Open Source Python System Software - Page 9
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Python System Software
View 2046 business solutionsBrowse free open source Python System Software and projects below. Use the toggles on the left to filter open source Python System Software by OS, license, language, programming language, and project status.
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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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Render Farm Manager, Project Tracker.
CGRU: Afanasy render farm manager and RULES project tracker.
CGRU is an open source CG tools pack, includes Afanasy render farm manager and RULES project tracker. -
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Zenoss Community Edition
Zenoss - Intelligent IT Operations Management
Zenoss provides software-defined IT operations for the world’s largest organizations. We deliver the ultimate level of IT service health with simplicity by providing the most granular and intelligent IT service modeling possible, at any scale, and sharing these unique insights with other IT operations management (ITOM) tools to make them more efficient. Zenoss Community Edition is not a “demo” or trial version of Zenoss Enterprise or Zenoss Cloud! Before You install Zenoss Community Edition, check out Zenoss Cloud, our new Saas-based platform for intelligent IT operations management, designed for enterprise hybrid IT environments. https://www.zenoss.com/product/zenoss-cloud-it-operations-management Zenoss Cloud extends your monitoring capabilities well beyond those available in our Community Edition. View the differences here: https://www.zenoss.com/get-started Features of Zenoss Cloud include: -
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pyexpander
A powerful macro processor based on python
Pyexpander is a powerful macro processor based on python. Instead of simple macro replacement it offers evaluation of arbitrary python expressions and execution of python code. Pyexpander is Turing Complete. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Python wrapper for global input hooks in Windows. The package provides callbacks for mouse and keyboard events; events can be monitored and filtered.
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PyMacroRecord
A completely free macro recorder, coded with Python.
PyMacroRecord is a completely free and open-source macro recorder. It is user friendly and doesn't require any scripting knowledge with lots of useful feature to make very good automation. -
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Open Automation
Building automation (like KNX or OpenHAB) software (like CometVisu).
This project aims to provide software usefull in automation tasks like building automation. Different backends are supported like KNX or OpenHAB. Note: The project has moved to GitHub. Please consult: https://github.com/CometVisu/CometVisu - for the CometVisu https://github.com/OpenAutomationProject - for the different OpenAutomation subprojects -
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synergy-stable-builds
Share one mouse, one keyboard and one clipboard between your computers
Synergy combines your desktop devices together in to one cohesive experience. It's software for sharing your mouse and keyboard between multiple computers on your desk. It works on Windows, Mac OS X and Linux. -
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Applied Infinity
CDN
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Earn up to 16% annual interest with Nexo.Generate interest, access liquidity without selling, and execute trades seamlessly. All in one platform. Geographic restrictions, eligibility, and terms apply.
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AWS ParallelCluster Node
Python package installed on the Amazon EC2 instances
aws-parallelcluster-node is the python package installed on the Amazon EC2 instances launched as part of AWS ParallelCluster. AWS ParallelCluster is an AWS-supported Open Source cluster management tool that makes it easy for you to deploy and manage High-Performance Computing (HPC) clusters in the AWS cloud. Built on the Open Source CfnCluster project, AWS ParallelCluster enables you to quickly build an HPC compute environment in AWS. It automatically sets up the required compute resources and a shared filesystem and offers a variety of batch schedulers such as AWS Batch and Slurm. AWS ParallelCluster facilitates both quick start proof of concepts (POCs) and production deployments. You can build higher-level workflows, such as a Genomics portal that automates the entire DNA sequencing workflow, on top of AWS ParallelCluster. -
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Android Emulator Container Scripts
Minimal scripts to run the emulator in a container for various systems
android-emulator-container-scripts turns the Android Emulator into a cloud-native service you can run in Docker and Kubernetes, so teams can provision ephemeral Android devices on demand. It includes scripts and container images that configure the emulator for headless operation, wire up networking, and expose endpoints for ADB and web access. A built-in WebRTC bridge lets you stream the emulator screen to a browser with interactive input, which is ideal for CI dashboards, remote debugging, or demo environments. The project focuses on reproducibility and scale: you define which system image to boot, how to persist or reset data, and how many instances to run, then schedule them like any other workload. GPU acceleration, audio, and sensors can be enabled depending on your host and cluster capabilities, while fallbacks like SwiftShader keep things usable when no GPU is available. -
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BotCity Framework Core Python
BotCity Framework - Python
Recognize and interact with UI elements using state-of-art computer vision module. Operate any UI interface independent of the technology or platform (desktop, web, terminal). BotCity is a platform to develop, deploy, manage and maintain automation. Automation can be developed in Python or Java using open-source libraries that are market standard. Develop, deploy, manage and scale your Automation Ops using All in One platform that provides task queue, runtime environment management, reports, alerts, logs and much more. -
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Crossbar.io
Crossbar.io - WAMP application router
Crossbar.io is an open-source networking platform for distributed and microservice applications. It implements the Web Application Messaging Protocol, which allows application components to communicate through routed remote procedure calls and publish-subscribe messaging. The platform is designed to handle the messaging layer so developers can focus on business logic instead of building custom connection, routing, and event systems. It supports real-time application architectures where services, browsers, devices, and backend components need to exchange messages reliably. Crossbar.io is especially useful for event-driven systems, IoT backends, dashboards, collaborative applications, and distributed service environments. It can be deployed as a router in the middle of an application network and used with compatible WAMP client libraries. -
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Deep Learning with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow. To date tensorflow comes in two different packages, namely tensorflow and tensorflow-gpu, whether you want to install the framework with CPU-only or GPU support, respectively. NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details. Since version 0.9 Theano introduced the libgpuarray in the stable release (it was previously only available in the development version). The goal of libgpuarray is (from the documentation) make a common GPU ndarray (n dimensions array) that can be reused by all projects that is as future proof as possible, while keeping it easy to use for simple need/quick test. The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. These are available for multiple architectures. -
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Maestral
Open-source Dropbox client for macOS and Linux
Maestral is a lightweight Dropbox client for macOS and Linux. It provides powerful command line tools, supports gitignore patterns to exclude local files from syncing, and allows syncing multiple Dropbox accounts. The CLI allows configuring an unlimited number of Dropbox accounts. Just pass a new config name when linking a new account. More fine-grained controls in the GUI and command line interface allow excluding individual files with selective sync. Maestral is not an official Dropbox App. It therefore does not count towards the three-device limit for Basic Dropbox accounts. Exclude local items from syncing by placing a .mignore file in the Dropbox root with patterns matching any number of items. -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
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Raven Python
Raven is the legacy Python client for Sentry
Raven is the official legacy Python client for Sentry, officially supports Python 2.6–2.7 & 3.3–3.7, and runs on PyPy and Google App Engine. It tracks errors and exceptions that happen during the execution of your application and provides instant notification with detailed information needed to prioritize, identify, reproduce, and fix each issue. It provides full out-of-the-box support for many of the popular Python frameworks, including Django, and Flask. Raven also includes drop-in support for any WSGI-compatible web application. -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
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SageMaker Experiments Python SDK
Experiment tracking and metric logging for Amazon SageMaker notebooks
Experiment tracking in SageMaker Training Jobs, Processing Jobs, and Notebooks. SageMaker Experiments is an AWS service for tracking machine learning Experiments. The SageMaker Experiments Python SDK is a high-level interface to this service that helps you track Experiment information using Python. Experiment tracking powers the machine learning integrated development environment Amazon SageMaker Studio. Experiment: A collection of related Trials. Add Trials to an Experiment that you wish to compare together. Trial: A description of a multi-step machine learning workflow. Each step in the workflow is described by a Trial Component. There is no relationship between Trial Components such as ordering. Trial Component: A description of a single step in a machine learning workflow. For example data cleaning, feature extraction, model training, model evaluation, etc. -
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The Reactive Extensions for Python
Reactive extensions for Python
RxPY is a library for composing asynchronous and event-based programs using observable collections and pipable query operators in Python. A library for composing asynchronous and event-based programs using observable collections and query operator functions in Python. Reactive Extensions for Python (RxPY) is a set of libraries for composing asynchronous and event-based programs using observable sequences and pipable query operators in Python. Using Rx, developers represent asynchronous data streams with Observables, query asynchronous data streams using operators, and parameterize concurrency in data/event streams using Schedulers. RxPY is a fairly complete implementation of Rx with more than 120 operators, and over 1300 passing unit-tests. RxPY is mostly a direct port of RxJS, but also borrows a bit from RxNET and RxJava in terms of threading and blocking operators. -
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Vorta Backup Client
Desktop Backup Client for Borg Backup
Vorta is a backup client for macOS and Linux desktops. It integrates the mighty Borg Backup with your favorite desktop environment to protect your data from disk failure, ransomware and theft. -
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Watchdog
Python library and shell utilities to monitor filesystem events
Python API library and shell utilities to monitor file system events. A simple program that uses watchdog to monitor directories specified as command-line arguments and logs events generated. Watchdog comes with an optional utility script called watchmedo. Please type watchmedo --help at the shell prompt to know more about this tool. You can use the shell-command subcommand to execute shell commands in response to events. watchmedo can read tricks.yaml files and execute tricks within them in response to file system events. Tricks are actually event handlers that subclass watchdog.tricks.Trick and are written by plugin authors. Trick classes are augmented with a few additional features that regular event handlers don't need. The directory containing the tricks.yaml file will be monitored. Each trick class is initialized with its corresponding keys in the tricks.yaml file as arguments and events are fed to an instance of this class as they arrive. -
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ZeroNet
Decentralized websites using Bitcoin crypto and BitTorrent network
Open, free and uncensorable websites, using Bitcoin cryptography and BitTorrent network. Your content distributed directly to other visitors without any central server. Uncensored: It's nowhere because it's everywhere! No hosting costs: Sites are served by visitors. Always accessible: No single point of failure. No configuration needed: Download, unpack and start using it. Decentralized domains using Namecoin cryptocurrency. Your account is protected by the same cryptography as your Bitcoin wallet. Page response time is not limited by your connection speed. Real-time updated, multi-user websites. Supports any modern browser on Windows, Linux or Mac platforms. You can easily hide your IP address using the Tor network. Browse the sites you're seeding even if your internet connection is down. Developed by the community for the community. We believe in open, free, and uncensored network and communication. -
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gitsome
A supercharged Git/GitHub command line interface (CLI)
Although the standard Git command line is a great tool to manage your Git-powered repos, it can be tough to remember the usage of 150+ porcelain and plumbing commands, countless command-specific options, and resources such as tags and branches. The Git command line does not integrate with GitHub, forcing you to toggle between command line and browser. gitsome aims to supercharge your standard git/shell interface by focusing on improving ease-of-use and increasing productivity. Not all GitHub workflows work well in a terminal; gitsome attempts to target those that do. gitsome includes 29 GitHub integrated commands that work with ALL shells. Run gh commands along with Git-Extras and hub commands to unlock even more GitHub integrations! gitsome supports Fish-style auto-suggestions. Use the right arrow key to complete a suggestion. -
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jetson-stats
Simple package for monitoring and control your NVIDIA Jetson
jetson_stats is a Python package for monitoring and controlling NVIDIA Jetson boards. It supports Jetson platforms such as Orin, Xavier, Nano, TX, and newer Jetson families. The project includes jtop, a terminal-based dashboard that gives users a live view of CPU, GPU, memory, power, temperature, engine usage, and system status. It can also be imported into Python scripts, which makes it useful for custom monitoring, robotics dashboards, automation, and diagnostics. Developers working with embedded AI systems can use it to understand performance limits and troubleshoot hardware behavior during deployment. Its main value is giving Jetson users a practical, readable, and scriptable view of board health and resource usage.
