JupyterLab Integrations

Docker takes away repetitive, mundane configuration tasks and is used throughout the development lifecycle for fast, easy and portable application development, desktop and cloud. Docker’s comprehensive end-to-end platform includes UIs, CLIs, APIs and security that are engineered to work together across the entire application delivery lifecycle. Get a head start on your coding by leveraging Docker images to efficiently develop your own unique applications on Windows and Mac. Create your multi-container application using Docker Compose. Integrate with your favorite tools throughout your development pipeline, Docker works with all development tools you use including VS Code, CircleCI and GitHub. Package applications as portable container images to run in any environment consistently from on-premises Kubernetes to AWS ECS, Azure ACI, Google GKE and more. Leverage Docker Trusted Content, including Docker Official Images and images from Docker Verified Publishers.

Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery. Kubernetes builds upon 15 years of experience of running production workloads at Google, combined with best-of-breed ideas and practices from the community. Designed on the same principles that allows Google to run billions of containers a week, Kubernetes can scale without increasing your ops team. Whether testing locally or running a global enterprise, Kubernetes flexibility grows with you to deliver your applications consistently and easily no matter how complex your need is. Kubernetes is open source giving you the freedom to take advantage of on-premises, hybrid, or public cloud infrastructure, letting you effortlessly move workloads to where it matters to you.

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.

The Domino platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record allows teams to easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Code Faster. Stay in Flow. Kite adds AI powered code completions to your code editor, giving developers superpowers. Download the Kite engine to add Kite’s AI powered code completions to all your code editors. Kite supports over 16 languages and 16 code editors. Experience lightning fast completions that are context aware of your code. Give your code editor super powers and get longer multi-line completions where you would typically get none. Code faster and stay in flow. Kite’s AI helps you cut keystrokes, by as much as 47% in this example. View Python docs with just one click or mouse-hover, plus find helpful examples and how-tos. Quickly find files in your codebase that may be related to the current file that you are coding in. Making thousands of developers more productive.

With JupyterHub you can create a multi-user Hub which spawns, manages, and proxies multiple instances of the single-user Jupyter notebook server. Project Jupyter created JupyterHub to support many users. The Hub can offer notebook servers to a class of students, a corporate data science workgroup, a scientific research project, or a high performance computing group. JupyterHub officially does not support Windows. You may be able to use JupyterHub on Windows if you use a Spawner and Authenticator that work on Windows, but the JupyterHub defaults will not. Bugs reported on Windows will not be accepted, and the test suite will not run on Windows. Small patches that fix minor Windows compatibility issues (such as basic installation) may be accepted, however. For Windows-based systems, we would recommend running JupyterHub in a docker container or Linux VM.

The solution provided by Activeeon is suited to fit modern challenges such as the growth of data, new infrastructures, cloud strategy evolving, new application architecture, etc. It provides orchestration and scheduling to automate and build a solid base for future growth. ProActive Workflows & Scheduling is a java-based cross-platform workflow scheduler and resource manager that is able to run workflow tasks in multiple languages and multiple environments (Windows, Linux, Mac, Unix, etc). ProActive Resource Manager makes compute resources available for task execution. It handles on-premises and cloud compute resources in an elastic, on-demand and distributed fashion. ProActive AI Orchestration from Activeeon empowers data engineers and data scientists with a simple, portable and scalable solution for machine learning pipelines. It provides pre-built and customizable tasks that enable automation within the machine learning lifecycle, which helps data scientists and IT Operations work.

Pieces™ is an on-device AI coding assistant that boosts developer productivity by helping you solve complex development tasks through a contextual understanding of your entire workflow. Leverage real-time context from all of your tools to ask questions about your daily interactions, capture important information, explain concepts or entire repositories, and generate ready-to-use code. Pieces works right in-flow, seamlessly integrating with your favorite tools to streamline, understand, and elevate your coding processes.

Log, store, query, display, organize, and compare all your model metadata in a single place. Know on which dataset, parameters, and code every model was trained on. Have all the metrics, charts, and any other ML metadata organized in a single place. Make your model training runs reproducible and comparable with almost no extra effort. Don’t waste time looking for folders and spreadsheets with models or configs. Have everything easily accessible in one place. Reduce context switching by having everything you need in a single dashboard. Find the information you need quickly in a dashboard that was built for ML model management. We optimize loggers/databases/dashboards to work for millions of experiments and models. We help your team get started with excellent examples, documentation, and a support team ready to help at any time. Don’t re-run experiments because you forgot to track parameters. Make experiments reproducible and run them once.

CSS, short for Cascading Style Sheets, is a style sheet language used by web developers to structure the HTML and other elements of a website. CSS is one of the most widely used languages on the web. For style sheets to work, it is important that your markup be free of errors. A convenient way to automatically fix markup errors is to use the HTML Tidy utility. This also tidies the markup making it easier to read and easier to edit. I recommend you regularly run Tidy over any markup you are editing. Tidy is very effective at cleaning up markup created by authoring tools with sloppy habits. Each style property starts with the property's name, then a colon and lastly the value for this property. When there is more than one style property in the list, you need to use a semicolon between each of them to delimit one property from the next.

Intel® DevCloud offers complimentary access to a wide range of Intel® architectures to help you get instant hands-on experience with Intel® software and execute your edge, AI, high-performance computing (HPC), and rendering workloads. With preinstalled Intel® optimized frameworks, tools, and libraries, you have everything you need to fast-track your learning and project prototyping. Learn, prototype, test, and run your workloads for free on a cluster of the latest Intel® hardware and software. Learn through a new suite of curated experiences, including market vertical samples, Jupyter Notebook tutorials, and more. Build your solution in JupyterLab and test with bare metal or develop your containerized solution. Quickly bring it to Intel DevCloud for testing. Optimize your solution for a specific target edge device with the deep learning workbench and take advantage of the new, more robust telemetry dashboard.

The Java™ Programming Language is a general-purpose, concurrent, strongly typed, class-based object-oriented language. It is normally compiled to the bytecode instruction set and binary format defined in the Java Virtual Machine Specification. In the Java programming language, all source code is first written in plain text files ending with the .java extension. Those source files are then compiled into .class files by the javac compiler. A .class file does not contain code that is native to your processor; it instead contains bytecodes — the machine language of the Java Virtual Machine1 (Java VM). The java launcher tool then runs your application with an instance of the Java Virtual Machine.

Scheme is a general-purpose computer programming language. It is a high-level language, supporting operations on structured data such as strings, lists, and vectors, as well as operations on more traditional data such as numbers and characters. While Scheme is often identified with symbolic applications, its rich set of data types and flexible control structures make it a truly versatile language. Scheme has been employed to write text editors, optimize compilers, operating systems, graphics packages, expert systems, numerical applications, financial analysis packages, virtual reality systems, and practically every other type of application imaginable. Scheme is a fairly simple language to learn since it is based on a handful of syntactic forms and semantic concepts and since the interactive nature of most implementations encourages experimentation. Scheme is a challenging language to understand fully.

Scala combines object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries. The Scala compiler is smart about static types. Most of the time, you need not tell it the types of your variables. Instead, its powerful type inference will figure them out for you. In Scala, case classes are used to represent structural data types. They implicitly equip the class with meaningful toString, equals and hashCode methods, as well as the ability to be deconstructed with pattern matching. In Scala, functions are values, and can be defined as anonymous functions with a concise syntax.

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

Julia was designed from the beginning for high performance. Julia programs compile to efficient native code for multiple platforms via LLVM. Julia uses multiple dispatch as a paradigm, making it easy to express many object-oriented and functional programming patterns. The talk on the Unreasonable Effectiveness of Multiple Dispatch explains why it works so well. Julia is dynamically typed, feels like a scripting language, and has good support for interactive use. Julia provides asynchronous I/O, metaprogramming, debugging, logging, profiling, a package manager, and more. One can build entire Applications and Microservices in Julia. Julia is an open source project with over 1,000 contributors. It is made available under the MIT license.

Maximize your security testing journey, from setting up your bench to analyzing your data processing results, esDynamic saves you valuable time and effort, empowering you to unleash the full potential of your attack workflow. Discover the flexible and comprehensive Python-based platform, perfectly suited for every phase of your security analysis. Customize your research space to meet your unique requirements by effortlessly adding new equipment, integrating tools, and modifying data. Additionally, esDynamic features an extensive collection of materials on complex topics that would typically require extensive research or a team of specialists, granting you instant access to expertise. Say goodbye to scattered data and fragmented knowledge. Welcome a cohesive workspace where your team can effortlessly share data and insights, fostering collaboration and accelerating discoveries. Centralize and solidify your efforts in JupyterLab notebooks to share with your team.

Zed is a next-generation code editor designed for high-performance collaboration with humans and AI. Written from scratch in Rust to efficiently leverage multiple CPU cores and your GPU. Integrate upcoming LLMs into your workflow to generate, transform, and analyze code. Chat with teammates, write notes together, and share your screen and project. Multibuffers compose excerpts from across the codebase in one editable surface. Evaluate code inline via Jupyter runtimes and collaboratively edit notebooks. Support for many languages via Tree-sitter, WebAssembly, and the Language Server Protocol. Fast native terminal tightly integrates with Zed's language-aware task runner and AI capabilities. First-class modal editing via Vim bindings, including features like text objects and marks. Zed is built by a global community of thousands of developers. Boost your Zed experience by choosing from hundreds of extensions that broaden language support, offer different themes, and more.

Focus on your core app and get to market faster. OAuth.io handles identity infrastructure, maintenance, and security overhead, so your team doesn’t have to. Identity can be difficult, OAuth.io makes it easy. Choose identity providers, add custom attributes, customize your login page or use our widget, integrate with your app - identity solved in minutes. Manage your users from our easy to use dashboard - find and manage users, reset passwords, enforce two-factor authentication, and add memberships and permissions through OAuth.io's simple and easy to use User Management. Fully-featured, hyper-secure user authentication using passwords or tokens. From multi-tenant to complex permissions, OAuth.io has your user authorization modeling covered. Force a second factor of user authentication with our popular integrations.

C++ is a simple and clear language in its expressions. It is true that a piece of code written with C++ may be seen by a stranger of programming a bit more cryptic than some other languages due to the intensive use of special characters ({}[]*&!|...), but once one knows the meaning of such characters it can be even more schematic and clear than other languages that rely more on English words. Also, the simplification of the input/output interface of C++ in comparison to C and the incorporation of the standard template library in the language, makes the communication and manipulation of data in a program written in C++ as simple as in other languages, without losing the power it offers. It is a programming model that treats programming from a perspective where each component is considered an object, with its own properties and methods, replacing or complementing structured programming paradigm, where the focus was on procedures and parameters.

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write. It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language Standard ECMA-262 3rd Edition - December 1999. JSON is a text format that is completely language independent but uses conventions that are familiar to programmers of the C-family of languages, including C, C++, C#, Java, JavaScript, Perl, Python, and many others. These properties make JSON an ideal data-interchange language. JSON is built on two structures: 1. A collection of name/value pairs. In various languages, this is realized as an object, record, struct, dictionary, hash table, keyed list, or associative array. 2. An ordered list of values. In most languages, this is realized as an array, vector, list, or sequence. These are universal data structures. Virtually all modern programming languages support them in one form or another.

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.

Switch between command and editor modes with a single keystroke. Navigate over cells with arrow keys. Use all of the standard Jupyter shortcuts. Enjoy fully interactive outputs – right under the cell. When editing code cells, enjoy smart code completion, on-the-fly error checking and quick-fixes, easy navigation, and much more. Work with local Jupyter notebooks or connect easily to remote Jupyter, JupyterHub, or JupyterLab servers right from the IDE. Run Python scripts or arbitrary expressions interactively in a Python Console. See the outputs and the state of variables in real-time. Split Python scripts into code cells with the #%% separator and run them individually as you would in a Jupyter notebook. Browse DataFrames and visualizations right in place via interactive controls. All popular Python scientific libraries are supported, including Plotly, Bokeh, Altair, ipywidgets, and others.

We have set up all the infrastructure, computing, and software (Cuda, Frameworks) required for you to train and deploy your favorite deep-learning models. You can spin up GPU/CPU-powered instances directly from your browser or automate it through our Python API.

Store, archive, manage, and collaborate on multi-omic datasets. Illumina Connected Analytics is a secure genomic data platform to operationalize informatics and drive scientific insights. Easily import, build, and edit workflows with tools like CWL and Nextflow. Leverage DRAGEN bioinformatics pipelines. Organize data in a secure workspace and share it globally in a compliant manner. Keep your data in your cloud environment while using our platform. Visualize and interpret your data with a flexible analysis environment, including JupyterLab Notebooks. Aggregate, query, and analyze sample and population data in a scalable data warehouse. Scale analysis operations by building, validating, automating, and deploying informatics pipelines. Reduce the time required to analyze genomic data, when swift results can be a critical factor. Enable comprehensive profiling to identify novel drug targets and drug response biomarkers. Flow data seamlessly from Illumina sequencing systems.

Baidu AI Cloud Machine Learning (BML), an end-to-end machine learning platform designed for enterprises and AI developers, can accomplish one-stop data pre-processing, model training, and evaluation, and service deployments, among others. The Baidu AI Cloud AI development platform BML is an end-to-end AI development and deployment platform. Based on the BML, users can accomplish the one-stop data pre-processing, model training and evaluation, service deployment, and other works. The platform provides a high-performance cluster training environment, massive algorithm frameworks and model cases, as well as easy-to-operate prediction service tools. Thus, it allows users to focus on the model and algorithm and obtain excellent model and prediction results. The fully hosted interactive programming environment realizes the data processing and code debugging. The CPU instance supports users to install a third-party software library and customize the environment, ensuring flexibility.

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

Everything you need to make better business decisions. The Fosfor Decision Cloud unifies the modern data ecosystem to deliver the long-sought promise of AI: enhanced business outcomes. The Fosfor Decision Cloud unifies the components of your data stack into a modern decision stack, built to amplify business outcomes. Fosfor works seamlessly with its partners to create the modern decision stack, which delivers unprecedented value from your data investments.

Octave lets you securely extract, orchestrate, and act on data from your industrial assets to the cloud. Pull and normalize data from virtually any type of industrial equipment via popular industrial protocols (Modbus, CANopen). Get the right data, at the right time, with the right priority, to the right system. Update your Industrial Internet of Things (IoT) application as your business needs change. Protect your data from the edge to the cloud even as new threats emerge. Octave securely integrates edge devices, networks, and cloud APIs into a single solution, so you can focus on your data. Octave eliminates the need to build IoT infrastructure from scratch, so you can concentrate on creating innovative Industrial IoT applications. Octave frees you from the complexities of Industrial IoT application development with an easy programming interface that uses a common JavaScript framework, supporting the most popular and extensively used industrial protocols such as Modbus, CANopen, etc.

UbiOps is an AI infrastructure platform that helps teams to quickly run their AI & ML workloads as reliable and secure microservices, without upending their existing workflows. Integrate UbiOps seamlessly into your data science workbench within minutes, and avoid the time-consuming burden of setting up and managing expensive cloud infrastructure. Whether you are a start-up looking to launch an AI product, or a data science team at a large organization. UbiOps will be there for you as a reliable backbone for any AI or ML service. Scale your AI workloads dynamically with usage without paying for idle time. Accelerate model training and inference with instant on-demand access to powerful GPUs enhanced with serverless, multi-cloud workload distribution.

HTML, short for HyperText Markup Language, is the markup language that is used by every website on the internet. HTML is code that websites use to build and structure every part of their website and web pages. HTML5 is a markup language used for structuring and presenting content on the World Wide Web. It is the fifth and final major HTML version that is a World Wide Web Consortium (W3C) recommendation. The current specification is known as the HTML Living Standard. It is maintained by the Web Hypertext Application Technology Working Group (WHATWG), a consortium of the major browser vendors (Apple, Google, Mozilla, and Microsoft). HTML5 includes detailed processing models to encourage more interoperable implementations; it extends, improves, and rationalizes the markup available for documents and introduces markup and application programming interfaces (APIs) for complex web applications. For the same reasons, HTML5 is also a candidate for cross-platform mobile applications.

Quickly write code by translating your comments into code, like in this example where we quickly create a Plotly bar chart. Create entire functions with ease, without searching for library methods and parameters. In this example, we created a function that loads df to AWS bucket in parquet format. Write SQL queries by providing CodeSquire with simple instructions on what you want to pull, join, and group by, like in the following example where we are trying to determine the top 10 most common names. CodeSquire can even help you understand someone else’s code, just ask to explain the function above, and get your explanation in plain text. CodeSquire can help you create complex functions that involve several logic steps. Brainstorm with it by starting simple and adding more complex features as you go.