Search Results for "apache framework" - Page 15
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Showing 456 open source projects for "apache framework"
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MMGeneration
MMGeneration is a powerful toolkit for generative models
MMGeneration has been merged in MMEditing. And we have supported new-generation tasks and models. MMGeneration is a powerful toolkit for generative models, especially for GANs now. It is based on PyTorch and MMCV. The master branch works with PyTorch 1.5+. We currently support training on Unconditional GANs, Internal GANs, and Image Translation Models. Support for conditional models will come soon. A plentiful toolkit containing multiple applications in GANs is provided to users. GAN... -
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PARL
A high-performance distributed training framework
PARL is a scalable reinforcement learning framework built on top of PaddlePaddle. It focuses on modularity and ease of use, supporting distributed training and a variety of RL algorithms. -
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Mars Framework
Mars is a tensor-based unified framework for large-scale data
Mars is a distributed computing framework designed to scale scientific computing and data science workloads across large clusters while preserving the familiar programming interfaces of common Python libraries. The project provides a tensor-based execution model that extends the capabilities of tools such as NumPy, pandas, and scikit-learn so that large datasets can be processed in parallel without rewriting code for distributed environments. Its architecture automatically divides large... -
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Amazon Braket Strawberry Fields Plugin
An open source framework for using Amazon Braket devices
An open-source framework for using Amazon Braket devices with the Strawberry Fields photonic device programming library. This plugin provides a BraketEngine class for running photonic quantum circuits created in Strawberry Fields on the Amazon Braket service. The Amazon Braket Python SDK is an open source library that provides a framework to interact with quantum computing hardware devices and simulators through Amazon Braket. This plugin provides the classes BraketEngine for submitting... -
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NanoDet-Plus
Lightweight anchor-free object detection model
Super fast and high accuracy lightweight anchor-free object detection model. Real-time on mobile devices. NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss. In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training. We also introduce a... -
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linux-factory
Framework used to create custom debian linux operating systems
linux-factory is a utility designed to streamline the process of building and customizing Linux kernel images using a simple, scriptable environment. It provides a modular framework for configuring kernel options, applying patches, and generating bootable images, making it ideal for embedded developers, kernel hackers, and OS maintainers. With a strong emphasis on repeatability and automation, linux-factory reduces the manual overhead of compiling and deploying Linux kernels across various... -
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GPT-NeoX
Implementation of model parallel autoregressive transformers on GPUs
This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training. For those looking for a TPU-centric codebase, we... -
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Sockeye
Sequence-to-sequence framework, focused on Neural Machine Translation
Sockeye is an open-source sequence-to-sequence framework for Neural Machine Translation built on PyTorch. It implements distributed training and optimized inference for state-of-the-art models, powering Amazon Translate and other MT applications. For a quickstart guide to training a standard NMT model on any size of data, see the WMT 2014 English-German tutorial. If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions... -
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FuzzBench
FuzzBench - Fuzzer benchmarking as a service
FuzzBench is a large-scale, open research platform developed by Google to evaluate and benchmark fuzzers — automated software testing tools that detect vulnerabilities through randomized input generation. It provides a standardized, reproducible environment for comparing the performance and effectiveness of different fuzzing algorithms on real-world software targets. FuzzBench integrates with the OSS-Fuzz infrastructure, allowing it to run experiments on authentic open source projects and... -
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UnionML
Build and deploy machine learning microservices
Creating ML apps should be simple and frictionless. UnionML is an open-source Python framework built on top of Flyte™, unifying the complex ecosystem of ML tools into a single interface. Combine the tools that you love using a simple, standardized API so you can stop writing so much boilerplate and focus on what matters: the data and the models that learn from them. Fit the rich ecosystem of tools and frameworks into a common protocol for machine learning. Using industry-standard machine... -
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d2l-zh
Chinese-language edition of Dive into Deep Learning
d2l‑zh is the Chinese-language edition of Dive into Deep Learning, an interactive, open‑source deep learning textbook that combines code, math, and explanatory text. It features runnable Jupyter notebooks compatible with multiple frameworks (e.g., PyTorch, MXNet, TensorFlow), comprehensive theoretical analysis, and exercises. Widely adopted in over 70 countries and used by more than 500 universities for teaching deep learning. -
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FedLab
A flexible Federated Learning Framework based on PyTorch
A Python-based framework for federated learning simulation, emphasizing modularity, communication efficiency, and algorithmic flexibility. Supports both server- and client-side customization for research and development purposes. -
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ProjectQ
An open source software framework for quantum computing
ProjectQ is an open-source effort for quantum computing. It features a compilation framework capable of targeting various types of hardware, a high-performance quantum computer simulator with emulation capabilities, and various compiler plug-ins. -
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MMTracking
OpenMMLab Video Perception Toolbox
MMTracking is an open-source video perception toolbox by PyTorch. It is a part of OpenMMLab project. We are the first open-source toolbox that unifies versatile video perception tasks include video object detection, multiple object tracking, single object tracking and video instance segmentation. We decompose the video perception framework into different components and one can easily construct a customized method by combining different modules. MMTracking interacts with other OpenMMLab... -
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AllenNLP
An open-source NLP research library, built on PyTorch
AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional... -
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BEVFormer
Implementation of BEVFormer, a camera-only framework
3D visual perception tasks, including 3D detection and map segmentation based on multi-camera images, are essential for autonomous driving systems. In this work, we present a new framework termed BEVFormer, which learns unified BEV representations with spatiotemporal transformers to support multiple autonomous driving perception tasks. In a nutshell, BEVFormer exploits both spatial and temporal information by interacting with spatial and temporal space through predefined grid-shaped BEV... -
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Opta
The next generation of Infrastructure-as-Code
Opta is an infrastructure-as-code framework. Rather than working with a low-level cloud configuration, Opta enables you to work with high-level constructs. Opta high-level constructs produce Terraform configuration files. This helps you avoid lock-in to Opta. You can write custom Terraform code or even take the Opta-generated Terraform and go your own way. Opta is a new kind of Infrastructure-as-Code (IaC) framework that lets engineers work with high-level constructs instead of getting lost... -
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Jraph
A Graph Neural Network Library in Jax
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time... -
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Apache MXNet (incubating)
A flexible and efficient library for deep learning
Apache MXNet is an open source deep learning framework designed for efficient and flexible research prototyping and production. It contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations. On top of this is a graph optimization layer, overall making MXNet highly efficient yet still portable, lightweight and scalable. -
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Amazon Braket Ocean Plugin
A Python plugin for using Ocean with Amazon Braket
The Amazon Braket Ocean Plugin is an open-source library in Python that provides a framework that you can use to interact with Ocean tools on top of Amazon Braket. Before you begin working with the Amazon Braket Ocean Plugin, make sure that you've installed or configured the following prerequisites. Download and install Python 3.7.2 or greater from Python.org. If you are using Windows, choose Add Python to environment variables before you begin the installation. Make sure that your AWS... -
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TensorFlowOnSpark
TensorFlowOnSpark brings TensorFlow programs to Apache Spark clusters
By combining salient features from the TensorFlow deep learning framework with Apache Spark and Apache Hadoop, TensorFlowOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. It enables both distributed TensorFlow training and inferencing on Spark clusters, with a goal to minimize the amount of code changes required to run existing TensorFlow programs on a shared grid. -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
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OpenPrompt
An Open-Source Framework for Prompt-Learning
Prompt-learning is the latest paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modifies the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. OpenPrompt is a library built upon PyTorch and provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. OpenPrompt supports loading PLMs directly from huggingface transformers. In the future, we will also support PLMs implemented by other... -
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EasyNLP
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
EasyNLP is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications. EasyNLP integrates knowledge distillation and few-shot learning for landing large pre-trained models, together with various popular multi-modality pre-trained models. It provides a unified framework of model training, inference, and... -
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Acme
A library of reinforcement learning components and agents
Acme is a framework from DeepMind for building scalable and reproducible reinforcement learning agents. It emphasizes modular components, distributed training, and ease of experimentation.
