Popular GitHub Repositories for Tensorflow Gpu

An Open Source Machine Learning Framework for Everyone

? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

ComfyUI docker images for use in GPU cloud and local environments. Includes AI-Dock base for authentication and improved user experience.

Deep Learning for humans

Clone a voice in 5 seconds to generate arbitrary speech in real-time

TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

AI-Powered Photos App for the Decentralized Web ???

:robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. Features: Generate Text, Audio, Video, Images, Voice Cloning, Distributed, P2P inference

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Visualizer for neural network, deep learning and machine learning models

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

The fastai deep learning library

Deezer source separation library including pretrained models.

DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

deep learning for image processing including classification and object-detection etc.