Besides watching recordings of live TV broadcast of Apollo 11, to celebrate Apollo’s 50th Anniversary, it is no better time to train my notebook computer with power far surpassing the Apollo guidance computer how to land on the moon. Suffice it to say the Apple ][ is after the Apollo made history to land mankind on moon.
With OpenAI Gym, a simulated environment for the lunar landing module on the tense moment landing on the moon is recreated. Training via TensorFlow to let the modern computer to practice and learn to how to land eventually accomplished the mission – the Eagle has landed.
Most of the time my work with deep learning is done in command line interface with Python and TensorFlow. The clean and efficient syntax of the Python language and package design of TensorFlow almost eliminated the need of a complex Integrated Development Environment (IDE). But after trying out the free Google Colab service that provide a web based interface in Jupyter, I am going to set up one on my desktop that sports an Nvidia RTX2060 GPU.
Installation is easy, but be sure to run Anaconda console as Administrator on Windows platform. For running TensorFlow with GPU:
Managing multiple packages is much easier with Anaconda as it separate configurations into environments that can be customized. On my development machine, I can simply create a TensorFlow environment with GPU and then install Jupyter to enjoy its graphical interface.
Finally to activate Jupyter:
To see how Anaconda with Jupyter is flexible on the same machine, a comparison of a simple image pattern recognition program runs under Jupyter with and without GPU support.