Search Results for "apache framework" - Page 16
Sort By:
Showing 439 open source projects for "apache framework"
View related business solutions-
Get full visibility and control over your tasks and projects with Wrike.Wrike offers world-class features that empower cross-functional, distributed, or growing teams take their projects from the initial request stage all the way to tracking work progress and reporting results.
-
The AI-powered unified PSA-RMM platform for modern MSPs.SuperOps.ai is the only PSA-RMM platform powered by intelligent automation and thoughtfully crafted for the new-age MSP. The platform also helps MSPs manage their projects, clients, and IT documents from a single place.
-
1
AdaNet
Fast and flexible AutoML with learning guarantees
AdaNet is a TensorFlow framework for fast and flexible AutoML with learning guarantees. AdaNet is a lightweight TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention. AdaNet builds on recent AutoML efforts to be fast and flexible while providing learning guarantees. Importantly, AdaNet provides a general framework for not only learning a neural network architecture but also for learning to the ensemble to obtain even better models. At each... -
2
PerfKit Benchmarker
PerfKit Benchmarker (PKB) contains a set of benchmarks
PerfKitBenchmarker is an open-source benchmarking framework designed to measure and compare the performance of cloud infrastructure across multiple providers in a consistent and reproducible way. It allows users to evaluate metrics such as latency, throughput, provisioning time, and system performance using a standardized set of benchmarks. The tool supports a wide range of environments, including major cloud platforms, Kubernetes clusters, and even local hardware, making it highly versatile... -
3
TFKit
Handling multiple nlp task in one pipeline
TFKit is a tool kit mainly for language generation. It leverages the use of transformers on many tasks with different models in this all-in-one framework. All you need is a little change of config. You can use tfkit for model training and evaluation with tfkit-train and tfkit-eval. The key to combine different task together is to make different task with same data format. All data will be in csv format - tfkit will use csv for all task, normally it will have two columns, first columns is the... -
4
Python Handout
Turn Python scripts into handouts with Markdown and figures
Handout is a lightweight library for embedding rich, interactive components such as exercises, charts, and interactive diagrams directly into static documents like Markdown, Jupyter notebooks, or static HTML pages, enabling authors to create more engaging technical handouts, tutorials, and interactive essays. It’s particularly aimed at educators, presenters, and researchers who want to make their written material come alive with runnable demonstrations and interactive problem sets without... -
DeskTime is a cloud-based time tracking softwareDeskTime is a high-performance, automated time tracking and workforce management solution for teams and freelancers. It runs silently in the background, logging computer activity from the moment of boot-up to ensure 100% accurate data without the need for manual timers.
-
5
SageMaker Chainer Containers
Docker container for running Chainer scripts to train and host Chainer
SageMaker Chainer Containers is an open-source library for making the Chainer framework run on Amazon SageMaker. This repository also contains Dockerfiles which install this library, Chainer, and dependencies for building SageMaker Chainer images. Amazon SageMaker utilizes Docker containers to run all training jobs & inference endpoints. The Docker images are built from the Dockerfiles specified in Docker/. The Docker files are grouped based on Chainer version and separated based on Python... -
6
BytePS
A high performance and generic framework for distributed DNN training
BytePS is a high-performance and generally distributed training framework. It supports TensorFlow, Keras, PyTorch, and MXNet, and can run on either TCP or RDMA networks. BytePS outperforms existing open-sourced distributed training frameworks by a large margin. For example, on BERT-large training, BytePS can achieve ~90% scaling efficiency with 256 GPUs (see below), which is much higher than Horovod+NCCL. In certain scenarios, BytePS can double the training speed compared with Horovod+NCCL.... -
7
MMSkeleton
A OpenMMLAB toolbox for human pose estimation, skeleton-based action
MMSkeleton is an open-source toolbox for skeleton-based human understanding. It is a part of the open-mmlab project in the charge of Multimedia Laboratory, CUHK. MMSkeleton is developed on our research project ST-GCN. MMSkeleton provides a flexible framework for organizing codes and projects systematically, with the ability to extend to various tasks and scale up to complex deep models. MMSkeleton addresses to multiple tasks in human understanding. Build a custom skeleton-based dataset.... -
8
Graph Nets library
Build Graph Nets in Tensorflow
Graph Nets, developed by Google DeepMind, is a Python library designed for constructing and training graph neural networks (GNNs) using TensorFlow and Sonnet. It provides a high-level, flexible framework for building neural architectures that operate directly on graph-structured data. A graph network takes graphs as inputs, consisting of edges, nodes, and global attributes, and produces updated graphs with modified feature representations at each level. This library implements the... -
9
Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments... -
MATRIX is a world-class, award-winning learning management system (LMS) for businesses.The platform is known for delivering a great user experience, while incorporating all the essential tools companies need to support efficient training and learning.
-
10
vid2vid
Pytorch implementation of our method for high-resolution
vid2vid is a deep learning framework for high-resolution video-to-video translation that generates photorealistic videos from structured inputs such as semantic maps, pose sequences, or edge maps. Built on top of image-to-image translation techniques like pix2pixHD, it extends these ideas into the temporal domain by ensuring consistency across video frames. The system can synthesize complex outputs such as realistic talking faces, human motion animations, or dynamic street scenes by learning... -
11
TestLink-API-Python-client
Python XML-RPC client for TestLink
TestLink-API-Python-client is a Python XML-RPC client for TestLink Initially based on James Stock testlink-api-python-client R7 and Olivier Renault JinFeng idea - an interaction of TestLink, Robot Framework and Jenkins. TestLink-API-Python-client delivers two main classes - TestlinkAPIGeneric - Implements the Testlink API methods as generic PY methods with error handling - TestlinkAPIClient - Inherits from TestlinkAPIGeneric and defines service methods like "copyTCnewVersion" and... -
12
Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main... -
13
Rasa Core
Rasa Core is now part of the Rasa repo
Rasa is an open source machine learning framework to automate text and voice-based conversations. With Rasa, you can build contextual assistants. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forth. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed – Rasa enables you to build assistants that can do this in a... -
14
Scalable Distributed Deep-RL
A TensorFlow implementation of Scalable Distributed Deep-RL
Scalable Agent is the open implementation of IMPALA (Importance Weighted Actor-Learner Architectures), a highly scalable distributed reinforcement learning framework developed by Google DeepMind. IMPALA introduced a new paradigm for efficiently training agents across large-scale environments by decoupling acting and learning processes. In this architecture, multiple actor processes interact with their environments in parallel to collect trajectories, which are then asynchronously sent to a... -
15
ShadowSocksShare
Python ShadowSocks framework
This project obtains the shared ss(r) account from the ss(r) shared website crawler, redistributes the account and generates a subscription link by parsing and verifying the account connectivity. Since Google plus will be closed on April 2, 2019, almost all the available accounts crawled before come from Google plus. So if you are building your own website, please keep an eye on the updates of this project and redeploy using the latest source code. -
16
Wally
Distributed Stream Processing
Wally is a fast-stream-processing framework. Wally makes it easy to react to data in real-time. By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency... -
17
Catalyst
An Algorithmic Trading Library for Crypto-Assets in Python
Catalyst is an algorithmic trading library for crypto-assets written in Python, originally developed to let quants and developers design, backtest, and deploy trading strategies in a unified environment. It builds on top of Zipline, extending that ecosystem to support crypto exchanges and high-resolution historical data (daily and minute bars). Users can express strategies in Python, run backtests against historical price data, and analyze performance through built-in metrics and analytics... -
18
OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
OpenSeq2Seq is a TensorFlow-based toolkit for efficient experimentation with sequence-to-sequence models across speech and NLP tasks. Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as... -
19
Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter... -
20
FastPhotoStyle
Style transfer, deep learning, feature transform
FastPhotoStyle is a deep learning-based image stylization framework designed to transfer the style of one photograph onto another while preserving photorealistic quality. Unlike traditional artistic style transfer methods that produce painterly outputs, this approach focuses on maintaining realistic textures, lighting, and spatial consistency. The method is based on a two-step process that includes a stylization phase followed by a smoothing operation, ensuring that the output image remains... -
21
Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
22
txObject ATK is an OS independent C++ & Python object library. Built in the 90s, it provides a 5-layer framework: Object Library, IO / Timers, Threads, Inter-Process / Distributed Object Communication
-
23
Learning to Learn in TensorFlow
Learning to Learn in TensorFlow
Learning to Learn, created by Google DeepMind, is an experimental framework that implements meta-learning—training neural networks to learn optimization strategies themselves rather than relying on manually designed algorithms like Adam or SGD. The repository provides code for training and evaluating learned optimizers that can generalize across different problem types, such as quadratic functions and image classification tasks (MNIST and CIFAR-10). Using TensorFlow, it defines a... -
24
SSL Logger
Decrypts and logs a process's SSL traffic
ssl_logger is a Python-based tool that decrypts and logs a target process’s SSL/TLS traffic on Linux and macOS. It attaches to a running process by name or PID and hooks SSL_read and SSL_write calls to capture plaintext data flowing through encrypted connections. Output can be streamed to the console with verbose metadata or written to a PCAP file for later analysis in standard tooling. The utility is powered by dynamic instrumentation using the Frida framework, allowing it to intercept... -
25
seq2seq
A general-purpose encoder-decoder framework for Tensorflow
seq2seq is an early, influential TensorFlow reference implementation for sequence-to-sequence learning with attention, covering tasks like neural machine translation, summarization, and dialogue. It packaged encoders, decoders, attention mechanisms, and beam search into a modular training and inference framework. The codebase showcased best practices for batching, bucketing by sequence length, and handling variable-length sequences efficiently on GPUs. Researchers used it as a baseline to...
