This guide walks you through the installation of Keras on Ubuntu, including setting up Python, creating a virtual environment, and installing TensorFlow (which includes Keras). It also covers optional GPU support for faster model training, making it an essential resource for deep learning enthusiasts.
10 min
Edited:12-10-2024
Step-by-Step guide to install Keras
To install Keras on Ubuntu, you'll first need to install Python and then install Keras using pip (Python's package installer). Keras is typically installed with TensorFlow, as it's a high-level API for building and training deep learning models that runs on top of TensorFlow.
Update and Upgrade the System
Before installing any new software, it's a good practice to update your system.
1. sudo apt update && sudo apt upgrade -y
Install Python
Ensure that you have Python 3.x installed on your system. You can check this by running:
1. python3 --version
If Python is not installed, you can install it with:
1. sudo apt install python3 python3-pip python3-venv -y
Create a Virtual Environment
It's a good practice to create a virtual environment to isolate your Python packages.
1. python3 -m venv keras-env
2. source keras-env/bin/activate
Install TensorFlow and Keras
Keras is included as part of TensorFlow, so you can install TensorFlow to get both.
Run the following command to install TensorFlow (which includes Keras):
1. pip install tensorflow
You can verify the installation of TensorFlow (and Keras) by running:
1. python -c "import tensorflow as tf; print(tf.keras.__version__)"
Test Keras Installation
You can write a simple script to ensure that Keras is installed correctly.
Create a Python file, for example test_keras.py:
1. import tensorflow as tf
2. from tensorflow.keras.models import Sequential
3. from tensorflow.keras.layers import Dense
4.
5. # Create a simple model
6. model = Sequential([
7. Dense(32, activation='relu', input_shape=(784,)),
8. Dense(10, activation='softmax')
9. ])
10.
11. # Print model summary
12. model.summary()
Run the script to ensure everything is working:
1. python test_keras.py
(Optional) Install GPU Support
If you have an NVIDIA GPU and want to use it to accelerate your model training, you’ll need to install TensorFlow with GPU support.
Install the necessary NVIDIA drivers and CUDA toolkit. You can follow the instructions on the NVIDIA website.
After installing the CUDA toolkit, install TensorFlow with GPU support:
1. pip install tensorflow-gpu
After following these steps, you should have Keras installed on Ubuntu, either with or without GPU support, depending on your setup. Now you can start building and training deep learning models
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