Kaggle and Colab - BNNLab/BN_Group_Wiki GitHub Wiki

Google Colab

Overview

Google Colab is a free, cloud-based notebook environment that allows users to run Python code directly in their browser. It is particularly popular for its ease of use and seamless cloud integration. By providing a Jupyter Notebook–like interface, Colab eliminates the need to set up a local environment or install specialised software.

Key Features

  1. Pre-Installed Libraries Colab comes with many common data science libraries pre-installed (e.g., NumPy, pandas, scikit-learn, TensorFlow, PyTorch).
  2. GPU and TPU Support Users can request and utilise GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) for accelerated computation at no cost (subject to usage limits).
  3. Large Dataset Handling You can easily stream large datasets from various sources, including Google Drive, Google Cloud Storage, and external data repositories.
  4. Collaborative Editing Multiple users can work together on the same notebook in real time, similar to other cloud-based collaboration tools.
  5. Environment Customisation You can install or update libraries via pip or perform system-level installations using Bash commands. This enables advanced workflows and specialised environment setups.
  6. Magic Commands Leverage powerful IPython magic commands (e.g., %%time, %%bash, %%writefile) for profiling code, running shell scripts, and saving files within the notebook environment.
  7. Version Control Integration Colab can integrate with GitHub, allowing you to save notebooks directly to a repository or open notebooks stored there.

Limitations

  1. Session Timeout and Resource Constraints Colab sessions automatically time out after periods of inactivity. GPU and TPU usage is also subject to quotas and resource sharing.
  2. Performance Constraints Free GPU/TPU resources are advantageous but can be limited or slower during peak times, especially if many users are requesting resources simultaneously.

1. Environment Setup and Customiaations

1.1 Installing Additional Libraries

Although Colab has popular libraries like NumPy, Pandas, TensorFlow, and PyTorch pre-installed, you can add or upgrade packages on the fly:

1.2 Magic Commands and Shell Commands

  • %ls, %pwd, %cd, etc.: IPython magic commands that provide shell-like functionality.
  • %%time: Measures cell execution time to help optimize performance.
  • %%capture: Suppresses output (useful for lengthy installation logs).
  • %%bash: Runs multi-line Bash scripts directly in a cell.

2. Data Handling

2.1 Accessing Google Drive

Mount Google Drive to your Colab session:

2.2 Accessing Google Cloud Storage (GCS)

For larger-scale storage:

  1. Enable the Google Cloud SDK within Colab:
  1. Authenticate using your Google account.
  2. Copy data from Cloud Storage buckets:

3. GPU/TPU Usage

3.1 Switching Runtime

  1. Go to Runtime > Change runtime type.
  2. Choose GPU or TPU under Hardware accelerator.

3.2 Best Practices

• GPU: Ideal for training deep learning models with frameworks like TensorFlow, PyTorch, or JAX. • TPU: Specialized for TensorFlow or JAX workloads. Code often requires specific TPU strategies (e.g., tf.distribute.TPUStrategy).

4. Version Control Integration

Colab supports integration with GitHub:

  1. Go to File > Save a Copy in GitHub (or Save to GitHub).
  2. Authenticate your GitHub account.
  3. Select the repository and branch where you want to store your notebook.

Kaggle

Overview

Kaggle is an online platform for data science competitions and collaborative projects. It also provides a vast collection of public datasets and an in-browser coding environment, making it easy for users to develop and share data science projects. Kaggle is part of the Google Cloud ecosystem.

Key Features

  1. Datasets A public repository of datasets covering a wide range of topics. Users can publish, share, and collaborate on data.
  2. Notebooks (Kaggle Kernels) An in-browser notebook environment (often called Kernels), preloaded with common machine learning libraries for Python or R.
  3. Discussion Forums and Community A highly active user community shares ideas, best practices, and solutions to data science problems.
  4. Learn Courses Free, short courses introducing various data science and machine learning topics—ideal for beginners.

Using Kaggle Notebooks (Kernels)

Creating a Kaggle Notebook

  1. Log into Kaggle.
  2. Navigate to Notebooks > New Notebook.
  3. Select the Python or R environment, and optionally enable GPU or TPU under the Settings tab.

How to Benefit from Both Google Colab and Kaggle

Although Google Colab and Kaggle are separate platforms, they can complement each other in many ways:

  1. Data Exploration and Prototyping o Explore or share datasets on Kaggle. o Refine and train more complex models on Colab if you need specialized environment customization or extended GPU/TPU usage.
  2. Learning Resources o Use Kaggle’s micro-courses to learn new concepts or techniques. o Reinforce those lessons by practicing in Colab notebooks, leveraging additional code examples or advanced GPU/TPU options.

Useful Links

  1. Google Colab Official Documentation https://colab.research.google.com/
  2. Kaggle Official Website https://www.kaggle.com/
  3. Kaggle Learn Courses https://www.kaggle.com/learn