deepracer-utils 1.0.11

A set of tools for working with DeepRacer training

Deepracer Utilities - Analyzing Your DeepRacer Model

This is a set of utilities that will take your DeepRacer experience to the next level by allowing you to analyze your model, step by step, episode by episode. Only through analyzing what your model does will you be able to write the right reward function, choose the right action space and to tune the hyperparameters!

Installation

You can install the latest version of deepracer-utils via pip through

pip install deepracer-utils

Otherwise you can build your own version with

python3 setup.py build
python3 setup.py install

AWS CLI and boto3 extension

This package contains an extension to the AWS CLI and Boto3 that allows you to interact with the Deepracer Console through commands starting with aws deepracer. For details run

aws deepracer help

Then run this to install:

python -m deepracer install-cli

To remove deepracer support from aws-cli and boto3, run:

python -m deepracer remove-cli

About the Utilities

The best reference on how to use the utilities can be found in the deepracer-analysis Jupyter notebooks.

An overview of the different modules provided, and the key classes involved:

Module	Class	Description
deepracer.logs	DeepRacerLog	Class that is pointed to a Deepracer Model folder, locally or in an S3 bucket, and that reads in and processes trace files from simtrace or robomaker log files.
deepracer.logs	AnalysisUtils	Class that processes the raw log input and summarizes by episode.
deepracer.logs	PlottingUtils	Class that visualises the track and plots each step in an episode.
deepracer.logs	TrainingMetrics	Class that reads in Metrics data and provides data similar to the training graph in the Console.
deepracer.console	ConsoleHelper	Class that reads out logfiles directly from the console, and together with e.g. TrainingMetrics can be used to visualize training progress in real time.
deepracer.tracks	TrackIO	Class that processes track routes (.npy files) and displays waypoints graphically.
deepracer.model	n/a	Methods to run inference on individual images and to perform visual analysis.

Other information

• Refer to development.md for instructions on coding standards, unit tests etc.
• Refer to examples.md for usage guidance.

License

This project retains the license of the aws-deepracer-workshops project which has been forked for the initial Community contributions. Our understanding is that it is a license more permissive than the MIT license and allows for removing of the copyright headers. We have decided to preserve the headers and only add copyright notice for the Community.

Standards and good practices, contributing

While doing our best to make deepracer-utils an outcome of best practices and standards, we are using what we learn, as we learn. If you see a solution that would be better to apply, if you see something that is a risk, do raise it with the Community. Thank you.

We are open to merge requests. Please open an issue first to agree on the outcomes of your work.

Contact

You can contact Tomasz Ptak through the Community Slack: http://join.deepracing.io