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Bumps pytorch-lightning from 2.0.1.post0 to 2.3.3.

Release notes

Sourced from pytorch-lightning's releases.

Patch release v2.3.3

This release removes the code from the main lightning package that was reported in CVE-2024-5980.

Patch release v2.3.2

Includes a minor bugfix that avoids a conflict with the entrypoint command with another package #20041.

Patch release v2.3.1

Includes minor bugfixes and stability improvements.

Full Changelog: Lightning-AI/pytorch-lightning@2.3.0...2.3.1

Lightning v2.3: Tensor Parallelism and 2D Parallelism

Lightning AI is excited to announce the release of Lightning 2.3 ⚡

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

This release introduces experimental support for Tensor Parallelism and 2D Parallelism, PyTorch 2.3 support, and several bugfixes and stability improvements.

Highlights

Tensor Parallelism (beta)

Tensor parallelism (TP) is a technique that splits up the computation of selected layers across GPUs to save memory and speed up distributed models. To enable TP as well as other forms of parallelism, we introduce a ModelParallelStrategy for both Lightning Trainer and Fabric. Under the hood, TP is enabled through new experimental PyTorch APIs like DTensor and torch.distributed.tensor.parallel.

PyTorch Lightning

Enabling TP in a model with PyTorch Lightning requires you to implement the LightningModule.configure_model() method where you convert selected layers of a model to paralellized layers. This is an advanced feature, because it requires a deep understanding of the model architecture. Open the tutorial Studio to learn the basics of Tensor Parallelism.

 

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@vivekkhimani vivekkhimani added the dependencies Pull requests that update a dependency file label Aug 18, 2024
@vivekkhimani vivekkhimani self-assigned this Aug 18, 2024
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