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https://blog.stephenwolfram.com/2019/05/launching-today-free-wolfram-engine-for-developers/
http://www.wolfram.com/engine/
The Free Wolfram Engine for Developers is available for pre-production software development.
You can use this product to:
Develop a product for yourself or your company
Conduct personal projects at home, at school, at work
Explore the Wolfram Language for future production projects
Download and install Python: https://www.python.org/downloads/windows/
Don't forget to to check "add python environment variables" / "add to PATH". Otherwise you will have to add python.exe to PATH manually.
Download the .paclet file from the "Assets" section in github : WLforJupyter > Releases
In the Command Prompt (running as Admin):
pip install jupyter
In the Wolfram Engine prompt (wolframscript):
PacletInstall @ "path/to/the.paclet"
<< WolframLanguageForJupyter`
ConfigureJupyter["Add"]
That's all! Now in the Command Prompt: jupyter notebook. This will launch a web browser. Select New -> Wolfram Language
And just for fun:
ConfigureJupyter["add", <|"JupyterBinary" -> "D:\\Anaconda3\\Scripts\\jupyter.exe"|>] to point at the Jupyter executable it doesn't work.
$\endgroup$
May 24, 2019 at 7:46
Instructions for Linux (tried on Ubuntu/Mint). I tried this yesterday and ran into some issues I think deserve attention in a separate answer. This answer might also contain relevant information for debugging problems on other operating systems.
jupyter notebook. If you can't, take a look at the answer below:https://stackoverflow.com/questions/35313876/after-installing-with-pip-jupyter-command-not-found
At this point you have two options. Either:
./configure-jupyter.wls add. In my own attempt, I got a message
Jupyter installation on Environment["PATH"] not found
this can be remedied by using the syntax
configure-jupyter.wls add "/absolute/path/to/Wolfram Engine binary" "path/to/Jupyter binary"
instead. The path to the Jupyter binary for me was ~/.local/bin/jupyter (see also the SO answer linked above). The path to the Wolfram binary you can figure out by starting wolframscript and then evaluating FileNameJoin[{$InstallationDirectory, "Executables", "WolframKernel"}].
Or:
$UserBaseDirectory is: start wolframscript and then evaluate $UserBaseDirectory. ./Applications directory. You should now have a directory structure that looks like: $UserBaseDirectory/Applications/WolframLanguageForJupyter/KernelNeeds["WolframLanguageForJupyter`"] (to load the package) and then ConfigureJupyter["Add"]. If you (still) get the error complaining about the Jupyter path, run ConfigureJupyter["Add", "JupyterInstallation" -> "path/to/Jupyter binary"] instead (see point 1 for the location of the jupyter binary). Instructions for macOS:
Make sure you installed wolframscript along with Mathematica or the Wolfram Engine. There is a separate installer for it in the disk image.
Install Jupyter. Detailed instructions are at https://jupyter.org/install I used Anaconda Python for this (conda install jupyter in the terminal if it's not already installed—but with Anaconda it should be).
Clone the WolframLanguageForJupyter repository. Open a terminal, enter the directory where you want it, then run git clone https://github.com/WolframResearch/WolframLanguageForJupyter.git. See the green button on GitHub for more help.
Enter the repository's directory, then run ./configure-jupyter.wls add
Run jupyter notebook to start the system. Note: Do not use jupyter lab because currently WolframLanguageForJupyter does not work well with it.
There is a new repo: https://github.com/njpipeorgan/wolfram-language-notebook
The author's introduction:
Using Wolfram Notebooks in VS Code
https://zhuanlan.zhihu.com/p/438701709
Translation:
The release of Wolfram Engine in 2019 marks the separation of the Wolfram language's runtime library and its notebook interface. The subtlety of this separation is that the Wolfram Engine is free, yet essentially has the functionality of both the kernel and the front end (just not the full notebook interface). That is, we can simply create an interface that can be adapted to the kernel to get a similar experience to using Mathematica [1].
Recently, I wrote a VS Code extension, Wolfram Language Notebook (Github), which makes it easy to write and run code in VS Code using the notebook interface. You can install this extension in VS Code's Marketplace at
Wolfram Language Notebook - Visual Studio Marketplace
marketplace.visualstudio.com/items?itemName=njpipeorgan.wolfram-language-notebook
To use this extension, you just need to install Wolfram Engine or Wolfram Mathematica[2] (v12.0+). If you can run wolframscript from the command line or shell, you are ready to go. If you want to use the remote kernel, then you need to install Engine on the remote machine, and just have ssh[3] on the local machine.
First create a new notebook: Search for and run Create New Wolfram Language Notebook in the Command Palette, or just create a new file with the .wlnb suffix.
Next, run the Manage Kernels command and select Use wolframscript. this will add wolframscript to the kernel configuration and then connect to this kernel; the status of the connection will be shown in the status bar.
You can then add code units, write some Wolfram Language code, and run it.
Syntax highlighting: The notebook will highlight some simple Wolfram Language syntax, common built-in functions, and the full names of special characters (such as [Alpha]).
Auto-completion and function descriptions: The notebook prompts for auto-completion of built-in functions. Inputting or hovering over built-in functions will show their usage descriptions.
Output rendering: The notebook renders common expressions to HTML and images to bitmaps.
Export notebooks: You can export .wlnb files to Wolfram notebooks, which include Markdown, input, and output.
Remote kernel: Notebooks can be configured with a remote kernel, and then booted and connected to the kernel via ssh. All calculations will be done remotely, but the code and output will be saved locally.
Structure of Wolfram Language Notebook
The Wolfram Language Notebook interface is based on the VS Code Notebook API, which is not yet fully finalized and includes an interface for passing messages between the notebook interface, renderer, and controller. As a result, the extension can only send results from the kernel in one direction and cannot receive feedback from the notebook, so some important features (such as Manipulate) are not yet available. This issue will be addressed in a later release.
The rendering of Graphics and Graphics3D is another area that needs improvement. Because of the many options, rendering expressions directly is not a realistic approach, so the current approach is to export them as bitmaps and display them [4]. In a future version, there may be a better way to display these images. (If you have a good method, feel free to contact me.)
You can download the Wolfram Engine here, register your Wolfram ID, and get a license. The use of the Wolfram Language is described here, and the examples in it can basically be run in the Wolfram Language Notebook, except for the interactive interface and natural language input.
Although you can use the Wolfram Language Notebook itself in almost any way, computations performed through the Wolfram Engine are still limited by the Wolfram Engine license.
I am trying to pick the best from all solutions we have now (Wolfram Notebook for VS Code extension for example), and follow the idea as Mathematica, where every graphics are just powerful syntax sugar
My preliminary results are there Wolfram Engine JS Frontend
i'd like to add for the setup on Windows for Anaconda.
After Anaconda has been installed,
open Anaconda Powershell Prompt
run wolframscript
and follow the installation process of Method 2 (https://github.com/WolframResearch/WolframLanguageForJupyter)
i did this without specifying any path for python or jupyter and it works. it seems the path has been set already after opening Anaconda Powershell Prompt.
If you mean front-end in the strict sense, i. e. the visual notebook interface for interactivity, the following is not for you. But if all you want is displaying visualizations / graphics (incl. typeset expressions), you could use some of the windowing functionality together with J/Link, as I explain in my video here: https://www.youtube.com/watch?v=S1maEG-0nvE. Because J/Link works just fine from the engine.
For Linux only: https://github.com/Ludwiggle/JWLX
Pros: code auto-completion, neat separation between Wolfram kernel and Jupyter kernel, special functions to handle Dynamics in the browser.
