Option 1: Running the notebook locally on your computer In case of questions, I’m happy to help - just contact me any way you prefer. In order to run the Notebook, you need to access an installation of SageMath somehow. ipynb-File containing SageMath computations. Here are some instructions on how to view and use a. The only point is that you won't have access to all Mathematica functionalities, as Manipulate, Mouse Over and the display of some images when output is truncated. Session = WolframLanguageSession('/usr/local/Wolfram/Mathematica/12.0/Executables/MathKernel') Now you can run your Mathematica scripts with backup of how many GPUs you need:įrom wolframclient.language import wl as w1įrom wolframclient.language import wlexprįrom wolframclient.evaluation import WolframLanguageSession Now that you've opened Mathematica in the command line with one SSH and after that Jupyter Notebook in other SSH, access the web address, port 8888, create a new Python 3 notebook and install wolframclient library: Important: You must run Mathematica ("math") before you open Jupyter Notebook. After providing them, Mathematica notebook starts in the command line This command will ask for your Activation key and Password. To install Mathematica run in another SSH session (terminal window): sudo bash link_to_mathematica.sh sh file: sudo wget link_to_mathematica.sh That will allow you to run Python notebooks in Jupyter. Now you will access Jupyter in the following address: So, to start Jupyter notebook you will need to run: jupyter notebook Go to GCP VPC and create a static IP for your instance, SSH into it. Now that you created the password, you are going to save "sha1:49b8799c22." Then you will edit Jupyter configuration file (use sudo or chmod -R 777 /home/anaconda3): sudo vi ~/.jupyter/jupyter_notebook_config.pyĪdd (type "i") these line of code: c=get_config()Ĭ.NotebookApp.password = paste your sha1 here Choose your CPUs, Memory, GPUs and regarding the boot disk, I used Debian GNU/Linux with Anaconda, PyTorch and CUDA already installed, as we also work with Deep Learning and NLP.Īfter that, you need to configure Jupyter in order to be able to open it in the local browser, by doing the following: ipython My co-worker, Gustavo Gouvea, also put efforts in this solution.įirst of all, go to Google Cloud Platform (GCP) Compute Engine and select Create Instance in a given region. So, I will present how I was able to run Mathematica inside a Jupyter notebook located in a Google Cloud instance with 8 V100 GPUs. However, I was told one can also use Wolfram Client Python library and run Mathematica in a Python notebook. One way it's to use webMathematica, installing Java and Apache Tomcat in a cloud instance. So, I started wondering how I could use GPUs with Wolfram Mathematica. And time is what you don't have in a startup. However, I was dealing with a drawback: if you choose to plot more than 10,000 connections in Mathematica, that can take some time. So, it's clear that Wolfram Mathematica is way ahead of networkx. More than that, you can highlight communities and check the number of connections of each individual: In my previous experience of Wolfram Mathematica, I was able to use some reasoning coming from cellular automata interactions to map, track people of interest (blue circle), and watch the evolution of mood (color), number of connections (size of circle) in a social network:īesides, one can use Mathematica's features of finding communities, coloring them and even highlight people of interest when passing the mouse over the social network and also speaking their names: It's an interesting perspective, but adds little value to generate strategic insights for our clients, given that we need to zoom it to analyze details of this network. Take the following picture as an example, done with networkx, 100,000 people: However, I was completely aware of the additional functionalities of Wolfram Mathematica. Recently I was working with Social Networks in Python using networkx library to analyse posts from a given hashtag for our client. I am a Data Scientist working in a startup in Brazil.
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