Local installation: quickstart 2020

Local installation

This is trying to be a short setup guide: a minimal guide to what you need to get your Dingocar running. The DonkeyCar docs have a more complete guide. If you get stuck or need more information, that's the place to go.

We don't want Python 2, and people have reported problems with Python 3.7 or later. So we currently use Python 3.6.

Miniconda instructions:

• Go to the Miniconda archive
• Download Miniconda3-4.5.4 in the right system for you.
• In your command line prompt, go to the directory holding the file
• Run the script: ./Miniconda3-4.5.4-Linux-x86_64.sh (or equivalent)

This will by default add the Miniconda directory to your path. Now you can check you have Python 3.6 installed and available:

• python3 -i
• This should show you Python 3.6.5 | Anaconda Inc.
• Use quit() to get out of the python shell

Get Dingocar

Go to which directory you like to keep your coding projects in.

• git clone https://github.com/tall-josh/dingocar.git
• cd dingocar
• git checkout master

Install Tensorflow for machine learning

• Ubuntu
  • apt-get install -y virtualenv # Note: You may be using a different software installer
  • mkvirtualenv donkeycar -p python3
  • pip install tensorflow==1.8.0 # Note: Probably requires Python 3.5 or 3.6. People are having problems with Python 3.7

(if you get errors, you can try (re-) installing pip: python -m pip install --upgrade pip )

• Debian, if virtualenv isn't there, try this instead
  • virtualenv donkeycar -p python3
  • cd donkeycar
  • export PATH=`pwd`/bin:$PATH
  • pip install tensorflow==1.8.0

(This seems to be v2, but 2019 instructions use 1.8?)

• conda install tensorflow-cpu

Install Dingocar

• pip install -e ./dingocar

Create an instance for your specific car

• donkey createcar --path ~/mycar #give your car its own unique name here!

Training

Run these commands on your laptop / desktop to train the Neural Network ...

• conda donkeycar
• cd ohmc_car
• python manage.py train --tub $HOME/ohmc_car/tub_$DATE --model ./models/model_$DATE.hdf5

 using donkey version: 2.5.7 ...
 loading config file: /Users/andyg/play/ai/roba_car/config.py
 config loaded
 tub_names ./tub_2019-01-15c
 train: 5740, validation: 1436
 steps_per_epoch 44
 Epoch 1/100
 2019-01-21 13:08:49.507048: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
 43/44 [============================>.] - ETA: 0s - loss: 58.5130 - angle_out_loss: 30.3421 - throttle_out_loss: 86.6839      
 Epoch 00001: val_loss improved from inf to 0.19699, saving model to ./models/roba0_2019-01-16c.hdf5
 44/44 [==============================] - 38s 874ms/step - loss: 57.1887 - angle_out_loss: 29.6601 - throttle_out_loss: 84.7172 - val_loss: 0.1970 - val_angle_out_loss: 0.3230 - val_throttle_out_loss: 0.0710

On a modern laptop, each epoch will take around 30 seconds to complete. For up-to 100 epochs. Typically, you can expect around 20 to 40 epochs before the Neural Network stop learning. That is around 10 to 20 minutes of training time.

The training command creates the Neural Network weights that represent what your DingoCar has "learned".