Figuring out which metrics you need to evaluate is key to deep learning. There are various metrics that we can evaluate the performance of ML algorithms. TorchMetrics is a collection of PyTorch metric implementations, originally a part of the PyTorch Lightning framework for high-performance deep learning. In this article, we will go over how you can use TorchMetrics to evaluate your deep learning models and even create your own metric with a simple to use API.
TorchMetrics is an open-source PyTorch native collection of functional and module-wise metrics for simple performance evaluations. You can use out-of-the-box implementations for common metrics…
Today we are excited to announce Lightning 1.3, containing highly anticipated new features including a new Lightning CLI, improved TPU support, integrations such as PyTorch profiler, new early stopping strategies, predict and validate trainer routines, and more.
In addition, we are standardizing our release schedule. We will be launching a new minor release (1.X.0) every quarter, where we will build new features for 8–10 weeks, and then freeze new additions (except bug fixes) for 2 weeks prior to each minor release. Between these launches will continue to maintain weekly bug fixes releases, as we do now.
New release includes a full set of metrics for information retrieval and other metrics requested by the community
TorchMetrics v0.3.0 includes 6 new metrics for evaluating information retrieval
We are happy to announce TorchMetrics v0.3.0 is now publicly available. It brings some general improvements to the library, the most prominent new feature is a set of metrics for information retrieval.
Information retrieval (IR) metrics are used to evaluate how well a system is retrieving information from a database or from a collection of documents. …
TLDR; This post introduces the PyTorch Lightning and DeepSpeed integration demonstrating how to scale models to billions of parameters with just a few lines of code.
We are happy to announce PyTorch Lightning V1.2.0 is now publicly available. It is packed with new integrations for anticipated features such as:
Autograd provides a profiler that lets you inspect the cost of different operations
Whether you are new to deep learning, or an experienced researcher, Flash offers a seamless experience from baseline experiments to state-of-the-art research. It allows you to build models without being overwhelmed by all the details, and then seamlessly override and experiment with Lightning for full flexibility. Continue reading to learn how to use Flash tasks to get state-of-the-art results in a flash.
Over the past year, PyTorch Lightning has received an enthusiastic response from the community for decoupling research from…
Lightning 1.1 is now available with some exciting new features. Since the launch of V1.0.0 stable release, we have hit some incredible milestones- 10K GitHub stars, 350 contributors, and many new members in our slack community! A few highlights include:
We're thrilled to introduce the beta version of our new sharded model training plugin, in collaboration with FairScale by Facebook. Sharded Training utilizes Data-Parallel Training under the hood, but optimizer states and gradients…
We were hard at work in the last couple of months fine-tuning our API, polishing our docs, recording tutorials, and it’s finally time to share with you all V1.0.0 of PyTorch Lightning. Want the lightning answer to scaling models on the cloud? continue reading.
AI research has evolved much faster than any single framework can keep up with. The field of deep learning is constantly evolving, mostly in complexity and scale. …
PyTorch Lightning framework was built to make deep learning research faster. Why write endless engineering boilerplate? Why limit your training to single GPUs? How can I easily reproduce my old experiments?
Our mission is to minimize engineering cognitive load and maximize efficiency, giving you all the latest AI features and engineering best practices to make your models scale at lightning speed. All Lightning automated features are rigorously tested with every change, reducing the footprints of potential errors.
To make the Lightning experience even more comprehensive, we want to share implementations with the same lightning standards. PyTorch Lightning Bolts is a…
PyTorch Lightning is a deep learning research frameworks to run complex models without the boilerplate.