Search Results for "compute" - Page 2
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Showing 122 open source projects for "compute"
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StableSwarmUI
Multi-user UI for managing and running Stable Diffusion workflows tool
...It abstracts much of the complexity involved in running diffusion models by offering a structured environment for handling prompts, outputs, and processing queues. StableSwarmUI is built to work alongside backend systems that execute the actual image generation, allowing separation between user interaction and compute workloads. It also emphasizes scalability, making it useful for setups where multiple jobs need to be processed efficiently. Overall, it serves as a coordination layer for Stable Diffusion usage rather than a standalone model implementation. -
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Argilla
The open-source data curation platform for LLMs
Argilla is a production-ready framework for building and improving datasets for NLP projects. Deploy your own Argilla Server on Spaces with a few clicks. Use embeddings to find the most similar records with the UI. This feature uses vector search combined with traditional search (keyword and filter based). Argilla is free, open-source, and 100% compatible with major NLP libraries (Hugging Face transformers, spaCy, Stanford Stanza, Flair, etc.). In fact, you can use and combine your preferred... -
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Transformers
State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX
Hugging Face Transformers provides APIs and tools to easily download and train state-of-the-art pre-trained models. Using pre-trained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch. These models support common tasks in different modalities. Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. Images, for tasks like image classification, object detection, and segmentation. ... -
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NVIDIA Model Optimizer
A unified library of SOTA model optimization techniques
Model Optimizer is a unified library that provides state-of-the-art techniques for compressing and optimizing deep learning models to improve inference efficiency and deployment performance. It brings together multiple optimization strategies such as quantization, pruning, distillation, and speculative decoding into a single cohesive framework. The library is designed to reduce model size and computational requirements while maintaining accuracy, making it particularly valuable for deploying... -
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Lithops
A multi-cloud framework for big data analytics
Lithops is an open-source serverless computing framework that enables transparent execution of Python functions across multiple cloud providers and on-prem infrastructure. It abstracts cloud providers like IBM Cloud, AWS, Azure, and Google Cloud into a unified interface and turns your Python functions into scalable, event-driven workloads. Lithops is ideal for data processing, ML inference, and embarrassingly parallel workloads, giving you the power of FaaS (Function-as-a-Service) without... -
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VoxelMorph
Unsupervised Learning for Image Registration
...VoxelMorph approaches the problem using neural networks that learn to predict deformation fields that transform one image so that it aligns with another. Once the model has been trained, it can rapidly compute the transformation required to register new image pairs, significantly reducing computational time compared to classical registration algorithms. The framework supports both supervised and unsupervised learning approaches and is commonly used in medical imaging applications such as MRI alignment, anatomical analysis, and longitudinal studies. -
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NovaSR
A lightning fast audio upsampler
...NovaSR is especially valuable for post-processing tasks in speech enhancement, TTS pipelines, and dataset restoration where low sampling rates degrade perceived audio clarity; the minimal model size also makes it suitable for edge and embedded use cases where memory is at a premium. Its performance can reach thousands of times realtime on modern GPUs, allowing massive audio batches to be processed with negligible compute overhead. -
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SentenceTransformers
Multilingual sentence & image embeddings with BERT
SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similar, semantic search, or paraphrase mining. The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. ... -
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DeepLabCut
Implementation of DeepLabCut
...We demonstrate the versatility of this framework by tracking various body parts in multiple species across a broad collection of behaviors. The package is open source, fast, robust, and can be used to compute 3D pose estimates or for multi-animals. Please see the original paper and the latest work below! This package is collaboratively developed by the Mathis Group & Mathis Lab at EPFL (releases prior to 2.1.9 were developed at Harvard University). The code is freely available and easy to install in a few clicks with Anaconda (and pypi). ... -
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TextDistance
Compute distance between sequences
Python library for comparing the distance between two or more sequences by many algorithms. For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature. Textdistance use benchmark results for algorithm optimization and try to call the fastest external lib first (if possible). TextDistance show benchmarks... -
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bitnet.cpp
Official inference framework for 1-bit LLMs
...At its core is bitnet.cpp, a highly optimized C++ backend that supports fast, low-memory inference on both CPUs and GPUs, enabling models such as BitNet b1.58 to run without requiring enormous compute infrastructure. The project’s focus on extreme quantization dramatically reduces memory footprint and energy consumption compared with traditional 16-bit or 32-bit LLMs, making it practical to deploy advanced language understanding and generation models on everyday machines. BitNet is built to scale across architectures, with configurable kernels and tiling strategies that adapt to different hardware, and it supports large models with impressive throughput even on modest resources. -
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PyTorch3D
PyTorch3D is FAIR's library of reusable components for deep learning
...It’s designed to make it easy to build and train neural networks that work directly with 3D data such as meshes, point clouds, and implicit surfaces. The library provides fast GPU-accelerated implementations of rendering pipelines, transformations, rasterization, and lighting—making it possible to compute gradients through full 3D rendering processes. Researchers use it for tasks like shape generation, reconstruction, view synthesis, and visual reasoning. PyTorch3D also includes utilities for loading, transforming, and sampling 3D assets, so models can be trained end-to-end from 2D supervision or partial data. Its modular design allows easy extension—components like differentiable rasterizers, mesh blending, or signed distance field (SDF) modules can be swapped or combined to test new architectures quickly. -
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LoLLMs Hub Fortress
A proxy server for multiple ollama instances with Key security
...It acts as a central gateway that connects different inference engines such as Ollama, llama.cpp, vLLM, and OpenAI-compatible services, allowing them to function as interchangeable compute nodes within one system. The architecture is built around a hierarchical “master and slave” hub model, enabling distributed deployments where multiple machines or clusters can be managed through a single entry point. This design allows organizations to scale horizontally, combining local hardware, cloud resources, and specialized inference servers into a unified infrastructure. ... -
14
BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
...The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention. -
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NVIDIA NeMo
Toolkit for conversational AI
...Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC. NGC collection of pre-trained speech processing models. -
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Omnilingual ASR
Omnilingual ASR Open-Source Multilingual SpeechRecognition
...The repo is aimed at pushing practical multilingual ASR—robust to accents, code-switching, and domain shifts—rather than language-by-language systems. For practitioners, it’s a starting point to study transfer, zero-shot behavior, and trade-offs between model size, compute cost, and coverage. -
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GenAI Processors
GenAI Processors is a lightweight Python library
...The library offers built-in processors for classic turn-based Gemini calls as well as Live API streaming, so you can mix “batch” and real-time interactions in the same graph. It leans on Python’s asyncio to coordinate concurrency, handle network I/O, and juggle background compute threads without blocking. -
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PML
The easiest way to use deep metric learning in your application
This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you pass into it. ... -
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Kornia
Open Source Differentiable Computer Vision Library
...It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. ... -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. -
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OpenTinker
OpenTinker is an RL-as-a-Service infrastructure for foundation models
...Traditional RL setups can be monolithic and difficult to configure, but OpenTinker separates concerns across agent definition, environment interaction, and execution, which lets developers focus on defining the logic of agents and environments separately from how training and inference are run. It introduces a centralized scheduler to manage distributed training jobs and shared compute resources, enabling workloads like reinforcement learning, supervised fine-tuning, and inference to run across multiple settings. The architecture supports a range of single-turn and multi-turn agentic tasks with a design that abstracts away infrastructure complexity while offering flexible Python APIs to define environments and workflows. -
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EPLB
Expert Parallelism Load Balancer
...The logic is implemented in eplb.py and supports predicting placements given estimated expert usage weights. EPLB aims to reduce hot-spotting and ensure more uniform usage of compute resources in large MoE deployments. -
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Ling
Ling is a MoE LLM provided and open-sourced by InclusionAI
Ling is a Mixture-of-Experts (MoE) large language model (LLM) provided and open-sourced by inclusionAI. The project offers different sizes (Ling-lite, Ling-plus) and emphasizes flexibility and efficiency: being able to scale, adapt expert activation, and perform across a range of natural language/reasoning tasks. Example scripts, inference pipelines, and documentation. The codebase includes inference, examples, models, documentation, and model download infrastructure. As more developers and... -
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SuperDuperDB
Integrate, train and manage any AI models and APIs with your database
Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. Just using Python. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on... -
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Colab-MCP
An MCP server for interacting with Google Colab
...Instead of relying on manual notebook usage, the system allows MCP-compatible agents to execute code, manage files, install dependencies, and orchestrate entire development workflows within Colab’s cloud infrastructure. This approach bridges the gap between local AI agents and remote high-performance compute environments, allowing users to offload heavy workloads such as machine learning training, data analysis, and dependency-heavy tasks to Colab’s GPU and TPU resources. By exposing Colab as an MCP server, the tool enables seamless integration with a wide range of AI assistants and agent frameworks, creating a standardized interface for tool use and execution.
