting also all oldest requirements which is linked to Torch == 2.0
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- image: "pytorchlightning/pytorch_lightning:base-cuda-py3.10-torch2.1-cuda12.1.1"
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- image: "pytorchlightning/pytorch_lightning:base-cuda12.1.1-py3.10-torch2.1"
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PACKAGE_NAME: "pytorch"
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- image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.7-cuda12.6.3"
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- image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.7"
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PACKAGE_NAME: "pytorch"
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# - image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.7-cuda12.6.3"
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# - image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.7"
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# PACKAGE_NAME: "pytorch"
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- image: "pytorchlightning/pytorch_lightning:base-cuda-py3.12-torch2.7-cuda12.6.3"
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- image: "pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.12-torch2.7"
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PACKAGE_NAME: "lightning"
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exclude: []
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‎dockers/README.md

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@@ -11,7 +11,13 @@ git clone https://github.com/Lightning-AI/lightning.git
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docker image build -t pytorch-lightning:latest -f dockers/base-cuda/Dockerfile .
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# build with specific arguments
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docker image build -t pytorch-lightning:base-cuda-py3.9-torch1.13-cuda11.7.1 -f dockers/base-cuda/Dockerfile --build-arg PYTHON_VERSION=3.9 --build-arg PYTORCH_VERSION=1.13 --build-arg CUDA_VERSION=11.7.1 .
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docker image build \
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-t pytorch-lightning:base-cuda12.6.3-py3.10-torch2.8 \
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-f dockers/base-cuda/Dockerfile \
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--build-arg PYTHON_VERSION=3.10 \
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--build-arg PYTORCH_VERSION=2.8 \
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--build-arg CUDA_VERSION=12.6.3 \
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.
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```
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To run your docker use
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and later run the docker image with `--gpus all`. For example,
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```
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docker run --rm -it --gpus all pytorchlightning/pytorch_lightning:base-cuda-py3.9-torch1.13-cuda11.7.1
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docker run --rm -it --gpus all pytorchlightning/pytorch_lightning:base-cuda12.6.3-py3.10-torch2.8
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```
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## Run Jupyter server
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1. Build the docker image:
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```bash
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docker image build -t pytorch-lightning:v1.6.5 -f dockers/nvidia/Dockerfile --build-arg LIGHTNING_VERSION=1.6.5 .
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docker image build -t pytorch-lightning:v2.5.1 -f dockers/nvidia/Dockerfile --build-arg LIGHTNING_VERSION=2.5.1 .
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```
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1. start the server and map ports:
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```bash
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docker run --rm -it --gpus=all -p 8888:8888 pytorch-lightning:v1.6.5
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docker run --rm -it --gpus=all -p 8888:8888 pytorch-lightning:v2.5.1
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```
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1. Connect in local browser:
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- copy the generated path e.g. `http://hostname:8888/?token=0719fa7e1729778b0cec363541a608d5003e26d4910983c6`

‎dockers/release/Dockerfile

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ARG PYTORCH_VERSION=2.8
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ARG CUDA_VERSION=12.6.3
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FROM pytorchlightning/pytorch_lightning:base-cuda-py${PYTHON_VERSION}-torch${PYTORCH_VERSION}-cuda${CUDA_VERSION}
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FROM pytorchlightning/pytorch_lightning:base-cuda${CUDA_VERSION}-py${PYTHON_VERSION}-torch${PYTORCH_VERSION}
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LABEL maintainer="Lightning-AI <https://github.com/Lightning-AI>"
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‎docs/source-pytorch/accelerators/gpu_intermediate.rst

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MASTER_ADDR=localhost MASTER_PORT=random() WORLD_SIZE=3 NODE_RANK=0 LOCAL_RANK=1 python my_file.py --accelerator 'gpu' --devices 3 --etc
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MASTER_ADDR=localhost MASTER_PORT=random() WORLD_SIZE=3 NODE_RANK=0 LOCAL_RANK=2 python my_file.py --accelerator 'gpu' --devices 3 --etc
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Using DDP this way has a few disadvantages over ``torch.multiprocessing.spawn()``:
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Using DDP this way has a few advantages over ``torch.multiprocessing.spawn()``:
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1. All processes (including the main process) participate in training and have the updated state of the model and Trainer state.
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2. No multiprocessing pickle errors

‎docs/source-pytorch/model/manual_optimization.rst

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d_opt = torch.optim.Adam(self.D.parameters(), lr=1e-5)
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return g_opt, d_opt
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Learning Rate Scheduling
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========================
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super().__init__()
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self.automatic_optimization = False
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
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return [optimizer], [scheduler]
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def training_step(self, batch, batch_idx):
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# do forward, backward, and optimization
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super().__init__()
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self.automatic_optimization = False
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)
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return [optimizer], [scheduler]
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def training_step(self, batch, batch_idx):
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# do forward, backward, and optimization
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super().__init__()
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self.automatic_optimization = False
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
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scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.1, patience=10)
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return [optimizer], [scheduler]
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def on_train_epoch_end(self):
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sch = self.lr_schedulers()
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# If the selected scheduler is a ReduceLROnPlateau scheduler.
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if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau):
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sch.step(self.trainer.callback_metrics["loss"])
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sch.step(self.trainer.callback_metrics["loss"])
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.. note::
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:meth:`~lightning.pytorch.core.LightningModule.configure_optimizers` supports 6 different ways to define and return
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optimizers and learning rate schedulers. Regardless of the way you define them, `self.optimizers()` will always return
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either a single optimizer if you defined a single optimizer, or a list of optimizers if you defined multiple
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optimizers. The same applies to the `self.lr_schedulers()` method, which will return a single scheduler
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if you defined a single scheduler, or a list of schedulers if you defined multiple schedulers
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Optimizer Steps at Different Frequencies

‎tests/legacy/back-compatible-versions.txt

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2.3.3
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2.5.1
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2.5.2
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2.5.3

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