Fast.ai is a high-level library built on top of PyTorch and is designed to make deep learning more accessible and easier to implement for beginners as well as experienced practitioners. In this article, we will provide a step-by-step guide on how to install and set up Fast.ai on your system.
Step 1: Check system requirements
First, it’s important to check the system requirements for installing Fast.ai. It is recommended to have a system with at least 8GB of RAM and an NVIDIA GPU for faster training of deep learning models. Additionally, it is advised to have Python version 3.6 or later.
Step 2: Install Anaconda
Anaconda is a popular open-source distribution of the Python and R programming languages, and is particularly useful for data science and machine learning projects. To install Fast.ai, we recommend using Anaconda to manage the Python environment and dependencies.
First, download and install Anaconda from the official website (https://www.anaconda.com/products/distribution). Follow the installation instructions provided on the website for your specific operating system.
Step 3: Create a new Anaconda environment
Once Anaconda is installed, open the Anaconda Navigator or use the Anaconda command line interface to create a new environment. A new environment provides a clean and isolated space to install specific packages and libraries.
To create a new environment, execute the following command in the Anaconda terminal:
“`
conda create -n fastai python=3.8
“`
Replace `fastai` with the desired name for your environment and `3.8` with the preferred version of Python.
Step 4: Activate the new environment
After creating the new environment, activate it by running the following command:
“`
conda activate fastai
“`
Step 5: Install Fast.ai and its dependencies
Once the environment is activated, use the following command to install Fast.ai and its dependencies:
“`
conda install -c pytorch -c fastai fastai
“`
This command will install Fast.ai, PyTorch, and other necessary packages. It is important to install Fast.ai from the `fastai` channel to ensure compatibility with the library and its dependencies.
Step 6: Test the installation
After the installation is completed, you can test whether Fast.ai is installed correctly by opening a Python interpreter and importing the `fastai` library:
“`python
import fastai
“`
If no errors are encountered, it indicates that Fast.ai has been successfully installed.
Step 7: Install Jupyter Notebook
Jupyter Notebook is a popular web-based interactive development environment for Python that is well-suited for experimenting with machine learning models. To install Jupyter Notebook, use the following command:
“`
conda install -c anaconda jupyter
“`
Step 8: Launch Jupyter Notebook
With Fast.ai and Jupyter Notebook installed, you can now launch Jupyter Notebook by running the following command in the Anaconda terminal:
“`
jupyter notebook
“`
This will open Jupyter Notebook in your default web browser, allowing you to create, edit, and run Python code in an interactive environment.
In conclusion, this article provided a step-by-step guide on how to install Fast.ai and set up the necessary environment for developing deep learning models. By following these instructions, you can start experimenting with Fast.ai and leverage its capabilities to build and train cutting-edge machine learning models.
