Difference between revisions of "Local installation: quickstart 2020"
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== Local installation == | == Local installation == | ||
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| + | This is trying to be a short setup guide: a minimal guide to what you need to get your Dingocar running. The [https://docs.donkeycar.com/ 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. | We don't want Python 2, and people have reported problems with Python 3.7 or later. So we currently use Python 3.6. | ||
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'''Install Tensorflow for machine learning''' | '''Install Tensorflow for machine learning''' | ||
| − | * | + | * conda install tensorflow |
| − | * | + | * conda env create -f install/envs/ubuntu.yml or conda env create -f install/envs/windows.yml or conda env create -f install/envs/mac.yml (on a different environment? Take a look at what's in install/envs and find one that's right for you) |
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'''Install Dingocar''' | '''Install Dingocar''' | ||
| + | * conda activate dingo | ||
* pip install -e ./dingocar | * pip install -e ./dingocar | ||
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Run these commands on your laptop / desktop to train the Neural Network ... | Run these commands on your laptop / desktop to train the Neural Network ... | ||
| − | * | + | * conda activate dingo |
* cd ohmc_car | * cd ohmc_car | ||
* python manage.py train --tub $HOME/ohmc_car/tub_$DATE --model ./models/model_$DATE.hdf5 | * python manage.py train --tub $HOME/ohmc_car/tub_$DATE --model ./models/model_$DATE.hdf5 | ||
Latest revision as of 06:06, 13 January 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
- conda install tensorflow
- conda env create -f install/envs/ubuntu.yml or conda env create -f install/envs/windows.yml or conda env create -f install/envs/mac.yml (on a different environment? Take a look at what's in install/envs and find one that's right for you)
Install Dingocar
- conda activate dingo
- 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 activate dingo
- 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".
