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CMYK to RGB color space mapping

Set up the following environment variable on your development PC:

Make sure those environment variables have been applied in the command window that you use for the next steps.

Set up your python environment as described in the Bonsai CLI Get started docs. Then install the Python requirements of this sample by:

pip install -r requirements.txt

Run the simulator locally by:

python main.py

The output should say Registered simulator followed by--every several seconds--a line saying Last Event: Idle.

NOTE: The next step uses Bonsai CLI commands. If you prefer, these opererations can also be performed using your Bonsai worspace GUI as described in Link an unmanaged simulator to Bonsai.

While main.py continues to run, open a new commmand window and use the Bonsai CLI to create a Bonsai brain and start training by:

bonsai brain create -n simple-adder-brain
bonsai brain version update-inkling -f machine_teacher.ink -n simple-adder-brain
bonsai brain version start-training -n simple-adder-brain
bonsai simulator unmanaged connect --simulator-name simple-adder-sim -a Train -b simple-adder-brain -c Concept

The output should say Simulators Connected: 1. After a minute or so, you should see lots of activity in the console window that is running main.py and if you open your Bonsai worspace you should see that the brain named simple-adder-brain is running training episodes. We'll complete training in a faster way in the next step, so for now you can manually stop training by:

bonsai brain version stop-training -n simple-adder-brain

Press Ctrl+C to stop the simulator running main.py in your first console window.

For this step, you must have Docker installed on your local machine. The community edition of Docker is available for Windows, Linux, and MacOS.

Build a Docker container image and push it to your registry. In the following commands, <SUBSCRIPTION> and <WORKSPACE_ACR_PATH> should be replaced with your workspace details:

docker build -t simple-adder-container:latest -f Dockerfile .
docker tag simple-adder-container:latest <WORKSPACE_ACR_PATH>/simple-adder-container:latest
az acr login --subscription <SUBSCRIPTION> --name <WORKSPACE_ACR_PATH>
docker push <WORKSPACE_ACR_PATH>/simple-adder-container:latest

NOTE: The next step uses Bonsai CLI commands. If you prefer, these opererations can also be performed using your Bonsai worspace GUI as described in Add a training simulator to your Bonsai workspace.

Creating a Bonsai simulator package and running training with it by:

bonsai simulator package container create -n simple-adder-pkg -u <WORKSPACE_ACR_PATH>/simple-adder-container:latest --max-instance-count 25 -r 1 -m 1 -p Linux
bonsai brain version start-training -n simple-adder-brain --simulator-package-name simple-adder-pkg

Next, open your Bonsai worspace and you should see your simple-adder-brain brain is running training. If you look in the Train tab, after a few minutes, you will see that simulators have started up and episodes are being executed. After approximately 200,000 iterations you should see in the training graph shows 100% goal satisfaction and 100% success rate. You can stop the training at this point or let training continue to run. It will eventually stop when it can no longer find improvements to reach the goal in a more optimal fashion.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

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