Binding Process to TCP/UDP Port Failure on Windows

Windows has the concept of reserved TCP/UDP ports. These ports can nonetheless be used by any other application. These can be annoying because the reserved ports would not shown be used when we query used ports using netstat. For instance, if we want to bind TCP port 23806 to an application, we determine the availability using the netstat command, such as

C:> netstat -anp tcp | find ":23806"

C:>

The output is blank, which means that the port is unused. However, when we attempt to bind the port to a process of our choice, we encounter an error, such as

bind [127.0.0.1]:23806: Permission denied

This is annoying. The reason is that the port somehow becomes a reserved port. To see this, we can query reserved ports, e.g.,

C:> netsh int ipv4 show excludedportrange protocol=tcp

Protocol tcp Port Exclusion Ranges

Start Port    End Port
----------    --------
      1155        1254
      ...          ...
     23733       23832
     23833       23932
     50000       50059     *

* - Administered port exclusions.


C:>
  

which shows that 23806 is now a served port. What is really annoying is that the range can be updated by Windows dynamically. There are several methods to deal with this.

1. Method 1. Stop and start the Windows NAT Driver service.

   
     net stop winnat
     net start winnat
     

   After this, query the reserved the ports again. It is often the reserved ports are much limited when compared to before, e.g.,

   
   C:>netsh int ipv4 show excludedportrange protocol=tcp
   
   Protocol tcp Port Exclusion Ranges
   
   Start Port    End Port
   ----------    --------
         2869        2869
         5357        5357
        50000       50059     *
   
   * - Administered port exclusions.
   
   C:>
     

2. Method 2. If you don't wish to use this feature of Windows, we can disable reserved ports.

   
   reg add HKLM\SYSTEM\CurrentControlSet\Services\hns\State /v EnableExcludedPortRange /d 0 /f
   

Installing and Using CUDA Tookit and cuDNN in Conda Virtual Environment of Python

This is straightforward.

1. Create a conda virtual environment, e.g.,

   
   conda create -n cudacudnn python=3.9 pip
         

2. Activate the virtual environment, i.e,

   
   conda activate cudacudnn
       

3. Assume we are using Pytorch 2.0, e.g., install it via

   
   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118  
       

4. Install CUDA toolkit and cuDNN, e.g.,

   
   conda install -c conda-forge cudnn=8 cudatoolkit=11.8
       

5. Add the library path of the conda environment to LD_LIBRARY_PATH. There are several approaches. Two appraoches are as follows, assuming the environment is at $HOME/.conda/envs/cudacudnn and we want to run foo.py,

   
   virtenv_path=$HOME/.conda/envs/cudacudnn
   export LD_LIBRARY_PATH=${virtenv_path}/lib:$LD_LIBRARY_PATH
   python foo.py
       

   or

   
   virtenv_path=$HOME/.conda/envs/cudacudnn
   LD_LIBRARY_PATH=${virtenv_path}/lib:$LD_LIBRARY_PATH python foo.py
       

Installing GPU Driver for PyTorch and Tensorflow

To use GPU for PyTorch and Tensorflow, a method I grow fond of is to install GPU driver from RPM fusion, in particular, on Debian or Fedora systems where only free packages are included in their repositories. Via this method, we only install the driver from RPM fusion, and use Python virtual environment to bring in CUDA libraries.

1. Configure RPM Fusion repo by following the instruction, e.g., as follows:

   
       sudo dnf install https://mirrors.rpmfusion.org/free/fedora/rpmfusion-free-release-$(rpm -E %fedora).noarch.rpm https://mirrors.rpmfusion.org/nonfree/fedora/rpmfusion-nonfree-release-$(rpm -E %fedora).noarch.rpm
       

2. Install driver, e.g.,

   
       sudo dnf install akmod-nvidia
       

3. Add CUDA support, i.e.,

   
       sudo dnf install xorg-x11-drv-nvidia-cuda
       

4. Check driver by running nvidia-smi. If it complains about not being able to connect to the driver, reboot the system.

If we use PyTorch or Tensorflow only, there is need to install CUDA from Nvidia.

Reference

1. https://rpmfusion.org/Configuration