Gokulprasath R’s Post

🚀 Accelerate your machine learning journey! 💻 Are you tired of waiting endlessly for your models to train? Unlock the power of your GPU and speed up your TensorFlow code on Windows 11! Check out my latest Medium article where I guide you through the steps to set up CUDA support for TensorFlow on your GPU. Say goodbye to sluggish training times and hello to lightning-fast convergence speeds! Don't let compatibility issues hold you back - I've got you covered with detailed instructions and tips to ensure a smooth setup process. Ready to supercharge your machine learning workflow? Read the full article here:https://lnkd.in/gi-C3PKu #MachineLearning #TensorFlow #GPU #Windows11 #TechTips #DeepLearning #DataScience #Python #CUDA #Performance #TechTutorial #AI #ArtificialIntelligence #Coding #Development Have you tried running TensorFlow on your GPU before? Share your experiences and let me know what methods you use for optimizing your machine learning workflow!

After exploring the world of AI for some time, I started with quick introductory ML / DL videos on YouTube to wet my appetite. Then, I discovered Medium articles showing how to set up a local LLM on my Windows 11 machine Equipped with an RTX 2070 GPU and LLM Studio software, I picked a model fit for my GPU and just like that, it was done! The satisfaction of taking small steps towards hands-on learning is unbeatable. Next up, RAG on python.

Kohya SS for Stable Diffusion Models!!! Kohya SS a powerful Python library tailored for stable diffusion-based models in image, text, and audio generation tasks. Here's why you should be excited: Purpose: Kohya SS simplifies model training and fine-tuning using cutting-edge techniques like LoRA and SDXL, ensuring optimal performance on consumer-grade GPUs. Gradio GUI: With Kohya SS, training models is easier than ever thanks to its intuitive Gradio GUI. Set parameters, generate CLI commands, and train models seamlessly. Platform Support: Whether you're on Linux or macOS, Kohya SS has got you covered, with robust support and ongoing community contributions. Image Generation: Experience real-time sample image generation during training, empowering you to iterate and improve your models efficiently. Troubleshooting: Kohya SS comes equipped with troubleshooting tips for common issues like page file limits and GPU utilization, ensuring smooth sailing in your ML journey. Learn more: Kohya SS: [https://lnkd.in/dd6i_NGy] Let's revolutionize AI together! #KohyaSS #MachineLearning #StableDiffusion #AIInnovation

🚩 I'm happy to share a new QualityScaler release, version 3.5 🚩And over 1.8k ⭐ on Github! Qualityscaler is a Windows app powered by AI to enhance, upscale and denoise photos and videos; compatible with any gpus. 📌 Github > https://lnkd.in/dpsNqZdg 🚀 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬: ✅ New AI engine powerd by onnxruntime-directml (up to 3x faster) ✅ AI-multithreading video upscaling (up to 2x faster than single thread) ✅ New AI model IRCNNx1 (only denoising, no upscaling) ✅ Selectable file output path ✅ and much much more⭐ 👉 full changelogs here > https://lnkd.in/eppm3vx7 #AI #onnx #pytorch #python #directml #upscale #denoise #app

NEW at PyTorch Conferenece: DL Compiler Mini-Summit: https://hubs.la/Q02LK29K0 Since PyTorch 2’s release, torch.compile() has sparked new ideas in DL compiler design. Leaders behind Triton, TorchInductor, Halide, TVM, IREE + Mojo will discuss building DL compilers + growing their ecosystems. Attend this Mini-Summit for FREE with your PyTorch Conference registration - register today: https://hubs.la/Q02LK1WT0 #PyTorchConf #PyTorch #PyTorchFoundation #AI #MachineLearning #ML #DeepLearning #OpenSource #OpenSourceSoftware #OpenSourceDevelopment #OpenSourceCommunity #OSS #LinuxFoundation #events #linux

I am thrilled to share that I have successfully completed a comprehensive course on CUDA programming! 🎉 This journey has been incredibly rewarding, and I have gained a basic understanding of parallel computing and GPU programming. A huge shoutout to the amazing tutorial series on YouTube that guided me through this learning process. You can check out the course here. https://lnkd.in/gQzWJii7 To solidify my knowledge and practice what I've learned, I have created a repository with all the exercise code from the course. If you need any help or want to learn , feel free to explore the code on my GitHub https://lnkd.in/ggFai5jP These exercises, mostly in C language, provide valuable insights into how deep learning models work on GPUs, including optimized techniques for tasks like matrix multiplication.

🔣 Machine Learning in Linux: Surya - multilingual document OCR toolkit adds text recognition —LinuxLinks "Surya is billed as a multilingual document OCR toolkit. It’s a CLI-based utility that can be used with a CPU or GPU. The latest release has added text recognition. And there’s also a Streamlit app, software which turns data scripts into shareable web apps." #surya #ml #ocr #python #pytorch #opensource https://bit.ly/3T1loGS

#snsinstitution #snsdesignthinking #snsdesignthinkers Scalable performance using all available CPU cores on laptops, desktops, and powerful servers Support for the latest CPU instructions Near-native performance through acceleration of core numerical and machine learning packages with libraries like the Intel® oneAPI Math Kernel Library (oneMKL) and Intel® oneAPI Data Analytics Library Productivity tools for compiling Python code into optimized instructions Essential Python bindings for easing integration of Intel native tools with your Python project

🚀 Announcing Datoviz 0.2.0 – a high-performance GPU scientific visualization library built on Vulkan, now available with precompiled Python wheels for Linux, macOS (ARM & Intel), and Windows! Datoviz targets the interactive visualization of large 2D/3D datasets and is a key part of the CZI-funded Vispy 2.0 project, where it will serve as the main GPU backend. While Datoviz provides core GPU visualization capabilities, VisPy 2.0 will deliver high-level plotting functionality—think of it like the relationship between NumPy and SciPy. 🔍 What I’m looking for: 1. Compatibility feedback: I’d appreciate any feedback on how these precompiled Python wheels perform across different operating systems and graphics hardware. 2. Library feedback: As Datoviz is still in its early stages and rapidly evolving, your input on its functionality and design would be invaluable. Since the initial v0.1 release in 2021, Datoviz has matured significantly, with a more robust and modular internal architecture. This paves the way for potential support of other rendering technologies like WebGPU and WebAssembly in the near future. While the feature set is still growing, the primary focus remains on stability, performance, and visual quality. Current visuals include points, markers, line segments, paths, glyphs, images, spheres, 3D meshes, and basic volume rendering. I’m planning to add key features like axes and picking in v0.3 and beyond. I invite you to try it out, report any issues on GitHub, and feel free to ask questions. Contributions are also welcome! 🔗 Check it out on GitHub: https://lnkd.in/eteSy6sU #visualization #dataviz #python #gpu #science #computing #vulkan