What’s new in PyTorch 1.11
14/03/2022
6:00 pm
functorch aims to provide composable vmap (vectorization) and autodiff transforms that work well with PyTorch modules and PyTorch autograd.
Stay updated on the latest PyTorch developments, releases, and innovations. This page covers breaking news, feature updates, community highlights, and industry applications of PyTorch. From version releases to ecosystem expansions, discover how this open-source machine learning framework is shaping the future of AI. Get insights on performance improvements, new tools, and real-world implementations across various domains.
functorch aims to provide composable vmap (vectorization) and autodiff transforms that work well with PyTorch modules and PyTorch autograd.
TFRS is built on top of Tensorflow 2 and Keras.
Pytorch Mobile allows for integration of QNNPACK for 8-bit quantized kernels.
Torchbearer is python based library which is basically a model fitting library for PyTorch models and offers a high-level metric and callback API that can be used in a variety of applications.
Various deep learning frameworks such as PyTorch do their computation on numbers in the form of tensors. Tensors are one of the basic fundamental aspects or types of data in deep learning.
Through this tutorial, we will demonstrate how to define and use a convolutional neural network (CNN) in a very easy way by explaining each of the steps in detail.
This article we will walk you through and compare the code usability and ease to use of TensorFlow and PyTorch on the most widely used MNIST dataset to classify handwritten digits.
In this article, we will learn how we can build a simple neural network using the PyTorch library in just a few steps. For this purpose, we will demonstrate a hands-on implementation where we will build a simple neural network for a classification problem.
PyTorch is a free and open-source software released under the Modified BSD license.
The data from Papers With Code suggests PyTorch is the most favourite library among researchers.
The New Multi-Weight API allows loading different pre-trained weights on the same model variant, keeps track of vital meta-data such as the classification labels, and includes the preprocessing transforms necessary for using the models
PyTorch Tabular is a framework for deep learning using tabular data that aims to make it simple and accessible to both real-world applications and academics. The following are the design principles for the library:
Ecosystem CI boosts compatibility, performs regular testing, and raises early warnings in case of possible collisions
Meta and AWS will help enterprises use PyTorch on AWS to bring deep learning models faster and easier into production
While PyTorch Live remained the biggest announcement of the PyTorch Developer Day Conference 2021, there are many important highlights.
PyTorch Live builds on PyTorch Mobile, a runtime that allows developers to go from training a model to deploying it while staying within the PyTorch ecosystem and the React Native library for creating visual user interfaces.
IceVision is a framework for object detection which allows us to perform object detection in a variety of ways using various pre-trained models provided by this framework. It also offers data curation features along with a dashboard for exploratory data analysis. The best feature it has is that it provides
The Lightning Team recently announced that it has collaborated closely with Neptune.ai and Weight & Biases Teams to provide the best experience in terms of Logging and Experiment Tracking.
PyTorch Lightning aims at becoming the simplest, most flexible framework for expediting any kind of deep learning research to production.
With six years passing by since its initial release in 2015, let us look back at TensorFlow’s journey
Stoke ‘wraps’ existing PyTorch code to automatically handle the necessary underlying wiring for all of the supported ‘accelerators’.
TorchDrug covers many recent techniques such as graph machine learning, deep generative models, and reinforcement learning.
The new update is focused on improving the training and performance, alongside developer usability.
A versatile and simple library for sequential agent learning, including reinforcement learning
Facebook AI released Captum 0.4 with new functionality for model understanding.
WarpDrive runs the entire MADRL workflow end-to-end on a single GPU, thereby using a single store of data for simulation roll-outs, inference, and training.
PySyft decouples private data from model training, using federated learning, differential privacy, multi-party computation (MPC) within the main deep learning framework like PyTorch, Keras and TensorFlow.
RTDL (Revisiting Tabular Deep Learning) is an open-source Python package based on implementing the paper “Revisiting Deep Learning Models for Tabular Data”. The library leverages the ease of creating a Deep Learning Model and can be used by practitioners and programmers looking to implement Deep Learning models in tabular data.
A comparative analysis of open-source deep learning optimization libraries DeepSpeed and Horovod for advancing large-scale model training.
At the NeurIPS conference in 2019, PyTorch appeared in 166 papers, whereas TensorFlow appeared in 74 papers.
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