Cocalc Index Md

The Panel How-to guides provide step by step recipes for solving essential problems and tasks that arise during your work. They assume that you've completed the Getting Started material and therefore already have some knowledge of how Panel works. There is no order to the guides, other than any potential prerequisites listed at the top of a page. Jump to the topic that is relevant to you now. :::{grid-item-card} {octicon}book;2.5em;sd-mr-1 sd-animate-grow50 Develop in a notebook 🔗 notebook/index :link-type: doc How to effectively develop apps in a notebook environment.

::: :::{grid-item-card} {octicon}file-code;2.5em;sd-mr-1 sd-animate-grow50 Develop in an editor 🔗 editor/index :link-type: doc How to effectively develop apps in a Python or Markdown file. ::: These Panel applications demonstrate what you can build with Panel and how to do it. Click on each thumbnail to see the app running live, and click on "See source" to look at how each of the components are configured and put together.

The source is fully runnable in your browser thanks to WASM & Pyodide; just press the ▶ button on the first code cell to run all the cells. Stock portfolio optimization by exploring the efficient frontier and optimizing the Sharpe ratio. +++ See source ::: :::{grid-item-card} Streaming Videostream Applying face detection and other image transforms on your webcam input using scikit-image. +++ See source :::

:::{grid-item-card} Windturbines Explorer About the CoCalc Implementation of Markdown Wikipedia defines Markdown as “a lightweight markup language for creating formatted text using a plain-text editor” (https://en.wikipedia.org/wiki/Markdown). Markdown makes it easy to add basic structure, such as headings and lists, to a plaintext document. Use of Markdown is ubiquitous in CoCalc (see below). When you start making notes into a text file, it is often most convenient to open the file as a Markdown document, i.e.

a file with name ending in “.md”. It is easy to produce pdf and html files from markdown. There is a brief tutorial on Markdown in the Project Library. To copy the introduction into a project of yours, open the project and follow the four steps below: The Library intro has three sample files, in format markdown (“.md”), Jupyter notebook (“.ipynb”), and Sage worksheet (“.sagews”). Here’s a view from the .md file:

If you need full control over how your GUI is set up then you can manually define widgets that link directly to other objects using either Python or JavaScript (JS) callbacks. Python callbacks are simple for Python users to write and can directly access Python data structures, while JS callbacks can directly manipulate the displayed HTML document and allow setting up dynamic behavior even for... This section contains how-to guides that address common tasks related the use of callbacks. :::{grid-item-card} {octicon}link;2.5em;sd-mr-1 sd-animate-grow50 Create High-Level Python Links with .link 🔗 links :link-type: doc How to use the convenient, high-level .link API to link parameters in Python. :::

:::{grid-item-card} {octicon}telescope;2.5em;sd-mr-1 sd-animate-grow50 Create Low-Level Python Links with .watch 🔗 watchers :link-type: doc How to use the flexible, low-level .watch API to trigger callbacks in Python. ::: The explanation section aims to clarify, deepen, and broaden the understanding of Panel with discussions about topics at a high-level or from alternate angles. This may include reasoning about design decisions, historical development, and technical constraints. Beyond the Getting Started > Core Concepts, which new users must complete before working with Panel, this explanation section is intended to help practitioners form and strengthen a conceptual web that facilitates new and...

:::{grid-item-card} {octicon}code;2.5em;sd-mr-1 sd-animate-grow50 Develop Seamlessly 🔗 develop_seamlessly :link-type: doc Learn how we enable you and your team to work seamlessly with Panel across a wide range of development environments ::: In this section we will discuss the principles and design decisions behind Panel's APIs in order to guide you towards the best approach for structuring your applications. We begin with a set of explanations behind the use of Param in Panel, how it unlocks reactive approaches to write applications and contrast function and class based approaches for writing apps. The Panel How-to guides provide step by step recipes for solving essential problems and tasks that arise during your work. They assume that you've completed the Getting Started material and therefore already have some knowledge of how Panel works.

There is no order to the guides, other than any potential prerequisites listed at the top of a page. Jump to the topic that is relevant to you now. :::{grid-item-card} {octicon}book;2.5em;sd-mr-1 sd-animate-grow50 Develop in a notebook 🔗 notebook/index :link-type: doc How to effectively develop apps in a notebook environment. ::: :::{grid-item-card} {octicon}file-code;2.5em;sd-mr-1 sd-animate-grow50 Develop in an editor 🔗 editor/index :link-type: doc

How to effectively develop apps in a Python or Markdown file. ::: The TensorFlow Core APIs provide a set of comprehensive, composable, and extensible low-level APIs for high-performance (distributed and accelerated) computation, primarily aimed at building machine learning (ML) models as well as authoring ML workflow... These APIs provide a foundation for creating highly configurable models with fine-grained control and new frameworks from the ground up. The Core APIs can be used as an alternative to high-level machine learning APIs like Keras. These high-level APIs are best suited for general machine learning needs.

They offer a variety of modules that abstract away the complexities of ML while also offering functionalities for customization through subclassing. If you are looking for an overview of TensorFlow using Keras, see the Quickstarts and Keras sections in the tutorials. The TensorFlow Core low-level APIs are designed with the following ML developers in mind: Researchers building complex models with high levels of configurability Developers interested in using TensorFlow as a high-performance scientific computing platform There are a number of common bottlenecks and pitfalls that can significantly reduce the performance of your applications and some approaches to improve the performance of your application.

This section provides various approaches to try to improve the performance of your applications. :::{grid-item-card} {octicon}versions;2.5em;sd-mr-1 sd-animate-grow50 Reuse sessions 🔗 reuse_sessions :link-type: doc Discover how to reuse sessions to improve the start render time. ::: :::{grid-item-card} {octicon}tab;2.5em;sd-mr-1 sd-animate-grow50 Enable throttling 🔗 throttling :link-type: doc Discover how to enable throttling to reduce the number of events being processed.

::: The Materials Project Workshop is two-day interactive workshop to learn to use th Materials Project (MP) open-source tools. Topics will include basic structure manipulation, querying data from MP using the API, automating DFT calculations, exploring new chemical systems and machine learning. A optional one-day python and MongoDB primer is provided for anyone new to python programming and using databases. We'll also provide a virtual poster-session for anyone interested in networking with other participants. The lessons for the 2021 virtual workshop are on YouTube and for 2020 are on YouTube and Bilibili.

Note that the 2021 workshop introduced a lesson on the new Materials Project API. Since this is still in development, some details may have changed. Consult our API page and documentation therein for the latest information. The 2021 Materials Project Workshop will be fully virtual due to COVID related travel and congregation restrictions. We'll use a cloud-based python environment to enable interactive tutorials where we provide close support. 09:20-10:20 Basics - Variables, Lists, Loops

When deploying a Panel application to be accessed by multiple users they will often access the same server simultaneously. To maintain responsiveness of the application when multiple users are interacting with it at the same time there are multiple approaches to concurrency, each with their own drawbacks and advantages: Load balancing: A load balancer distributes network traffic between multiple instances of the Panel application. This ensures that the load is distributed across multiple servers but also requires a lot configuration and resources. Multi-process server instance: Launches your app with multiple processes on a single machine. Much simpler to set up than a load balancer but the load is not distributed equally across processes and you are limited by the compute and memory resources on one machine.

Threading: Attempts to distribute processing across multiple threads. Effectiveness depends on the operations being performed, I/O bound and CPU bound operations that release the GIL can easily be made concurrent in this way. AsyncIO: Allows asynchronously processing I/O bound operations. Effective for many concurrent I/O operations but requires rewriting your application and callbacks to make use of async/await paradigm.

People Also Search

• CoCalc -- index.md
• Markdown — CoCalc Manual documentation