NumPy & SciPy for GPU

A flexible framework of neural networks for deep learning

GPU-accelerated Deep Learning on Windows 10 native

The purpose of this tutorial is to learn how to install and prepare TensorFlow framework to train your own convolutional neural network object detection classifier for multiple objects, starting from scratch

Diagnose and Fix CUDA / GPU environments compatibility issues locally, in Docker, and CI/CD. CLI + MCP server available.

Script to remotely check GPU servers for free GPUs

Minimal Deep Learning library is written in Python/Cython/C++ and Numpy/CUDA/cuDNN.

Lightweight turnkey solution for AI

Set up CI in DL/ cuda/ cudnn/ TensorRT/ onnx2trt/ onnxruntime/ onnxsim/ Pytorch/ Triton-Inference-Server/ Bazel/ Tesseract/ PaddleOCR/ NVIDIA-docker/ minIO/ Supervisord on AGX or PC from scratch.

Run YOLOv4 directly with OpenCV using the CUDA enabled DNN module.

Social Distancing and Face Mask Detection using TensorFlow. Install all required Libraries and GPU drivers as well. Refer to README.md or REPORT for know to installation requirement

Revives the last functional CUDA deep-learning stack on macOS High Sierra. NVIDIA CUDA 10.2 + cuDNN 7.6.5 + PyTorch 1.7.0 fully working on GTX Pascal GPUs — a discontinued ecosystem resurrected.

Image recognition and classification using Convolutional Neural Networks with TensorFlow

[IJCNLP 2017 - Accepted] Multi-tasking deep learning framework that achieves state-of-the-art results in sentiment analysis, topic prediction, and hashtag recommendation.

How to install CUDA & cuDNN for Machine Learning

Simple tests for JAX, PyTorch, and TensorFlow to test if the installed NVIDIA drivers are being properly picked up

A python library to control nvidia gpu fan to use more aggressive cooling for instance for DL applications. Use at your own risk.

AI podcast summary from a youtube video using Anthropic or XAI and Elevenlabs voices

TensorFlow installation on windows10 CUDA and cudnn