With the growing role of artificial intelligence in climate and weather research, efficient model training and inference are in high demand. Current models like FourCastNet and AI-GOMS depend heavily on GPUs, limiting hardware independence, especially for Chinese domestic hardware and frameworks. To address this issue, we present a framework for migrating large-scale atmospheric and oceanic models from PyTorch to MindSpore and optimizing for Chinese chips, and evaluating their performance against GPUs. The framework focuses on software-hardware adaptation, memory optimization, and parallelism. Furthermore, the model's performance is evaluated across multiple metrics, including training speed, inference speed, model accuracy, and energy efficiency, with comparisons against GPU-based implementations. Experimental results demonstrate that the migration and optimization process preserves the models' original accuracy while significantly reducing system dependencies and improving operational efficiency by leveraging Chinese chips as a viable alternative for scientific computing. This work provides valuable insights and practical guidance for leveraging Chinese domestic chips and frameworks in atmospheric and oceanic AI model development, offering a pathway toward greater technological independence.

State-of-the-art language models like T5 have revolutionized the NLP landscape, but their computational demands hinder a large portion of the research community. To address this challenge, we present nanoT5, a specially-optimized PyTorch framework for efficient pre-training and fine-tuning of T5 models. Drawing on insights from optimizer differences and prioritizing efficiency, nanoT5 allows a T5-Base model to be pre-trained on a single GPU in just 16 hours, without any loss in performance. With the introduction of this open-source framework, we hope to widen the accessibility to language modelling research and cater to the community's demand for more user-friendly T5 (Encoder-Decoder) implementations. We make our contributions, including configurations, codebase, pre-training insights, and pre-trained models, available to the public.

The two codes above are similar to what you guys might have seen before in developing a Machine Learning Model. The first object, loss_fn, is created to measure the error rate from the predicted results, while the second object, optimizer, is to reduce the loss in every iteration of the training.

In 2016, Meta (then but a simple country Facebook) launched its open-source AI research library, the Pytorch framework. Six years and 150,000 projects from 2,400 contributors later, Meta announced on Monday that the Pytorch project will soon spin out from the company's direct control to become its own entity, the Pytorch Foundation, a subsidiary within the larger Linux Foundation nonprofit hegemony. Over the last half decade, Pytorch has grown to become a leading standard for the AI research community with Meta CEO Mark Zuckerberg noting in Monday's press release that some 80 percent of "researchers who submit their work at major ML conferences, such as NeurIPS or ICML, harness the framework." "We have built libraries that support some of the principal domains of the AI field, such as torchvision, which powers most of the world's modern computer vision research," Zuckerberg continued. "The framework will continue to be a part of Meta's AI research and engineering work."

With the release of the new version of Pytorch 1.12, Pytorch has come up with a new DataFrame library for data visualization or preprocessing named Torcharrow. Torcharrow is a Pytorch library for data processing and visualization with support for processing tabular data and is more suitable for deep learning data. Torcharrow has come up with the ability of faster processing of data by lighter usage of the processing unit. In this article let us get a brief overview of the latest preprocessing library of Pytorch 1.12 named Torcharrow. Pytorch, an open-sourced machine learning and deep learning framework based on the torch library is used in various applications like computer vision and Natural Language processing.

Two-dimensional tensors are analogous to two-dimensional metrics. Like a two-dimensional metric, a two-dimensional tensor also has $n$ number of rows and columns. Let's take a gray-scale image as an example, which is a two-dimensional matrix of numeric values, commonly known as pixels. Ranging from '0' to '255', each number represents a pixel intensity value. Here, the lowest intensity number (which is '0') represents black regions in the image while the highest intensity number (which is '255') represents white regions in the image.

GAN is a generative ML model that is widely used in advertising, games, entertainment, media, pharmaceuticals, and other industries. You can use it to create fictional characters and scenes, simulate facial aging, change image styles, produce chemical formulas synthetic data, and more. For example, the following images show the effect of picture-to-picture conversion. The following images show the effect of synthesizing scenery based on semantic layout. This post walks you through building your first GAN model using Amazon SageMaker. This is a journey of learning GAN from the perspective of practical engineering experiences, as well as opening a new AI/ML domain of generative models.

This tutorial focus on the implementation of the image segmentation architecture called UNET in the PyTorch framework. It's a simple encoder-decoder architecture developed by Olaf Ronneberger et al. for Biomedical Image Segmentation in 2015 at the University of Freiburg, Germany. An image consists of multiple objects inside it, such as people, cars, animals, or any other object. To classify the image, we use image classification, where the task is to predict the label or class of the input image. Now imagine, we need to find the exact location of the object, i.e, which pixel belongs to the which object.

My original post is Here and the code notebook is on my GitHub Here! You can follow this tutorial if you would like to know about Graph Neural Networks (GNNs) through a practical example using PyTorch framework. A BIG caveat here is to emphasize that I do not mean GNNs are just CNNs that operate on graphs, but what I want to say is that I felt comfortable with GNNs when I linked it to my understanding of CNNs and learnt something about graphs. For sure there are many other variations of GNNs but let us stick to this for those 10 minutes of reading. I hope this works for you as well, note that I put the sign to avoid causing some people to cringe. Graph neural networks, as their name tells, are neural networks that work on graphs.

Today, corporations like Google, Facebook and Microsoft have been dominating tools and deep learning frameworks that AI researchers use globally. Many of their open-source libraries are now gaining popularity on GitHub, which is helping budding AI developers across the world build flexible and scalable machine learning models. From conversational chatbot, self-driving cars to the weather forecast and recommendation systems, AI developers are experimenting with various neural network architectures, hyperparameters, and other features to fit the hardware constraints of edge platforms. Some of the popular deep learning frameworks include Google's TensorFlow and Facebook's Caffe2, PyTorch, Torchcraft AI and Hydra, etc. According to Statista, AI business operations global revenue is expected to touch $10.8 billion by 2023, and the natural language processing (NLP) market size globally is expected to reach $43.3 billion by 2025. With the rise of AI adoption across businesses, the need for open-source libraries and architecture will only increase in the coming months.