Jupyter Hub for Reinforcement Learning using Python packaged by Data Science Dojo

Data Science Dojo

Jupyter Hub for Reinforcement Learning using Python packaged by Data Science Dojo

Data Science Dojo

Our Jupyter Instance provides easy to use environment for Reinforcement Learning applications.

Data Science Dojo delivers data science education, consulting, and technical services to harvest the power of data.

Trademarks: This software listing is packaged by Data Science Dojo. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

About the offer:

Jupyter Hub for Reinforcement Learning using Python gives you an effortless coding environment in the cloud with pre-installed Reinforcement Learning python libraries, which reduces the burden of installation and maintenance of tasks. Through this offer, a user can work on different applications of Reinforcement Learning including autonomous driving, robotics for Industry automation, text summarization, question answering, machine translation, healthcare, business strategy planning, games optimization, manufacturing, online recommendation and bid optimization. The heavy computations required for these applications are not performed on the user's local machine. Instead, They are performed in the Azure cloud, which increases responsiveness and processing speed.

Who benefits from this offer:

  • Teams of developers
  • Data scientists
  • Machine learning engineers
  • Scientific researcher groups
  • And anyone else interested in data science tools

What is included in this offer:
  • Pre-installed Python libraries and packages for Reinforcement Learning.
  • Ready to go notebooks which consist of example codes through which user can get guidance for working on Reinforcement Learning applications.
  • Code consoles to run code interactively, with full support for rich output.
  • Kernel-backed documents enable code in any text file (Markdown, Python, etc.) to be run interactively in Jupyter kernel.
  • Work with multiple notebooks at the same time.
Technical Specifications:

  • Minimum Recommended memory: 16GB RAM
  • Minimum Recommended vCPU: 4 vCPUs
  • Operating System: Ubuntu 20.04
  • GPU required (for heavy computations and Deep Reinforcement Learning)

Following Authoring Tools are supported in this offer:

  • Jupyter Hub
  • Jupyter Lab
  • Terminal

Our instance supports following Python Reinforcement learning libraries:

  • Numpy
  • Matplotlib
  • Pandas
  • Seaborn
  • Tensorflow
  • PyTorch
  • Keras
  • KerasRL
  • Tensorforce
  • Pyqlearning
  • TFAgents
  • MushroomRL
  • RLlib
  • DeeR
  • Chainer RL
  • Gym
  • RL Graph
  • Tqdm

Our offer provides repositories from following sources:

  • Github repository of book Hands On Reinforcement Learning with Python, by author Sudharsan Ravichandiran.
  • Github repository of The Reinforcement Learning Workshop, published by Packt.
  • Github repository of book Applied Reinforcement Learning with Python, by author Taweh Beysolow.
  • Github repository of bookReinforcement Learning: an Introduction, by author Richard Sutton and Andrew Barto.

The default HTTP port JupyterHub listen to is 8000. You can access the web interface at http://yourip:8000

Use following credentials:
  • Username: guest
  • Password: guest@123
The Jupyter Trademark is registered with the U.S. Patent & Trademark Office.