The evolution of Machine Learning (ML) has been revolutionized by Google Colab, which provides a Jupyter Notebook-based environment that can be accessed directly from the browser. Developers and data scientists can easily train AI models without having to create a local environment due to free GPU and TPU access.
In addition, Colab is ideal for creating, experimenting with, and collaborating on machine learning notebooks because it supports TensorFlow, PyTorch, and other popular libraries. From this blog, you will learn how to develop and train machine learning models in Google Colab.
Machine Learning-Ready Environment
Google Colab has ML libraries pre-installed, thus there's no need to set up a local environment like with standard Jupyter notebooks.
Primary libraries accessible:
Google's primary deep learning framework is called TensorFlow.
PyTorch: Framework researchers and technologists working on AI
Scikit-learn: Toolkit for machine learning.
NumPy and Pandas: Data manipulation libraries.
Seaborn and Matplotlib: Tools for visualizing data.
To install an additional library, simply use: !pip install library-name
Free GPU and TPU Access for Faster Training
Access to free GPUs and TPUs, which greatly speed up the training of deep learning models, is one of Google Colab's greatest benefits.
How do I turn on a GPU in Colab?
Select Runtime from the top menu.
Select "Change Runtime Type."
Choose GPU in the Hardware Accelerator section, then click Save.
Use the code below to confirm that the GPU has been properly activated:
Your GPU has been successfully enabled if it returns "True". If not, it indicates that your current session does not have any GPU accessible.
If a GPU is not available in Google Colab, Kaggle Notebooks offers free GPU access as well, so take that into consideration.
How do I activate a TPU in Colab?
Just choose TPU from the same Change runtime type box if you wish to use a TPU to optimize model processing. Next, configure the TPU environment using the code below:
"Yes" indicates that your TPU has been successfully enabled. If not, it indicates that there isn't a TPU accessible during your current session.
Large-scale deep learning model training with TensorFlow is the primary application for TPUs. GPUs are a superior choice when utilizing PyTorch.
Google Drive and GitHub connectivity
By allowing you to access datasets directly from Google Drive and import projects from GitHub, Google Colab makes file organization and team collaboration easier.
How can I connect Google Drive to Colab?
Execute the following code in your notebook:
Click the link to provide access after it has been generated.Your Google Drive files will be located in the /content/drive/ folder.
How can I import a notebook from GitHub?
Navigate to File.
Open Notebook.
Select the "GitHub" tab.
Type the repository's name or paste the URL.
Once the necessary file has been selected, click Open.
Use Google Colab to train machine learning models
After setting up the environment, let's use TensorFlow to train a basic handwritten digit classification model (MNIST).
Conclusion
Google Colab's free GPUs, readily available setup, and connectivity with GitHub and Drive make machine learning work easier. One of the best options for training and testing AI models without a powerful computer is this one, which is appropriate for data scientists, developers, and students.
Comments