Guardrails AI’s Post

Simple helloGPU program to configure number of threads and threadblocks to run on GPU https://lnkd.in/gSUxw64N

Excited to see llm.c using Ubicloud’s GPU runners! https://lnkd.in/eaVWYZHM Also, thanks for the shout out to Ubicloud and Nvidia in the commit message! We love llm.c because it removes a lot of the mystery around what it takes to train LLMs. train_gpt2.c provides a clean, minimal, and reference implementation to train an LLM in 1175 lines of C code. The commit message for adding GPU runners shows in a few easy steps how to do pre-processing, train models in various configurations, and test that C and PyTorch outputs agree. https://lnkd.in/eiUd4z5E llm.c helped us understand what goes into training LLMs & made these concepts accessible to a broad audience. With Ubicloud’s new runners, we aspire to make CI/CD more accessible for GPU workflows. If you have use cases around enabling GPUs in your development pipeline, drop us a line anytime! https://lnkd.in/eaHPU8kX

https://lnkd.in/g3Dde3ZM rocAlution is AMD's equivalence of Nvidia AMGX - sparse matrix solver using a single/many GPUs with MPI. HIP library/APIs are almost 1:1 matching of CUDA library/API - some device functions are not supported yet - and conversion is quite straightforward. However, rocAlution has completely different interfaces than AMGX, and any existing codes may need lots of re-writing if a porting is required. Anyway, Hypre for CPU, AMGX for Nvidia, and rocALUTION for AMD. Now equations are completed 😀

Say Goodbye to the GIL with PEP 703! PEP 703 proposes making the GIL optional, letting CPython run more smoothly on multi-core processors. This means faster, more efficient multi-threaded programs. For those using `pytest` for system testing, this change promises better performance and quicker test runs. It's like giving your tests a turbo boost! I still don’t know what would happen in the future of xdist plugin, but we’ll see. Source: https://lnkd.in/ddqnM_-f

An open source system, based on FSDP and QLoRA, that can Fine-Tune a 70b model on two 24GB GPUs. - FSPD is a kind of abstraction on top of Pytorch - Apple GPU NOT Supported

We are thrilled to share a groundbreaking development in leveraging the capabilities of #GPUs in solving linear programming problems using PDLP. This milestone is the result of a collaborative effort among the talented developers at Cardinal Operations and esteemed researchers from University of Chicago, Stanford University, and Shanghai University of Finance and Economics. Our experiments reveal that utilizing PDLP on GPUs can achieve comparable, and in many cases, superior performance in solving LPs when comparing with COPT's state-of-the-art Simplex and Barrier methods on CPUs. In an exciting move to foster innovation and collaboration within the OR community, the team has made the codes #opensource at https://lnkd.in/ditFKkkW. We believe this finding leads to new opportunities in solving tough continuous optimization problems (and hopefully MILPs too!) and new possibilities for real-life OR applications with specialized hardwares. Stay tuned for more updates as we continue to push the boundaries and redefine what's possible in optimization. #operationsresearch #optimization #datascience #gpu Reference: Haihao Lu, Jinwen Yang, Haodong Hu, Qi Huangfu, Jinsong Liu, Tianhao Liu, Yinyu Ye, Chuwen Zhang, Dongdong Ge. (2023) cuPDLP-C: A Strengthened Implementation of cuPDLP for Linear Programming by C language. Arxiv: https://lnkd.in/dqBcYcSV

Imagine training a LLM from scratch in pure C without PyTorch or cPython!! and its Done!! Check out the below #repo in #github #technews #llm #generativeai #ganfinityai

Tip and Tricks to correct a Cuda Toolkit installation in Conda https://lnkd.in/d3bVV_j7

Simplifying AI Development with Mojo and MAX Current Generative AI applications struggle with complex, multi-language workloads across various hardware types. The Modular Mojo language and MAX platform offer a solution by unifying CPU and GPU programming into a single Pythonic model. This approach aims to simplify development, boost productivity, and accelerate AI innovation. Presented by Chris Lattner, co-founder and CEO of Modular, at the AI Engineer World's Fair in San Francisco. Check it out: https://lnkd.in/dQxT9ejY #Mojo #Python #PyTorch #MAX #Modular

Karpathy just released a new tutorial where he trains a LLM from scratch in pure C without PyTorch or cPython. Starts with GPT-2 training on CPU/fp32 in only ~1,000 lines of clean code. It compiles and runs instantly, and exactly matches the PyTorch reference implementation. Link in comments. ↓ Are you technical? Check out https://AlphaSignal.ai to get a weekly summary of the top trending models, repos and papers in AI. Read by 175,000+ engineers and researchers.