Search Results for "building" - Page 6
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Showing 208 open source projects for "building"
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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Model Search
Framework that implements AutoML algorithms
...Instead of hand-crafting models, you define a search space and objectives, then the system explores candidate architectures using controllers and population-based strategies. It supports multiple tasks (such as vision or text) by letting you express reusable building blocks—layers, cells, and topologies—that the search can recombine. Training, evaluation, and promotion of candidates are orchestrated automatically, with strong emphasis on reproducibility and fair comparisons. The framework logs trials, metrics, and artifacts so you can analyze what the search learned and why certain designs dominate. ... -
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AWS Step Functions Data Science SDK
For building machine learning (ML) workflows and pipelines on AWS
The AWS Step Functions Data Science SDK is an open-source library that allows data scientists to easily create workflows that process and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create machine learning workflows in Python that orchestrate AWS infrastructure at scale, without having to provision and integrate the AWS services separately. The best way to quickly review how the AWS Step Functions Data Science SDK works is to review the related... -
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Nameko
Python framework for building microservices
Write regular Python methods and classes to implement your service. Nameko will manage connections, transports, and concurrency for you. Spin up multiple service instances to easily scale out. Nameko gives you effortless concurrency by yielding workers when they wait for I/O, leaving you free to handle many requests without the worry of threading. Nameko is compatible with almost any protocol, transport or database. Simply use the built-in extensions, build your own or leverage the... -
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TorchGAN
Research Framework for easy and efficient training of GANs
...The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to provide building blocks for popular GANs and also to allow customization for cutting-edge research. Using TorchGAN's modular structure allows. -
Streamline Azure Security with Palo Alto Networks VM-SeriesImprove your security posture and reduce incident response time. Use the VM-Series to natively analyze Azure traffic and dynamically drive policy updates based on workload changes.
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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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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
...The Dockerfiles are grouped based on the version of TensorFlow Serving they support. Each supported processor type (e.g. "cpu", "gpu", "ei") has a different Dockerfile in each group. If your are testing locally, building the image is enough. But if you want to your updated image in SageMaker, you need to publish it to an ECR repository in your account. You can also run your container locally in Docker to test different models and input inference requests by hand. -
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Gooey
Turn Python command line programs into a full GUI application
...Whether you've built your application in Java, Node, or Haskell, or you just want to put a pretty interface on an existing tool like FFMPEG, Gooey can be used to create a fast, practically free UI with just a little bit of Python (about 20 lines!). To show how this all fits together, and that it really works for anything, we're going to walk through building a graphical interface to one of my favorite tools of all time: FFMPEG. These steps apply to anything, though! You could swap out FFMPEG for a .jar you've written, or an arbitrary windows .exe, an OSX .app bundle, or anything on linux that's executable! In short, it will transform a "scary" terminal command line into an easy to use desktop application that you could hand over to users. -
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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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
...We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. ... -
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molten
A minimal, extensible, fast and productive framework
molten is a minimal, extensible, fast and productive framework for building HTTP APIs with Python. molten can automatically validate requests according to predefined schemas, ensuring that your handlers only ever run if given valid input. Schemas are PEP484-compatible, which means mypy and molten go hand-in-hand, making your code more easy to maintain. Schema instances are automatically serializable and you can pick and choose which fields to exclude from responses and requests. ... -
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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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SageMaker Containers
Create SageMaker-compatible Docker containers
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. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. ... -
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TACO is a toolkit for building distributed control systems or any other distributed system. It is based on a C/C++ core. It is based on the client-server model. It supports writing clients and server on Unix+Windows. Clients and servers can be written in
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Clay Golem
Golem is creating a global market for computing power
The Golem Network fosters a global group of creators building ambitious software solutions that will shape the technological landscape of future generations by accessing computing resources across the platform. Golem Network is an accessible, reliable, open access and censorship-resistant protocol, democratizing access to digital resources and connecting users through a flexible, open-source platform. -
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Kopf
A Python framework to write Kubernetes operators
Kopf —Kubernetes Operator Pythonic Framework, is a framework and a library to make Kubernetes operator's development easier, just in a few lines of Python code. The main goal is to bring the Domain-Driven Design to the infrastructure level, with Kubernetes being an orchestrator/database of the domain objects (custom resources), and the operators containing the domain logic (with no or minimal infrastructure logic). -
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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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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 version and processor type. The Docker images, used to run training & inference jobs, are built from both corresponding "base" and "final" Dockerfiles. ... -
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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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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 to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. ...