Learning about JupyterLab

JupyterLab is the next-generation web-based user interface for Project Jupyter. It is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture. The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

The initial release of the JupyterLab Notebook Service for Mesosphere DC/OS contains:

  • Apache Spark 2.2.1 - Apache Spark™ is a unified analytics engine for large-scale data processing.
  • BeakerX 1.0.0 - BeakerX is a collection of kernels and extensions to the Jupyter interactive computing environment. It provides JVM support, Spark cluster support, polyglot programming, interactive plots, tables, forms, publishing, and more.
  • Dask 0.18.2 - Dask is a flexible parallel computing library for analytic computing.
  • Distributed 1.22.0 - Dask.distributed is a lightweight library for distributed computing in Python. It extends both the concurrent.futures and dask APIs to moderate sized clusters.
  • JupyterLab 0.33.4 - JupyterLab is the next-generation web-based user interface for Project Jupyter.
  • PyTorch 0.4.0 - Tensors and Dynamic neural networks in Python with strong GPU acceleration. PyTorch is a deep learning framework for fast, flexible experimentation.
  • Ray 0.5.0 - Ray is a flexible, high-performance distributed execution framework.
    • Ray Tune: Hyperparameter Optimization Framework
    • Ray RLlib: Scalable Reinforcement Learning
  • TensorFlow 1.9.0 - TensorFlow™ is an open source software library for high performance numerical computation.
  • TensorFlowOnSpark 1.3.2 - TensorFlowOnSpark brings TensorFlow programs onto Apache Spark clusters.
  • XGBoost 0.72 - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more.

It also includes support for:

  • OpenID Connect Authentication and Authorization based on email address or User Principal Name (UPN) (for Windows Integrated Authentication and AD FS 4.0 with Windows Server 2016)
  • HDFS connectivity
  • S3 connectivity
  • GPUs with the <image>:<tag>-gpu Docker Image variant built from Dockerfile-cuDNN

Further resources

Pre-built JupyterLab Docker Images for Mesosphere DC/OS: https://hub.docker.com/r/dcoslabs/dcos-jupyterlab/tags/

Related Docker Images: