Search Results for "python compile source code" - Page 7
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Showing 389 open source projects for "python compile source code"
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Denoising Diffusion Probabilistic Model
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model. -
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SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
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Sonnet
TensorFlow-based neural network library
... ship with Sonnet, making it quite powerful and yet simple at the same time. Users are also encouraged to build their own modules. Sonnet is designed to be extremely unopinionated about your use of modules. It is simple to understand, and offers clear and focused code. -
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segment-geospatial
A Python package for segmenting geospatial data with the SAM
... adapted the source code of segment-geospatial from the segment-anything-eo repository, and credit for its original version goes to Aliaksandr Hancharenka. -
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Taipy
Turns Data and AI algorithms into production-ready web applications
.... Struggle with sluggish performance and excessive memory usage, as every data point demands processing. Large datasets become cumbersome, complicating the user experience and data analysis. Scenarios are made easy with Taipy Studio. A powerful VS Code extension that unlocks a convenient graphical editor. Get your methods invoked at a certain time or intervals. Enjoy a variety of predefined themes or build your own. -
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EvaDB
Database system for building simpler and faster AI-powered application
Over the last decade, AI models have radically changed the world of natural language processing and computer vision. They are accurate on various tasks ranging from question answering to object tracking in videos. To use an AI model, the user needs to program against multiple low-level libraries, like PyTorch, Hugging Face, Open AI, etc. This tedious process often leads to a complex AI app that glues together these libraries to accomplish the given task. This programming complexity prevents... -
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Norfair
Lightweight Python library for adding real-time multi-object tracking
Norfair is a customizable lightweight Python library for real-time multi-object tracking. Using Norfair, you can add tracking capabilities to any detector with just a few lines of code. Any detector expressing its detections as a series of (x, y) coordinates can be used with Norfair. This includes detectors performing tasks such as object or keypoint detection. It can easily be inserted into complex video processing pipelines to add tracking to existing projects. At the same time... -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply. ktrain is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, ktrain is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners. With only a few lines... -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
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Django friendly finite state machine
Django friendly finite state machine support
Django-fsm adds simple declarative state management for Django models. If you need parallel task execution, view, and background task code reuse over different flows - check my new project Django-view flow. Instead of adding a state field to a Django model and managing its values by hand, you use FSMField and mark model methods with the transition decorator. These methods could contain side effects of the state change. You may also take a look at the Django-fsm-admin project containing a mixin... -
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PraisonAI
PraisonAI application combines AutoGen and CrewAI or similar framework
PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customization, and efficient human-agent collaboration. Chat with your ENTIRE Codebase. Praison AI, leveraging both AutoGen and CrewAI or any other agent framework, represents a low-code, centralized framework designed to simplify the creation and orchestration of multi-agent systems for various LLM applications, emphasizing... -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Automatic batch size selection, distributed training (DDP, DeepSpeed... -
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FastChat
Open platform for training, serving, and evaluating language models
FastChat is an open platform for training, serving, and evaluating large language model-based chatbots. If you do not have enough memory, you can enable 8-bit compression by adding --load-8bit to the commands above. This can reduce memory usage by around half with slightly degraded model quality. It is compatible with the CPU, GPU, and Metal backend. Vicuna-13B with 8-bit compression can run on a single NVIDIA 3090/4080/T4/V100(16GB) GPU. In addition to that, you can add --cpu-offloading to... -
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SageMaker Inference Toolkit
Serve machine learning models within a Docker container
Serve machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. Once you have a trained model, you can include it in a Docker container that runs your inference code. A container provides an effectively isolated environment, ensuring a consistent runtime regardless of where... -
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TensorFlow.NET
.NET Standard bindings for Google's TensorFlow for developing models
... with a powerful Machine Learning tool set without reinventing the wheel. Since the APIs are kept as similar as possible you can immediately adapt any existing TensorFlow code in C# or F# with a zero learning curve. Take a look at a comparison picture and see how comfortably a TensorFlow/Python script translates into a C# program with TensorFlow.NET. -
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aisuite
Simple, unified interface to multiple Generative AI providers
Simple, unified interface to multiple Generative AI providers. aisuite makes it easy for developers to use multiple LLM through a standardized interface. Using an interface similar to OpenAI's, aisuite makes it easy to interact with the most popular LLMs and compare the results. It is a thin wrapper around Python client libraries and allows creators to seamlessly swap out and test responses from different LLM providers without changing their code. Today, the library is primarily focused on chat... -
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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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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... -
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Asteroid
The PyTorch-based audio source separation toolkit for researchers
The PyTorch-based audio source separation toolkit for researchers. Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code thats supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers. Building blocks are thought and designed to be seamlessly plugged together. Filterbanks, encoders, maskers, decoders and losses are all common building blocks that can be combined... -
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The Operator Splitting QP Solver
The Operator Splitting QP Solver
OSQP uses a specialized ADMM-based first-order method with custom sparse linear algebra routines that exploit structure in problem data. The algorithm is absolutely division-free after the setup and it requires no assumptions on problem data (the problem only needs to be convex). It just works. OSQP has an easy interface to generate customized embeddable C code with no memory manager required. OSQP supports many interfaces including C/C++, Fortran, Matlab, Python, R, Julia, Rust. -
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TEN
Open-source framework for conversational voice AI agents
TEN (Transformative Extensions Network) is an open source framework designed to empower developers to build real-time multimodal AI agents capable of voice, video, text, image, and data-stream interaction with ultra-low latency. It includes a full ecosystem, TEN Turn Detection, TEN Agent, and TMAN Designer, allowing developers to rapidly assemble human-like, responsive agents that can see, speak, hear, and interact. With support for languages like Python, C++, and Go, it offers flexible... -
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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. The codebase includes inference, examples, models, documentation, and model download infrastructure. As more developers and researchers engage with the platform, we can expect rapid... -
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Semantic Router
Superfast AI decision making and processing of multi-modal data
Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow, unreliable LLM generations to make tool-use or safety decisions, we use the magic of semantic vector space — routing our requests using semantic meaning. Combining LLMs with deterministic rules means we can be confident that our AI systems behave as intended. Cramming agent tools into the limited context window is expensive, slow, and fundamentally limited. Semantic Router enables... -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
... they pass into a neural network (if you use augmentation). The general recommendation is to use suitable augs for your data and as many as possible, then after some time of training disable the most destructive (for image) augs. You can turn on automatic mixed precision with one flag --amp. You should expect it to be 33% faster and save up to 40% memory. Aim is an open-source experiment tracker that logs your training runs, and enables a beautiful UI to compare them. -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging...
