Open Source Python Software Development Software - Page 98
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Python Software Development Software
View 5769 business solutionsBrowse free open source Python Software Development Software and projects below. Use the toggles on the left to filter open source Python Software Development Software by OS, license, language, programming language, and project status.
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Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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Failed Payment Recovery for Subscription BusinessesFlexPay’s innovative platform uses multiple technologies to achieve the highest number of retained customers, resulting in reduced involuntary churn, longer life span after recovery, and higher revenue. Leading brands like LegalZoom, Hooked on Phonics, and ClinicSense trust FlexPay to recover failed payments, reduce churn, and increase customer lifetime value.
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SUIT (Scripting Using Integrated Templates) is a template framework that allows you to define your own syntax through user-defined rules. There are PHP and Python versions. This page is also the home of subprojects, Such as TIE, a template manager.
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SVNChecker is a framework for Subversion pre-commit hook scripts. See the new project page http://svnchecker.tigris.org/.
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SVNGroup is a collection of tools designed to provide dynamically configurable access control groups for Subversion repository access.
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SVNLocalChangesBackup
Tool to take Backup of Local File Changes in the SVN sandbox
Simple tool to take backup of files that are changed/added in SVN sandbox and not yet committed into the.SVN repository (in a ZIP file format). -
Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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SVNStartCommitHelper
Useful form to support SVN Commits as an SVN Start-Commit Hook Script
Professional environments focus on high development standards in Source Code Management. E.g. by usage of server side commit hooks to check for minimum acceptance levels on code and documentation quality including commit message structure and content. TortoiseSVN offers only a free form text field to edit inside the Commit Dialog. Developers might recall situations when struggling with commit message structure and fighting the server side commit hooks instead of focusing on message content! Thus being annoyed instead of feeling an incentive to deliver high quality descriptions here. The SVNStartCommitHelper is a client side start commit hook script (as a first version written in Python / Tkinter) exactly offering a well-structured form to fill in. The edited content is transformed and forwarded to the SVN commit dialog then. You still have full control on the commit message then. While using the helper you focus on message quality now instead struggling with message structure. -
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SaaS Base Application
SaaS base application (Flask, Vue, Bootstrap, Webpack)
A base application for SaaS products built with Flask, Vue.js, Bootstrap, and Webpack. -
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Safe Harbor Deidentification
Safe Harbor Deidentification for medical documents
Phalanx - Deidentify Safe Harbor Deidentification Mode of Phalanx is an abridged pipeline of NLP annotators culminating in NER annotators which write output of text offsets. It uses the Safe Harbor deidentification method. -
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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. The "base" Dockerfile encompasses the installation of the framework and all of the dependencies needed. All "final" Dockerfiles build images using base images that use the tagging scheme. -
Deliver trusted data with dbtData teams use dbt to codify business logic and make it accessible to the entire organization—for use in reporting, ML modeling, and operational workflows.
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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. If you use a prebuilt SageMaker Docker image for training, this library may already be included. Very often, an entry point needs additional information from the container that is not available in hyperparameters. SageMaker Containers writes this information as environment variables that are available inside the script. -
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SageMaker Hugging Face Inference Toolkit
Library for serving Transformers models on Amazon SageMaker
SageMaker Hugging Face Inference Toolkit is an open-source 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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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. If you have your own algorithms built into SageMaker compatible Docker containers, you can train and host models using these as well. -
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SageMaker Scikit-Learn Extension
A library of additional estimators and SageMaker tools based on scikit
A library of additional estimators and SageMaker tools based on scikit-learn. This project contains standalone scikit-learn estimators and additional tools to support SageMaker Autopilot. Many of the additional estimators are based on existing scikit-learn estimators. SageMaker Scikit-Learn Extension is a Python module for machine learning built on top of scikit-learn. In order to use the I/O functionalies in the sagemaker_sklearn_extension.externals module, you will also need to install the mlio version 0.7 package via conda. The mlio package is only available through conda at the moment. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. For unit tests, tox will use pytest to run the unit tests in a Python 3.7 interpreter. tox will also run flake8 and pylint for style checks. -
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SageMaker TensorFlow Serving Container
A TensorFlow Serving solution for use in SageMaker
SageMaker TensorFlow Serving Container is an a open source project that builds docker images for running TensorFlow Serving on Amazon SageMaker. Some of the build and tests scripts interact with resources in your AWS account. Be sure to set your default AWS credentials and region using aws configure before using these scripts. Amazon SageMaker uses Docker containers to run all training jobs and inference endpoints. The Docker images are built from the Dockerfiles in docker/. 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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SageMaker TensorFlow Training Toolkit
Toolkit for running TensorFlow training scripts on SageMaker
Toolkit for running TensorFlow training scripts on SageMaker. SageMaker TensorFlow Training Toolkit is an open-source library for using TensorFlow to train models on Amazon SageMaker. To use your TensorFlow Serving model on SageMaker, you first need to create a SageMaker Model. After creating a SageMaker Model, you can use it to create SageMaker Batch Transform Jobs for offline inference, or create SageMaker Endpoints for real-time inference. A SageMaker Model contains references to a model.tar.gz file in S3 containing serialized model data, and a Docker image used to serve predictions with that model. A Batch Transform job runs an offline-inference job using your TensorFlow Serving model. Input data in S3 is converted to HTTP requests, and responses are saved to an output bucket in S3. -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. 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. If you use a prebuilt SageMaker Docker image for training, this library may already be included. Write a training script (eg. train.py). Define a container with a Dockerfile that includes the training script and any dependencies. -
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Sakura is a Knowledge Navigator and User Interface for UNIX, which implements HyperMedia and its own windowing and packing system, both in the main program and in an extensive API for Tcl and other languages.
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Saleor Commerce
A modular, high performance, headless e-commerce platform
An open-source, GraphQL-first e-commerce platform delivering ultra-fast, dynamic and personalized shopping experiences. A headless, GraphQL commerce platform delivering ultra-fast, dynamic, personalized shopping experiences. Beautiful online stores, anywhere, on any device. Saleor is a rapidly-growing open source e-commerce platform that has served high-volume companies from branches like publishing and apparel since 2012. Based on Python and Django, the latest major update introduces a modular front end powered by a GraphQL API and written with React and TypeScript. A comprehensive system for orders, dispatch, and refunds. Advanced payment and tax options, with full control over discounts and promotions. Packed with features that get stores to a wider audience. -
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SamChanEd is a command line tool to organize channels list on Samsung TV. Currently it supports only analog channels on C series of TV sets. TV icon by http://cemagraphics.deviantart.com/
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Named after a well know product from Microsoft, SandStorm is a framework for creating modular middle-end web products.
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Sanic
Async Python 3.6+ web server/framework
Build fast, run fast with Sanic! Sanic is a Python 3.6+ web server and web framework designed to go fast. It provides a way to get a highly performant HTTP server up and running fast, while also making it easy to build, expand, and eventually scale. Sanic aspires to be as simple as possible while delivering the performance that you require. It allows the usage of the async/await syntax added in Python 3.5, so your code is guaranteed to be non-blocking and speedy. It's also ASGI compliant, so it's possible to deploy with an alternative ASGI webserver. -
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Satine is a XML Data Binding technology for the Python platform. It supports random access to files, validation, read and write documents
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The OO component based XML processing framework.The goal is to make the XML developer's life easier.It's going ot achieve that by providing reusable components for all stages of XML processsing with easy language and application binding.
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