Diskussion:JetRacer: Spurführung mit künstlicher Intelligenz: Unterschied zwischen den Versionen

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! #  !! Termin  !!  Planung für die Woche !! Fortschritt
! #  !! Termin  !!  Planung für die Woche !! Fortschritt
|-
|-
| 1  || 12.06.2024- 21.06.2024  ||   
| 1  || 12.06.2024- 19.06.2024  || 
* Safety briefing from Mr Marc Ebmeyer's laboratories
* Familiarization with the existing frame work
* Familiarize with the existing system using the wiki article. Gantt chart
* Handing over the keys to Marc Ebmeyer's laboratories
||
*Make Research on the topic
*Draft a timeline for the project.
 
|-
| 2  || 20.06.2024-26.06.2024  ||   
* Build the Jetracer
* Build the Jetracer
* Setup Integrated Nvidia OS
* Setup and Flash Nvidia OS
* Connect JetRacer to local WLAN
* Connect JetRacer to local WLAN
* Setup Headless control of jetracer
* Setup Headless control of jetracer
||  
||  
*Make Research on the topic
*Errors and Crashes on the OS (details on Errors and solutions)
*Draft the timeline of the project.
*Find the optimal OS for the jet racer
*Optimization of the AI for the circuit, the controller (e.g. PD controller) in the laboratory Autonomous systems (speed, robustness).


|-
|-
| 2 || 24.06.2024 - 05.07.2024 ||   
| 3 || 26.06.2024 - 05.07.2024 ||   
 
* Fixing, debuging errors and testing different Flashversions of the JetRacer
* Teach the JetRacer automated driving
* Teach the JetRacer automated driving
* Steering JetRacer with Gamepad
* Steering JetRacer with Gamepad
* Teaching Jetracer interactive_regression
* Teaching Jetracer interactive_regression
* Testing different Flashversions of the JetRacer:
 
||
*Make Research on Matlab, Deep learning and ROS
*Document Errors
 
|-
| 4  || 05.07.2024 - 26.07.2024 || 
 
* Link Jetracer to MATLAB
* Install and check prerequisite for the jetracer like: CUDA, Cudnn, TensorRT
* Reinstall CUDA 10.2, CuDNN and SDL1.2 Paths
* Attempt to programme Jetracer using MATLAB:
#Image recognition
#Line following
 


||
||
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
*Document Errors + Solutions (E.g. Incompatible GPU, Missing Environment Variables paths like: CUDA_PATH and NVIDIA_CUDNN)
*Speak with other student working on Autonome Spurführung mit einem JetRacer ROS AI Robot
*Update the Professor by Email for Feedback
 
|-
|-
| 2 || 08.07.2024 - 02.08.2024 ||   
| 6 || 29.07.2024 - ||   
* Research for resources and links on how to programme Jetracer using MATLAB/ROS2
 
* programme Jetracer using MATLAB
* Programme Jetracer using ROS2
* Programme Jetracer using ROS2
* Evaluation of the advantages and disadvantages of the programming environments.
* Evaluation of the advantages and disadvantages of the programming environments.
||
* Successfully install and run ROS on jetracer
* Recreate Alexnet Example on ROS image classification with GPU coder
|-
| 7  || 12.08.2024 ||
* Easy start without ROS: Run Image detection on GPU with Professor's assistance
*[https://de.mathworks.com/videos/deploying-a-deep-learning-network-on-nvidia-jetson-using-gpu-coder-1506357891312.html Deploying a Deep Learning Network on NVIDIA Jetson Using GPU Coder]
* [https://de.mathworks.com/matlabcentral/fileexchange/64534-getting-started-with-matlab-simulink-and-ros?s_tid=ta_fx_results Getting Started with MATLAB, Simulink, and ROS]
|-
| 8  || 19.08.2024 || 
* Use the Game controller to steer
||
||
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
|-
| 9  || 26.08.2024 || 


* Programme and teach Jetracer using Matlab and Simulink
* Understand the Libraries used in JupytrLab
||
* Working on previous Task
|-
| 10  || 04.09.2024 || 
* Discussion of subject for the bachelorthesis.
* Drive the JetRacer in the right lane counterclockwise with the gamepad controller. Limit the speed via Software to a maximum (e.&thinsp;g. 1&thinsp;m/s).
* Take a video while driving a lap with MATLAB® using a MATLAB®-script.
* Load the pretrained NN.
* Train the pretrained NN with MATLAB® with a MATLAB®-App (GUI) by clicking the desired path in the images.
* Use classic lane tracking algorithms to teach the NN automatically.
*Write a PD-contoller that uses the NN to drive in the right lane. Program this in MATLAB® and let it run on the JetRacer-GPU using GPU Coder.
*Goal: the car should drive autonomously several laps in the right lane as fast as possible.
||
* Student choosed a name fot the bachelorthesis.
* Provide debug option to adjust speed(Throttle) of jetracer to a maximum (e.&thinsp;g. 1&thinsp;m/s) only on matlab.
*
|-
| 11  || 09.09.2024 - 13.09.2024 || 
* Student did not attend the meeting.
* Please cancle the meeting if you are not coming.
* Drive the JetRacer in the right lane counterclockwise with the gamepad controller. Limit the speed via Software to a maximum (e.&thinsp;g. 1&thinsp;m/s) wit Professor's assistance.
* Take a video while driving a lap with MATLAB® using a MATLAB®-script.
* Load the pretrained NN.
||
* Successfully Drive the JetRacer in the right lane counterclockwise with the gamepad controller on matlab. Hard code the speed Limit via Software to a maximum (e.&thinsp;g. 1&thinsp;m/s).
* Successfully took a video while driving a lap with MATLAB<sup>®</sup> using a MATLAB<sup>®</sup>-script.
* Load the pretrained NN.
* Attempt to train the pretrained NN with MATLAB® with a MATLAB<sup>®</sup>-App (GUI) by clicking the desired path in the images.
* Documented all my work on wiki platform
|-
| 12  || 16.09.2024 - 20.09.2024 || 
* Use classic lane tracking algorithms to teach the NN automatically.
*Write a PD-contoller that uses the NN to drive in the right lane. Program this in MATLAB® and let it run on the JetRacer-GPU using GPU Coder.
*Goal: the car should drive autonomously several laps in the right lane as fast as possible.
||
Send professor recorded video online
|-
| 13  || 21.09.2024 || 
Student did not appear to the weekly appointment.
Further research on how to train jetracer
||
Feedback from professor to use hshl gpt for advance research on how to train a NN
|-
| 13  || 30.09.2024 || 
Asking hshl gpt on how to train a NN with the video input from jetracer
||
Training jetracer based on the researches
Uning alternative method(using gamepad to train the jetracer frame by frame with linear image processing method)
|-
| 13  || 07.1.2024 || 
Show professor my work progress on NN traning and approche to teach jetracer with controller instead of mouse(Clicking each desirable path on the frame)
||
*Testing the jetracer steering and develop PID control
|-
| 13  || 14.10.2024 || 
*Testing jetracer to complete a complete lap on the right lane
*Recieved feedback from professor to train jetracer to get both input from controller and camera
||
*Training the jetracer to get both input from controller and camera
*Documenting all work progress
|-
| 13  || 21.10.2024 || 
*Programming jetracer to get input from both video and controller
*Testing and debugging
*Documentation feedback
||
*Meeting with professor
*Documenting all work progress
|-
| 13  || 28.10.2024 || 
*Training NN with new inputs from the jetracer (controller and camera)
||
*
*Documenting all work progress
|}
|}


Zeile 51: Zeile 167:
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
|-
|-
|  ||
|  || * jetbot-043_nano-4gb-jp45.zip
||   
||   


   * jetbot-043_nano-4gb-jp45.zip
    
# Warning!! this is for Jetbot, not Jetracer.
# Warning!! this is for Jetbot, not Jetracer.
# Jetracer cannot detect SD-card
# Jetracer cannot detect SD-card
Zeile 64: Zeile 180:


|-
|-
|  ||  ||  * jetson-nano-jp461-sd-card-image.zip(Jetson-nano developer kit)
|  ||  * jetson-nano-jp461-sd-card-image.zip(Jetson-nano developer kit) ||
 
# No build in Jetracer packages like: basic_motion, teleoperation, road_following etc
# No build in Jetracer packages like: basic_motion, teleoperation, road_following etc
# Incompatible with Jetracer and gives error : jetson nano Adafruit-Blinka requires Python '>=3.7.0' but the running Python is 3.6.9
# Incompatible with Jetracer and gives error : jetson nano Adafruit-Blinka requires Python '>=3.7.0' but the running Python is 3.6.9
Zeile 72: Zeile 189:


|-
|-
|  ||  ||  * jetcard_nano-4gb-jp451.zip (Jetpack4.5.1)
|  ||  * jetcard_nano-4gb-jp451.zip (Jetpack4.5.1) ||
 
# Working but no build in Jetracer packages like: teleoperation, road_following etc
# Working but no build in Jetracer packages like: teleoperation, road_following etc
# Solution is to use github repository: https://github.com/NVIDIA-AI-IOT/jetracer to clone the  teleoperation, road_following etc   
# Solution is to use github repository: https://github.com/NVIDIA-AI-IOT/jetracer to clone the  teleoperation, road_following etc   
Zeile 79: Zeile 197:


|-
|-
|  ||   ||  * jetrace.zip (Jetpack4.5)
|  || * jetrace.zip (Jetpack4.5) ||
 
# Incompatible with Jetracer and gives error :ValueError: No I2C device at address: 0x60 (error comes from step 5 of https://www.waveshare.com/wiki/JetRacer_AI_Kit. do not immplement it for pro versions )
# Incompatible with Jetracer and gives error :ValueError: No I2C device at address: 0x60 (error comes from step 5 of https://www.waveshare.com/wiki/JetRacer_AI_Kit. do not immplement it for pro versions )
# only interactive_regression package works with no error
# only interactive_regression package works with no error
# Solution is to use github repository: https://github.com/NVIDIA-AI-IOT/jetracer to clone the  basic_movement, teleoperation, road_following etc
# Solution is to use github repository: https://github.com/NVIDIA-AI-IOT/jetracer to clone the  basic_movement, teleoperation, road_following etc
# Can't connect to SSH header  || <span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
# Can't connect to SSH header   
 
|| <span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
|-
|-


|-
| 2  || 08.07.2024 - 02.09.2024 ||   
| 2  || 08.07.2024 - 02.08.2024 ||   
* Research for resources and links on how to programme Jetracer using MATLAB/ROS2
* Research for resources and links on how to programme Jetracer using MATLAB/ROS2
* programme Jetracer using MATLAB
* programme Jetracer using MATLAB
* Programme Jetracer using ROS2
* Use of ROS2 to teach the Jetson Nano.
* Evaluation of the advantages and disadvantages of the programming environments.
||
||
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
|-
|-
|  ||  ||  ||
|  ||  Cuda 10.2 set path ||
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
  ||
 
|-
||  dpkg: error processing package nvidia-l4t-bootloader  ||  ||
https://forums.developer.nvidia.com/t/solution-dpkg-error-processing-package-nvidia-l4t-bootloader-configure/208627
 
```Code:
sudo mv /var/lib/dpkg/info/ /var/lib/dpkg/backup/
sudo mkdir /var/lib/dpkg/info/
 
```
 
Next update repos and force install .
 
```Code:
 
sudo apt-get update
sudo apt-get install -f
 
```
 
Move the new structure dpkg/info to old info
 
```Code:
 
sudo mv /var/lib/dpkg/info/* /var/lib/dpkg/backup/
 
```
 
Remove the new dpkg structure folder and back the old
 
```Code:
 
sudo rm -rf /var/lib/dpkg/info
sudo mv /var/lib/dpkg/backup/ /var/lib/dpkg/info/
```
 
resources: https://elinux.org/Jetson/Installing_CUDA
|-  
|-  
| ||   ||
| || Nvcc not Found  ||


||
||
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
- **Edit `.bashrc`:**
Since `vim` and `nano` are unavailable, use `vi` if it's installed:
   
    ``Code:
    vi ~/.bashrc
   
    ```
   
- **Clean Up Environment Variables:**
Update the `.bashrc` file to remove duplicates and ensure only one set of `PATH` and `LD_LIBRARY_PATH` entries:
   
    ```Code:
    # Add CUDA bin & library paths
    export PATH=/usr/local/cuda-10.2/bin:$PATH
    export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
   
    ```
   
- **Save and Exit:**
    - In `vi`, press `i` to enter insert mode.
    - Make your changes.
    - Press `Esc`, then type `:wq` and press `Enter` to save and exit.
- **Source `.bashrc`:**
Code:
source ~/.bashrc
|-  
|-  
|-
|-
|  ||  || ||
|  || Install Cudnn   ||
[[Datei:Cudnn error.jpg|mini]]
||
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
<span style="color:red">Dokumentieren Sie bitte hier Ihren Fortschritt.</span>
|-
|-
|  || SDL 1.2 installation  ||
[[Datei:Screenshot (2).png|mini]]
  ||
1. **`nvcc`**: Ensure CUDA is correctly installed and its path is added to your `.bashrc` file.
2. **SDL 1.2**: Install the development libraries for SDL 1.2 on your system. You can typically do this with:
   
    ```code:
    sudo apt-get install libsdl1.2-dev
   
    ```
|-
|  || No publick key(update error)  ||
The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC
Reading package lists... Done
W: GPG error: http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC
E: The repository 'http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64  InRelease' is not signed.
N: Updating from such a repository can't be done securely, and is therefore disabled by default.
N: See apt-secure(8) manpage for repository creation and user configuration details.
||
The error message indicates that the GPG key for the NVIDIA CUDA repository is missing. To resolve this, you can manually add the key:
1. **Add the missing key**:
   
    ```Code:
 
    sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
   
    ```
   
2. **Update the package list again**:
   
    ```Code:
    sudo apt-get update
   
    ```
   
This should fix the GPG error and allow you to update the repositories.
|-
|-
|}
|}
-----
Structure of the thesis
# Introduction
## Task overview
## Gantt-Chart
## Organisation Plan/Outline
Where do I find the solutuins to the tasks.
# Theoretical Background
# Solution Way/Alternatives
# Solution/Result
# Conclusion
## Discussion of the results
## Outlook
Literature/source
Attachments/SVN Links

Aktuelle Version vom 28. Oktober 2024, 10:11 Uhr

# Termin Planung für die Woche Fortschritt
1 12.06.2024- 19.06.2024
  • Safety briefing from Mr Marc Ebmeyer's laboratories
  • Familiarization with the existing frame work
  • Familiarize with the existing system using the wiki article. Gantt chart
  • Handing over the keys to Marc Ebmeyer's laboratories
  • Make Research on the topic
  • Draft a timeline for the project.
2 20.06.2024-26.06.2024
  • Build the Jetracer
  • Setup and Flash Nvidia OS
  • Connect JetRacer to local WLAN
  • Setup Headless control of jetracer
  • Errors and Crashes on the OS (details on Errors and solutions)
  • Find the optimal OS for the jet racer
  • Optimization of the AI for the circuit, the controller (e.g. PD controller) in the laboratory Autonomous systems (speed, robustness).
3 26.06.2024 - 05.07.2024
  • Fixing, debuging errors and testing different Flashversions of the JetRacer
  • Teach the JetRacer automated driving
  • Steering JetRacer with Gamepad
  • Teaching Jetracer interactive_regression
  • Make Research on Matlab, Deep learning and ROS
  • Document Errors
4 05.07.2024 - 26.07.2024
  • Link Jetracer to MATLAB
  • Install and check prerequisite for the jetracer like: CUDA, Cudnn, TensorRT
  • Reinstall CUDA 10.2, CuDNN and SDL1.2 Paths
  • Attempt to programme Jetracer using MATLAB:
  1. Image recognition
  2. Line following


  • Document Errors + Solutions (E.g. Incompatible GPU, Missing Environment Variables paths like: CUDA_PATH and NVIDIA_CUDNN)
  • Speak with other student working on Autonome Spurführung mit einem JetRacer ROS AI Robot
  • Update the Professor by Email for Feedback
6 29.07.2024 -
  • Programme Jetracer using ROS2
  • Evaluation of the advantages and disadvantages of the programming environments.
  • Successfully install and run ROS on jetracer
  • Recreate Alexnet Example on ROS image classification with GPU coder
7 12.08.2024
8 19.08.2024
  • Use the Game controller to steer

Dokumentieren Sie bitte hier Ihren Fortschritt.

9 26.08.2024
  • Programme and teach Jetracer using Matlab and Simulink
  • Understand the Libraries used in JupytrLab
  • Working on previous Task
10 04.09.2024
  • Discussion of subject for the bachelorthesis.
  • Drive the JetRacer in the right lane counterclockwise with the gamepad controller. Limit the speed via Software to a maximum (e. g. 1 m/s).
  • Take a video while driving a lap with MATLAB® using a MATLAB®-script.
  • Load the pretrained NN.
  • Train the pretrained NN with MATLAB® with a MATLAB®-App (GUI) by clicking the desired path in the images.
  • Use classic lane tracking algorithms to teach the NN automatically.
  • Write a PD-contoller that uses the NN to drive in the right lane. Program this in MATLAB® and let it run on the JetRacer-GPU using GPU Coder.
  • Goal: the car should drive autonomously several laps in the right lane as fast as possible.
  • Student choosed a name fot the bachelorthesis.
  • Provide debug option to adjust speed(Throttle) of jetracer to a maximum (e. g. 1 m/s) only on matlab.
11 09.09.2024 - 13.09.2024
  • Student did not attend the meeting.
  • Please cancle the meeting if you are not coming.
  • Drive the JetRacer in the right lane counterclockwise with the gamepad controller. Limit the speed via Software to a maximum (e. g. 1 m/s) wit Professor's assistance.
  • Take a video while driving a lap with MATLAB® using a MATLAB®-script.
  • Load the pretrained NN.
  • Successfully Drive the JetRacer in the right lane counterclockwise with the gamepad controller on matlab. Hard code the speed Limit via Software to a maximum (e. g. 1 m/s).
  • Successfully took a video while driving a lap with MATLAB® using a MATLAB®-script.
  • Load the pretrained NN.
  • Attempt to train the pretrained NN with MATLAB® with a MATLAB®-App (GUI) by clicking the desired path in the images.
  • Documented all my work on wiki platform
12 16.09.2024 - 20.09.2024
  • Use classic lane tracking algorithms to teach the NN automatically.
  • Write a PD-contoller that uses the NN to drive in the right lane. Program this in MATLAB® and let it run on the JetRacer-GPU using GPU Coder.
  • Goal: the car should drive autonomously several laps in the right lane as fast as possible.

Send professor recorded video online

13 21.09.2024

Student did not appear to the weekly appointment. Further research on how to train jetracer

Feedback from professor to use hshl gpt for advance research on how to train a NN

13 30.09.2024

Asking hshl gpt on how to train a NN with the video input from jetracer

Training jetracer based on the researches Uning alternative method(using gamepad to train the jetracer frame by frame with linear image processing method)

13 07.1.2024

Show professor my work progress on NN traning and approche to teach jetracer with controller instead of mouse(Clicking each desirable path on the frame)

  • Testing the jetracer steering and develop PID control
13 14.10.2024
  • Testing jetracer to complete a complete lap on the right lane
  • Recieved feedback from professor to train jetracer to get both input from controller and camera
  • Training the jetracer to get both input from controller and camera
  • Documenting all work progress
13 21.10.2024
  • Programming jetracer to get input from both video and controller
  • Testing and debugging
  • Documentation feedback
  • Meeting with professor
  • Documenting all work progress
13 28.10.2024
  • Training NN with new inputs from the jetracer (controller and camera)
  • Documenting all work progress

Errors and Solutions

# Dates Error Descriptions Solutions
1 12.06.2024- 05.07.2024
  • Build the Jetracer
  • Setup Integrated Nvidia OS
  • Connect JetRacer to local WLAN
  • Setup Headless control of jetracer
  • Teach the JetRacer automated driving
  • Steering JetRacer with Gamepad
  • Teaching Jetracer interactive_regression
  • Testing different Flashversions of the JetRacer

Dokumentieren Sie bitte hier Ihren Fortschritt.

* jetbot-043_nano-4gb-jp45.zip


  1. Warning!! this is for Jetbot, not Jetracer.
  2. Jetracer cannot detect SD-card
  3. Connot lunch Nvidia OS because HMI Screen always dark and Jetson Nano turns on but doesn’t boot


Dokumentieren Sie bitte hier Ihren Fortschritt.

* jetson-nano-jp461-sd-card-image.zip(Jetson-nano developer kit)
  1. No build in Jetracer packages like: basic_motion, teleoperation, road_following etc
  2. Incompatible with Jetracer and gives error : jetson nano Adafruit-Blinka requires Python '>=3.7.0' but the running Python is 3.6.9
  3. Incompatible with Jetracer and gives error :No matching distribution found for Adafruit-Blinka>=7.0.0 (from adafruit-circuitpython-servokit)
  4. Cannot run "sudo nvpmodel -m1" and shows nvpmodel not found ||


* jetcard_nano-4gb-jp451.zip (Jetpack4.5.1)
  1. Working but no build in Jetracer packages like: teleoperation, road_following etc
  2. Solution is to use github repository: https://github.com/NVIDIA-AI-IOT/jetracer to clone the teleoperation, road_following etc
Dokumentieren Sie bitte hier Ihren Fortschritt.


* jetrace.zip (Jetpack4.5)
  1. Incompatible with Jetracer and gives error :ValueError: No I2C device at address: 0x60 (error comes from step 5 of https://www.waveshare.com/wiki/JetRacer_AI_Kit. do not immplement it for pro versions )
  2. only interactive_regression package works with no error
  3. Solution is to use github repository: https://github.com/NVIDIA-AI-IOT/jetracer to clone the basic_movement, teleoperation, road_following etc
  4. Can't connect to SSH header
Dokumentieren Sie bitte hier Ihren Fortschritt.
2 08.07.2024 - 02.09.2024
  • Research for resources and links on how to programme Jetracer using MATLAB/ROS2
  • programme Jetracer using MATLAB
  • Use of ROS2 to teach the Jetson Nano.

Dokumentieren Sie bitte hier Ihren Fortschritt.

Cuda 10.2 set path
dpkg: error processing package nvidia-l4t-bootloader

https://forums.developer.nvidia.com/t/solution-dpkg-error-processing-package-nvidia-l4t-bootloader-configure/208627

```Code: sudo mv /var/lib/dpkg/info/ /var/lib/dpkg/backup/ sudo mkdir /var/lib/dpkg/info/

```

Next update repos and force install .

```Code:

sudo apt-get update sudo apt-get install -f

```

Move the new structure dpkg/info to old info

```Code:

sudo mv /var/lib/dpkg/info/* /var/lib/dpkg/backup/

```

Remove the new dpkg structure folder and back the old

```Code:

sudo rm -rf /var/lib/dpkg/info sudo mv /var/lib/dpkg/backup/ /var/lib/dpkg/info/ ```

resources: https://elinux.org/Jetson/Installing_CUDA

Nvcc not Found

- **Edit `.bashrc`:** Since `vim` and `nano` are unavailable, use `vi` if it's installed:

   ``Code:
   vi ~/.bashrc
   
   ```
   

- **Clean Up Environment Variables:** Update the `.bashrc` file to remove duplicates and ensure only one set of `PATH` and `LD_LIBRARY_PATH` entries:

   ```Code:
   # Add CUDA bin & library paths
   export PATH=/usr/local/cuda-10.2/bin:$PATH
   export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH
   
   ```
   

- **Save and Exit:**

   - In `vi`, press `i` to enter insert mode.
   - Make your changes.
   - Press `Esc`, then type `:wq` and press `Enter` to save and exit.

- **Source `.bashrc`:** Code: source ~/.bashrc

Install Cudnn

Dokumentieren Sie bitte hier Ihren Fortschritt.

SDL 1.2 installation

1. **`nvcc`**: Ensure CUDA is correctly installed and its path is added to your `.bashrc` file. 2. **SDL 1.2**: Install the development libraries for SDL 1.2 on your system. You can typically do this with:

   ```code:
   sudo apt-get install libsdl1.2-dev
   
   ```
No publick key(update error)

The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC Reading package lists... Done W: GPG error: http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease: The following signatures couldn't be verified because the public key is not available: NO_PUBKEY A4B469963BF863CC E: The repository 'http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64 InRelease' is not signed. N: Updating from such a repository can't be done securely, and is therefore disabled by default. N: See apt-secure(8) manpage for repository creation and user configuration details.

The error message indicates that the GPG key for the NVIDIA CUDA repository is missing. To resolve this, you can manually add the key:

1. **Add the missing key**:

   ```Code:
  
   sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
   
   ```
   

2. **Update the package list again**:

   ```Code:
   sudo apt-get update
   
   ```
   

This should fix the GPG error and allow you to update the repositories.


Structure of the thesis

  1. Introduction
    1. Task overview
    2. Gantt-Chart
    3. Organisation Plan/Outline

Where do I find the solutuins to the tasks.

  1. Theoretical Background
  2. Solution Way/Alternatives
  3. Solution/Result
  4. Conclusion
    1. Discussion of the results
    2. Outlook

Literature/source Attachments/SVN Links