AI Notebooks - Tutorial - Create your first Machine Learning model
How to build your first Machine Learning model thanks to Miniconda
Last updated 1st September, 2022.
Objective
This tutorial will allow you to create your first OVHcloud AI notebook based on a very simple Machine Learning model: the simple linear regression.
At the end of this tutorial, you will have learned to master OVHcloud AI Notebooks and be able to predict the scores obtained by students as a function of the number of hours worked.
We will be able to predict a student's exam score based on the amount of time he has studied using a dataset available on Kaggle: Students Score Dataset.
Requirements
- Access to the OVHcloud Control Panel
- An AI Notebooks project created inside a Public Cloud project in your OVHcloud account
- A user for AI Notebooks
Instructions
You can launch your notebook from the OVHcloud Control Panel or via the ovhai CLI.
Launching a Jupyter notebook with "Miniconda" via UI
To launch your notebook from the OVHcloud Control Panel, refer to the following steps.
Code editor
Choose the Jupyterlab code editor.
Framework
In this tutorial, the Miniconda framework is used.
With Miniconda, you will be able to set up your environment by installing the Python libraries you need.
You can choose the conda version you want.
The default version of conda is functional for this tutorial.
Resources
You can choose the number of CPUs or GPUs you want.
Here, using 1 CPU is sufficient.
Launching a Jupyter notebook with "Miniconda" via CLI
If you want to launch it with the CLI, choose the jupyterlab editor and the conda framework.
To access the different versions of conda available, run the following command.
ovhai capabilities framework list -o yaml
If you do not specify a version, your notebook starts with the default version of conda.
Choose the number of CPUs (<nb-cpus>) to use in your notebook and use the following command.
ovhai notebook run conda jupyterlab \
--name <notebook-name> \
--framework-version <conda-version> \
--cpu <nb-cpus>
You can then reach your notebook’s URL once the notebook is running.
Accessing the notebook
Once the repository has been cloned, find your notebook by following this path: ai-training-examples > notebooks > getting-started > miniconda > ai-notebooks-introduction > notebook-introduction-linear-regression.ipynb.
A preview of this notebook can be found on GitHub here.
Go further
- If you want to learn more about the field of Computer vision and Image Classification, check out this notebook.
- If you are interested in NLP (Natural Language Processing), familiarise yourself with speech to text by following this tutorial.
Feedback
Please send us your questions, feedback and suggestions to improve the service:
- On the OVHcloud Discord server
Czy ten przewodnik był pomocny?
Zachęcamy do przesyłania sugestii, które pomogą nam ulepszyć naszą dokumentację.
Obrazy, zawartość, struktura - podziel się swoim pomysłem, my dołożymy wszelkich starań, aby wprowadzić ulepszenia.
Zgłoszenie przesłane za pomocą tego formularza nie zostanie obsłużone. Skorzystaj z formularza "Utwórz zgłoszenie" .
Dziękujemy. Twoja opinia jest dla nas bardzo cenna.