Search Results for "py2exe for python 3.6" - Page 2
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Showing 93 open source projects for "py2exe for python 3.6"
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TextAttack
Python framework for adversarial attacks, and data augmentation
Generating adversarial examples for NLP models. TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP. -
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Django Ninja
Fast, Async-ready, Openapi, type hints based framework
Django Ninja is a web framework for building APIs with Django and Python 3.6+ type hints. Designed to be easy to use and intuitive. Very high performance thanks to Pydantic and async support. Type hints and automatic docs lets you focus only on business logic. Based on the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema. Django friendly (obviously) has good integration with the Django core and ORM. Used by multiple companies on live projects. -
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YouTube Saver 3.6 [I.S.A]
YouTube Saver 3.6 [Improved.Simplified.Alternative]
'YouTube Saver' is an desktop application developed using python 3.6.8 and other add-on libaries. Can download YouTube videos and audios. YouTube Saver has two parts: 1) Download Video - downloads YouTube video (.mp4) 2) Download Audio - downloads YouTube video (.mp3) Compatible only for windows OS. -
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uwsgi-nginx-flask
Docker image with uWSGI and Nginx for Flask applications in Python
Image with uWSGI and Nginx for Flask apps in Python running in a single container. Docker image with uWSGI and Nginx for Flask web applications in Python running in a single container. Optionally using Alpine Linux. This Docker image allows you to create Flask web applications in Python that run with uWSGI and Nginx in a single container. The combination of uWSGI with Nginx is a common way to deploy Python Flask web applications. It is widely used in the industry and would give you decent... -
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CodeChecker
CodeChecker is an analyzer tooling, defect database
CodeChecker is a static analysis infrastructure built on the LLVM/Clang Static Analyzer toolchain, replacing scan-build in a Linux or macOS (OS X) development environment. Executes Clang-Tidy and Clang Static Analyzer with Cross-Translation Unit analysis, Statistical Analysis (when checkers are available). Creates the JSON compilation database by wiretapping any build process (e.g., CodeChecker log -b "make"). Automatically analyzes GCC cross-compiled projects: detecting GCC or Clang... -
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Dulwich
Pure-Python Git implementation
Dulwich is a Python implementation of the Git file formats and protocols, which does not depend on Git itself. All functionality is available in pure Python. Optional C extensions can be built for improved performance. Dulwich takes its name from the area in London where the friendly Mr. and Mrs. Git once attended a cocktail party. Supported Python versions are Python 3.5 and later. Versions of Dulwich prior to 0.20 also supported Python 2.7. Supported platforms include Linux, Mac OS X... -
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GQL
A GraphQL client in Python
This is a GraphQL client for Python 3.7+. Plays nicely with graphene, graphql-core, graphql-js and any other GraphQL implementation compatible with the spec. GQL architecture is inspired by React-Relay and Apollo-Client. GQL 3 is a GraphQL Client for Python 3.6+ which plays nicely with other graphql implementations compatible with the spec. Under the hood, it uses GraphQL-core which is a Python port of GraphQL.js, the JavaScript reference implementation for GraphQL. GQL supports multiple... -
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Lightly
A python library for self-supervised learning on images
... through advanced filtering. We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. Lightly requires Python 3.6+ but we recommend using Python 3.7+. We recommend installing Lightly in a Linux or OSX environment. With lightly, you can use the latest self-supervised learning methods in a modular way using the full power of PyTorch. Experiment with different backbones, models, and loss functions. -
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GluonTS
Probabilistic time series modeling in Python
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models. GluonTS requires Python 3.6 or newer, and the easiest way to install it is via pip. We train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts... -
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aws-encryption-sdk-cli
CLI wrapper around aws-encryption-sdk-python
This command line tool can be used to encrypt and decrypt files and directories using the AWS Encryption SDK. If you have not already installed cryptography, you might need to install additional prerequisites as detailed in the cryptography installation guide for your operating system. Installation using a python virtual environment is recommended to avoid conflicts between system packages and user-installed packages. For the most part, the behavior of aws-encryption-cli in handling files... -
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Luigi
Python module that helps you build complex pipelines of batch jobs
Luigi is a Python (3.6, 3.7, 3.8, 3.9 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more. The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to chain many tasks, automate them, and failures will happen. These tasks can be anything, but are typically long running things like Hadoop... -
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atpbar
Progress bars for threading and multiprocessing tasks on terminal
Progress bars for threading and multiprocessing tasks on the terminal and Jupyter Notebook. atpbar can display multiple progress bars simultaneously growing to show the progresses of iterations of loops in threading or multiprocessing tasks. atpbar can display progress bars on the terminal and Jupyter Notebook. atpbar can be used with Mantichora. atpbar started its development in 2015 as part of Alphatwirl. atpbar prevented physicists from terminating their running analysis codes, which... -
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pybaselines
Library of algorithms for baseline correction of experimental data
pybaselines is a Python library that provides many different algorithms for performing baseline correction on data from experimental techniques such as Raman, FTIR, NMR, XRD, XRF, PIXE, etc. The aim of the project is to provide a semi-unified API to allow quick testing and comparing multiple baseline correction algorithms to find the best one for a set of data. pybaselines has 50+ baseline correction algorithms. These include popular algorithms, such as AsLS, airPLS, ModPoly, and SNIP, as well... -
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Mobly
E2E test framework for tests with complex environment requirements
Mobly is a Python-based test framework that specializes in supporting test cases that require multiple devices, complex environments, or custom hardware setups. P2P data transfer between two devices. Conference calls across three phones. Wearable device interacting with a phone. Internet-Of-Things devices interacting with each other. Testing RF characteristics of devices with special equipment. Testing LTE network by controlling phones, base stations, and eNBs. Mobly can support many different... -
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Spektral
Graph Neural Networks with Keras and Tensorflow 2
... deep learning. Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects. Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. Other Linux distros should work as well. The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements. -
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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... -
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Django Notifications
GitHub notifications alike app for Django
django-notifications is a GitHub notification alike app for Django, it was derived from django-activity-stream. Notifications are actually actions events, which are categorized by four main components. To generate a notification anywhere in your code, simply import the notify signal and send it with your actor, recipient, and verb. Generating notifications is probably best done in a separate signal. Using django-model-utils, we get the ability to add queryset methods to not only the manager,... -
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django-push-notifications
Send push notifications to mobile devices through GCM or APNS
A minimal Django app that implements Device models that can send messages through APNS, FCM/GCM, WNS and WebPush. The app also implements an admin panel, through which you can test single and bulk notifications. Select one or more FCM/GCM, APNS, WNS or WebPush devices and in the action dropdown, select "Send test message" or "Send test message in bulk", accordingly. Note that sending a non-bulk test message to more than one device will just iterate over the devices and send multiple single... -
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PyTorch Implementation of SDE Solvers
Differentiable SDE solvers with GPU support and efficient sensitivity
This library provides stochastic differential equation (SDE) solvers with GPU support and efficient backpropagation. examples/demo.ipynb gives a short guide on how to solve SDEs, including subtle points such as fixing the randomness in the solver and the choice of noise types. examples/latent_sde.py learns a latent stochastic differential equation, as in Section 5 of [1]. The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can be... -
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OpsManage
Automated operation and maintenance platform
..., Python3.6 + HTML + JScripts. Front-end web framework, Bootstrap. Backend Web Framework, Django. Backend Task Framework, Celery + Redis. Programming language is Python 3.6. Operating System is CentOS 6+. Ansible version 2.6+. Deployment platform and node server Rsync 3+. -
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GIXY
Nginx configuration static analyzer
Gixy is a tool to analyze Nginx configuration. The main goal of Gixy is to prevent security misconfiguration and automate flaw detection. Currently supported Python versions are 2.7, 3.5, 3.6 and 3.7. Gixy is well tested only on GNU/Linux, other OSs may have some issues. You can find things that Gixy is learning to detect at Issues labeled with "new plugin". By default Gixy will try to analyze Nginx configuration placed in /etc/nginx/nginx.conf. Or something else, you can find all other gixy... -
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BCI
BCI: Breast Cancer Immunohistochemical Image Generation
Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix. We have released the trained model on BCI and LLVIP datasets. We host a competition for breast cancer immunohistochemistry image generation on Grand Challenge. Project pix2pix provides a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene, these can be pairs {HE, IHC}. Then we can learn to translate A(HE images... -
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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... -
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Grassroots DICOM
Cross-platform DICOM implementation
Grassroots DiCoM is a C++ library for DICOM medical files. It is accessible from Python, C#, Java and PHP. It supports RAW, JPEG, JPEG 2000, JPEG-LS, RLE and deflated transfer syntax. It comes with a super fast scanner implementation to quickly scan hundreds of DICOM files. It supports SCU network operations (C-ECHO, C-FIND, C-STORE, C-MOVE). PS 3.3 & 3.6 are distributed as XML files. It also provides PS 3.15 certificates and password based mecanism to anonymize and de-identify DICOM... -
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Catalyst
Accelerated deep learning R&D
... something totally new. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. It's part of the PyTorch Ecosystem, as well as the Catalyst Ecosystem which includes Alchemy (experiments logging & visualization) and Reaction (convenient deep learning models serving).