8 Easy Ways to Run Your Jupyter Notebook in the Cloud (2023 Update)
Introduction
Jupyter Notebooks are a widely popular tool among data scientists and researchers, allowing them to combine code, visualizations, and rich text in a single, interactive document. Running Jupyter Notebooks in the cloud can offer several advantages, such as increased computational power, storage, and collaboration capabilities. In this article, we’ll explore eight easy ways to run your Jupyter Notebooks in the cloud.
In this post, we dive into each of the top 8 ways to run you notebook as well as provide you with convenient comparison tables for both data scientists and engineers evaluating the options.
For Data Scientists:
| Platform | Free Tier | GPU/TPU Support | Real-time Collaboration | Pre-installed Libraries/Frameworks | Integration with Other Services | Supported Languages | DevOps Required |
|---|---|---|---|---|---|---|---|
| Saturn Cloud | Yes | Yes | No | Various | AWS, GCP, Azure | Python, R, Julia | No |
| Google Colab | Yes | Yes | No | TensorFlow, PyTorch, Keras | Google Drive | Python | No |
| Azure Notebooks | Yes | No | Yes | TensorFlow, Keras, Scikit-learn | Azure services | Python, R, F# | No |
| Amazon SageMaker | Yes | Yes | No | Various | AWS services | Python | Moderate |
| IBM Watson Studio | No | Yes | Yes | Various | IBM services | Python, R, Scala | Extensive |
| Databricks | Yes | Yes | Yes | Various | Apache Spark, other data services | Python, R, Scala | Minimal |
| Deepnote | Yes | Yes | Yes | TensorFlow, PyTorch | Various | Python, R, SQL | No |
| CoCalc | Yes | Yes | Yes | Various | Various | Python, R, Julia | No |
For Software Engineers/DevOps:
| Platform | Deployment & CI/CD Integration | Version Control | Docker Support | REST API Support | Infrastructure Management | Cost Management | Monitoring & Logging |
|---|---|---|---|---|---|---|---|
| Saturn Cloud | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Google Colab | Limited | Yes | No | Limited | No | Limited | Limited |
| Azure Notebooks | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Amazon SageMaker | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| IBM Watson Studio | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Databricks | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Deepnote | Limited | Yes | Yes | Limited | No | Limited | Limited |
| CoCalc | Limited | Yes | No | Limited | No | Limited | Limited |
1. Saturn Cloud
Saturn Cloud is a free, powerful data science platform that offers a cloud-based Jupyter Notebook environment with minimal DevOps requirements. Users can focus on data science tasks without worrying about infrastructure management, deployment, or other operational concerns. It provides access to multi-GPU instances and parallel computing, making it suitable for large-scale data processing and machine learning projects. Saturn Cloud integrates with popular data storage services such as Amazon S3, Google Cloud Storage, and Azure Blob Storage. It also comes pre-installed with libraries and frameworks such as TensorFlow, PyTorch, and scikit-learn, so users can start their projects without manually setting up their environments.
Saturn Cloud is ideal for teams that require robust infrastructure management, cost management, and monitoring capabilities.
2. Google Colab
Google Colab is a free, cloud-based Jupyter Notebook environment that provides access to GPUs and TPUs. It integrates with Google Drive, making collaboration and sharing easy. While it has limited support for deployment, CI/CD integration, and infrastructure management, it is an excellent choice for individual data scientists or small teams with limited budgets who need GPU/TPU access for their projects.
3. Microsoft Azure Notebooks
Microsoft Azure Notebooks is a free, cloud-based Jupyter Notebook environment offered by Microsoft. It supports integration with Azure services, enabling teams to leverage cloud storage, machine learning, and other resources. Azure Notebooks offer comprehensive deployment, CI/CD integration, and infrastructure management capabilities, making it suitable for teams already using or planning to use the Azure ecosystem.
4. Amazon SageMaker
Amazon SageMaker is a fully managed service designed for building, training, and deploying machine learning models. It provides a Jupyter Notebook interface with access to powerful hardware like GPUs and high-performance storage. SageMaker is ideal for teams that require seamless integration with AWS services, advanced deployment options, and robust infrastructure management. It is well-suited for large-scale machine learning projects and teams familiar with the AWS ecosystem.
5. IBM Watson Studio
IBM Watson Studio is a cloud-based platform that offers tools for data scientists and machine learning engineers, including a Jupyter Notebook environment. It provides access to various libraries and frameworks and integrates with IBM services such as Watson APIs and IBM Cloud Object Storage. Watson Studio is ideal for teams that require advanced monitoring, deployment options, and infrastructure management within the IBM ecosystem. It suits teams working on machine learning and data science projects that leverage IBM cloud services.
6. Databricks
Databricks is a cloud-based platform for big data analytics and machine learning. It offers a collaborative workspace that includes a Jupyter Notebook environment and supports multiple programming languages. Databricks integrates various data storage and processing services, including Apache Spark. It is well-suited for teams working on large-scale data processing and machine learning projects that require advanced deployment options, monitoring, and infrastructure management within the Databricks ecosystem.
7. Deepnote
Deepnote is a cloud-based Jupyter Notebook platform for data scientists and machine learning engineers. It offers real-time collaboration features and access to powerful hardware like GPUs. Deepnote has limited support for deployment, CI/CD integration, and infrastructure management. It is a good choice for individual data scientists or small teams focused on collaboration and GPU access but does not require advanced deployment or infrastructure management options.
8. CoCalc
CoCalc is a cloud-based platform that offers a wide range of tools for computational mathematics, including a Jupyter Notebook environment. It supports multiple programming languages and provides real-time collaboration features. CoCalc has limited support for deployment, CI/CD integration, and infrastructure management. It is suitable for academic teams or individual researchers working on computational mathematics and related projects that require collaboration features and support for multiple languages.
Conclusion
Running Jupyter Notebooks in the cloud can significantly enhance your data science workflow, providing access to powerful hardware, storage, and collaboration capabilities. With numerous cloud-based platforms, such as Saturn Cloud, Google Colab, Microsoft Azure Notebooks, etc., you can easily find a service that meets your needs and requirements.
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