Training a PyTorch Model with DataLoader and Dataset

When you build and train a PyTorch deep learning model, you can provide the training data in several different ways. Ultimately, a PyTorch model works like a function that takes a PyTorch tensor and returns you another tensor. You have a lot of freedom in how to get the input tensors. Probably the easiest is …

Scaled2520Hero

Pre-training generalist agents using offline reinforcement learning

Posted by Aviral Kumar, Student Researcher, and Sergey Levine, Research Scientist, Google Research Reinforcement learning (RL) algorithms can learn skills to solve decision-making tasks like playing games, enabling robots to pick up objects, or even optimizing microchip designs. However, running RL algorithms in the real world requires expensive active data collection. Pre-training on diverse datasets …

fig1 fa2eb5f6 7f0d 4899 ba50 8109b08ece0a

Modular functions design for Advanced Driver Assistance Systems (ADAS) on AWS

Over the last 10 years, a number of players have developed autonomous vehicle (AV) systems using deep neural networks (DNNs). These systems have evolved from simple rule-based systems to Advanced Driver Assistance Systems (ADAS) and fully autonomous vehicles. These systems require petabytes of data and thousands of compute units (vCPUs and GPUs) to train. This …

DIY Urban AI: Researchers Drive Hyper-Local Climate Modeling Movement

The do-it-yourself climate modeling movement is here. Researchers from Northwestern University and Argonne National Laboratory have been launching NVIDIA Jetson-driven edge computing Waggle devices across the globe to collect hyper-local climate information. Waggle is an open source sensor platform for edge computing developed by Argonne. Working with this, scientists share open-source AI code designed for …

Alternate framework for distributed computing tames Big Data’s ever growing costs

The sheer volume of ‘Big Data’ produced today by various sectors is beginning to overwhelm even the extremely efficient computational techniques developed to sift through all that information. But a new computational framework based on random sampling looks set to finally tame Big Data’s ever-growing communication, memory and energy costs into something more manageable.