Search Results for "gpu" - Page 4
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Showing 315 open source projects for "gpu"
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Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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1
MoCo (Momentum Contrast)
Self-supervised visual learning using momentum contrast in PyTorch
...The repository includes implementations for both MoCo v1 and MoCo v2, the latter improving training stability and performance through architectural and augmentation enhancements. Training is optimized for distributed multi-GPU environments, using DistributedDataParallel for speed and simplicity. -
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HunyuanVideo-I2V
A Customizable Image-to-Video Model based on HunyuanVideo
...The repository includes pretrained weights, inference and sampling scripts, training code for LoRA effects, and support for parallel inference via xDiT. Resolution, video length, stability mode, flow shift, seed, CPU offload etc. Parallel inference support using xDiT for multi-GPU speedups. LoRA training / fine-tuning support to add special effects or customize generation. -
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Aviary
Ray Aviary - evaluate multiple LLMs easily
...Aviary has native support for autoscaling and multi-node deployments thanks to Ray and Ray Serve. Aviary can scale to zero and create new model replicas (each composed of multiple GPU workers) in response to demand. Ray ensures that the orchestration and resource management is handled automatically. Aviary is able to support hundreds of replicas and clusters of hundreds of nodes, deployed either in the cloud or on-prem. -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment. However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. It remains a challenge for AI researchers to implement complex distributed training solutions for their models. ... -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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Text Generation Inference
Large Language Model Text Generation Inference
Text Generation Inference is a high-performance inference server for text generation models, optimized for Hugging Face's Transformers. It is designed to serve large language models efficiently with optimizations for performance and scalability. -
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DeepSpeed MII
MII makes low-latency and high-throughput inference possible
...Incredibly powerful text generation models such as the Bloom 176B, or image generation model such as Stable Diffusion are now available to anyone with access to a handful or even a single GPU through platforms such as Hugging Face. While open-sourcing has democratized access to AI capabilities, their application is still restricted by two critical factors: inference latency and cost. DeepSpeed-MII is a new open-source python library from DeepSpeed, aimed towards making low-latency, low-cost inference of powerful models not only feasible but also easily accessible. ... -
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FLUX.2
Official inference repo for FLUX.2 models
FLUX.2 is a state-of-the-art open-weight image generation and editing model released by Black Forest Labs aimed at bridging the gap between research-grade capabilities and production-ready workflows. The model offers both text-to-image generation and powerful image editing, including editing of multiple reference images, with fidelity, consistency, and realism that push the limits of what open-source generative models have achieved. It supports high-resolution output (up to ~4 megapixels),... -
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exo
Run your own AI cluster at home with everyday devices
Run your own AI cluster at home with everyday devices. Maintained by exo labs. Forget expensive NVIDIA GPUs, unify your existing devices into one powerful GPU, iPhone, iPad, Android, Mac, Linux, or pretty much any device. Now the default models, run 8B, 70B, and 405B parameter models on your own devices. -
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PyTorch Image Models
The largest collection of PyTorch image encoders / backbones
timm (PyTorch Image Models) is a premier library hosting a vast collection of state-of-the-art image classification models and backbones such as ResNet, EfficientNet, NFNet, Vision Transformer, ConvNeXt, and more. Created by Ross Wightman and now maintained by Hugging Face, it includes pretrained weights, data loaders, augmentations, optimizers, schedulers, and reference scripts for training, evaluation, inference, and model export. It's an essential toolkit for vision research and... -
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DINOv3
Reference PyTorch implementation and models for DINOv3
DINOv3 is the third-generation iteration of Meta’s self-supervised visual representation learning framework, building upon the ideas from DINO and DINOv2. It continues the paradigm of learning strong image representations without labels using teacher–student distillation, but introduces a simplified and more scalable training recipe that performs well across datasets and architectures. DINOv3 removes the need for complex augmentations or momentum encoders, streamlining the pipeline while... -
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Selkies-GStreamer
Open-Source Low-Latency Accelerated Linux WebRTC HTML5 Remote Desktop
...This module acts as a high-performance media pipeline that captures video, encodes it with low latency, and streams it via WebRTC to client browsers. It is optimized for GPU-accelerated encoding and integrates with Kubernetes-based deployments to enable scalable, real-time remote desktop sessions. This component plays a critical role in delivering smooth, responsive experiences for cloud-based workstations, gaming, or visualization tools. -
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Style-Bert-VITS2
Style-Bert-VITS2: Bert-VITS2 with more controllable voice styles
...It includes a full GUI editor to script dialogue, set different styles per line, edit dictionaries, and save/load projects, plus a separate web UI and Colab notebooks for training and experimentation. For those who only need synthesis, the project is published as a Python library (pip install style-bert-vits2) and can run on CPU without an NVIDIA GPU, though training still requires GPU hardware. -
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Bert-VITS2
VITS2 backbone with multilingual-bert
...The repository includes everything needed to train, fine-tune, and run the model, from configuration files to preprocessing scripts, spectrogram utilities, and training entrypoints for multi-GPU and multi-node setups. It provides emotional modeling through “emo embeddings,” allowing voices to be conditioned on different affective states during synthesis. Releases include optimizations for Japanese and English alignment, expanded training data, spec caching and pre-generation tools, as well as ONNX export for more lightweight inference deployments. -
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Depth Pro
Sharp Monocular Metric Depth in Less Than a Second
...Unlike many prior approaches, it does not require camera intrinsics or extra metadata, yet still outputs metric depth suitable for downstream 3D tasks. Apple highlights both accuracy and speed: the model can synthesize a ~2.25-megapixel depth map in around 0.3 seconds on a standard GPU, enabling near real-time applications. The repo and research page emphasize boundary fidelity and crisp geometry, addressing a common weakness in monocular depth where edges can blur. Community integrations (e.g., inference wrappers and UI nodes) have sprung up around the model, reflecting practical interest in video, AR, and generative pipelines. ... -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming. Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). Losses: BinaryFocalLoss, Focal, ReducedFocal, Lovasz, Jaccard and Dice losses, Wing Loss and more. Extras for Catalyst library (Visualization of batch predictions, additional metrics). ... -
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TorchAudio
Data manipulation and transformation for audio signal processing
The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the same philosophy of providing strong GPU acceleration, having a focus on trainable features through the autograd system, and having consistent style (tensor names and dimension names). Therefore, it is primarily a machine learning library and not a general signal processing library. The benefits of PyTorch can be seen in torchaudio through having all the computations be through PyTorch operations which makes it easy to use and feel like a natural extension. -
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VisPy
Main repository for Vispy
Vispy is an open-source, high-performance interactive visualization library in Python, designed for creating scientific visualizations and interactive plots. It leverages the power of modern Graphics Processing Units (GPUs) through OpenGL to render large datasets efficiently. Vispy supports a wide range of visualization types, including 2D plots, 3D visualizations, volume rendering, and more, making it suitable for scientific research, data analysis, and educational purposes. -
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Axolotl
Go ahead and axolotl questions
Axolotl is a powerful and flexible framework for fine-tuning large language models on custom datasets. Built for researchers and developers, Axolotl simplifies the process of adapting LLMs for specific tasks, including chat, code generation, and instruction following. It supports a wide variety of model architectures and offers out-of-the-box optimization strategies for efficient training. -
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API-for-Open-LLM
Openai style api for open large language models
API-for-Open-LLM is a lightweight API server designed for deploying and serving open large language models (LLMs), offering a simple way to integrate LLMs into applications. -
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Bailing
Bailing is a voice dialogue robot similar to GPT-4o
...The project is modular: each core function — ASR, VAD, LLM, TTS — exists as a separately replaceable component, which allows flexibility in picking your preferred models depending on resources or languages. It aims to be light enough to run without a GPU, making it usable on modest hardware or edge devices, while still maintaining low latency and smooth interaction. Bailing includes a memory system, giving the assistant the ability to remember user preferences and context across sessions, which enables more personalized and context-aware conversations. -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
...Tasks span heterogeneous domains—catalysis (OC20-style), inorganic materials (OMat), molecules (OMol), MOFs (ODAC), and molecular crystals (OMC)—allowing one model family to serve many simulations. The README provides quick paths for pulling models (e.g., via Hugging Face access), then running energy/force predictions on GPU or CPU. -
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Qwen
The official repo of Qwen chat & pretrained large language model
Qwen is a series of large language models developed by Alibaba Cloud, consisting of various pretrained versions like Qwen-1.8B, Qwen-7B, Qwen-14B, and Qwen-72B. These models, which range from smaller to larger configurations, are designed for a wide range of natural language processing tasks. They are openly available for research and commercial use, with Qwen's code and model weights shared on GitHub. Qwen's capabilities include text generation, comprehension, and conversation, making it a... -
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TensorLy
Tensor Learning in Python
...It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, TensorFlow, CuPy or Paddle, and run methods at scale on CPU or GPU. -
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Mosec
A high-performance ML model serving framework, offers dynamic batching
Mosec is a high-performance and flexible model-serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. -
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Stable Diffusion Version 2
High-Resolution Image Synthesis with Latent Diffusion Models
...The repository provides code for training and running Stable Diffusion-style models, instructions for installing dependencies (with notes about performance libraries like xformers), and guidance on hardware/driver requirements for efficient GPU inference and training. It’s organized as a practical, developer-focused toolkit: model code, scripts for inference, and examples for using memory-efficient attention and related optimizations are included so researchers and engineers can run or adapt the model for their own projects. The project sits within a larger ecosystem of Stability AI repositories (including inference-only reference implementations like SD3.5 and web UI projects) and the README points users toward compatible components, recommended CUDA/PyTorch versions.
