Alternatives to luminoth

The Python multi-platform windowing and multimedia library. pyglet, a powerful and easy-to-use Python library, allows you to create games and other visually rich applications on Windows, Mac OS X and Linux. It supports windowing, user event handling, Joysticks and OpenGL graphics. It also supports loading images and videos. This is all done with a friendly Pythonic API that's easy to use and doesn't get in the way. pyglet is available under the BSD open source license. This allows you to use it for commercial and open-source projects without restriction. No external dependencies or installation requirements. pyglet doesn't require any additional Python to meet most application or game requirements. This simplifies distribution and installation. This makes it easy for you to package your project using freezers like PyInstaller. pyglet offers real platform native windows that allow you to use multiple windows and multi-monitor desktops.

Tox aims automate and standardize Python testing. It is part of a larger vision to simplify the packaging, testing, and release of Python software. Tox is a generic virtualenv management tool and test command-line tool that you can use to verify that your package works with different Python versions. You can also run your tests in each environment. It also acts as a frontend for continuous integration servers, reducing boilerplate and merging CI. First, install tox using pip install tox. Next, add basic information about your project as well as the test environments that you would like your project to run into a tox.ini.py file. To generate a tox.ini automatically, run tox-quickstart. Answer a few questions. Test your project against Python2.7 or Python3.6.

Pexpect makes Python an easier tool for controlling other programs. Pexpect can be used to create child applications, control them and respond to their output. Pexpect works like Don Libes' Expect. Your script can use Pexpect to create a child application. You can also control it like a human would. Pexpect is a tool that automates interactive applications like ssh and FTP, passwd and telnet. It can also be used to automate setup scripts that duplicate software package installations on different servers. It can also be used to automate software testing. Pexpect has a similar spirit to Don Libes' Expect but is pure Python. Pexpect is not like other Expect-like Python modules. It doesn't require Expect or TCL to be compiled. It should work on any platform that can support the standard Python pty modules. The Pexpect interface is easy to use.

Properties allow you to access all URL parts, such as scheme, user password, host, path, query, path, port, and password. All URL manipulations result in a new URL object. Strings passed to constructor or modification methods are automatically encoded and give canonical representations as a result. Regular properties are percent-decoded. To get encoded strings, you can use raw_ versions. As.human_repr(), URLs can be represented in a human-readable format. PyPI has binary wheels for Linux and MacOS. You can use the tarball to compile yarl from the source code if you want to install it on another operating system (such as Alpine Linux, which isn't manylinux-compliant due to the missing glibc). It requires Python headers and a C compiler. The pure-Python version (uncompiled) is slower. PyPy uses a pure Python implementation and is therefore unaffected by this variable.

The Python Imaging Library provides image processing capabilities for your Python interpreter. This library supports many file formats, an efficient internal representation, as well as powerful image processing capabilities. The core image library allows for quick access to data stored only in basic pixel formats. It should be a solid foundation for an image processing tool. Tidelift subscribers can get Pillow for Enterprise. The Python Imaging Library is perfect for batch processing and image archival applications. The library can be used to create thumbnails, convert file formats, and print images. The current version can identify and read a wide range of formats. The most common interchange and presentation formats are the only ones that support writing. The library includes basic image processing functionality such as point operations, filtering using a set of convolution kernels and color space conversions.

This package can be used in any Python project independently. It is maintained by Zope Toolkit. This package implements Python's "object interfaces". Interfaces allow objects to be labeled as conforming to a contract or API. This package can be considered an implementation of Python's Design By Contract support. Interfaces are objects that document the external behavior of objects "provided" by them. An interface describes behavior through informal documentation, such as doc strings, attribute definitions and invariants. These are conditions that must be met for objects to provide the interface. Attribute definitions specify specific attributes. They provide documentation and constraints for attribute values. There are many ways to define attributes.

TensorBoard, TensorFlow’s comprehensive visualization toolkit, is designed to facilitate machine-learning experimentation. It allows users to track and visual metrics such as accuracy and loss, visualize the model graph, view histograms for weights, biases or other tensors over time, display embeddings in a lower-dimensional area, and display images and text. TensorBoard also offers profiling capabilities for optimizing TensorFlow programmes. These features provide a suite to help understand, debug and optimize TensorFlow, improving the machine learning workflow. To improve something in machine learning, you need to be able measure it. TensorBoard provides the measurements and visualisations required during the machine-learning workflow. It allows tracking experiment metrics, visualizing model graphs, and projecting embedded embeddings into a lower-dimensional space.

WTForms allows you to easily validate and render forms for Python web development. It works with any web framework or template engine. It supports data validation, CSRF Protection, Internationalization (I18N), among other features. There are many community libraries that allow for closer integration with popular frameworks. WTForms strives to make APIs as easy as possible. WTForms can be used by a variety of libraries. However, if a library is not listed it doesn't necessarily mean it won't work. Any ORM or object-DB should be able to work as long as they allow access to members via data objects. WTForms uses unicode string throughout the source code. It assumes that input to forms has been forced to unicode by your framework. WTForms fields default to unicode strings. As long as your templating engine is capable of handling that, you should not have unicode issues.

TFlearn, a modular and transparent deep-learning library built on top Tensorflow, is modular and transparent. It is a higher-level API for TensorFlow that allows experimentation to be accelerated and facilitated. However, it is fully compatible and transparent with TensorFlow. It is an easy-to-understand, high-level API to implement deep neural networks. There are tutorials and examples. Rapid prototyping with highly modular built-in neural networks layers, regularizers and optimizers. Tensorflow offers full transparency. All functions can be used without TFLearn and are built over Tensors. You can use these powerful helper functions to train any TensorFlow diagram. They are compatible with multiple inputs, outputs and optimizers. A beautiful graph visualization with details about weights and gradients, activations, and more. The API supports most of the latest deep learning models such as Convolutions and LSTM, BiRNN. BatchNorm, PReLU. Residual networks, Generate networks.

AWS Deep Learning AMIs are a secure and curated set of frameworks, dependencies and tools that ML practitioners and researchers can use to accelerate deep learning in cloud. Amazon Machine Images (AMIs), designed for Amazon Linux and Ubuntu, come preconfigured to include TensorFlow and PyTorch. To develop advanced ML models at scale, you can validate models with millions supported virtual tests. You can speed up the installation and configuration process of AWS instances and accelerate experimentation and evaluation by using up-to-date frameworks, libraries, and Hugging Face Transformers. Advanced analytics, ML and deep learning capabilities are used to identify trends and make forecasts from disparate health data.

Imageio is an image library written in Python that allows you to easily read and write a variety of image data including animated images, volumetrics data, and scientific formats. It runs on Python 3.5+ and is cross-platform. Imageio is written entirely in Python, making it easy to install. Imageio is compatible with Python 3.5+. It works with Pypy. Imageio is dependent on Numpy, Pillow. For some formats, imageio needs additional libraries/executables (e.g. Imageio can help you download/install ffmpeg. You need to know where to look for the causes of problems if something doesn't work. This overview will help you understand how things work and where they might go wrong.

Openpyxl is an open-source Python library that can read/write Excel 2010 files xlsx/xlsm/xltx/xltm. It was created because there wasn't an existing library that could natively read/write Python in the Office Open XML format. Openpyxl was originally built on PHPExcel. All credit to the PHPExcel team. Openpyxl doesn't guard against quadratic blowup attacks or billion laughs XML attacks by default. Defusedxml can be installed to protect against these attacks. Install openpyxl with pip This should be done in a Python virtualenv with no system packages. Sometimes, you may want to work with a particular version's checkout. This could be true if bugs have been fixed, but no release has been made. To get started with openpyxl, you don't need to create a file in the filesystem. Simply import the Workbook class to get started. When a sheet is created, it automatically gives it a name. You can give a worksheet a name to make it a key in the workbook.

statsmodels is an Python module that provides classes, functions, and methods for the estimation of various statistical models. It also allows for statistical data exploration and statistical tests. Each estimator has a comprehensive list of available result statistics. To ensure accuracy, the results are compared to existing statistical packages. The package is available under the Modified BSD (3 clause) license. statsmodels allows you to specify models using R-style formulas or pandas DataFrames. To see the available results, take a look at dir (results). Results describe attributes. __doc__, results methods have their own documentation strings. Numpy arrays can be used in place of formulas. Installing statsmodels as part of the Anaconda distribution is the easiest way to do so. This cross-platform distribution allows for data analysis and scientific computing. This is the preferred method of installation for most users.

The ROOT data analysis framework can be used extensively in High Energy Physics (HEP). It also has its own output format (.root). ROOT can be interfaced with C++ software. pyROOT is a Python software tool. Unfortunately, pyROOT is not compatible with python3.4. broot is a small library which converts data from python numpy to ROOT files. These ROOT files contain trees with a branch each array. This library provides a generic way to convert python numpy datastructures into ROOT files. The library should be portable, and it should support python2, ROOT v5 or ROOT v6 (no modifications to the ROOT part required). The library should be easy to install. Users will only need to compile the library once.

Django-MySQL extends Django’s built-in MySQL support and MariaDB support. A cache backend that makes MySQL's upsert statements available and performs compression. Named locks allow for easy locking of e.g. External resources. Django's check framework has additional checks to ensure optimal Django/MySQL configurations. Django-MySQL includes a variety of extensions to QuerySet. These extensions can be installed in a variety of ways, e.g. QuerySetMixin can be added to an existing QuerySet subclass.

Oracle Data Access Components are a collection of Windows and.NET data accessibility drivers and tools. They support.NET data access, Microsoft Visual Studio tools to develop Oracle database applications, and ASP.NET providers. ODAC offers comprehensive client support for advanced Oracle database functionality. This includes performance, high availability, security, and many other features. Visual Studio is seamlessly integrated with ODAC to offer developers a seamless development experience. Oracle Data Provider is a Microsoft implementation of ADO.NET that provides access to Oracle databases. OLAP Data Manipulation Language is used to create and manipulate analytic workspace objects. It is a high-performance, feature-rich tool for Microsoft ADO and OLE DB access to Oracle databases. It also allows for its configuration and usage. This section provides information about the usage, installation, configuration, and post-installation.

Bootstrap your application using beautiful Material Design components React Toolbox is a collection of React components that implements the Google Material Design specification. It is built on top of some of today's most popular proposals, such as CSS Modules (written using SASS, Webpack, and ES6). The library seamlessly integrates with your Webpack workflow. It's also easily customizable and flexible. Check out our amazing documentation, which is built using React Toolbox. You can also try all the components with real examples. Our components are so much fun that you don't even need to install them. The app bar is a unique type of toolbar that can be used for branding, navigation and search as well as actions. It usually has controls on the right- and left-sides, as well as a title that displays the current section or the app name. The content should contain elements for children. You can use text, images, or both to create content that is consistent with your app's colors theme.

Unirest is a collection of lightweight HTTP libraries that are available in multiple languages. It was created and maintained by Kong who also maintains the open source API Gateway Kong. Install the npm module to use Unirest for node.js. Unirest makes it easier to create requests. Unirest supports gzip and can parse responses. You can also start with basic examples. To initiate a request, invoke the appropriate method on Unirest object and then call.end() to send it. You can also send the request directly by sending a callback with the URL. This provides simple and straightforward methods to manipulate the request before it is sent. This object is created when Unirest is invoked. This object has methods that can be chained like other libraries, such as jQuery or the popular request module Superagent (which is slightly modeled after this library).

DaisyUI is a Tailwind CSS library that improves development efficiency through semantic class names. It provides these names for UI components such as cards and toggles. This approach reduces the requirement to write extensive utility class, resulting in a cleaner and more maintainable HTML. DaisyUI is built upon Tailwind CSS and allows developers to customize components by using Tailwind's Utility Classes. It is a pure CSS plug-in that does not require JavaScript, which ensures compatibility with various JavaScript frameworks. It is easy to install and supports unlimited themes with customizable color names using CSS variables. This allows features like dark mode to be implemented without the need for additional class names. DaisyUI is built over Tailwind CSS, so you can customize all of it using utility classes. DaisyUI is an extension for Tailwind CSS. It works with all JS frameworks, and does not require a JS package file.

We have put a lot of effort into making ngx_bootstrap modular. This allows you to implement your templates, styles, and whatnot. All components are designed with adaptability and extensibility in mind. They will work on both Mobile and Desktop with equal performance. To improve code readability and maintainability, we have included a set style guidelines and guidelines. We provide full coverage for unit-test coverage and support all Angular versions. We are developers ourselves so we know the importance of documentation. Documentation that is well-written and regularly updated can greatly simplify the lives of developers and improve the quality of software. We do our best to provide the best documentation possible. You need to understand what you are getting when working with visually rich libraries. We have created a set of great demos for the majority of the component's methods.

Ignite UI is the most comprehensive library of enterprise-grade JavaScript components and.NET UI components. It makes it easier to create data-rich, responsive web applications faster. Libraries for Angular and ASP.NET (Core, MVC), Blazor and jQuery, React, and other web components. Our Command Line Interface (CLI), which is a powerful tool, makes it easy to use component libraries that have multiple dependencies. Our WYSIWYG Page Designer makes it easy to drag-and-drop your way towards a modern web experience. You can also leverage our JavaScript UI components and data connectors to design your next app. It can be difficult to be productive when you have to be an expert in multiple JavaScript library. Typescript is used to augment our libraries. This allows for stronger typing and class-based inheritance, which makes development much easier and less error-prone.

GDAL is a translator for raster and geospatial formats. It is released under an MIT-style Open Source License by Open Source Geospatial Foundation. It is a library that provides a single raster and one vector abstract data model to the calling app for all supported formats. It also includes a number of useful command-line utilities that allow data processing and translation.

These.NET spreadsheet components are completely independent and allow you to explore the possibilities of your.NET enterprise applications. .NET spreadsheet components allow professional developers to add Excel-like functionality into their desktop apps. The.NET spreadsheet control supports Excel import/export, full customization, and an extensive calculation engine with more than 450 functions. All of this is possible with zero dependencies on Excel. The powerful.NET spreadsheet API allows you to create financial, analytical, budgeting, dashboards, data collection and management, scientific and financial applications. Spread.NET's modular structure allows you to only add what you need to your.NET spreadsheet applications.

Xlrd is a library that allows developers to extract data from Microsoft Excel™,.xls spreadsheet files. Xlrd is a library that reads and formats data from Excel files in the historical format.xls. This library cannot read files other than.xls. Charts, macros and pictures are ignored. However, formula calculations are extracted and comments, hyperlinks, advanced filters, pivot table, conditional formatting and data validation are all possible. This library cannot read password-protected files. This command will display the first, second, or last rows of each sheet within each file. xlrd is licensed by the BSD license.

IMSL numerical libraries can improve performance and reduce development time. IMSL's tools allow you to achieve your strategic goals. Your IMSL library can be used to model regression, create decision trees, build neural networks, forecast time series, and make decisions. The IMSL C Numerical Library has been proven reliable and robust across all industries. It provides companies with a high-ROI solution to building cutting-edge analytics tools. The IMSL C Numerical Library is able to quickly add sophisticated functionality to your analytic applications, including data mining, forecasting, and advanced statistical analysis. Integration and deployment are easy with the IMSL C library. Easy migrations, support common platforms and combination of platforms, and no additional infrastructure to embed in applications or databases, all possible with the IMSL C library.

urllib3 is a powerful and user-friendly HTTP client that Python uses. You should use urllib3 as a client for your Python environment. Many critical features are missing from the Python standard library, and urllib3 provides them. Thread safety, connection pooling and client-side SSL/TLS verification. Multipart encryption is used for file uploads. Helpers for dealing with HTTP redirects and retrying requests. Support for deflate, gzip, and brotli encryption. Proxy support for HTTPS and SOCKS. 100% coverage of the test. urllib3 is one the most downloaded Python packages. It is a dependency on many popular Python packages such as Requests, Pip and more! urllib3 is available under the MIT License. API Reference documentation provides API-level documentation. The User Guide explains how to use the library to accomplish common tasks. For lower-level tweaking, the Advanced Usage Guide is a more detailed guide.

Requests is an elegant and simple HTTP library. Requests makes it easy to send HTTP/1.1 queries. You don't need to manually add query strings or form-encode your PUT and POST data to your URLs. Instead, you can use the JSON method. Requests is the most downloaded Python package today, pulling in approximately 30M downloads per week, according to GitHub. Requests is currently dependent upon 1,000,000+ repositories. This code is certainly worth your trust. PyPI has Requests. Requests is available to meet the demands of building reliable and robust HTTP-speaking applications for today's needs. Automated content decompression and decoding. International URLs and domains. Sessions with cookie persistence. Browser-style TLS/SSL verification. Basic and digest authentication, as well as familiar dict-like cookies. Multi-part file uploads. SOCKS proxy support. Connection timeouts and streaming downloading.

Many domains have shown program autotuning to be more efficient and portable. Autotuners are not always portable between projects. This is because they need to be able to use a domain-informed search space representation. Also, no single search technique will work for all problems. OpenTuner is a new framework that allows you to create multi-objective, domain-specific program autotuners. OpenTuner allows for fully customizable configuration representations and extensible technique representations to allow for domain-specific methods. It also provides an easy-to use interface for communicating with the tuned programs. OpenTuner allows you to use multiple search techniques simultaneously. Techniques that perform well will get larger testing budgets while techniques that perform poorly will be disabled.

You can quickly provision a VM with everything you need for your deep learning project on Google Cloud. Deep Learning VM Image makes it simple and quick to create a VM image containing all the most popular AI frameworks for a Google Compute Engine instance. Compute Engine instances can be launched pre-installed in TensorFlow and PyTorch. Cloud GPU and Cloud TPU support can be easily added. Deep Learning VM Image supports all the most popular and current machine learning frameworks like TensorFlow, PyTorch, and more. Deep Learning VM Images can be used to accelerate model training and deployment. They are optimized with the most recent NVIDIA®, CUDA-X AI drivers and libraries, and the Intel®, Math Kernel Library. All the necessary frameworks, libraries and drivers are pre-installed, tested and approved for compatibility. Deep Learning VM Image provides seamless notebook experience with integrated JupyterLab support.

Keepsake, an open-source Python tool, is designed to provide versioning for machine learning models and experiments. It allows users to track code, hyperparameters and training data. It also tracks metrics and Python dependencies. Keepsake integrates seamlessly into existing workflows. It requires minimal code additions and allows users to continue training while Keepsake stores code and weights in Amazon S3 or Google Cloud Storage. This allows for the retrieval and deployment of code or weights at any checkpoint. Keepsake is compatible with a variety of machine learning frameworks including TensorFlow and PyTorch. It also supports scikit-learn and XGBoost. It also has features like experiment comparison that allow users to compare parameters, metrics and dependencies between experiments.

Kubeflow is a project that makes machine learning (ML), workflows on Kubernetes portable, scalable, and easy to deploy. Our goal is not create new services, but to make it easy to deploy the best-of-breed open source systems for ML to different infrastructures. Kubeflow can be run anywhere Kubernetes is running. Kubeflow offers a custom TensorFlow job operator that can be used to train your ML model. Kubeflow's job manager can handle distributed TensorFlow training jobs. You can configure the training controller to use GPUs or CPUs, and to adapt to different cluster sizes. Kubeflow provides services to create and manage interactive Jupyter Notebooks. You can adjust your notebook deployment and compute resources to meet your data science requirements. You can experiment with your workflows locally and then move them to the cloud when you are ready.

NVIDIA DeepLearning GPU Training System (DIGITS), puts deep learning in the hands of data scientists and engineers. DIGITS is a fast and accurate way to train deep neural networks (DNNs), for image classification, segmentation, and object detection tasks. DIGITS makes it easy to manage data, train neural networks on multi-GPU platforms, monitor performance with advanced visualizations and select the best model from the results browser for deployment. DIGITS is interactive, so data scientists can concentrate on designing and training networks and not programming and debugging. TensorFlow allows you to interactively train models and TensorBoard lets you visualize the model architecture. Integrate custom plugs to import special data formats, such as DICOM, used in medical imaging.

Flutter is Google's UI Toolkit that allows you to create beautiful, natively compiled apps for mobile, web and desktop using a single codebase. Stateful Hot Reload makes your app come to life in a matter of seconds. You can quickly create native interfaces with a rich collection of widgets that you can fully customize. You can quickly ship features that are focused on native end-user experiences. The layering architecture allows for full customization which results in extremely fast rendering and expressive and flexible design. Flutter's widgets include all platform differences, such as scrolling and navigation. Your Flutter code is compiled using Dart's native compilers to native ARM machine codes. Flutter's hot reload allows you to quickly and easily experiment, create UIs, add features and fix bugs quicker. Flutter's hot reload speeds are sub-second and you won't lose state on simulators, emulators, or hardware.

SQLite offers unparalleled performance and endless possibilities. SQLite Data Access Components, also known as LiteDAC, is a collection of components that allows native connectivity to SQLite using C++Builder and Delphi. It also includes Lazarus (and Free Pascal), as well as C++Builder and Community Edition. This library can be used on Windows, Linux and macOS as well as Android for 32-bit and 64 bit platforms. LiteDAC allows programmers to create truly cross-platform desktop or mobile SQLite databases applications without the need to deploy additional libraries. LiteDAC-based DB apps are simple to deploy and don't require additional data provider layers (such BDE or ODBC). They work faster than those based on standard Delphi data connectivity options. LiteDAC also allows you to directly work with SQLite and C++Builder by linking the client library statically to your application.

Seaborn is a Python data visualization tool that uses matplotlib. It provides a high-level interface to draw attractive and informative statistical graphs. The paper and introductory notes provide a brief overview of the library's ideas. To download the package and get started, visit the installation page. To see some of the seaborn-related tasks, you can look through the example gallery. Then, check out the tutorials and API reference. Visit the GitHub repository to view the code and report a bug. You can find general support questions on StackOverflow. There is also a channel for seaborn.

Azure Databricks allows you to unlock insights from all your data, build artificial intelligence (AI), solutions, and autoscale your Apache Spark™. You can also collaborate on shared projects with other people in an interactive workspace. Azure Databricks supports Python and Scala, R and Java, as well data science frameworks such as TensorFlow, PyTorch and scikit-learn. Azure Databricks offers the latest version of Apache Spark and allows seamless integration with open-source libraries. You can quickly spin up clusters and build in an Apache Spark environment that is fully managed and available worldwide. Clusters can be set up, configured, fine-tuned, and monitored to ensure performance and reliability. To reduce total cost of ownership (TCO), take advantage of autoscaling or auto-termination.

Pygame is a collection of Python modules that can be used to create video games. Pygame is an extension of the SDL library. This allows you create full-featured games and multimedia programs using the python programming language. Pygame is portable and can be used on almost every platform and operating system. Pygame is completely free. Pygame is free and open-source. You can use it to create commercial, freeware, shareware, or open-source games. Dual-core CPUs are common and 8-core CPUs easily available on desktop systems, multi-core CPUs allow you to do more with your game. Select pygame functions will release the dreaded Python GIL. This is something you can do with C code. Optimized C and assembly code are used for core functions. C code is often 10-20x faster than Python code, while assembly code can easily be 100x faster than Python code. It is compatible with many operating systems. You just need to apt-get or emerge.

Universal Data Access Components is a collection of components that allows direct access to multiple databases including C++Builder and Delphi, as well as Lazarus and Free Pascal on Windows, Linux and macOS. It also supports Android for 32-bit and 64 bit platforms. Key Features: Access to all databases servers via universal access; Support for the most popular databases; Support for the most recent server versions is available; Support for the most recent IDE versions; Library available in VCL, LCL, and FMX versions; High performance; Easy to deploy.

This is Ant Design's internal standard to evaluate design quality. We have added the values of "Meaningfulness", and "Growth" to Ant Design's internal standard for evaluating design quality. This is based on the assumption that everyone seeks happiness at work. Ant Design React is committed to providing a great development experience for programmers. It is recommended that you first learn React and ES2015. Then, install and configure Node.js v8 and above correctly. The official guide assumes intermediate knowledge of HTML, CSS, JavaScript, and react. It may not be a good idea to start with the UI framework if you are just beginning to learn React or front-end.

Amazon EC2 Inf1 instances were designed to deliver high-performance, cost-effective machine-learning inference. Amazon EC2 Inf1 instances offer up to 2.3x higher throughput, and up to 70% less cost per inference compared with other Amazon EC2 instance. Inf1 instances are powered by up to 16 AWS inference accelerators, designed by AWS. They also feature Intel Xeon Scalable 2nd generation processors, and up to 100 Gbps of networking bandwidth, to support large-scale ML apps. These instances are perfect for deploying applications like search engines, recommendation system, computer vision and speech recognition, natural-language processing, personalization and fraud detection. Developers can deploy ML models to Inf1 instances by using the AWS Neuron SDK. This SDK integrates with popular ML Frameworks such as TensorFlow PyTorch and Apache MXNet.

The library has been completely rewritten in Typescript/ES6 since v1.0.0. The old and massive jQuery plugin is gone. The library's core is now very small thanks to the plugin-based architecture. Everything else is built around a plugin. Every file in the dist/css or dist/js folders contains two versions: the original code in.css or.js files and the minified code within the.min.css or.min.js. files. You should use the.min.css and.min.js files on your production website to speed up page loading times and improve the user experience. To debug the code faster, you can use the normal files in development mode.

IBM Watson Machine Learning, a full-service IBM Cloud offering, makes it easy for data scientists and developers to work together to integrate predictive capabilities into their applications. The Machine Learning service provides a set REST APIs that can be called from any programming language. This allows you to create applications that make better decisions, solve difficult problems, and improve user outcomes. Machine learning models management (continuous-learning system) and deployment (online batch, streaming, or online) are available. You can choose from any of the widely supported machine-learning frameworks: TensorFlow and Keras, Caffe or PyTorch. Spark MLlib, scikit Learn, xgboost, SPSS, Spark MLlib, Keras, Caffe and Keras. To manage your artifacts, you can use the Python client and command-line interface. The Watson Machine Learning REST API allows you to extend your application with artificial intelligence.

Groq's mission is to set the standard in GenAI inference speeds, enabling real-time AI applications to be developed today. LPU, or Language Processing Unit, inference engines are a new end-to-end system that can provide the fastest inference possible for computationally intensive applications, including AI language applications. The LPU was designed to overcome two bottlenecks in LLMs: compute density and memory bandwidth. In terms of LLMs, an LPU has a greater computing capacity than both a GPU and a CPU. This reduces the time it takes to calculate each word, allowing text sequences to be generated faster. LPU's inference engine can also deliver orders of magnitude higher performance on LLMs than GPUs by eliminating external memory bottlenecks. Groq supports machine learning frameworks like PyTorch TensorFlow and ONNX.

virtualenvwrapper adds extensions to Ian Bicking’s virtualenv tool. These extensions allow you to create and delete virtual environments, manage your development workflow, and make it easier to work on multiple projects at once without creating conflicts between their dependencies. All of your virtual environments can be organized in one place. Wrappers to manage your virtual environments (create/delete/copy). Switch between environments with a single command Tab completion is used for commands that use a virtual environment argument. All operations can be configured by the user. Plugin system to create more shareable extensions. Virtualenvwrapper's features can be best explained by showing it in action. The workon command includes tab completion for environment names and invokes customization Scripts when an environment is activated.

Clarity is an open-source, scalable, customizable design system that can be customized to fit any need. It's supported by the people who build it, the people who use it, and the community that made us who we are. Figma is our toolkit. Every component, pattern and guideline that we offer is rooted in real customer interactions. Clarity can be relied upon for over 30 components and 200 icons, so you can concentrate on solving product-specific problems. Clarity was founded by a small team of developers, designers and content creators. Clarity has grown from that strong start by supporting the three big three: Angular React, Vue and React. All of them are supported by our latest Core components. Clarity offers code examples and detailed API documentation to help you build your next app.

A set of over 150 Ajax enabled JSF components that allow you to build a richer web user experience for Java EE applications. Oracle ADF Essentials allows you to create and deploy Oracle ADF Faces components. More than 150+ AJAX-enabled parts. Extensive data visualization component, with JSF2 support. Complete JavaScript API. Drag and drop framework. Dialog and popup framework. Navigation menu framework. Accessibility and internationalization support. Tablet/touch gesture support. Declarative partial page rendering. Support for skinning. Support for page templates, reusable pages regions, and declarative parts. Support for the Oracle Alta UI. Integration with ADFc for TaskFlows and bookmarking. Integration with ADFm for data bindings. Oracle ADF security support.

Flowbite, an open-source library for interactive UI components, is built on Tailwind CSS. It was designed to speed up web development through a comprehensive collection of pre-designed elements. It includes over 56 components, such as buttons, alerts and breadcrumbs. Interactive elements, like dropdowns and modals are also available. Developers can integrate Flowbite with Tailwind CSS projects using npm or CDN. It supports various frameworks including React, Vue.js Angular and Next.js. The library includes a Figma system that allows designers to prototype and design apps before development. Flowbite Pro is available for those who want advanced features. It includes additional components and templates. Flowbite Blocks provides over 450 website components and advanced UI elements built with Tailwind CSS.

TRichView is a set of native Delphi/C++Builder/FireMonkey/Lazarus (Free Pascal), LCL components that allows you to display, edit, and print complex rich text documents. TRichView is used to create advanced text editors, web/help/book authoring apps, chats and messages, organizers, diaries, multimedia books, multimedia encyclopedias, and any other applications that require a high-quality richtext editor or 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 pictures, tables, images from image list, footnotes, endnotes, as well as any Delphi controls. Center, left, right, center, justify paragraph alignment, custom margins, indents, images from image lists, footnotes and endnotes, background images and data-aware versions.

gTTS (Google Text-to-Speech) is a Python library and CLI to interface with Google Translate’s text-to-speech API. For further audio manipulation or to write spoken mp3 data to files, you can use bytestring (file-like object) or stdout. You can also pre-generate Google Translate request URLs to feed an external program. A speech-specific sentence tokenizer that can be customized to allow for unlimited text lengths, while maintaining proper intonation, abbreviations and decimals. You can also make pronunciation corrections with custom-designed text pre-processors.

Scapy is an interactive packet manipulation program. It can forge and decode packets from a variety of protocols, send them over the wire, capture them and match requests and responses. It can handle most of the classic tasks such as scanning, tracerouting and probing, attacks, or network detection (it can replace Hping, 85% nmap, arpspoofsk, arping), tcpdumpshark, p0f and tshark). It can also handle a lot more specific tasks than most other tools, such as sending invalid frames, injecting 802.11 frames, combining technologynics (VLAN hopping+ARP caching poisoning, VOIP descoding on WEP encrypted channel), and so forth. Scapy works natively on Linux and Windows as well as OSX and most Unixes that have libpcap. The same code base runs natively on Python 2 and Python 3 Scapy development uses Git version control system. The GitHub repository hosts the Scapy reference repository.