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Donkey Car Detailed Explanation (2) – Raspberry Pi Installation

Donkey Car Detailed Explanation (2) – Raspberry Pi Installation

robocarstore/173807615525023113

Now the official Donkey Car is no longer provided with a lazy person's version, so this time we will share the process of installing Donkey Car on a Raspberry Pi (Raspberry Pi 3 B+).

Reference: http://docs.donkeycar.com/guide/robot_sbc/setup_raspberry_pi/

Environment

The official recommendation is to use Raspian Lite(Stretch) (352MB). However, I used the desktop version, as follows:

Raspberry Pi OS:
– Raspbian Buster with desktop
Version:July 2019
Release date:2019-07-10
Kernel version:4.19

Download Url:

https://www.raspberrypi.org/downloads/raspbian/

How to flash install Raspberry Pi, here is no more.

Assume you have already installed the Raspberry Pi system and can connect to the network, you can choose to connect to the display directly (this example's operation mode), or remotely SSH. You like it.

Open the command line, let's start!

donkeycar-rpi

Start Installation

1，Update your system before installation

To update your system, enter the following command:

Updating the system takes a long time and may need to be repeated multiple times to complete. Please be patient.

2，Configure Raspberry Pi to enable camera, I2C, expand file system

To open the Raspberry Pi configuration dialog, enter the following command in the command line:

① Enable I2C

05 Interfacing Options -> P5 I2C ->「Yes」

rpi_i2c

② Enable Camera:

05 Interfacing Options -> P1 Camera -> 「Yes」

robocarstore/173807630025267416

③ Expand File System

7 Advanced Options -> A1 Exapand Filesystem

robocarstore/173807631425048117

Set it up, then exit the configuration dialog, it will prompt whether to restart now, select 「Yes」 to restart the system.

robocarstore/173807632825076218

3，Install the dependencies required for Donkey Car

Enter the following command in the command line:

sudo apt-get install -y build-essential python3 python3-dev python3-pip python3-virtualenv python3-numpy python3-picamera python3-pandas python3-rpi.gpio i2c-tools avahi-utils joystick libopenjp2-7-dev libtiff5-dev gfortran libatlas-base-dev libopenblas-dev libhdf5-serial-dev git

After a long wait, it will be installed, if an error occurs, please run it again.

（If the problem is still not resolved, please share the problem you encountered in the comments section, and post your system version, as much information as possible, so we can help you.）

4，Install OpenCV dependencies (optional)

This step is optional, you can skip it if you don't use the OpenCV feature.

If you still want to install it, enter the following command in the command line:

sudo apt-get install libilmbase-dev libopenexr-dev libgstreamer1.0-dev libjasper-dev libwebp-dev libatlas-base-dev libavcodec-dev libavformat-dev libswscale-dev libqtgui4 libqt4-test

5，Install Virtual Environment

Enter the following command in the command line:

python3 -m virtualenv -p python3 env --system-site-packages

echo "source env/bin/activate" >> ~/.bashrc

After running the above commands, the command line will have a (env) prefix, indicating that we are operating in an environment called env.

robocarstore/173807634625230519

The purpose of the virtual environment is to avoid errors in other projects when updating a dependency or automatically clearing a dependency on the Raspberry Pi running multiple projects.

Now, every time the Raspberry Pi starts, it will automatically enter the env virtual environment. If you want to exit, enter the command line: deactivate .

6，Install Donkey Car's Python Code

Create Directory, and Enter the Directory

In the user directory, create a folder named projects (directory), and enter the folder, enter the following command:

Note: Directly enter the user directory, just enter the command line: cd , press Enter.

Clone the entire Donkey car project from GitHub

Enter the following command:

git clone https://github.com/autorope/donkeycar

Then, start a long wait, because the speed of github in China is not fast, please be patient, if it doesn't work, run it again.

Note: If you are familiar with command line instructions, you can use the scp command to download some software projects after packaging, and upload them to the current directory of the Raspberry Pi. For specific usage, please search for it yourself.

When git clone (clone) is complete, we use the following command to operate:

pip install tensorflow==1.13.1

Note:
– When running pip install -e .[pi] installation, if an error occurs, you can try to run it again.
– When running pip install tensorflow==1.13.1 , it may prompt that the downloaded file's hash code is incorrect, causing it to be unable to download. You can manually use the wget command to download it according to the error prompt, download it, and then use pip install 「downloaded file name」 to install it.

Verify Tensorflow Installation Success

Enter the following command to enter the Python interactive programming mode:

Enter the following code to check:

print(tensorflow.__version__)

robocarstore/173807637225182120

When you see the version 1.13.1, it proves that you have successfully installed Tensorflow.

Note: Python will have some prompts when loading tensorflow for the first time, please don't worry, this is not an error prompt.

7，Install Python's OpenCV (optional)

Install OpenCV

This is also not required to be installed, if you don't use the OpenCV feature.

But if you still want to install it, please enter the following command:

pip install opencv-python

Verify Whether OpenCV Installation Success

Enter the following code to enter the Python interactive programming mode:

python import cv2 print(cv2.__version__)

Donkey Car Detailed Explanation (2) – Raspberry Pi Installation

Donkey Car Detailed Explanation (2) – Raspberry Pi Installation

robocarstore/173807615525023113

Now the official Donkey Car is no longer provided with a lazy person's version, so this time we will share the process of installing Donkey Car on a Raspberry Pi (Raspberry Pi 3 B+).

Reference: http://docs.donkeycar.com/guide/robot_sbc/setup_raspberry_pi/

Environment

The official recommendation is to use Raspian Lite(Stretch) (352MB). However, I used the desktop version, as follows:

Raspberry Pi OS:
– Raspbian Buster with desktop
Version:July 2019
Release date:2019-07-10
Kernel version:4.19

Download Url:

https://www.raspberrypi.org/downloads/raspbian/

How to flash install Raspberry Pi, here is no more.

Assume you have already installed the Raspberry Pi system and can connect to the network, you can choose to connect to the display directly (this example's operation mode), or remotely SSH. You like it.

Open the command line, let's start!

donkeycar-rpi

Start Installation

1，Update your system before installation

To update your system, enter the following command:

Updating the system takes a long time and may need to be repeated multiple times to complete. Please be patient.

2，Configure Raspberry Pi to enable camera, I2C, expand file system

To open the Raspberry Pi configuration dialog, enter the following command in the command line:

① Enable I2C

05 Interfacing Options -> P5 I2C ->「Yes」

rpi_i2c

② Enable Camera:

05 Interfacing Options -> P1 Camera -> 「Yes」

robocarstore/173807630025267416

③ Expand File System

7 Advanced Options -> A1 Exapand Filesystem

robocarstore/173807631425048117

Set it up, then exit the configuration dialog, it will prompt whether to restart now, select 「Yes」 to restart the system.

robocarstore/173807632825076218

3，Install the dependencies required for Donkey Car

Enter the following command in the command line:

sudo apt-get install -y build-essential python3 python3-dev python3-pip python3-virtualenv python3-numpy python3-picamera python3-pandas python3-rpi.gpio i2c-tools avahi-utils joystick libopenjp2-7-dev libtiff5-dev gfortran libatlas-base-dev libopenblas-dev libhdf5-serial-dev git

After a long wait, it will be installed, if an error occurs, please run it again.

（If the problem is still not resolved, please share the problem you encountered in the comments section, and post your system version, as much information as possible, so we can help you.）

4，Install OpenCV dependencies (optional)

This step is optional, you can skip it if you don't use the OpenCV feature.

If you still want to install it, enter the following command in the command line:

sudo apt-get install libilmbase-dev libopenexr-dev libgstreamer1.0-dev libjasper-dev libwebp-dev libatlas-base-dev libavcodec-dev libavformat-dev libswscale-dev libqtgui4 libqt4-test

5，Install Virtual Environment

Enter the following command in the command line:

python3 -m virtualenv -p python3 env --system-site-packages

echo "source env/bin/activate" >> ~/.bashrc

After running the above commands, the command line will have a (env) prefix, indicating that we are operating in an environment called env.

robocarstore/173807634625230519

The purpose of the virtual environment is to avoid errors in other projects when updating a dependency or automatically clearing a dependency on the Raspberry Pi running multiple projects.

Now, every time the Raspberry Pi starts, it will automatically enter the env virtual environment. If you want to exit, enter the command line: deactivate .

6，Install Donkey Car's Python Code

Create Directory, and Enter the Directory

In the user directory, create a folder named projects (directory), and enter the folder, enter the following command:

Note: Directly enter the user directory, just enter the command line: cd , press Enter.

Clone the entire Donkey car project from GitHub

Enter the following command:

git clone https://github.com/autorope/donkeycar

Then, start a long wait, because the speed of github in China is not fast, please be patient, if it doesn't work, run it again.

Note: If you are familiar with command line instructions, you can use the scp command to download some software projects after packaging, and upload them to the current directory of the Raspberry Pi. For specific usage, please search for it yourself.

When git clone (clone) is complete, we use the following command to operate:

pip install tensorflow==1.13.1

Note:
– When running pip install -e .[pi] installation, if an error occurs, you can try to run it again.
– When running pip install tensorflow==1.13.1 , it may prompt that the downloaded file's hash code is incorrect, causing it to be unable to download. You can manually use the wget command to download it according to the error prompt, download it, and then use pip install 「downloaded file name」 to install it.

Verify Tensorflow Installation Success

Enter the following command to enter the Python interactive programming mode:

Enter the following code to check:

print(tensorflow.__version__)

robocarstore/173807637225182120

When you see the version 1.13.1, it proves that you have successfully installed Tensorflow.

Note: Python will have some prompts when loading tensorflow for the first time, please don't worry, this is not an error prompt.

7，Install Python's OpenCV (optional)

Install OpenCV

This is also not required to be installed, if you don't use the OpenCV feature.

But if you still want to install it, please enter the following command:

pip install opencv-python

Verify Whether OpenCV Installation Success

Enter the following code to enter the Python interactive programming mode:

python import cv2 print(cv2.__version__)

Use Colab to Accelerate Donkey Car Training (Tensorflow GPU)

colab

This notebook helps you quickly train your Donkey car or autonomous car model.

Reference and improved the notebook of @sachindroid8, thanks to the previous generation!

The Pros and Cons of Using Colab for Training

  1. Efficiency

Using Colab requires a VPN, and also needs to upload data to Google, the upload time varies from person to person, I uploaded it for about 2-3 minutes, the rest is executing the code and training. The first execution of the code needs to understand what the principle is, and the subsequent execution basically does not take much time, but the training time using GPU, I have 6k images, training about 30-40 epochs early, the training time is less than 1 minute! Each Epoch only needs 1-2 seconds.

Then using my Macbook Pro training, because of the lack of GPU, each Epoch needs 30-50 seconds, training down, it takes about 40 minutes to 1 hour to complete.

  1. Convenience

If you don't have a VPN, of course, Colab is not a choice, I know Baidu also provides some limited free servers, which can be tested later, but with a VPN, Google Colab is the best choice. Below is how to start training

Load My Notebook

Open the Using Google_Colab to Donkey_Car

Install TensorFlow 1.14.0

TensorFlow 2.x and Donkey Car 3.x still have compatibility issues, so it is not recommended to use them temporarily (2019.8.17)In [1]:

!pip install tensorflow-gpu==1.14.0

Collecting tensorflow-gpu==1.14.0
  Downloading https://files.pythonhosted.org/packages/76/04/43153bfdfcf6c9a4c38ecdb971ca9a75b9a791bb69a764d652c359aca504/tensorflow_gpu-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (377.0MB)
     |████████████████████████████████| 377.0MB 86kB/s 
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.15.0)
Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.7.1)
Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.14.0)
Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.11.2)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.1.0)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.8.0)
Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.0.8)
Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.14.0)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.33.4)
Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (3.7.1)
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.12.0)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.1.0)
Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.1.7)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.2.2)
Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.16.4)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==1.14.0) (2.8.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (3.1.1)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (0.15.5)
Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (41.0.1)
Installing collected packages: tensorflow-gpu
Successfully installed tensorflow-gpu-1.14.0

Check if GPU is working

If you see “Found GPU at: / device: GPU: 0”, then the GPU is working normally

If you don't see the above output, you need to check if the Runtime (Run Type) has selected the GPU hardware acceleratorIn [2]:

device_name = tf.test.gpu_device_name()

if device_name != '/device:GPU:0':

raise SystemError('GPU device not found')

print('Found GPU at: {}'.format(device_name))

Found GPU at: /device:GPU:0

克隆Donkey respository

In [3]:

!git clone https://github.com/autorope/donkeycar.git donkey

Cloning into 'donkey'...
remote: Enumerating objects: 120, done.
remote: Counting objects: 100% (120/120), done.
remote: Compressing objects: 100% (76/76), done.
remote: Total 10558 (delta 65), reused 73 (delta 31), pack-reused 10438
Receiving objects: 100% (10558/10558), 58.74 MiB | 46.70 MiB/s, done.
Resolving deltas: 100% (6528/6528), done.

Install Donkey car

In [4]:

Obtaining file:///content/donkey
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (1.16.4)
Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (4.3.0)
Requirement already satisfied: docopt in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.6.2)
Requirement already satisfied: tornado in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (4.5.3)
Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (2.21.0)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (2.8.0)
Requirement already satisfied: moviepy in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.2.3.5)
Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.24.2)
Requirement already satisfied: PrettyTable in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.7.2)
Collecting paho-mqtt (from donkeycar==3.1.0)
  Downloading https://files.pythonhosted.org/packages/25/63/db25e62979c2a716a74950c9ed658dce431b5cb01fde29eb6cba9489a904/paho-mqtt-1.4.0.tar.gz (88kB)
     |████████████████████████████████| 92kB 4.2MB/s 
Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->donkeycar==3.1.0) (0.46)
Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (1.24.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (2019.6.16)
Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (3.0.4)
Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (2.8)
Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from h5py->donkeycar==3.1.0) (1.12.0)
Requirement already satisfied: decorator<5.0,>=4.0.2 in /usr/local/lib/python3.6/dist-packages (from moviepy->donkeycar==3.1.0) (4.4.0)
Requirement already satisfied: tqdm<5.0,>=4.11.2 in /usr/local/lib/python3.6/dist-packages (from moviepy->donkeycar==3.1.0) (4.28.1)
Requirement already satisfied: imageio<3.0,>=2.1.2 in /usr/local/lib/python3.6/dist-packages (from moviepy->donkeycar==3.1.0) (2.4.1)
Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->donkeycar==3.1.0) (2018.9)
Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas->donkeycar==3.1.0) (2.5.3)
Building wheels for collected packages: paho-mqtt
  Building wheel for paho-mqtt (setup.py) ... done
  Created wheel for paho-mqtt: filename=paho_mqtt-1.4.0-cp36-none-any.whl size=48333 sha256=9a67d0c95fae2b9495c20980895d9569ef53859cbc22bb57fc2466d7a581af7b
  Stored in directory: /root/.cache/pip/wheels/82/e5/de/d90d0f397648a1b58ffeea1b5742ac8c77f71fd43b550fa5a5
Successfully built paho-mqtt
Installing collected packages: paho-mqtt, donkeycar
  Running setup.py develop for donkeycar
Successfully installed donkeycar paho-mqtt-1.4.0

Create a project

I used mycar as the project name, you can change it, but you need to modify the code after renamingIn [5]:

!donkey createcar --path /content/mycar

using donkey v3.1.0 ...
Creating car folder: /content/mycar
making dir  /content/mycar
Creating data & model folders.
making dir  /content/mycar/models
making dir  /content/mycar/data
making dir  /content/mycar/logs
Copying car application template: complete
Copying car config defaults. Adjust these before starting your car.
Copying train script. Adjust these before starting your car.
Copying my car config overrides
Donkey setup complete.

Prepare data: Upload data.zip and unzip

Now you need to package the data directory that you collected from pi, save it as data.zip.

Run the following command on the pi data directory:

$ zip -r data.zip tub_3_19-08-17/

Then copy it back to your computer, ready to upload to Colab

Upload data.zip to Colab

Run the following code, you will see an upload button, click to upload the data.zip you just packagedIn [7]:

from google.colab import files

if(os.path.exists("/content/data.zip")):

os.remove("/content/data.zip")

if(os.path.exists("/content/mycar/data/data.zip")):

os.remove("/content/mycar/data/data.zip")

uploaded = files.upload()

WORK_FOLDER = "/content/mycar/data/"

if(os.path.exists(WORK_FOLDER) == False):

!mv /content/data.zip /content/mycar/data/

Clean up the uploaded files

You need to ensure that the content/mycar/data directory has a tub directory, and the directory contains images and corresponding json files

data.zip is not neededIn [0]:

!rm /content/mycar/data/data.zip

Train the model

In [21]:

!python /content/mycar/manage.py train --model /content/mycar/models/mypilot.h5

using donkey v3.1.0 ...
loading config file: /content/mycar/config.py
loading personal config over-rides

config loaded
WARNING: Logging before flag parsing goes to stderr.
W0818 04:53:15.944086 140399927523200 deprecation_wrapper.py:119] From /content/donkey/donkeycar/parts/keras.py:18: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

W0818 04:53:15.944333 140399927523200 deprecation_wrapper.py:119] From /content/donkey/donkeycar/parts/keras.py:18: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2019-08-18 04:53:15.954857: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-08-18 04:53:15.959505: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-08-18 04:53:16.083690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.084227: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2455100 executing computations on platform CUDA. Devices:
2019-08-18 04:53:16.084262: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
2019-08-18 04:53:16.086194: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
2019-08-18 04:53:16.086460: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x65e6380 executing computations on platform Host. Devices:
2019-08-18 04:53:16.086508: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-08-18 04:53:16.086686: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.087039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:04.0
2019-08-18 04:53:16.087321: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-08-18 04:53:16.088548: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-08-18 04:53:16.089621: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-08-18 04:53:16.089935: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-08-18 04:53:16.091399: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-08-18 04:53:16.092394: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-08-18 04:53:16.095470: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-08-18 04:53:16.095606: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.096009: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.096350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-08-18 04:53:16.096401: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-08-18 04:53:16.097166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-08-18 04:53:16.097190: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
2019-08-18 04:53:16.097201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
2019-08-18 04:53:16.097482: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.097878: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.098226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14089 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)
"get_model_by_type" model Type is: linear
W0818 04:53:16.444941 140399927523200 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
training with model type <class 'donkeycar.parts.keras.KerasLinear'>
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
img_in (InputLayer)             [(None, 120, 160, 3) 0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 58, 78, 24)   1824        img_in[0][0]                     
__________________________________________________________________________________________________
dropout (Dropout)               (None, 58, 78, 24)   0           conv2d_1[0][0]                   
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 27, 37, 32)   19232       dropout[0][0]                    
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 27, 37, 32)   0           conv2d_2[0][0]                   
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 12, 17, 64)   51264       dropout_1[0][0]                  
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 12, 17, 64)   0           conv2d_3[0][0]                   
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 10, 15, 64)   36928       dropout_2[0][0]                  
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 10, 15, 64)   0           conv2d_4[0][0]                   
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 8, 13, 64)    36928       dropout_3[0][0]                  
__________________________________________________________________________________________________
dropout_4 (Dropout)             (None, 8, 13, 64)    0           conv2d_5[0][0]                   
__________________________________________________________________________________________________
flattened (Flatten)             (None, 6656)         0           dropout_4[0][0]                  
__________________________________________________________________________________________________
dense (Dense)                   (None, 100)          665700      flattened[0][0]                  
__________________________________________________________________________________________________
dropout_5 (Dropout)             (None, 100)          0           dense[0][0]                      
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 50)           5050        dropout_5[0][0]                  
__________________________________________________________________________________________________
dropout_6 (Dropout)             (None, 50)           0           dense_1[0][0]                    
__________________________________________________________________________________________________
n_outputs0 (Dense)              (None, 1)            51          dropout_6[0][0]                  
__________________________________________________________________________________________________
n_outputs1 (Dense)              (None, 1)            51          dropout_6[0][0]                  
==================================================================================================
Total params: 817,028
Trainable params: 817,028
Non-trainable params: 0
__________________________________________________________________________________________________
None
found 0 pickles writing json records and images in tub /content/mycar/data/tub_3_19-08-17
/content/mycar/data/tub_3_19-08-17
collating 5799 records ...
train: 4639, val: 1160
total records: 5799
steps_per_epoch 36
Epoch 1/100
2019-08-18 04:53:19.663907: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-08-18 04:53:19.984999: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
35/36 [============================>.] - ETA: 0s - loss: 0.1681 - n_outputs0_loss: 0.1555 - n_outputs1_loss: 0.0126
Epoch 00001: val_loss improved from inf to 0.14482, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 8s 213ms/step - loss: 0.1677 - n_outputs0_loss: 0.1553 - n_outputs1_loss: 0.0124 - val_loss: 0.1448 - val_n_outputs0_loss: 0.1409 - val_n_outputs1_loss: 0.0039
Epoch 2/100
34/36 [===========================>..] - ETA: 0s - loss: 0.1278 - n_outputs0_loss: 0.1234 - n_outputs1_loss: 0.0044
Epoch 00002: val_loss improved from 0.14482 to 0.08814, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 53ms/step - loss: 0.1257 - n_outputs0_loss: 0.1214 - n_outputs1_loss: 0.0043 - val_loss: 0.0881 - val_n_outputs0_loss: 0.0870 - val_n_outputs1_loss: 0.0012
Epoch 3/100
35/36 [============================>.] - ETA: 0s - loss: 0.0943 - n_outputs0_loss: 0.0910 - n_outputs1_loss: 0.0033
Epoch 00003: val_loss improved from 0.08814 to 0.07490, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0938 - n_outputs0_loss: 0.0905 - n_outputs1_loss: 0.0033 - val_loss: 0.0749 - val_n_outputs0_loss: 0.0738 - val_n_outputs1_loss: 0.0011
Epoch 4/100
35/36 [============================>.] - ETA: 0s - loss: 0.0840 - n_outputs0_loss: 0.0813 - n_outputs1_loss: 0.0027
Epoch 00004: val_loss improved from 0.07490 to 0.07108, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 51ms/step - loss: 0.0835 - n_outputs0_loss: 0.0808 - n_outputs1_loss: 0.0027 - val_loss: 0.0711 - val_n_outputs0_loss: 0.0702 - val_n_outputs1_loss: 8.9668e-04
Epoch 5/100
35/36 [============================>.] - ETA: 0s - loss: 0.0767 - n_outputs0_loss: 0.0741 - n_outputs1_loss: 0.0026
Epoch 00005: val_loss did not improve from 0.07108
36/36 [==============================] - 2s 50ms/step - loss: 0.0765 - n_outputs0_loss: 0.0739 - n_outputs1_loss: 0.0026 - val_loss: 0.0722 - val_n_outputs0_loss: 0.0717 - val_n_outputs1_loss: 5.4629e-04
Epoch 6/100
35/36 [============================>.] - ETA: 0s - loss: 0.0750 - n_outputs0_loss: 0.0727 - n_outputs1_loss: 0.0023
Epoch 00006: val_loss improved from 0.07108 to 0.06483, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0749 - n_outputs0_loss: 0.0725 - n_outputs1_loss: 0.0024 - val_loss: 0.0648 - val_n_outputs0_loss: 0.0641 - val_n_outputs1_loss: 7.5336e-04
Epoch 7/100
35/36 [============================>.] - ETA: 0s - loss: 0.0711 - n_outputs0_loss: 0.0689 - n_outputs1_loss: 0.0022
Epoch 00007: val_loss improved from 0.06483 to 0.06324, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0711 - n_outputs0_loss: 0.0689 - n_outputs1_loss: 0.0021 - val_loss: 0.0632 - val_n_outputs0_loss: 0.0626 - val_n_outputs1_loss: 5.9058e-04
Epoch 8/100
35/36 [============================>.] - ETA: 0s - loss: 0.0693 - n_outputs0_loss: 0.0675 - n_outputs1_loss: 0.0018
Epoch 00008: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0690 - n_outputs0_loss: 0.0672 - n_outputs1_loss: 0.0018 - val_loss: 0.0644 - val_n_outputs0_loss: 0.0637 - val_n_outputs1_loss: 7.8261e-04
Epoch 9/100
35/36 [============================>.] - ETA: 0s - loss: 0.0693 - n_outputs0_loss: 0.0675 - n_outputs1_loss: 0.0018
Epoch 00009: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 50ms/step - loss: 0.0690 - n_outputs0_loss: 0.0672 - n_outputs1_loss: 0.0018 - val_loss: 0.0653 - val_n_outputs0_loss: 0.0646 - val_n_outputs1_loss: 6.6657e-04
Epoch 10/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0651 - n_outputs0_loss: 0.0633 - n_outputs1_loss: 0.0018
Epoch 00010: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0657 - n_outputs0_loss: 0.0639 - n_outputs1_loss: 0.0018 - val_loss: 0.0699 - val_n_outputs0_loss: 0.0692 - val_n_outputs1_loss: 6.9729e-04
Epoch 11/100
35/36 [============================>.] - ETA: 0s - loss: 0.0630 - n_outputs0_loss: 0.0612 - n_outputs1_loss: 0.0018
Epoch 00011: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0630 - n_outputs0_loss: 0.0612 - n_outputs1_loss: 0.0018 - val_loss: 0.0633 - val_n_outputs0_loss: 0.0619 - val_n_outputs1_loss: 0.0014
Epoch 12/100
35/36 [============================>.] - ETA: 0s - loss: 0.0643 - n_outputs0_loss: 0.0626 - n_outputs1_loss: 0.0016
Epoch 00012: val_loss improved from 0.06324 to 0.06034, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0644 - n_outputs0_loss: 0.0628 - n_outputs1_loss: 0.0017 - val_loss: 0.0603 - val_n_outputs0_loss: 0.0598 - val_n_outputs1_loss: 4.9525e-04
Epoch 13/100
35/36 [============================>.] - ETA: 0s - loss: 0.0611 - n_outputs0_loss: 0.0597 - n_outputs1_loss: 0.0014
Epoch 00013: val_loss improved from 0.06034 to 0.05636, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0613 - n_outputs0_loss: 0.0599 - n_outputs1_loss: 0.0014 - val_loss: 0.0564 - val_n_outputs0_loss: 0.0557 - val_n_outputs1_loss: 6.3083e-04
Epoch 14/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0593 - n_outputs0_loss: 0.0580 - n_outputs1_loss: 0.0013
Epoch 00014: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 51ms/step - loss: 0.0590 - n_outputs0_loss: 0.0577 - n_outputs1_loss: 0.0013 - val_loss: 0.0580 - val_n_outputs0_loss: 0.0575 - val_n_outputs1_loss: 5.4033e-04
Epoch 15/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0552 - n_outputs0_loss: 0.0540 - n_outputs1_loss: 0.0012
Epoch 00015: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 51ms/step - loss: 0.0555 - n_outputs0_loss: 0.0542 - n_outputs1_loss: 0.0012 - val_loss: 0.0565 - val_n_outputs0_loss: 0.0559 - val_n_outputs1_loss: 5.6679e-04
Epoch 16/100
35/36 [============================>.] - ETA: 0s - loss: 0.0538 - n_outputs0_loss: 0.0527 - n_outputs1_loss: 0.0012
Epoch 00016: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 50ms/step - loss: 0.0538 - n_outputs0_loss: 0.0526 - n_outputs1_loss: 0.0012 - val_loss: 0.0624 - val_n_outputs0_loss: 0.0619 - val_n_outputs1_loss: 5.0014e-04
Epoch 17/100
35/36 [============================>.] - ETA: 0s - loss: 0.0536 - n_outputs0_loss: 0.0525 - n_outputs1_loss: 0.0011
Epoch 00017: val_loss improved from 0.05636 to 0.05615, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 53ms/step - loss: 0.0534 - n_outputs0_loss: 0.0523 - n_outputs1_loss: 0.0011 - val_loss: 0.0561 - val_n_outputs0_loss: 0.0556 - val_n_outputs1_loss: 5.2067e-04
Epoch 18/100
35/36 [============================>.] - ETA: 0s - loss: 0.0502 - n_outputs0_loss: 0.0492 - n_outputs1_loss: 0.0011
Epoch 00018: val_loss improved from 0.05615 to 0.05594, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0501 - n_outputs0_loss: 0.0490 - n_outputs1_loss: 0.0011 - val_loss: 0.0559 - val_n_outputs0_loss: 0.0555 - val_n_outputs1_loss: 4.4089e-04
Epoch 00018: early stopping
Training completed in 0:00:40.


----------- Best Eval Loss :0.055939 ---------
<Figure size 640x480 with 1 Axes>

Display the Loss curve of the training result

In [26]:

import matplotlib.pyplot as plt

list_of_png = glob.glob('/content/mycar/models/*png')

latest_png = max(list_of_png, key=os.path.getctime)

image = cv2.imread(latest_png)

/content/mycar/models/mypilot.h5_loss_acc_0.055939.png

Out[26]:

<matplotlib.image.AxesImage at 0x7f867c896be0>

robocarstore/173807730625008222

Put the trained model back into Donkey Car.

Once the model is trained, you can find the trained model in the mycar/models directory
1. Download the mypilot file to your PC or Mac

  1. Copy the model from your PC or Mac to your pi

Finally, use the Autopilot mode to drive Donkey car

Enjoy!

Use Colab to Accelerate Donkey Car Training (Tensorflow GPU)

colab

This notebook helps you quickly train your Donkey car or autonomous car model.

Reference and improved the notebook of @sachindroid8, thanks to the previous generation!

The Pros and Cons of Using Colab for Training

  1. Efficiency

Using Colab requires a VPN, and also needs to upload data to Google, the upload time varies from person to person, I uploaded it for about 2-3 minutes, the rest is executing the code and training. The first execution of the code needs to understand what the principle is, and the subsequent execution basically does not take much time, but the training time using GPU, I have 6k images, training about 30-40 epochs early, the training time is less than 1 minute! Each Epoch only needs 1-2 seconds.

Then using my Macbook Pro training, because of the lack of GPU, each Epoch needs 30-50 seconds, training down, it takes about 40 minutes to 1 hour to complete.

  1. Convenience

If you don't have a VPN, of course, Colab is not a choice, I know Baidu also provides some limited free servers, which can be tested later, but with a VPN, Google Colab is the best choice. Below is how to start training

Load My Notebook

Open the Using Google_Colab to Donkey_Car

Install TensorFlow 1.14.0

TensorFlow 2.x and Donkey Car 3.x still have compatibility issues, so it is not recommended to use them temporarily (2019.8.17)In [1]:

!pip install tensorflow-gpu==1.14.0

Collecting tensorflow-gpu==1.14.0
  Downloading https://files.pythonhosted.org/packages/76/04/43153bfdfcf6c9a4c38ecdb971ca9a75b9a791bb69a764d652c359aca504/tensorflow_gpu-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (377.0MB)
     |████████████████████████████████| 377.0MB 86kB/s 
Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.15.0)
Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.7.1)
Requirement already satisfied: tensorflow-estimator<1.15.0rc0,>=1.14.0rc0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.14.0)
Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.11.2)
Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.1.0)
Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.8.0)
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Requirement already satisfied: tensorboard<1.15.0,>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.14.0)
Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.33.4)
Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (3.7.1)
Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.12.0)
Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.1.0)
Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.1.7)
Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (0.2.2)
Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==1.14.0) (1.16.4)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==1.14.0) (2.8.0)
Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (3.1.1)
Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (0.15.5)
Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow-gpu==1.14.0) (41.0.1)
Installing collected packages: tensorflow-gpu
Successfully installed tensorflow-gpu-1.14.0

Check if GPU is working

If you see “Found GPU at: / device: GPU: 0”, then the GPU is working normally

If you don't see the above output, you need to check if the Runtime (Run Type) has selected the GPU hardware acceleratorIn [2]:

device_name = tf.test.gpu_device_name()

if device_name != '/device:GPU:0':

raise SystemError('GPU device not found')

print('Found GPU at: {}'.format(device_name))

Found GPU at: /device:GPU:0

克隆Donkey respository

In [3]:

!git clone https://github.com/autorope/donkeycar.git donkey

Cloning into 'donkey'...
remote: Enumerating objects: 120, done.
remote: Counting objects: 100% (120/120), done.
remote: Compressing objects: 100% (76/76), done.
remote: Total 10558 (delta 65), reused 73 (delta 31), pack-reused 10438
Receiving objects: 100% (10558/10558), 58.74 MiB | 46.70 MiB/s, done.
Resolving deltas: 100% (6528/6528), done.

Install Donkey car

In [4]:

Obtaining file:///content/donkey
Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (1.16.4)
Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (4.3.0)
Requirement already satisfied: docopt in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.6.2)
Requirement already satisfied: tornado in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (4.5.3)
Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (2.21.0)
Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (2.8.0)
Requirement already satisfied: moviepy in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.2.3.5)
Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.24.2)
Requirement already satisfied: PrettyTable in /usr/local/lib/python3.6/dist-packages (from donkeycar==3.1.0) (0.7.2)
Collecting paho-mqtt (from donkeycar==3.1.0)
  Downloading https://files.pythonhosted.org/packages/25/63/db25e62979c2a716a74950c9ed658dce431b5cb01fde29eb6cba9489a904/paho-mqtt-1.4.0.tar.gz (88kB)
     |████████████████████████████████| 92kB 4.2MB/s 
Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow->donkeycar==3.1.0) (0.46)
Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (1.24.3)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->donkeycar==3.1.0) (2019.6.16)
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Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->donkeycar==3.1.0) (2018.9)
Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas->donkeycar==3.1.0) (2.5.3)
Building wheels for collected packages: paho-mqtt
  Building wheel for paho-mqtt (setup.py) ... done
  Created wheel for paho-mqtt: filename=paho_mqtt-1.4.0-cp36-none-any.whl size=48333 sha256=9a67d0c95fae2b9495c20980895d9569ef53859cbc22bb57fc2466d7a581af7b
  Stored in directory: /root/.cache/pip/wheels/82/e5/de/d90d0f397648a1b58ffeea1b5742ac8c77f71fd43b550fa5a5
Successfully built paho-mqtt
Installing collected packages: paho-mqtt, donkeycar
  Running setup.py develop for donkeycar
Successfully installed donkeycar paho-mqtt-1.4.0

Create a project

I used mycar as the project name, you can change it, but you need to modify the code after renamingIn [5]:

!donkey createcar --path /content/mycar

using donkey v3.1.0 ...
Creating car folder: /content/mycar
making dir  /content/mycar
Creating data & model folders.
making dir  /content/mycar/models
making dir  /content/mycar/data
making dir  /content/mycar/logs
Copying car application template: complete
Copying car config defaults. Adjust these before starting your car.
Copying train script. Adjust these before starting your car.
Copying my car config overrides
Donkey setup complete.

Prepare data: Upload data.zip and unzip

Now you need to package the data directory that you collected from pi, save it as data.zip.

Run the following command on the pi data directory:

$ zip -r data.zip tub_3_19-08-17/

Then copy it back to your computer, ready to upload to Colab

Upload data.zip to Colab

Run the following code, you will see an upload button, click to upload the data.zip you just packagedIn [7]:

from google.colab import files

if(os.path.exists("/content/data.zip")):

os.remove("/content/data.zip")

if(os.path.exists("/content/mycar/data/data.zip")):

os.remove("/content/mycar/data/data.zip")

uploaded = files.upload()

WORK_FOLDER = "/content/mycar/data/"

if(os.path.exists(WORK_FOLDER) == False):

!mv /content/data.zip /content/mycar/data/

Clean up the uploaded files

You need to ensure that the content/mycar/data directory has a tub directory, and the directory contains images and corresponding json files

data.zip is not neededIn [0]:

!rm /content/mycar/data/data.zip

Train the model

In [21]:

!python /content/mycar/manage.py train --model /content/mycar/models/mypilot.h5

using donkey v3.1.0 ...
loading config file: /content/mycar/config.py
loading personal config over-rides

config loaded
WARNING: Logging before flag parsing goes to stderr.
W0818 04:53:15.944086 140399927523200 deprecation_wrapper.py:119] From /content/donkey/donkeycar/parts/keras.py:18: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.

W0818 04:53:15.944333 140399927523200 deprecation_wrapper.py:119] From /content/donkey/donkeycar/parts/keras.py:18: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2019-08-18 04:53:15.954857: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-08-18 04:53:15.959505: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-08-18 04:53:16.083690: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.084227: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x2455100 executing computations on platform CUDA. Devices:
2019-08-18 04:53:16.084262: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): Tesla T4, Compute Capability 7.5
2019-08-18 04:53:16.086194: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz
2019-08-18 04:53:16.086460: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x65e6380 executing computations on platform Host. Devices:
2019-08-18 04:53:16.086508: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-08-18 04:53:16.086686: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.087039: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties: 
name: Tesla T4 major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:00:04.0
2019-08-18 04:53:16.087321: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-08-18 04:53:16.088548: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-08-18 04:53:16.089621: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcufft.so.10.0
2019-08-18 04:53:16.089935: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcurand.so.10.0
2019-08-18 04:53:16.091399: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusolver.so.10.0
2019-08-18 04:53:16.092394: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcusparse.so.10.0
2019-08-18 04:53:16.095470: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-08-18 04:53:16.095606: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.096009: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.096350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1763] Adding visible gpu devices: 0
2019-08-18 04:53:16.096401: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudart.so.10.0
2019-08-18 04:53:16.097166: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-08-18 04:53:16.097190: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]      0 
2019-08-18 04:53:16.097201: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1200] 0:   N 
2019-08-18 04:53:16.097482: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.097878: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-08-18 04:53:16.098226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1326] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14089 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)
"get_model_by_type" model Type is: linear
W0818 04:53:16.444941 140399927523200 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
training with model type <class 'donkeycar.parts.keras.KerasLinear'>
Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
img_in (InputLayer)             [(None, 120, 160, 3) 0                                            
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (None, 58, 78, 24)   1824        img_in[0][0]                     
__________________________________________________________________________________________________
dropout (Dropout)               (None, 58, 78, 24)   0           conv2d_1[0][0]                   
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (None, 27, 37, 32)   19232       dropout[0][0]                    
__________________________________________________________________________________________________
dropout_1 (Dropout)             (None, 27, 37, 32)   0           conv2d_2[0][0]                   
__________________________________________________________________________________________________
conv2d_3 (Conv2D)               (None, 12, 17, 64)   51264       dropout_1[0][0]                  
__________________________________________________________________________________________________
dropout_2 (Dropout)             (None, 12, 17, 64)   0           conv2d_3[0][0]                   
__________________________________________________________________________________________________
conv2d_4 (Conv2D)               (None, 10, 15, 64)   36928       dropout_2[0][0]                  
__________________________________________________________________________________________________
dropout_3 (Dropout)             (None, 10, 15, 64)   0           conv2d_4[0][0]                   
__________________________________________________________________________________________________
conv2d_5 (Conv2D)               (None, 8, 13, 64)    36928       dropout_3[0][0]                  
__________________________________________________________________________________________________
dropout_4 (Dropout)             (None, 8, 13, 64)    0           conv2d_5[0][0]                   
__________________________________________________________________________________________________
flattened (Flatten)             (None, 6656)         0           dropout_4[0][0]                  
__________________________________________________________________________________________________
dense (Dense)                   (None, 100)          665700      flattened[0][0]                  
__________________________________________________________________________________________________
dropout_5 (Dropout)             (None, 100)          0           dense[0][0]                      
__________________________________________________________________________________________________
dense_1 (Dense)                 (None, 50)           5050        dropout_5[0][0]                  
__________________________________________________________________________________________________
dropout_6 (Dropout)             (None, 50)           0           dense_1[0][0]                    
__________________________________________________________________________________________________
n_outputs0 (Dense)              (None, 1)            51          dropout_6[0][0]                  
__________________________________________________________________________________________________
n_outputs1 (Dense)              (None, 1)            51          dropout_6[0][0]                  
==================================================================================================
Total params: 817,028
Trainable params: 817,028
Non-trainable params: 0
__________________________________________________________________________________________________
None
found 0 pickles writing json records and images in tub /content/mycar/data/tub_3_19-08-17
/content/mycar/data/tub_3_19-08-17
collating 5799 records ...
train: 4639, val: 1160
total records: 5799
steps_per_epoch 36
Epoch 1/100
2019-08-18 04:53:19.663907: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcublas.so.10.0
2019-08-18 04:53:19.984999: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
35/36 [============================>.] - ETA: 0s - loss: 0.1681 - n_outputs0_loss: 0.1555 - n_outputs1_loss: 0.0126
Epoch 00001: val_loss improved from inf to 0.14482, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 8s 213ms/step - loss: 0.1677 - n_outputs0_loss: 0.1553 - n_outputs1_loss: 0.0124 - val_loss: 0.1448 - val_n_outputs0_loss: 0.1409 - val_n_outputs1_loss: 0.0039
Epoch 2/100
34/36 [===========================>..] - ETA: 0s - loss: 0.1278 - n_outputs0_loss: 0.1234 - n_outputs1_loss: 0.0044
Epoch 00002: val_loss improved from 0.14482 to 0.08814, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 53ms/step - loss: 0.1257 - n_outputs0_loss: 0.1214 - n_outputs1_loss: 0.0043 - val_loss: 0.0881 - val_n_outputs0_loss: 0.0870 - val_n_outputs1_loss: 0.0012
Epoch 3/100
35/36 [============================>.] - ETA: 0s - loss: 0.0943 - n_outputs0_loss: 0.0910 - n_outputs1_loss: 0.0033
Epoch 00003: val_loss improved from 0.08814 to 0.07490, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0938 - n_outputs0_loss: 0.0905 - n_outputs1_loss: 0.0033 - val_loss: 0.0749 - val_n_outputs0_loss: 0.0738 - val_n_outputs1_loss: 0.0011
Epoch 4/100
35/36 [============================>.] - ETA: 0s - loss: 0.0840 - n_outputs0_loss: 0.0813 - n_outputs1_loss: 0.0027
Epoch 00004: val_loss improved from 0.07490 to 0.07108, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 51ms/step - loss: 0.0835 - n_outputs0_loss: 0.0808 - n_outputs1_loss: 0.0027 - val_loss: 0.0711 - val_n_outputs0_loss: 0.0702 - val_n_outputs1_loss: 8.9668e-04
Epoch 5/100
35/36 [============================>.] - ETA: 0s - loss: 0.0767 - n_outputs0_loss: 0.0741 - n_outputs1_loss: 0.0026
Epoch 00005: val_loss did not improve from 0.07108
36/36 [==============================] - 2s 50ms/step - loss: 0.0765 - n_outputs0_loss: 0.0739 - n_outputs1_loss: 0.0026 - val_loss: 0.0722 - val_n_outputs0_loss: 0.0717 - val_n_outputs1_loss: 5.4629e-04
Epoch 6/100
35/36 [============================>.] - ETA: 0s - loss: 0.0750 - n_outputs0_loss: 0.0727 - n_outputs1_loss: 0.0023
Epoch 00006: val_loss improved from 0.07108 to 0.06483, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0749 - n_outputs0_loss: 0.0725 - n_outputs1_loss: 0.0024 - val_loss: 0.0648 - val_n_outputs0_loss: 0.0641 - val_n_outputs1_loss: 7.5336e-04
Epoch 7/100
35/36 [============================>.] - ETA: 0s - loss: 0.0711 - n_outputs0_loss: 0.0689 - n_outputs1_loss: 0.0022
Epoch 00007: val_loss improved from 0.06483 to 0.06324, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0711 - n_outputs0_loss: 0.0689 - n_outputs1_loss: 0.0021 - val_loss: 0.0632 - val_n_outputs0_loss: 0.0626 - val_n_outputs1_loss: 5.9058e-04
Epoch 8/100
35/36 [============================>.] - ETA: 0s - loss: 0.0693 - n_outputs0_loss: 0.0675 - n_outputs1_loss: 0.0018
Epoch 00008: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0690 - n_outputs0_loss: 0.0672 - n_outputs1_loss: 0.0018 - val_loss: 0.0644 - val_n_outputs0_loss: 0.0637 - val_n_outputs1_loss: 7.8261e-04
Epoch 9/100
35/36 [============================>.] - ETA: 0s - loss: 0.0693 - n_outputs0_loss: 0.0675 - n_outputs1_loss: 0.0018
Epoch 00009: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 50ms/step - loss: 0.0690 - n_outputs0_loss: 0.0672 - n_outputs1_loss: 0.0018 - val_loss: 0.0653 - val_n_outputs0_loss: 0.0646 - val_n_outputs1_loss: 6.6657e-04
Epoch 10/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0651 - n_outputs0_loss: 0.0633 - n_outputs1_loss: 0.0018
Epoch 00010: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0657 - n_outputs0_loss: 0.0639 - n_outputs1_loss: 0.0018 - val_loss: 0.0699 - val_n_outputs0_loss: 0.0692 - val_n_outputs1_loss: 6.9729e-04
Epoch 11/100
35/36 [============================>.] - ETA: 0s - loss: 0.0630 - n_outputs0_loss: 0.0612 - n_outputs1_loss: 0.0018
Epoch 00011: val_loss did not improve from 0.06324
36/36 [==============================] - 2s 51ms/step - loss: 0.0630 - n_outputs0_loss: 0.0612 - n_outputs1_loss: 0.0018 - val_loss: 0.0633 - val_n_outputs0_loss: 0.0619 - val_n_outputs1_loss: 0.0014
Epoch 12/100
35/36 [============================>.] - ETA: 0s - loss: 0.0643 - n_outputs0_loss: 0.0626 - n_outputs1_loss: 0.0016
Epoch 00012: val_loss improved from 0.06324 to 0.06034, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0644 - n_outputs0_loss: 0.0628 - n_outputs1_loss: 0.0017 - val_loss: 0.0603 - val_n_outputs0_loss: 0.0598 - val_n_outputs1_loss: 4.9525e-04
Epoch 13/100
35/36 [============================>.] - ETA: 0s - loss: 0.0611 - n_outputs0_loss: 0.0597 - n_outputs1_loss: 0.0014
Epoch 00013: val_loss improved from 0.06034 to 0.05636, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0613 - n_outputs0_loss: 0.0599 - n_outputs1_loss: 0.0014 - val_loss: 0.0564 - val_n_outputs0_loss: 0.0557 - val_n_outputs1_loss: 6.3083e-04
Epoch 14/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0593 - n_outputs0_loss: 0.0580 - n_outputs1_loss: 0.0013
Epoch 00014: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 51ms/step - loss: 0.0590 - n_outputs0_loss: 0.0577 - n_outputs1_loss: 0.0013 - val_loss: 0.0580 - val_n_outputs0_loss: 0.0575 - val_n_outputs1_loss: 5.4033e-04
Epoch 15/100
34/36 [===========================>..] - ETA: 0s - loss: 0.0552 - n_outputs0_loss: 0.0540 - n_outputs1_loss: 0.0012
Epoch 00015: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 51ms/step - loss: 0.0555 - n_outputs0_loss: 0.0542 - n_outputs1_loss: 0.0012 - val_loss: 0.0565 - val_n_outputs0_loss: 0.0559 - val_n_outputs1_loss: 5.6679e-04
Epoch 16/100
35/36 [============================>.] - ETA: 0s - loss: 0.0538 - n_outputs0_loss: 0.0527 - n_outputs1_loss: 0.0012
Epoch 00016: val_loss did not improve from 0.05636
36/36 [==============================] - 2s 50ms/step - loss: 0.0538 - n_outputs0_loss: 0.0526 - n_outputs1_loss: 0.0012 - val_loss: 0.0624 - val_n_outputs0_loss: 0.0619 - val_n_outputs1_loss: 5.0014e-04
Epoch 17/100
35/36 [============================>.] - ETA: 0s - loss: 0.0536 - n_outputs0_loss: 0.0525 - n_outputs1_loss: 0.0011
Epoch 00017: val_loss improved from 0.05636 to 0.05615, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 53ms/step - loss: 0.0534 - n_outputs0_loss: 0.0523 - n_outputs1_loss: 0.0011 - val_loss: 0.0561 - val_n_outputs0_loss: 0.0556 - val_n_outputs1_loss: 5.2067e-04
Epoch 18/100
35/36 [============================>.] - ETA: 0s - loss: 0.0502 - n_outputs0_loss: 0.0492 - n_outputs1_loss: 0.0011
Epoch 00018: val_loss improved from 0.05615 to 0.05594, saving model to /content/mycar/models/mypilot.h5
36/36 [==============================] - 2s 52ms/step - loss: 0.0501 - n_outputs0_loss: 0.0490 - n_outputs1_loss: 0.0011 - val_loss: 0.0559 - val_n_outputs0_loss: 0.0555 - val_n_outputs1_loss: 4.4089e-04
Epoch 00018: early stopping
Training completed in 0:00:40.


----------- Best Eval Loss :0.055939 ---------
<Figure size 640x480 with 1 Axes>

Display the Loss curve of the training result

In [26]:

import matplotlib.pyplot as plt

list_of_png = glob.glob('/content/mycar/models/*png')

latest_png = max(list_of_png, key=os.path.getctime)

image = cv2.imread(latest_png)

/content/mycar/models/mypilot.h5_loss_acc_0.055939.png

Out[26]:

<matplotlib.image.AxesImage at 0x7f867c896be0>

robocarstore/173807730625008222

Put the trained model back into Donkey Car.

Once the model is trained, you can find the trained model in the mycar/models directory
1. Download the mypilot file to your PC or Mac

  1. Copy the model from your PC or Mac to your pi

Finally, use the Autopilot mode to drive Donkey car

Enjoy!

Introducing Donkey Car!

Donkey Car

Donkey Car is an open-source, small-scale DIY autonomous driving platform, and it might be your best entry-level AI learning platform. Donkey is a high-level autonomous driving library written in Python, characterized by its ability to help you quickly learn machine learning, experiment rapidly, and integrate applications of computer vision and machine learning tools (such as tornado, keras, tensorflow, opencv, etc.).

What is Donkey Car?

Donkey Car is a Python-based open-source project designed to help users build and train autonomous vehicles. It is intended to allow users to easily experiment with autonomous driving technology at home or in a lab. The hardware part of Donkey Car typically consists of a small remote-controlled car, a Raspberry Pi, a camera, and a battery. The software part is a powerful Python library that supports multiple machine learning frameworks.

Why Choose Donkey Car?

  1. Easy to Get Started: Donkey Car is designed to be very intuitive, allowing even those without programming experience to get started quickly. It provides detailed documentation and tutorials to guide users through the entire process from hardware assembly to software configuration.

  2. Community Support: As an open-source project, Donkey Car has an active community. Whatever issues you encounter, you can find answers or seek help within the community.

  3. Flexibility: Donkey Car supports multiple machine learning frameworks, such as TensorFlow and Keras, allowing users to choose the right tools for their experiments based on their needs.

  4. Cost-Effective: Compared to other autonomous driving platforms, Donkey Car is relatively low-cost, making it ideal for hobbyists and educational institutions.

How to Get Started?

To start using Donkey Car, you need to prepare the following materials:

  • A small remote-controlled car
  • Raspberry Pi
  • Camera
  • Battery
  • 3D printed car frame (optional)

Next, you can follow the official documentation to assemble the hardware and install the software. Donkey Car's official GitHub page provides detailed installation steps and example code to help you quickly build your own autonomous vehicle.

Conclusion

Donkey Car is a project well-suited for AI beginners. Through this platform, you can learn the basic principles of autonomous driving and master the fundamentals of machine learning and computer vision. Whether you want to enter the AI field or are simply interested in autonomous driving technology, Donkey Car is an ideal starting point.

I hope this article helps you better understand Donkey Car and inspires your interest in autonomous driving technology. Join the Donkey Car community and start your AI learning journey today!

Donkey Car Detailed Explanation (1) – Assembly

Donkey Car

Overview

Donkey Car is an AI autonomous driving model car project led by Adam Conway (Twitter) since October 2016. With the rise of AI and autonomous driving technologies in recent years, this project has attracted more enthusiasts, forming the DonkeyCar community.

This tutorial aims to help DonkeyCar beginners get started quickly. If you have any questions or suggestions, feel free to provide feedback.

Choosing a Chassis

The original Donkey Car was modified from the "Exceed" brand 1/16 four-wheel-drive toy car. The official documentation recommends the following models:

These cars have the same electronic drive modules, differing only in tires and body mounting methods. Note that the Desert Monster, Short Course Truck, and Blaze require an adapter to secure the Donkey Car motherboard and handle, which can be purchased or 3D printed. These cars are standard configurations, mostly plug-and-play. They come in brushed and brushless motor versions; the author suggests using the brushed motor version as it's easier to train, better for rough surfaces, and cheaper.

Later, enthusiasts began modifying 1/10 scale toy cars, making them larger and upgrading to the Donkey Car Pro version. The Pro version offers better performance but at a higher cost. Supported models include:

  • HobbyKing Trooper (non-Pro) Link
  • HobbyKing Mission-D Link
  • Tamaya TT01 or its clones, which are easy to buy but require assembly, posing some difficulty.

Fewer people use the Pro version due to documentation gaps, so if you start with Donkey Car Pro, you should have good hands-on skills and patience.

Required Parts

Donkey Car has two versions based on the mainboard: one using Raspberry Pi and the other using Jetson Nano. Apart from the mainboard, the required parts and software installation differ.

Option 1 – Purchase through Donkey Car official channels

If you're in the US, you can buy from the original author's Donkey store.

In Asia, you can choose Hong Kong's Robocar Store or mainland China's Robocar Store China.

Official kits or complete cars include:

PartApproximate Cost
Chassis (from the toy cars above)Approx. ¥600
Battery and ChargerApprox. ¥120
Raspberry Pi 3b+Approx. ¥300
128G SD CardApprox. ¥120
Other AccessoriesApprox. ¥600 – ¥875

Option 2 – Purchase parts individually

If you need to buy parts individually, refer to the author's links. Note that if you assemble it yourself, you'll need to 3D print Donkey Car parts. If you don't have a 3D printer, consider buying one from Beets3D.

For specific printed parts, refer to the official documentation, link.

Jetson Nano Upgrade

For faster performance, choose Jetson Nano with a GPU as the mainboard. Besides changing the mainboard, you'll need a Jetson Nano adapter, network card, and compatible camera (see this article).

JetsonNano Adapter

Jetson Nano Adapter

JetsonNano Expansion Board

Note that Jetson Nano's pin layout is opposite to Raspberry Pi's, so be careful when using other expansion boards.

Battery

We strongly recommend using lithium polymer batteries for their higher energy density and smoother voltage drop curve, providing more stable voltage.

Battery

Hardware

If you purchased Donkey Car from the official store, you can skip the first two steps.

Step 1: Print Parts

If you don't have a 3D printer, buy printed parts from our store or find a third-party printer. For PLA printing, set layer height to 2mm and infill to 30%. Printing a set of parts takes about 2 days. Here's the model download link.

Step 2: Clean Parts

All 3D printed parts need some cleaning, including re-drilling holes and removing strings.

robocarstore/17380757722502808

robocarstore/17380757862515729

Step 3: Install Baseplate and Handle

The installation is straightforward; use M3 screws to connect the baseplate and handle. Ensure the screws are tight, as you'll use the handle to lift the car.

Step 4: Connect Servo Controller

Install the circuit board on the baseplate before wiring, as shown below.

robocarstore/173807580225062010

robocarstore/173807581725157711

This is the Raspberry Pi layout. Connect 3.3v, two I2C pins (SDA and SCL), and Ground.

Step 5: Install Raspberry Pi on Baseplate

At this point, insert the pre-burned SD card into the Raspberry Pi slot. Then, mount the Raspberry Pi and servo controller on the baseplate using screws. Use M2.6 screws.

(Add image)

Step 6: Install Camera

Connect the camera cable, then mount it on the front of the handle, as shown:

Ensure the Raspberry Pi is installed correctly. If the camera has a protective film, remove it to avoid affecting training results.

Step 7: Mount on Chassis

Use R-clips to secure the baseplate to the chassis, then connect the power cable.

robocarstore/173807583625096212

Congratulations, your car is assembled!

To drive your car, continue with the next article on software installation.

Introducing Donkey Car!

Donkey Car

Donkey Car is an open-source, small-scale DIY autonomous driving platform, and it might be your best entry-level AI learning platform. Donkey is a high-level autonomous driving library written in Python, characterized by its ability to help you quickly learn machine learning, experiment rapidly, and integrate applications of computer vision and machine learning tools (such as tornado, keras, tensorflow, opencv, etc.).

What is Donkey Car?

Donkey Car is a Python-based open-source project designed to help users build and train autonomous vehicles. It is intended to allow users to easily experiment with autonomous driving technology at home or in a lab. The hardware part of Donkey Car typically consists of a small remote-controlled car, a Raspberry Pi, a camera, and a battery. The software part is a powerful Python library that supports multiple machine learning frameworks.

Why Choose Donkey Car?

  1. Easy to Get Started: Donkey Car is designed to be very intuitive, allowing even those without programming experience to get started quickly. It provides detailed documentation and tutorials to guide users through the entire process from hardware assembly to software configuration.

  2. Community Support: As an open-source project, Donkey Car has an active community. Whatever issues you encounter, you can find answers or seek help within the community.

  3. Flexibility: Donkey Car supports multiple machine learning frameworks, such as TensorFlow and Keras, allowing users to choose the right tools for their experiments based on their needs.

  4. Cost-Effective: Compared to other autonomous driving platforms, Donkey Car is relatively low-cost, making it ideal for hobbyists and educational institutions.

How to Get Started?

To start using Donkey Car, you need to prepare the following materials:

  • A small remote-controlled car
  • Raspberry Pi
  • Camera
  • Battery
  • 3D printed car frame (optional)

Next, you can follow the official documentation to assemble the hardware and install the software. Donkey Car's official GitHub page provides detailed installation steps and example code to help you quickly build your own autonomous vehicle.

Conclusion

Donkey Car is a project well-suited for AI beginners. Through this platform, you can learn the basic principles of autonomous driving and master the fundamentals of machine learning and computer vision. Whether you want to enter the AI field or are simply interested in autonomous driving technology, Donkey Car is an ideal starting point.

I hope this article helps you better understand Donkey Car and inspires your interest in autonomous driving technology. Join the Donkey Car community and start your AI learning journey today!

Donkey Car Detailed Explanation (1) – Assembly

Donkey Car

Overview

Donkey Car is an AI autonomous driving model car project led by Adam Conway (Twitter) since October 2016. With the rise of AI and autonomous driving technologies in recent years, this project has attracted more enthusiasts, forming the DonkeyCar community.

This tutorial aims to help DonkeyCar beginners get started quickly. If you have any questions or suggestions, feel free to provide feedback.

Choosing a Chassis

The original Donkey Car was modified from the "Exceed" brand 1/16 four-wheel-drive toy car. The official documentation recommends the following models:

These cars have the same electronic drive modules, differing only in tires and body mounting methods. Note that the Desert Monster, Short Course Truck, and Blaze require an adapter to secure the Donkey Car motherboard and handle, which can be purchased or 3D printed. These cars are standard configurations, mostly plug-and-play. They come in brushed and brushless motor versions; the author suggests using the brushed motor version as it's easier to train, better for rough surfaces, and cheaper.

Later, enthusiasts began modifying 1/10 scale toy cars, making them larger and upgrading to the Donkey Car Pro version. The Pro version offers better performance but at a higher cost. Supported models include:

  • HobbyKing Trooper (non-Pro) Link
  • HobbyKing Mission-D Link
  • Tamaya TT01 or its clones, which are easy to buy but require assembly, posing some difficulty.

Fewer people use the Pro version due to documentation gaps, so if you start with Donkey Car Pro, you should have good hands-on skills and patience.

Required Parts

Donkey Car has two versions based on the mainboard: one using Raspberry Pi and the other using Jetson Nano. Apart from the mainboard, the required parts and software installation differ.

Option 1 – Purchase through Donkey Car official channels

If you're in the US, you can buy from the original author's Donkey store.

In Asia, you can choose Hong Kong's Robocar Store or mainland China's Robocar Store China.

Official kits or complete cars include:

PartApproximate Cost
Chassis (from the toy cars above)Approx. ¥600
Battery and ChargerApprox. ¥120
Raspberry Pi 3b+Approx. ¥300
128G SD CardApprox. ¥120
Other AccessoriesApprox. ¥600 – ¥875

Option 2 – Purchase parts individually

If you need to buy parts individually, refer to the author's links. Note that if you assemble it yourself, you'll need to 3D print Donkey Car parts. If you don't have a 3D printer, consider buying one from Beets3D.

For specific printed parts, refer to the official documentation, link.

Jetson Nano Upgrade

For faster performance, choose Jetson Nano with a GPU as the mainboard. Besides changing the mainboard, you'll need a Jetson Nano adapter, network card, and compatible camera (see this article).

JetsonNano Adapter

Jetson Nano Adapter

JetsonNano Expansion Board

Note that Jetson Nano's pin layout is opposite to Raspberry Pi's, so be careful when using other expansion boards.

Battery

We strongly recommend using lithium polymer batteries for their higher energy density and smoother voltage drop curve, providing more stable voltage.

Battery

Hardware

If you purchased Donkey Car from the official store, you can skip the first two steps.

Step 1: Print Parts

If you don't have a 3D printer, buy printed parts from our store or find a third-party printer. For PLA printing, set layer height to 2mm and infill to 30%. Printing a set of parts takes about 2 days. Here's the model download link.

Step 2: Clean Parts

All 3D printed parts need some cleaning, including re-drilling holes and removing strings.

robocarstore/17380757722502808

robocarstore/17380757862515729

Step 3: Install Baseplate and Handle

The installation is straightforward; use M3 screws to connect the baseplate and handle. Ensure the screws are tight, as you'll use the handle to lift the car.

Step 4: Connect Servo Controller

Install the circuit board on the baseplate before wiring, as shown below.

robocarstore/173807580225062010

robocarstore/173807581725157711

This is the Raspberry Pi layout. Connect 3.3v, two I2C pins (SDA and SCL), and Ground.

Step 5: Install Raspberry Pi on Baseplate

At this point, insert the pre-burned SD card into the Raspberry Pi slot. Then, mount the Raspberry Pi and servo controller on the baseplate using screws. Use M2.6 screws.

(Add image)

Step 6: Install Camera

Connect the camera cable, then mount it on the front of the handle, as shown:

Ensure the Raspberry Pi is installed correctly. If the camera has a protective film, remove it to avoid affecting training results.

Step 7: Mount on Chassis

Use R-clips to secure the baseplate to the chassis, then connect the power cable.

robocarstore/173807583625096212

Congratulations, your car is assembled!

To drive your car, continue with the next article on software installation.