- Google Colab Tutorial
- Google Colab - Home
- Google Colab - Introduction
- What is Google Colab?
- Your First Colab Notebook
- Documenting Your Code
- Google Colab - Saving Your Work
- Google Colab - Sharing Notebook
- Invoking System Commands
- Executing External Python Files
- Google Colab - Graphical Outputs
- Google Colab - Code Editing Help
- Google Colab - Magics
- Google Colab - Adding Forms
- Google Colab - Installing ML Libraries
- Google Colab - Using Free GPU
- Google Colab - Conclusion
- Google Colab Useful Resources
- Google Colab - Quick Guide
- Google Colab - Useful Resources
- Google Colab - Discussion
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Google Colab - Installing ML Libraries
Colab supports most of machine learning libraries available in the market. In this chapter, let us take a quick overview of how to install these libraries in your Colab notebook.
To install a library, you can use either of these options −
Keras, written in Python, runs on top of TensorFlow, CNTK, or Theano. It enables easy and fast prototyping of neural network applications. It supports both convolutional networks (CNN) and recurrent networks, and also their combinations. It seamlessly supports GPU.
To install Keras, use the following command −
!pip install -q keras
PyTorch is ideal for developing deep learning applications. It is an optimized tensor library and is GPU enabled. To install PyTorch, use the following command −
!pip3 install torch torchvision
Apache MxNet is another flexible and efficient library for deep learning. To install MxNet execute the following commands −
!apt install libnvrtc8.0 !pip install mxnet-cu80
OpenCV is an open source computer vision library for developing machine learning applications. It has more than 2500 optimized algorithms which support several applications such as recognizing faces, identifying objects, tracking moving objects, stitching images, and so on. Giants like Google, Yahoo, Microsoft, Intel, IBM, Sony, Honda, Toyota use this library. This is highly suited for developing real-time vision applications.
To install OpenCV use the following command −
!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python
XGBoost is a distributed gradient boosting library that runs on major distributed environments such as Hadoop. It is highly efficient, flexible and portable. It implements ML algorithms under the Gradient Boosting framework.
To install XGBoost, use the following command −
!pip install -q xgboost==0.4a30
Graphviz is an open source software for graph visualizations. It is used for visualization in networking, bioinformatics, database design, and for that matter in many domains where a visual interface of the data is desired.
To install GraphViz, use the following command −
!apt-get -qq install -y graphviz && pip install -q pydot
By this time, you have learned to create Jupyter notebooks containing popular machine learning libraries. You are now ready to develop your machine learning models. This requires high processing power. Colab provides free GPU for your notebooks.
In the next chapter, we will learn how to enable GPU for your notebook.