Domino Compute Environments are completely customizable, using the flexibility of Docker images. To jumpstart your workflows, we provide the Domino Analytics Distribution, an optimized scientific computing stack for work in Python, R, Julia, and more. The distribution contains free or open source software we’ve aggregated over years, tailored for modern data science workflows.
We have developed streamlined environments for production deployment to ensure performant models with the tools data scientists use most. Users and IT teams can configure and customize these environments to meet their specific needs.
R 3+, Python 2 and 3, Julia, Octave — all properly compiled and linked against high-performance BLAS libraries. Anaconda Python available upon request.
Interactive tools and notebooks
Jupyter, RStudio, Zeppelin, Beaker and more. Popular packages in R and Python, including pandas, scikit-learn, nltk, dplyr, caret, H2O, and hundreds more.
Including ggplot, matplotlib, bokeh, plotly, seaborn and more.
Deep learning packages and GPU drivers
Properly configured to leverage GPUs: TensorFlow, Theano, Keras, and more.
Access Spark clusters from your code without painful configuration.
For Postgres, Oracle, MySQL and other popular databases.