Managed JupyterLab notebook instances
Deploy with one click
You can deploy new JupyterLab instances with one click and start analyzing your data immediately. Each instance comes pre-configured with optimized versions of the most popular data science and machine learning libraries including TensorFlow, Keras, PyTorch, fast.ai, RAPIDS, NumPy, scikit-learn, pandas, and Matplotlib.
Scale on demand
You can start small and scale up by adding CPUs, RAM, and GPUs. When your data gets too big for one machine, seamlessly switch to distributed services like BigQuery, Dataproc, Dataflow, and Vertex Training and Prediction. You pay for the instances only while they are running.
You’ll go from data to a deployed machine learning model without leaving Notebooks. Pull data from BigQuery, use Cloud Dataproc to transform it, and leverage Vertex AI services or Kubeflow for distributed training and online prediction.
Managed JupyterLab experience
Notebooks is built on the industry standard JupyterLab. So you can use it with the RPython and R data science community and customize your environment by installing JupyterLab plugins.
Notebooks supports popular enterprise security architectures through VPC-SC, shared VPC, and private IP controls. You can also encrypt your data on disk with CMEK.
Controlled user access
You can choose between two predefined user access modes: restrict Notebooks to a single-user or use a service account. You can also customize access based on your enterprise security architecture based on Cloud Identity and Access Management.
You can select any virtual private cloud for their Notebook instances, provided that they have access either through Google Private Access or the internet to Cloud Storage. You can also turn off public IP address and access your instance via proxy.
Support for data science frameworks
We provide a pre-configured environment that supports the most popular data science libraries, including R, pandas, NumPy, SciPy, scikit-learn, and Matplotlib, and ML frameworks like TensorFlow, Keras, fast.ai, RAPIDS, XGBoost, and PyTorch.
Optimized for machine learning
Notebooks' optimized versions of TensorFlow and PyTorch enable you to get the most out of Google Cloud hardware and seamlessly add and remove GPUs from your instance.
It’s easy to pull and push notebooks from your Git repository, making it also easy to share your notebooks with colleagues.
Bring your own container
You can run a Notebook instance on a container of your choice. This provides you the flexibility to install specific libraries mandated by your organization or preconfigure the environment running JupyterLab to your preference.
Explainable AI support
Notebooks come pre-installed with Google Cloud's Explainable AI, which allows you to generate feature attributions on-the-fly for rapid model prototyping and debugging.
Vertex AI documentation
Try a codelab to learn about Notebooks
Take up a data engineering course
Jupyter Notebook Manifesto
Use AutoML Tables from a Jupyter Notebook
Use the Pipelines SDK from Notebooks
Overview of Notebooks on Google Cloud (Next ’20: On Air)
Learn about data engineering, big data, and machine learning on Google Cloud
There are no minimum fees or up-front commitments, and there’s no charge for using Notebooks. You pay only for the cloud resources you use with the Notebooks instance: Compute Engine, Cloud Storage, Vertex Training, Vertex Predictions' BigQuery, and others. Our pricing calculator can help you estimate the costs of running your workloads.