This repository is used to emphasize UDACITY self-driving car dataset.
- tensorflow=2.5.0
- opencv-python=4.5.2.54
- numpy=1.19.5
- sklearn=0.0
- matplotlib=3.4.2
- pandas=1.3.0
- scikit-learn=0.24.2
- imgaug=0.4.0
- python-engineio=3.13.2
- eventlet=0.31.1
- Pillow=8.3.1
- Flask=2.0.1
$ pip3 install -r requirements.txtThe CNN Model used here is being proposed by NVIDIA for self-driving Cars
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 31, 98, 24) 1824
_________________________________________________________________
conv2d_1 (Conv2D) (None, 14, 47, 36) 21636
_________________________________________________________________
conv2d_2 (Conv2D) (None, 5, 22, 48) 43248
_________________________________________________________________
conv2d_3 (Conv2D) (None, 3, 20, 64) 27712
_________________________________________________________________
conv2d_4 (Conv2D) (None, 1, 18, 64) 36928
_________________________________________________________________
flatten (Flatten) (None, 1152) 0
_________________________________________________________________
dense (Dense) (None, 100) 115300
_________________________________________________________________
dense_1 (Dense) (None, 50) 5050
_________________________________________________________________
dense_2 (Dense) (None, 10) 510
_________________________________________________________________
dense_3 (Dense) (None, 1) 11
=================================================================
Total params: 252,219
Trainable params: 252,219
Non-trainable params: 0
- CPU Intel i7
- RAM 16GB
- NVIDIA 1050 GTX
- Ubuntu 20.04
Self-driving-car
|__ train.ipynb
|__ .gitignore
|__ README.md
|__ LICENSE
|__ Utils
| |__ utils.py
| |__ test_gpu.py
|__ dataset
| |__ driving_log.csv
| |__ IMG
|_________|__ ....jpg
# Add NVIDIA package repositories
$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
$ sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
$ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
$ sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /"
$ sudo apt-get update
$ wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo apt-get update
$ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
$ sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
$ sudo apt-get update
# Install development and runtime libraries (~4GB)
$ sudo apt-get install --no-install-recommends \
cuda-11-0 \
libcudnn8=8.0.4.30-1+cuda11.0 \
libcudnn8-dev=8.0.4.30-1+cuda11.0
# Reboot. Check that GPUs are visible using the command: nvidia-smi
$ nvidia-smi# Output Example of nvidia-smi
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.42.01 Driver Version: 470.42.01 CUDA Version: 11.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... On | 00000000:01:00.0 Off | N/A |
| N/A 42C P8 N/A / N/A | 11MiB / 4042MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| 0 N/A N/A 1268 G /usr/lib/xorg/Xorg 4MiB |
| 0 N/A N/A 1753 G /usr/lib/xorg/Xorg 4MiB |
+-----------------------------------------------------------------------------+
# Install TensorRT. Requires that libcudnn8 is installed above.
$ sudo apt-get install -y --no-install-recommends libnvinfer7=7.1.3-1+cuda11.0 \
libnvinfer-dev=7.1.3-1+cuda11.0 \
libnvinfer-plugin7=7.1.3-1+cuda11.0# Add Soft link to libcusolver
$ cd /usr/local/cuda/lib64
$ sudo ln -s $(pwd)/libcusolver.so.10 $(pwd)/libcusolver.so.11# Set LD_LIBRARY_PATH to ~/.bashrc
$ cd /usr/local/cuda/lib64
$ echo 'export LD_LIBRARY=$(pwd)' >> ~/.bashrcPycharm LD_LIBRARY Setup Stackoverflow #33812902
$ pip3 install tensorflow
$ python3 test_gpu.py# output:
2021-07-10 20:56:56.563751: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-10 20:56:57.336122: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2021-07-10 20:56:57.358240: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-10 20:56:57.358784: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:01:00.0 name: NVIDIA GeForce GTX 1050 computeCapability: 6.1
coreClock: 1.493GHz coreCount: 5 deviceMemorySize: 3.95GiB deviceMemoryBandwidth: 104.43GiB/s
2021-07-10 20:56:57.358801: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2021-07-10 20:56:57.360613: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2021-07-10 20:56:57.360648: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2021-07-10 20:56:57.361401: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10
2021-07-10 20:56:57.361643: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10
2021-07-10 20:56:57.363797: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11
2021-07-10 20:56:57.364300: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11
2021-07-10 20:56:57.364410: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2021-07-10 20:56:57.364483: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-10 20:56:57.365078: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-07-10 20:56:57.365533: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]Used Dataset found on Kaggle Download the dataset and unzip it at the root folder
Use train.ipynb to train the model.
Complete Training can be followed at: Kaggle/pranjalchanda/udacity-sdc-train