Search Results for "building" - Page 2
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Showing 55 open source projects for "building"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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SageMaker Hugging Face Inference Toolkit
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 Face Inference Toolkit implements various additional environment variables to simplify your deployment experience. The Hugging Face Inference Toolkit allows user to override the default methods of the HuggingFaceHandlerService. SageMaker Hugging Face Inference Toolkit is licensed under the Apache 2.0 License. -
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Pwntools
CTF framework and exploit development library
Pwntools is a CTF framework and exploit development library. Written in Python, it is designed for rapid prototyping and development, and intended to make exploit writing as simple as possible. Whether you’re using it to write exploits, or as part of another software project will dictate how you use it. Historically pwntools was used as a sort of exploit-writing DSL. Simply doing from pwn import in a previous version of pwntools would bring all sorts of nice side-effects. This version... -
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Groq Python
The official Python Library for the Groq API
...This makes it easy to integrate Groq-powered AI capabilities into backend services, data pipelines, research notebooks, or applications written in Python. For those building AI-based tooling, automation scripts, or ML-backed backends, groq-python abstracts away HTTP request plumbing and exposes a clean API, accelerating development and reducing boilerplate. -
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Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
...It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. Use BERT and other state-of-the-art deep learning models to solve classification, NER, Q&A and other NLP tasks. DeepPavlov Agent allows building industrial solutions with multi-skill integration via API services. -
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AudioCraft
Audiocraft is a library for audio processing and generation
...The repo provides inference scripts, checkpoints, and simple Python APIs so you can generate clips from prompts or incorporate the models into applications. It also contains training code and recipes, so researchers can fine-tune on custom data or explore new objectives without building infrastructure from scratch. Example notebooks, CLI tools, and audio utilities help with prompt design, conditioning on reference audio, and post-processing to produce ready-to-share outputs. -
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Prompt Engineering Interactive Tutorial
Anthropic's Interactive Prompt Engineering Tutorial
...The course leans heavily on realistic failure modes (ambiguity, hallucination, brittle instructions) and shows how to iteratively debug prompts the way you would debug code. Lessons include building prompts from scratch for common tasks like extraction, classification, transformation, and step-by-step reasoning, with checkpoints that let you compare your outputs against solid baselines. You’ll also practice advanced patterns such as tool use, constrained generation, and response validation so outputs are trustworthy and machine-consumable. -
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tacklelib
The C++11 library, msvc2015u3/gcc5.4 + cmake,python,bash,vbs modules
https://sourceforge.net/p/tacklelib/tacklelib/ci/master/tree/README.md https://sourceforge.net/p/tacklelib/tacklelib/ci/master/tree/README_EN.txt https://sourceforge.net/p/tacklelib/tacklelib/ci/master/tree/README_EN.deps.txt https://sourceforge.net/p/tacklelib/tacklelib/ci/master/tree/README_EN.linux_x86_64.txt https://sourceforge.net/p/tacklelib/tacklelib/ci/master/tree/changelog.txt -
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grafanalib
Python library for building Grafana dashboards
Grafanalib is a Python library for building Grafana dashboards programmatically, allowing users to automate dashboard creation and configuration. -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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Neural Tangents
Fast and Easy Infinite Neural Networks in Python
Neural Tangents is a high-level neural network API for specifying complex, hierarchical models at both finite and infinite width, built in Python on top of JAX and XLA. It lets researchers define architectures from familiar building blocks—convolutions, pooling, residual connections, and nonlinearities—and obtain not only the finite network but also the corresponding Gaussian Process (GP) kernel of its infinite-width limit. With a single specification, you can compute NNGP and NTK kernels, perform exact GP inference, and study training dynamics analytically for infinitely wide networks. ... -
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quantitative
Quantized transactions python3
The “quantitative” repository by Jack-Cherish is a tutorial-style codebase for quantitative trading written in Python — essentially a learning resource that guides users through building algorithmic trading strategies step by step. It’s organized as a sequence of lessons (lesson1, lesson2, etc.), making it approachable for learners who want to understand both theory and practice in quantitative finance. The repo is evidently tied to a popular video series (on Bilibili) that reportedly drew substantial attention, suggesting the material is meant to be both educational and hands-on. ... -
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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 compilation. ... -
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Spyne
A transport agnostic sync/async RPC library
...Spyne aims to save the protocol implementers the hassle of implementing their own remote procedure call api and the application programmers the hassle of jumping through hoops just to expose their services using multiple protocols and transports. In other words, Spyne is a framework for building distributed solutions that strictly follow the MVC pattern, where Model = spyne.model, View = spyne.protocol and Controller = user code. Spyne comes with the implementations of popular transport, protocol and interface document standards along with a well-defined API that lets you build on existing functionality. -
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TensorNetwork
A library for easy and efficient manipulation of tensor networks
TensorNetwork is a high-level library for building and contracting tensor networks—graphical factorizations of large tensors that underpin many algorithms in physics and machine learning. It abstracts networks as nodes and edges, then compiles efficient contraction orders across multiple numeric backends so users can focus on model structure rather than index bookkeeping. Common network families (MPS/TT, PEPS, MERA, tree networks) are expressed with concise APIs that encourage experimentation and comparison. ... -
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PyTorchVideo
A deep learning library for video understanding research
PyTorchVideo is a deep learning library for video understanding, providing modular components and pretrained models for tasks like action recognition, video classification, detection, and self-supervised learning. It is tightly integrated with PyTorch and PyTorch Lightning, offering flexible APIs for building and training spatiotemporal networks. The library includes efficient implementations of state-of-the-art architectures such as SlowFast, X3D, and MViT, optimized for both research prototyping and production inference. It supports video I/O pipelines, data augmentation, distributed training, and mixed precision computation for large-scale experiments. ... -
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TRFL
TensorFlow Reinforcement Learning
TRFL, developed by Google DeepMind, is a TensorFlow-based library that provides a collection of essential building blocks for reinforcement learning (RL) algorithms. Pronounced “truffle,” it simplifies the implementation of RL agents by offering reusable components such as loss functions, value estimation tools, and temporal difference (TD) learning operators. The library is designed to integrate seamlessly with TensorFlow, allowing users to define differentiable RL objectives and train models using standard optimization routines. ... -
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gradslam
gradslam is an open source differentiable dense SLAM library
gradslam is an open-source framework providing differentiable building blocks for simultaneous localization and mapping (SLAM) systems. We enable the usage of dense SLAM subsystems from the comfort of PyTorch. The question of “representation” is central in the context of dense simultaneous localization and mapping (SLAM). Newer learning-based approaches have the potential to leverage data or task performance to directly inform the choice of representation. -
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Zipline
Zipline, a Pythonic algorithmic trading library
...It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python package. For a development installation (used to develop Zipline itself), create and activate a virtualenv, then run the etc/dev-install script. ... -
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SageMaker MXNet Training Toolkit
Toolkit for running MXNet training scripts on SageMaker
SageMaker MXNet Training Toolkit is an open-source library for using MXNet to train models on Amazon SageMaker. For inference, see SageMaker MXNet Inference Toolkit. For the Dockerfiles used for building SageMaker MXNet Containers, see AWS Deep Learning Containers. For information on running MXNet jobs on Amazon SageMaker, please refer to the SageMaker Python SDK documentation. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow. You can also train and deploy models with Amazon algorithms, which are scalable implementations of core machine learning algorithms that are optimized for SageMaker and GPU training. ... -
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Higher
higher is a pytorch library
...It also provides differentiable implementations of common optimizers like SGD and Adam, making it possible to backpropagate through an arbitrary number of inner-loop optimization steps. By offering a clear and flexible interface, higher simplifies building complex learning algorithms that require gradient tracking across multiple update levels. Its design ensures compatibility with existing PyTorch models. -
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Forecasting Best Practices
Time Series Forecasting Best Practices & Examples
...Almost every business needs to predict the future in order to make better decisions and allocate resources more effectively. This repository provides examples and best practice guidelines for building forecasting solutions. The goal of this repository is to build a comprehensive set of tools and examples that leverage recent advances in forecasting algorithms to build solutions and operationalize them. Rather than creating implementations from scratch, we draw from existing state-of-the-art libraries and build additional utilities around processing and featuring the data, optimizing and evaluating models, and scaling up to the cloud. ... -
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API Correios
API correios.com.br in Python
...The library abstracts the raw SOAP or REST endpoints exposed by Correios, providing Pythonic methods to perform common tasks like tracking a package by its code or computing shipping cost/lead time between postal codes. It handles serialization and mapping of API responses into Python objects so developers don’t manually parse raw XML or JSON. With this tool, developers building Brazilian market e-commerce or logistics solutions can integrate postal services smoothly. Because it is open source, improvements can be contributed to support new endpoints, changes in the postal service API, or additional features like caching or async requests. -
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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 foundational ideas from DeepMind’s paper “Relational Inductive Biases, Deep Learning, and Graph Networks”, offering tools to explore relational reasoning and message-passing neural networks. ... -
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TF Quant Finance
High-performance TensorFlow library for quantitative finance
...Users can value options and fixed-income instruments, simulate paths, fit curves, and calibrate models while leveraging TensorFlow’s jit compilation and automatic differentiation. The codebase is organized as modular math and finance primitives so you can combine building blocks or target end-to-end examples. It includes Bazel builds, tests, and example notebooks to accelerate learning and adoption in real workflows. With hardware acceleration and differentiable models, it enables modern techniques like gradient-based calibration and end-to-end learning of market dynamics. -
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AeroPython
Classical Aerodynamics of potential flow using Python
The AeroPython series of lessons is the core of a university course (Aerodynamics-Hydrodynamics, MAE-6226) by Prof. Lorena A. Barba at the George Washington University. The first version ran in Spring 2014 and these Jupyter Notebooks were prepared for that class, with assistance from Barba-group PhD student Olivier Mesnard. In Spring 2015, we revised and extended the collection, adding student assignments to strengthen the learning experience. The course is also supported by an open learning...
