Blog: Breaking in to the Top 10 of AWS Deepracer Competition - May 2020
This is a fork of the original deepracer-analysis which can be found at the AWS DeepRacer Community.
- Modified Training_Analysis to incorporate to better view training progress and optimise viewing of model speed.
- Create_Trackfile can be used to generate a track numpy file (especially for new tracks). Takes in printed waypoints and calculates the inner and outer borders.
- Track_Analysis is used to create your custom racing line + to view the track and racing line characteristics.
Many thanks to the DeepRacer community getting me started on this journey.
-Michael
This is a set of notebooks and utilities to enable analysis of logs for AWS DeepRacer.
This project is a redo of analysis solutions provided in the AWS DeepRacer Workshops repository.
There are a few motivations leading a decision to reorganise the repository
- Having a community version of the project in a folder on a branch of a fork of the original git repository has been causing issues when looking for it
- Jupyter notebooks are difficult to manage through source control as a generated file in json format is hardly readable at all. This needed a new approach
- The project relied on a bunch of Python files which acted as a bag for code. There was a need to extract it into a separate project to maintain the versioning and apply some hopefully good practices
Separate repository makes it easier to find it. Jupyter combined with Jupytext enables maintaining the notebook as a set of Python files from which a notebook can then be genereted. Finally, the project files have been moved to DeepRacer utils.
It is assumed that you have aws-cli installed and configured in your account. You will find instructions in documentation. Without this the notebooks will not fail to load but fetching logs from AWS will not work. If you realise this after running the notebook, you can open a terminal withing Jupyter Lab and perform the configuration.
If this happens in Docker, just bear in mind that ~/.aws folder will be created for you with root privileges - this may lead to permissions problems at some point.
The recommended way to work with this project is by using Docker containers. Containers provide an isolated, disposable environment for your use. If you however prefer not to use Docker (or are using Windows), see "Using the notebooks without Docker" below.
Since you're using DeepRacer Analysis, chances are you've already got Docker installed. If not, find instructions in Docker documentation.
Docker setup comes with Jupytext configured.
Before you run your notebooks, you will have to build the docker image:
bin/build-docker-image.sh
I'd recommend that you do it every time when you pull changes from the git repository.
This builds a Docker image on top of a jupyter-minimal image and installs required dependencies.
To start using the analysis you have to first start the container and then open the notebook
in a browser. The startup script starts Jupyter Notebook but is you add lab argument
it will open Jupyter Lab - this is my preferred way
bin/start.sh lab
bin/open-notebook.sh
If you're running on a remote system, you can use url-to-notebook.sh to obtain a url with
a token to open in your browser. You can provide your url as an argument, otherwise you will
get a localhost address:
bin/url-to-notebook.sh http://someurl.com:8888
will return
http://someulr.com:8888/?token=123fab41...
if the container is running.
The notebooks require Jupyter to run, together with deepracer-utils. While not needed for using the notebooks, it's worth to also have Jupytext installed.
If you only plan to use the notebooks, I recommend that you make a copy of them to enable seamless pulls of any updates.
If you pull latest changes for the notebooks, do also run
pip install --upgrade -r requirements.txt
in your venv. This way you will also get upgrades on the requirements.
python3 -m venv venv
source venv/bin/activate
pip install --upgrade -r requirements.txt
jupyter lab
If you want to use the notebooks as a user and don't intend to submit changes, simply use them through Jupyter Notebook or Jupyter Lab.
If you would like to submit changes to a notebook however, follow instruction in the Jupytext README to enable pairing of the notebook with a light script. This means that any changes you apply to the notebook or the .py file paired with it will be synched.
When applying changes to the notebook, make sure you can use them with the sample log resources and at the end of work restart the Kernel and run all the cells to provide a clean view in the notebook.
- Recreate the training and evaluation notebooks on top of the deepracer-utils
- Apply changes from the original notebook commited since the creation of the log analysis branch
- Add docker runtime
- Redo log analysis challenge PRs and apply them to notebooks
- Prepare simpler, specialised notebooks for everyday use
- Prepare a tutorial on how to use and contribute to a notebook
We would like to thank:
- AWS employees who have given birth to these tools as part of the AWS DeepRacer Workshops repository.
- All involved AWS DeepRacer Community members to contributed to its development
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.
Unless clearly sated otherwise, this license applies to all files in this repository.
If you face problems, do reach out to the AWS DeepRacer Community.
Channel #dr-training-log-analysis has been created for this purpose.
When you face an issue, it is worth running pip freeze and saving the output as it may be
due to a specific version of the dependencies installed.
You can contact Tomasz Ptak through the Community Slack: http://join.deepracing.io.