Introduction: Cloud Classification

This project is implemented in two methods:

1. Standard CPU based - "cloudclassify-cpu.py" script is to train the DNN model for detecting cloudy pixels of Cross-track Infrared sounder using the top 75 Principal Components (PC) of it's spectrum.
2. GPU based

A playlist detailing the installation process may be found at this link.

Software requirements

1. Python 3.7 or 3.8
2. NVIDIA 450.51.06 (For Ubuntu Server 18.04) and 441.12 (For Windows Server 2019)
3. CUDA 10.2
4. TensorFlow 2.2

Note: Python 3.9 is incompatible with Tensorflow 2.2.0. Please download Python 3.7 or 3.8.

• Introduction: Cloud Classification
  • Software requirements
• For Windows
  • Standard CPU-based implementation
  • GPU-based implementation
• For Ubuntu
  • GPU-based implementation
• Videos
  • Walkthrough video for AWS Ubuntu Server multi-GPU instance
  • Walkthrough Video for AWS Windows multi-GPU instance
  • Walkthrough Video for AWS Windows single GPU instance

For Windows

Standard CPU-based implementation

1. Clone the repository

2. Set up the virtual environment

1. Create a new project folder and name it as per your wish. For example "cloudclassify".

2. In command prompt, go to cloudclassify folder and run the below command. This creates a virtual environment called "cloudclassify-venv"

        python -m venv cloudclassify-venv
   

3. In command prompt, run below line to activate the virtual environment

        cloudclassify-venv\Scripts\activate.bat
   

3. Install python packages Copy requirements.txt file from cloned repository and place it inside cloudclassify folder. Use the following command to install all necessary packages at once:

        pip install -r requirements_cpu.txt     

4. Download the data

1. Click the link and download the cloud_pc.sav file.
2. Place the downloaded "cloud_pc.sav" file inside cloudclassify folder

5. Download train and test data

1. Download four folders train_10mm, tes_10mm, logs, and florence_10mm placed in the link.
2. Extract the downloaded folder. Copy the four folders and paste them inside your cloudclassify folder.

6. Run the script Now, we are all set to run the script. Run the cloudclassify-cpu.py script using below command. This script accepts an argument called flag (-f). The default value of the flag is detection.

1. In command prompt, run the script with default value "detection"

        set CUDA_VISIBLE_DEVICES='' & python cloudclassify-cpu.py
   

2. In command prompt, run the script with flag "rainy cloud"

        set CUDA_VISIBLE_DEVICES='' & python cloudclassify-cpu.py -f "rainy cloud"
   

7. Output

1. The model weights can be found in inside my_model folder.
2. The accuracy metrics (highlighted in yellow) such as probability of detection (POD), probability of false detection (POFD), false alarm ratio (FAR), bias, critical success index (CSI), and model accuracy and runtime (in seconds) will be printed in the terminal when the process finishes.

   POD	POFD	FAR	Bias	CSI	Accuracy
   0.6933	0.0431	0.1106	0.7795	0.6382	0.8690

3. The accuracy of the model is 86.90% and it took 91.69 minutes to finish.

GPU-based implementation

The GPU-based implementation is tested in three environments.

1. Windows Desktop with NVIDIA GeForce GTX 1650 Ti with Max-Q Design
2. AWS g4dn instance with NVIDIA Tesla T4
3. Google Colab

---

1. Windows Desktop with NVIDIA GeForce GTX 1650 Ti with Max-Q Design Enable and install GPU driver 1. Verfiy graphic card details.

1. Go to Windows Start menu and type device manager. Expand Display Adapters, graphic cards will be displayed.

2. Download and install the NVIDIA driver

1. Go to NVIDIA drive download link
2. Provide NVIDIA driver details according to your NVIDIA product. Below screenshot shows the selection based on “NVIDIA GeForce GTX 1640 Ti with Max-Q Design”.
3. Click on Search and download the driver.
4. Install downloaded NVIDIA driver.
5. You will find CUDA subfolder inside this path C:\Program Files\NVIDIA GPU Computing Toolkit.

3. CUDA toolkit

1. Go to CUDA Toolkit Archive.
2. Find the latest release of CUDA Toolkit.
3. Select the Operating System (Linux or Windows), architecture, and version based on your machine preference.
4. Click download.
5. Double click the downloaded exe file (Example: cuda_11.4.1_471.41_win10.exe) and follow the on-screen prompts.

4. Download cuDNN library

1. Go to cuDNN link.
2. Click Download cuDNN. If you are a first-time user, you need to create a user account and consent to the cuDNN Software License Agreement.
3. Select the right version of cuDNN. Please note that the version of CUDA and cuDNN should match. In this case, we should download version 11.
4. It will download as a compressed folder. Extract the compressed folder.
5. The extracted folder has “cuda” subfolder that matches with the “CUDA” subfolder in C:\Program Files\NVIDIA GPU Computing Toolkit.
6. Now, copy cudnn64_8.dll from the bin of the extracted folder (C:\Users\anush\Downloads\cudnn-11.4-windows-x64-v8.2.2.26\cuda\bin) and paste it in the bin folder inside CUDA folder of NVIDIA GPU Computing Toolkit (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\bin).
7. Copy cudnn.h file from include of the extracted folder (C:\Users\anush\Downloads\cudnn-11.4-windows-x64-v8.2.2.26\cuda\include) and paste it in the include folder inside CUDA folder of NVIDIA_GPU_Computing Toolkit (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\include).
8. Copy cudnn.lib file from lib/x64 folder inside extracted folder (C:\Users\anush\Downloads\cudnn-11.4-windows-x64-v8.2.2.26\cuda\lib\x64) and paste it in the similar folder of NVIDIA_GPU_Computing_Tookit (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\lib\x64).

5. Set up the virtual environment

1. Create a new project folder and name it as per your wish. For example "cloudclassify-gpu".

2. Go to Start menu and type “command prompt”.

3. Open command prompt. Change to your project folder.

4. Copy and paste the below line in your command prompt. This creates a virtual environment named “cloudclassify-gpu”. You can name virtual environment as per your wish.

     python -m venv cloudclassify-gpu
   

5. Copy and paste below line in command prompt. This activates the virtual environment.

     cloudclassify-gpu\Scripts\activate.bat
   

6. Download train and test data

1. Download data using this link.
2. Download all four folders to your local machine inside project folder.

7. Install python packages

1. In command prompt, copy and paste below line to install python packages.

     pip install -r requirements_gpu.txt
   

8. Run the script

1. In command prompt, run the python script using below command.

     set CUDA_VISIBLE_DEVICES=0 & python cloudcode-multi-gpu.py
   

9. Output of the script

1. The model weights can be found inside my_model folder.
2. The accuracy metrics (highlighted in yellow) such as probability of detection (POD), probability of false detection (POFD), false alarm ratio (FAR), bias, critical success index (CSI), and model accuracy and runtime (in seconds) will be printed in the terminal when the process finishes.

   POD	POFD	FAR	Bias	CSI	Accuracy
   0.6789	0.0413	0.1086	0.7617	0.6271	0.8654

3. The accuracy of the model is 86.54% and it took 122.73 minutes to finish.

---

2. AWS g4dn instance with NVIDIA Tesla T4

Install NVIDIA drivers on Windows instances

The following steps are based on AWS help document to install NVIDIA driver in g4dn instance.

1. Download and install the NVIDIA driver

1. Connect to the Windows instance.

2. Go to Start menu and open a PowerShell window.

3. Copy and paste the below in Powershell. This command will download driver inside Desktop.

        $Bucket = "nvidia-gaming"
        $KeyPrefix = "windows"
        $LocalPath = "$home\Desktop\NVIDIA"
        $Objects = Get-S3Object -BucketName $Bucket -KeyPrefix $KeyPrefix -Region us-east-1
        foreach ($Object in $Objects) {
           $LocalFileName = $Object.Key
           if ($LocalFileName -ne '' -and $Object.Size -ne 0) {
              $LocalFilePath = Join-Path $LocalPath $LocalFileName
              Copy-S3Object -BucketName $Bucket -Key $Object.Key -LocalFile $LocalFilePath -Region us-east-1
           }
        }
   

4. Navigate to the desktop->NVIDIA folder->windows.

5. You will find 441.12_grid_vgaming_server2016_server2019_64bit_international.exe file inside windows directory.

6. Double-click the installation file (exe) to launch it.

7. Follow the instructions to install the driver

8. Reboot your instance as required.

9. To verify that the GPU is available, check Device Manager. Go to Start menu and open Device Manager. Expand Display adapters.

11. Create a registry value in the HKEY_LOCAL_MACHINE\SOFTWARE\NVIDIA Corporation\Global key with the name vGamingMarketplace, the type DWord, and the value 2.

    1. Use Powershell and run the below command.

        New-ItemProperty -Path "HKLM:\SOFTWARE\NVIDIA Corporation\Global" -Name "vGamingMarketplace" -PropertyType "DWord" -Value "2"
       

    2. Use Powershell and run the below command.

       reg add "HKLM\SOFTWARE\NVIDIA Corporation\Global" /v vGamingMarketplace /t REG_DWORD /d 2
       

12. Use Powershell and run the below command to download the certification file.

        Invoke-WebRequest -Uri "https://nvidia-gaming.s3.amazonaws.com/GridSwCert-Archive/GridSwCertWindows_2021_10_2.cert" -OutFile "$Env:PUBLIC\Documents\GridSwCert.txt"
    

13. Reboot the instance.

2. Install CUDA

1. Go to NVIDIA website and select the version of CUDA that you need.
2. For version, choose based on your windows type and for instance type: choose exe (local)
3. Click download.
4. Double click the download exe file and follow the on-screen prompts.
5. Reboot the instance.

3. Download cuDNN library

1. Go to cuDNN link.
2. Click Download cuDNN. If you are a first-time user, you need to create a user account and consent to the cuDNN Software License Agreement.
3. Select the right version of cuDNN. Please note that the version of CUDA and cuDNN should match. In this case, we should download version 11.
4. It will download as a compressed folder. Extract the compressed folder.
5. The extracted folder has “cuda” subfolder that matches with the “CUDA” subfolder in C:\Program Files\NVIDIA GPU Computing Toolkit.
6. Now, copy cudnn64_8.dll from the bin of the extracted folder (C:\Users\anush\Downloads\cudnn-11.4-windows-x64-v8.2.2.26\cuda\bin) and paste it in the bin folder inside CUDA folder of NVIDIA GPU Computing Toolkit (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\bin).
7. Copy cudnn.h file from include of the extracted folder (C:\Users\anush\Downloads\cudnn-11.4-windows-x64-v8.2.2.26\cuda\include) and paste it in the include folder inside CUDA folder of NVIDIA_GPU_Computing Toolkit (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\include).
8. Copy cudnn.lib file from lib/x64 folder inside extracted folder (C:\Users\anush\Downloads\cudnn-11.4-windows-x64-v8.2.2.26\cuda\lib\x64) and paste it in the similar folder of NVIDIA_GPU_Computing_Tookit (C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.4\lib\x64).

4. Set up the virtual environment

1. Create a new project folder and name it as per your wish. For example "cloudclassifygpu".

2. Go to Start menu and type “command prompt”.

3. Open command prompt. Change to your project folder.

4. Copy and paste the below line in your command prompt. This creates a virtual environment named “cloudclassify-gpu”. You can name virtual environment as per your wish.

     python -m venv cloudclassify-gpu
   

5. Copy and paste below line in command prompt. This activates the virtual environment.

     cloudclassify-gpu\Scripts\activate.bat
   

5. Install python packages

1. In command prompt, copy and paste below line to install python packages.

     pip install -r requirements_gpu.txt
   

6. Download train and test data

1. Download data using this link.
2. Download all four folders to your local machine inside project folder.

7. Run the script

1. In command prompt, run the python script using below command.

   For single GPU

     set CUDA_VISIBLE_DEVICES=0 & python cloudcode-multi-gpu.py
   

   For multi-GPU

     set CUDA_VISIBLE_DEVICES=0,1,2,3 & python cloudcode-multi-gpu.py
   

NVIDIA SMI Screenshot

Note: While running the multi gpu code if you encounter this issue: No OpKernel was registered to support Op 'NcclAllReduce, please change line #109 to strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())

Note: In the above command set CUDA_VISIBLE_DEVICES=0,1,2,3 & python cloudclassify-gpu.py uses 4 GPUs. For example, set CUDA_VISIBLE_DEVICES=X,Y,Z,... & python cloudclassify-gpu.py. Here 'X' , 'Y', and 'Z' are variables specifying the number of GPUs you want to use.

8. Output

1. The model weights can be found inside my_model folder.

2. The accuracy metrics (highlighted in yellow) such as probability of detection (POD), probability of false detection (POFD), false alarm ratio (FAR), bias, critical success index (CSI), and model accuracy and runtime (in seconds) will be printed in the terminal when the process finishes.

   POD	POFD	FAR	Bias	CSI	Accuracy
   0.6816	0.0436	0.1134	0.7687	0.6269	0.8648

3. For single GPU

   It took 3976.503949403763 seconds to finish.

   For multi GPU

   It took 9036.8576 seconds to finish.

   

---

3. Google Colab

1. Go to Google Colab.
2. Click on the Upload tab.
3. Choose the "CloudClassifyGPU.ipynb" downloaded from the Git repository.

1. Download train and test data and upload them in your Google Drive

1. Download data using this link.
2. Download all four folders to your local machine.
3. Then upload it to your Google drive.
4. Finally, your folder should have four folders. See screenshot below:

2. Change Runtime to GPU

1. Go to Runtime in menu.
2. Click "Change runtime type".
3. In Notebook setting, change Hardware accelerator to "GPU" and click Save.

3. Run the notebook

1. Run each code block sequentially.

2. Note when you run the "GPU available" block, you should see below if GPU is properly enabled. Note: If you encounter Import error (screenshot below): cannot import name LayerNormalization, go to Runtime -> Factory Reset runtime. Then set "Hardware Accelerator" to GPU and save it.

3. When you run the code block "Mount the Google drive".

   1. Click on the URL.
   2. Obtain the authorization code and paste it in the "Enter your authorization code" box.
   3. Verify Google drive is mounted correctly.
      1. Click file icon in the left panel.
      2. Click Mount Drive (third option).
      3. Click "Connect to Google Drive" when it asks for permission. It takes few minutes to mount.
      4. Once it is mounted, you should see drive folder. See screenshot below:

4. Please make sure to check your data path if you get an IOError in the below code block.

4. Output

1. The model weights can be found inside the my_model folder.
2. The accuracy metrics (highlighted in yellow) such as probability of detection (POD), probability of false detection (POFD), false alarm ratio (FAR), bias, critical success index (CSI), and model accuracy and runtime (in seconds) will be printed in the code block.

   POD	POFD	FAR	Bias	CSI	Accuracy
   0.6903	0.0440	0.1131	0.7783	0.6344	0.8674

3. The accuracy of the model is 86.74% and it took 91.62 minutes to finish.

---

For Ubuntu

GPU-based implementation

Step 1 The AWS Deep Learning AMI comes with different versions of CUDA. Please switch to the correct CUDA version, 10.2, by using the following commands:

sudo rm /usr/local/cuda

sudo ln -s /usr/local/cuda-10.2 /usr/local/cuda

After running both commands, verify your CUDA version by running NVIDIA's nvcc program:

nvcc --version

Step 2 Create a folder inside your prefered directory in which you will store all the necessary files for the cloud classification program. For the purposes of this guide, I will refer to this folder as 'cloudClassification'

Step 3 Copy the dataset files (florence_10mm, train_10mm, test_10mm) into the cloudClassification folder alongside the 'requirements_gpu.txt' file and make an empty 'logs' folder for the TensorBoard Profiler results.

Step 4 Create a conda environment with the required Python version, 3.8, using the following command (you may name your conda environment however you like; for the purposes of this guide, I will use the name 'cloudclassification'):

conda create -n cloudclassification python=3.8

Step 5 After you create your conda environment, activate it using the following command:

conda activate cloudClassification

Step 6 Use the following command to install all of the necessary packages:

pip install -r requirements_gpu.txt

Step 7 Navigate to the /usr/local/cuda/extras/CUPTI/lib64 directory and use the following command to copy and paste the libcupti.so.10.2 file into the same directory with a different name:

sudo cp libcupti.so.10.2 libcupti.so.10.1

Step 8 Run the cloud classification program using the following command:

For single GPU

CUDA_VISIBLE_DEVICES=0 python cloudcode-multi-gpu.py

For multi-GPU

CUDA_VISIBLE_DEVICES=0,1,2,3 python cloudcode-multi-gpu.py

Note:The program will output the runtime alongside metrics such as accuracy after it finishes training. You may use the nvidia-smi command to check GPU load.

NVIDIA SMI Screenshot

---

Videos

Walkthrough video for AWS Ubuntu Server multi-GPU instance

Walkthrough Video for AWS Windows multi-GPU instance

Walkthrough Video for AWS Windows single GPU instance

Walkthrough Video for Windows CPU-based implementation

\

Walkthrough Video for Google Colab