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tox aims to automate and standardize testing in Python. It is part of a larger vision of easing the packaging, testing and release process of Python software. tox is a generic virtualenv management and test command-line tool you can use for checking that your package installs correctly with different Python versions and interpreters, running your tests in each of the environments, configuring your test tool of choice, and acting as a frontend to continuous integration servers, greatly reducing boilerplate and merging CI and shell-based testing. First, install tox with pip install tox. Then put basic information about your project and the test environments you want your project to run in into a tox.ini file residing right next to your setup.py file. You can also try generating a tox.ini file automatically, by running tox-quickstart and then answering a few simple questions. Install and test your project against Python2.7 and Python3.6.

The cross-platform windowing and multimedia library for Python. pyglet is a powerful, yet easy-to-use Python library for developing games and other visually-rich applications on Windows, Mac OS X, and Linux. It supports windowing, user interface event handling, Joysticks, OpenGL graphics, loading images, and videos, and playing sounds and music. All of this with a friendly Pythonic API, that's simple to learn and doesn't get in your way. pyglet is provided under the BSD open-source license, allowing you to use it for both commercial and other open-source projects with very little restriction. No external dependencies or installation requirements. For most application and game requirements, pyglet needs nothing else besides Python, simplifying distribution and installation. This makes it easy to package your project with freezers such as PyInstaller. pyglet provides real platform native windows, allowing you to take advantage of multiple windows and multi-monitor desktops.

All URL parts, scheme, user, password, host, port, path, query, and fragment are accessible by properties. All URL manipulations produce a new URL object. Strings passed to constructor and modification methods are automatically encoded giving canonical representation as result. Regular properties are percent-decoded, use raw_ versions for getting encoded strings. Human-readable representation of URL is available as .human_repr(). PyPI contains binary wheels for Linux, Windows and MacOS. If you want to install yarl on another operating system (like Alpine Linux, which is not manylinux-compliant because of the missing glibc and therefore, cannot be used with our wheels) the tarball will be used to compile the library from the source code. It requires a C compiler and Python headers installed. Please note that the pure-Python (uncompiled) version is much slower. However, PyPy always uses a pure-Python implementation, and, as such, it is unaffected by this variable.

Pexpect makes Python a better tool for controlling other applications. Pexpect is a pure Python module for spawning child applications; controlling them, and responding to expected patterns in their output. Pexpect works like Don Libes’ Expect. Pexpect allows your script to spawn a child application and control it as if a human were typing commands. Pexpect can be used for automating interactive applications such as ssh, FTP, passwd, telnet, etc. It can be used to automate setup scripts for duplicating software package installations on different servers. It can be used for automated software testing. Pexpect is in the spirit of Don Libes’ Expect, but Pexpect is pure Python. Unlike other Expect-like modules for Python, Pexpect does not require TCL or Expect nor does it require C extensions to be compiled. It should work on any platform that supports the standard Python pty module. The Pexpect interface was designed to be easy to use.

This package is intended to be independently reusable in any Python project. It is maintained by the Zope Toolkit project. This package provides an implementation of “object interfaces” for Python. Interfaces are a mechanism for labeling objects as conforming to a given API or contract. So, this package can be considered as an implementation of the Design By Contract methodology support in Python. Interfaces are objects that specify (document) the external behavior of objects that “provide” them. An interface specifies behavior through informal documentation in a doc string, attribute definitions, and invariants, which are conditions that must hold for objects that provide the interface. Attribute definitions specify specific attributes. They define the attribute name and provide documentation and constraints of attribute values. Attribute definitions can take a number of forms.

The Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities. The core image library is designed for fast access to data stored in a few basic pixel formats. It should provide a solid foundation for a general image processing tool. Pillow for enterprise is available via the Tidelift subscription. The Python Imaging Library is ideal for image archival and batch processing applications. You can use the library to create thumbnails, convert between file formats, print images, etc. The current version identifies and reads a large number of formats. Write support is intentionally restricted to the most commonly used interchange and presentation formats. The library contains basic image processing functionality, including point operations, filtering with a set of built-in convolution kernels, and color space conversions.

WTForms is a flexible forms validation and rendering library for Python web development. It can work with whatever web framework and template engine you choose. It supports data validation, CSRF protection, internationalization (I18N), and more. There are various community libraries that provide closer integration with popular frameworks. WTForms tries to provide as usable an API as possible. We’ve listed here some of the known libraries to work with WTForms, but if it’s not listed, it doesn’t mean it won’t work. Pretty much any ORM or object-DB should work, as long as data objects allow attribute access to their members. WTForms uses unicode strings throughout the source code and assumes that form input has already been coerced to unicode by your framework. WTForms fields render to unicode strings by default, and therefore as long as your templating engine can work with that, you should have no unicode issues.

AWS Deep Learning AMIs (DLAMI) provides ML practitioners and researchers with a curated and secure set of frameworks, dependencies, and tools to accelerate deep learning in the cloud. Built for Amazon Linux and Ubuntu, Amazon Machine Images (AMIs) come preconfigured with TensorFlow, PyTorch, Apache MXNet, Chainer, Microsoft Cognitive Toolkit (CNTK), Gluon, Horovod, and Keras, allowing you to quickly deploy and run these frameworks and tools at scale. Develop advanced ML models at scale to develop autonomous vehicle (AV) technology safely by validating models with millions of supported virtual tests. Accelerate the installation and configuration of AWS instances, and speed up experimentation and evaluation with up-to-date frameworks and libraries, including Hugging Face Transformers. Use advanced analytics, ML, and deep learning capabilities to identify trends and make predictions from raw, disparate health data.

TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks.

openpyxl is a Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files. It was born from a lack of an existing library to read/write natively from Python in the Office Open XML format. All kudos to the PHPExcel team as openpyxl was initially based on PHPExcel. By default, openpyxl does not guard against quadratic blowup or billion laughs XML attacks. To guard against these attacks install defusedxml. Install openpyxl using pip. It is advisable to do this in a Python virtualenv without system packages. Sometimes you might want to work with the checkout of a particular version. This may be the case if bugs have been fixed but a release has not yet been made. There is no need to create a file on the filesystem to get started with openpyxl. Just import the Workbook class and start work. Sheets are given a name automatically when they are created. Once you gave a worksheet a name, you can get it as a key of the workbook.

Imageio is a Python library that provides an easy interface to read and write a wide range of image data, including animated images, volumetric data, and scientific formats. It is cross-platform, runs on Python 3.5+, and is easy to install. Imageio is written in pure Python, so installation is easy. Imageio works on Python 3.5+. It also works on Pypy. Imageio depends on Numpy and Pillow. For some formats, imageio needs additional libraries/executables (e.g. ffmpeg), which imageio helps you to download/install. If something doesn’t work as it should, you need to know where to search for causes. The overview on this page aims to help you in this regard by giving you an idea of how things work, and - hence - where things may go sideways.

The ROOT data analysis framework is used much in High Energy Physics (HEP) and has its own output format (.root). ROOT can be easily interfaced with software written in C++. For software tools in Python there exists pyROOT. Unfortunately, pyROOT does not work well with python3.4. broot is a small library that converts data in python numpy ndarrays to ROOT files containing trees with a branch for each array. The goal of this library is to provide a generic way of writing python numpy datastructures to ROOT files. The library should be portable and supports both python2, python3, ROOT v5 and ROOT v6 (requiring no modifications on the ROOT part, just the default installation). Installation of the library should only require a user to compile to library once or install it as a python package.

statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. An extensive list of result statistics is available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open-source Modified BSD (3-clause) license. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings. You can also use numpy arrays instead of formulas. The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. This is the recommended installation method for most users.

Oracle Data Access Components (ODAC) are a set of Windows and .NET data access drivers and tools. They include support for .NET data access, Microsoft Visual Studio tools for developing Oracle database applications; and ASP.NET providers. ODAC provides comprehensive client support for advanced Oracle database functionality, including performance, high availability, and security, among other features. It is tightly integrated with Visual Studio to provide a seamless development experience for developers. Oracle Data Provider for .NET is an implementation of Microsoft's ADO.NET interface and provides access to Oracle databases. OLAP Data Manipulation Language (OLAP DML) is used to define and manipulate analytic workspace objects. High performance, a feature-rich mechanism for Microsoft ADO and OLE DB data access to Oracle databases, its usage, and its configuration. Provides introductory, installation, post-installation configuration, and usage information.

Django-MySQL extends Django’s built-in MySQL and MariaDB support their specific features not available on other databases. A new cache backend that makes use of MySQL’s upsert statement and does compression. Named locks for easy locking of e.g. external resources. Extra checks added to Django’s check framework to ensure your Django and MySQL configurations are optimal. Django-MySQL comes with a number of extensions to QuerySet that can be installed in a number of ways - e.g. adding the QuerySetMixin to your existing QuerySet subclass.

Unirest is a set of lightweight HTTP libraries available in multiple languages, built and maintained by Kong, who also maintains the open-source API Gateway Kong. To utilize Unirest for node.js install the npm module. You're probably wondering how using Unirest makes creating requests easier. Besides automatically supporting gzip, and parsing responses, you can start with basic examples. A request can be initiated by invoking the appropriate method on the Unirest object, then calling .end() to send the request. Alternatively, you can send the request directly by providing a callback along with the URL. Provides simple and easy-to-use methods for manipulating the request prior to being sent. This object is created when a Unirest method is invoked. This object contains methods that are chainable like other libraries such as jQuery and popular request module Superagent (which this library is modeled after slightly).

Bootstrap your application with beautiful Material Design components. React Toolbox is a set of React components that implements Google Material Design specification. It's built on top of some of the trendiest proposals like CSS Modules (written in SASS), Webpack, and ES6. The library harmoniously integrates with your Webpack workflow and it's easily customizable and very flexible. Check our awesome documentation (which is built using React Toolbox) and try all the components with live examples. We've created a playground so you don't need to install anything to fall in love with our components, so fun! The app bar is a special kind of toolbar that’s used for branding, navigation, search, and actions. Usually, it contains controls on the right and left side and a title with the current section or app name. You should give the content with children elements. Consists of text, an image, or both, designed in accordance with your app’s color theme.

We put much effort into making ngx-bootstrap modular so you can implement your templates, styles, whatnot. All components are designed with extensibility and adaptivity in mind. You can expect them to work on Mobile and Desktop with the same level of performance. We have incorporated a set of style guides and guidelines to enhance both code maintainability and readability. Also, we always support the latest Angular versions and provide full unit-test coverage. Being developers ourselves, we understand the importance of documentation. Well-written and continually updated docs significantly ease up the life of developers and improve overall software quality. We are doing our best to provide you with the most complete and easy-to-understand documentation out there. While working with visually rich libraries, you need to know what you are getting without going through the installation hassle. That is why we have developed a great set of demos for most of the component’s methods.

GDAL is a translator library for raster and vector geospatial data formats that is released under an MIT style Open Source License by the Open Source Geospatial Foundation. As a library, it presents a single raster abstract data model and single vector abstract data model to the calling application for all supported formats. It also comes with a variety of useful command line utilities for data translation and processing.

Build data-rich, responsive web apps much faster with Ignite UI - the most complete library of enterprise-grade JavaScript and .NET UI components available. Includes libraries for Angular, ASP.NET (Core and MVC), Blazor, jQuery, React, web components and more. Our advanced Command Line Interface (CLI) tools simplify the use of component libraries with multiple dependencies. While our WYSIWYG Page Designer allows you to drag-n-drop your way to a modern web experience – leveraging our JavaScript UI components, data connectors, layout and theme support to design, and code your next app. It is hard to be productive when you need to be an expert in multiple JavaScript libraries. We augment our libraries with Typescript for strong typing, class-based inheritance that makes development a lot less error-prone and easier to manage.

xlrd is a library for developers to extract data from Microsoft Excel (tm) .xls spreadsheet files. xlrd is a library for reading data and formatting information from Excel files in the historical .xls format. This library will no longer read anything other than .xls files. Ignores charts, macros, pictures, any other embedded object, including embedded worksheets, VBA modules, and formulas, but results of formula calculations are extracted, comments, hyperlinks, auto filters, advanced filters, pivot tables, conditional formatting, and data validation. Password-protected files are not supported and cannot be read by this library. From the command line, this will show the first, second, and last rows of each sheet in each file. xlrd is licensed under the BSD license.

Explore the possibilities of your .NET enterprise apps with these dependency-free .NET spreadsheet components. .NET spreadsheet components are advanced software components that allow professional developers to add complete Excel-like functionality to their desktop applications. The .NET spreadsheet control includes support for Excel import/export, full cell customization, an extensive calculation engine with over 450 functions and more, all with zero dependencies on Excel. Leverage the extensive .NET spreadsheet API and powerful calculation engine to create analysis, budgeting, dashboard, data collection and management, scientific, and financial applications. Every platform of Spread.NET ensures maximum performance and speed for your enterprise apps, and its modular structure means you only need to add what you use to your .NET spreadsheet apps.

urllib3 is a powerful, user-friendly HTTP client for Python. Much of the Python ecosystem already uses urllib3 and you should too. urllib3 brings many critical features that are missing from the Python standard libraries. Thread safety, connection pooling, client-side TLS/SSL verification. File uploads with multipart encoding. Helpers for retrying requests and dealing with HTTP redirects. Support for gzip, deflate, and brotli encoding. Proxy support for HTTP and SOCKS. 100% test coverage. urllib3 is one of the most downloaded packages on PyPI and is a dependency of many popular Python packages like Requests, Pip, and more! urllib3 is made available under the MIT License. The API Reference documentation provides API-level documentation. The User Guide is the place to go to learn how to use the library and accomplish common tasks. The more in-depth Advanced Usage guide is the place to go for lower-level tweaking.

Enhance performance and save development time with IMSL numerical libraries. Achieve your strategic objectives using IMSL's build tools. Model regression, make decision trees, establish neural networks, and forecast time series with your IMSL library. Rigorously tested and proven for decades across all industries, the IMSL C Numerical Library gives companies a dependable, high-ROI solution for building cutting-edge analytics tools. From data mining and forecasting, to advanced statistical analysis, the IMSL C Numerical Library can help teams quickly add sophisticated functionality to analytic applications. The IMSL C library makes integration and deployment easy. Enjoy easy migrations, support for common platforms and platform combinations, and no added infrastructure on embed in databases or applications.

Program autotuning has been demonstrated in many domains to achieve better or more portable performance. However, autotuners themselves are often not very portable between projects because using a domain-informed search space representation is critical to achieving good results and because no single search technique performs best for all problems. OpenTuner is a new framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy-to-use interface for communicating with the tuned program. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously, techniques that perform well will receive larger testing budgets, and techniques which perform poorly will be disabled.

Requests is a simple, yet elegant, HTTP library. Requests allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your PUT & POST data, but nowadays, just use the JSON method! Requests is one of the most downloaded Python packages today, pulling in around 30M downloads/week, according to GitHub, Requests is currently depended upon by 1,000,000+ repositories. You may certainly put your trust in this code. Requests is available on PyPI. Requests is ready for the demands of building robust and reliable HTTP–speaking applications, for the needs of today. Automatic content decompression and decoding. International domains and URLs. Sessions with cookie persistence. Browser-style TLS/SSL verification. Basic & digest authentication, and familiar dict–like cookies. Multi-part file uploads. SOCKS proxy support. Connection timeouts and streaming downloads.

Provision a VM quickly with everything you need to get your deep learning project started on Google Cloud. Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular AI frameworks on a Google Compute Engine instance without worrying about software compatibility. You can launch Compute Engine instances pre-installed with TensorFlow, PyTorch, scikit-learn, and more. You can also easily add Cloud GPU and Cloud TPU support. Deep Learning VM Image supports the most popular and latest machine learning frameworks, like TensorFlow and PyTorch. To accelerate your model training and deployment, Deep Learning VM Images are optimized with the latest NVIDIA® CUDA-X AI libraries and drivers and the Intel® Math Kernel Library. Get started immediately with all the required frameworks, libraries, and drivers pre-installed and tested for compatibility. Deep Learning VM Image delivers a seamless notebook experience with integrated support for JupyterLab.

The NVIDIA Deep Learning GPU Training System (DIGITS) puts the power of deep learning into the hands of engineers and data scientists. DIGITS can be used to rapidly train the highly accurate deep neural network (DNNs) for image classification, segmentation and object detection tasks. DIGITS simplifies common deep learning tasks such as managing data, designing and training neural networks on multi-GPU systems, monitoring performance in real-time with advanced visualizations, and selecting the best performing model from the results browser for deployment. DIGITS is completely interactive so that data scientists can focus on designing and training networks rather than programming and debugging. Interactively train models using TensorFlow and visualize model architecture using TensorBoard. Integrate custom plug-ins for importing special data formats such as DICOM used in medical imaging.

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready.

Enjoy the highest performance and unlimited possibilities when working with SQLite. SQLite Data Access Components (LiteDAC) is a library of components that provides native connectivity to SQLite from Delphi and C++Builder including Community Edition, as well as Lazarus (and Free Pascal) on Windows, Linux, macOS, iOS, and Android for both 32-bit and 64-bit platforms. LiteDAC is designed for programmers to develop truly cross-platform desktop and mobile SQLite database applications with no need to deploy any additional libraries. LiteDAC-based DB applications are easy to deploy and do not require the installation of other data provider layers (such as BDE or ODBC), and that's why they can work faster than the ones based on standard Delphi data connectivity solutions. Moreover, LiteDAC provides an additional opportunity to work with SQLite in Delphi and C++Builder directly by linking the client library statically in your application.

Unlock insights from all your data and build artificial intelligence (AI) solutions with Azure Databricks, set up your Apache Spark™ environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn. Azure Databricks provides the latest versions of Apache Spark and allows you to seamlessly integrate with open source libraries. Spin up clusters and build quickly in a fully managed Apache Spark environment with the global scale and availability of Azure. Clusters are set up, configured, and fine-tuned to ensure reliability and performance without the need for monitoring. Take advantage of autoscaling and auto-termination to improve total cost of ownership (TCO).

Flutter is Google’s UI toolkit for building beautiful, natively compiled applications for mobile, web, and desktop from a single codebase. Paint your app to life in milliseconds with Stateful Hot Reload. Use a rich set of fully-customizable widgets to build native interfaces in minutes. Quickly ship features with a focus on native end-user experiences. Layered architecture allows for full customization, which results in incredibly fast rendering and expressive and flexible designs. Flutter’s widgets incorporate all critical platform differences such as scrolling, navigation, icons and fonts, and your Flutter code is compiled to native ARM machine code using Dart's native compilers. Flutter's hot reload helps you quickly and easily experiment, build UIs, add features, and fix bugs faster. Experience sub-second reload times without losing state on emulators, simulators, and hardware.

IBM Watson Machine Learning is a full-service IBM Cloud offering that makes it easy for developers and data scientists to work together to integrate predictive capabilities with their applications. The Machine Learning service is a set of REST APIs that you can call from any programming language to develop applications that make smarter decisions, solve tough problems, and improve user outcomes. Take advantage of machine learning models management (continuous learning system) and deployment (online, batch, streaming). Select any of widely supported machine learning frameworks: TensorFlow, Keras, Caffe, PyTorch, Spark MLlib, scikit learn, xgboost and SPSS. Use the command-line interface and Python client to manage your artifacts. Extend your application with artificial intelligence through the Watson Machine Learning REST API.

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. For a brief introduction to the ideas behind the library, you can read the introductory notes or the paper. Visit the installation page to see how you can download the package and get started with it. You can browse the example gallery to see some of the things that you can do with seaborn, and then check out the tutorials or API reference to find out how. To see the code or report a bug, please visit the GitHub repository. General support questions are most at home on StackOverflow, which has a dedicated channel for seaborn.

Universal Data Access Components (UniDAC) is a library of components that provides direct access to multiple databases from Delphi and C++Builder including Community Edition, as well as Lazarus (and Free Pascal) on Windows, Linux, macOS, iOS, and Android for both 32-bit and 64-bit platforms. Key Features: Universal access to different database servers; Support for most popular databases; Full support for the latest server versions; Support for the latest IDE versions; VCL, LCL and FMX versions of library available; High performance; Easy to deploy.

Pygame is a set of Python modules designed for writing video games. Pygame adds functionality on top of the excellent SDL library. This allows you to create fully featured games and multimedia programs in the python language. Pygame is highly portable and runs on nearly every platform and operating system. Pygame is free. Released under the LGPL license, you can create open-source, freeware, shareware, and commercial games with it. With dual-core CPUs common, and 8-core CPUs cheaply available on desktop systems, making use of multi-core CPUs allows you to do more in your game. Selected pygame functions release the dreaded python GIL, which is something you can do from C code. Uses optimized C and assembly code for core functions. C code is often 10-20 times faster than python code, and assembly code can easily be 100x or more times faster than python code. Comes with many operating systems. Just an apt-get, emerge, pkg_add, or just install away.

This is Ant Design's internal standard for evaluating design quality. Based on the assumption that "everyone is pursuing happiness at work", we have added the two values of "Meaningfulness" and "Growth" on the basis of "Certainty" and "Naturalness" to guide each designer towards better judgment and decision-making. Ant Design React is dedicated to providing a good development experience for programmers. Before starting, it is recommended to learn React and ES2015 first, and correctly install and configure Node.js v8 or above. The official guide also assumes that you have intermediate knowledge about HTML, CSS, and JavaScript, and React. If you are just starting to learn front-end or React, it may not be the best idea to use the UI framework as your first step.

Groq is on a mission to set the standard for GenAI inference speed, helping real-time AI applications come to life today. An LPU inference engine, with LPU standing for Language Processing Unit, is a new type of end-to-end processing unit system that provides the fastest inference for computationally intensive applications with a sequential component, such as AI language applications (LLMs). The LPU is designed to overcome the two LLM bottlenecks, compute density and memory bandwidth. An LPU has greater computing capacity than a GPU and CPU in regards to LLMs. This reduces the amount of time per word calculated, allowing sequences of text to be generated much faster. Additionally, eliminating external memory bottlenecks enables the LPU inference engine to deliver orders of magnitude better performance on LLMs compared to GPUs. Groq supports standard machine learning frameworks such as PyTorch, TensorFlow, and ONNX for inference.

From v1.0.0, the library is rewritten in Typescript and ES6 completely. The giant and old jQuery plugin doesn't exist anymore. With the new plugin-based architecture, the library has really small core. Everything else is built around as a plugin. Each file in the dist/css and dist/js folders has two versions: the normal code in the .css, .js files, and the minified code in the .min.css, .min.js files. In order to reduce the page loading time and enhance the user experience when visiting your site, you should use the .min.css, .min.js files in the production website. Meanwhile, in the development mode, you should use the normal files without .min part to debug the code more easy.

virtualenvwrapper is a set of extensions to Ian Bicking’s virtualenv tool. The extensions include wrappers for creating and deleting virtual environments and otherwise managing your development workflow, making it easier to work on more than one project at a time without introducing conflicts in their dependencies. Organizes all of your virtual environments in one place. Wrappers for managing your virtual environments (create, delete, copy). Use a single command to switch between environments. Tab completion for commands that take a virtual environment as an argument. User-configurable hooks for all operations. Plugin system for creating more sharable extensions. The best way to explain the features virtualenvwrapper gives you is to show it in use. The workon command also includes tab completion for the environment names and invokes customization scripts as an environment is activated or deactivated.

Faster, simpler, and richer user interface development Oracle ADF Faces components is a set of over 150 Ajax-enabled JSF components that let you build a richer web user interface for Java EE applications. Oracle ADF Faces components are free to develop and deploy through Oracle ADF Essentials. Over 150+ AJAX-enabled components. Extensive data visualization components, with JSF 2 support. Complete JavaScript API. Drag and drop framework. Dialog and popup framework. Navigation menu framework. Internationalization and accessibility support. Tablet/touch gesture support. Declarative partial page rendering. Support for skinning. Support for page templates, reusable page regions, and declarative components. Support for the Oracle Alta UI. Integration with ADFc to support TaskFlows and bookmarking. Integration with ADFm to support data bindings. Oracle ADF security support.

Clarity is a scalable, customizable, open source design system bolstered by the people that build with it, the people we build it for, and the community that makes us who we are. Our toolkit is Figma. Every component, pattern, and guideline we ship is rooted in real customer interactions. As a result, you can rely on Clarity for 30+ components and over 200 icons so you can focus on product-specific user problems. The first folx that built Clarity from scratch were a scrappy team of developers, designers, and content creators. Clarity has continued to grow on that strong start by supporting the big three: Angular, React and Vue. Our latest Core components provide support for them all. Clarity provides code examples and detailed API documentation that guide you as you build your next application.

TRichView is a suite of native Delphi/C++Builder VCL/FireMonkey and Lazarus (Free Pascal) LCL components for displaying, editing, and printing complex rich text documents. TRichView can be used to develop advanced text editors, web/help/book authoring applications, chats and messengers, organizers and diaries, multimedia encyclopedias, and other applications that need a high-quality rich text editor or a hypertext user interface. Supported FireMonkey platforms, Windows, 64-bit macOS. The components support various character attributes (fonts, subscripts/superscripts, colored text background, custom drawn). Documents can contain tables, pictures, images from image lists, footnotes and endnotes, and any Delphi controls. Left, right, center, or justify paragraph alignment, custom margins and indents, multilevel bullets and numbering, background images, print preview, data-aware versions, and more.

Scapy is a powerful interactive packet manipulation program. It is able to forge or decode packets of a wide number of protocols, send them on the wire, capture them, match requests and replies, and much more. It can easily handle most classical tasks like scanning, tracerouting, probing, unit tests, attacks, or network discovery (it can replace hping, 85% of nmap, arpspoof, arp-sk, arping, tcpdump, tshark, p0f, etc.). It also performs very well at a lot of other specific tasks that most other tools can’t handle, like sending invalid frames, injecting your own 802.11 frames, combining technics (VLAN hopping+ARP cache poisoning, VOIP decoding on WEP encrypted channel), etc. Scapy runs natively on Linux, Windows, OSX, and on most Unixes with libpcap. The same code base now runs natively on both Python 2 and Python 3. Scapy development uses the Git version control system. Scapy reference repository is hosted on GitHub.

JDeli is able to read and write images as HEIC files, giving a complete HEIC converter between HEIC file format and lots of other image file formats. JDeli can do this in one step with the convert method or separately read or write the HEIC image if you want to process the image in Java. Boost the performance of your image reading and writing code with threading. Unlike ImageIO, JDeli is safe to use with threads. JDeli offers a very similar API to ImageIO and is easy to switch over in your code. JDeli is actively developed by the IDRsolutions development team who rely on it in their other products. Unable to add a platform dependency? JDeli is 100% Java and does not require any native dependencies or plugins.

gTTS (Google Text-to-Speech), a Python library and CLI tool to interface with Google Translate's text-to-speech API. Write spoken mp3 data to a file, a file-like object (bytestring) for further audio manipulation, or stdout. Or simply pre-generate Google Translate TTS request URLs to feed to an external program. Customizable speech-specific sentence tokenizer that allows for unlimited lengths of text to be read, all while keeping proper intonation, abbreviations, decimals and more. Customizable text pre-processors which can, for example, provide pronunciation corrections.

The Polymer library provides a set of features for creating custom elements. These features are designed to make it easier and faster to make custom elements that work like standard DOM elements. Similar to standard DOM elements, Polymer elements can be instantiated using a constructor or document creation element, configured using attributes or properties, populated with internal DOM inside each instance, responsive to property and attribute changes, styled with internal defaults or externally, and responsive to methods that manipulate its internal state. Registering an element associates a class with a custom element name. The element provides callbacks to manage its lifecycle. Polymer also lets you declare properties, to integrate your element's property API with the Polymer data system. Shadow DOM provides a local, encapsulated DOM tree for your element. Polymer can automatically create and populate a shadow tree for your element from a DOM template.

Write the backend and frontend of a feature in a single component and let the framework decide where the code should run. Nullstack provides you with all the tools you need to stay focused on the product. On the first render, you'll get SEO-ready HTML optimized for the first paint of your route in a single request using local functions with zero JavaScript dependencies in the client bundle. After the content is served and the network is idle Nullstack JavaScript is loaded, the state of the application is restored through hydration and it becomes a single-page application. Subsequent server functions will fetch JSON from an automatically generated microservice API, deserialize the response, update the application state, and rerender the page out of the box. A full stack lifecycle combined with a feature-driven mindset allows you to write clean and reusable code without the need to create APIs manually.

Build responsive and accessible mobile-first projects for the web with an easy-to-use the component library. A function that recieves two theme objects and returns a theme object that includes both theme values. In case different styling are found for the same components on the two themes, the second argument theme will determine the styling. Tailor composite components with grommet. embrace atomic design methods and build a library that fits your needs. Keyboard navigation, screen reader tags, and more. grommet provides support for W3c’s WCAG 2.1 spec out of the box. Support all the devices! use Flexbox and CSS Grid to provide layouts for all those new phones and widescreen displays.

Beautiful Soup is a library that makes it easy to scrape information from web pages. It sits atop an HTML or XML parser, providing Pythonic idioms for iterating, searching, and modifying the parse tree. Beautiful Soup's support for Python 2 was discontinued on December 31, 2020: one year after the sunset date for Python 2 itself. From this point onward, new Beautiful Soup development will exclusively target Python 3. The final release of Beautiful Soup 4 to support Python 2 was 4.9.3. Beautiful Soup is licensed under the MIT license, so you can also download the tarball, drop the bs4/ directory into almost any Python application (or into your library path) and start using it immediately.