Installing Miniconda

TensorFlow and other third-party packages on which DLRM data preprocessing and code training depend can be managed in a unified manner using Miniconda, avoiding incompatibility issues.

1. Use PuTTY to log in to the server as the root user.
2. Create a Miniconda installation directory.

   mkdir -p /path/to/miniconda3

3. Download miniconda.sh to /path/to/miniconda3.

   wget https://repo.anaconda.com/miniconda/Miniconda3-py39_24.3.0-0-Linux-aarch64.sh  -O  /path/to/miniconda3/miniconda.sh

4. Install Miniconda.

   bash /path/to/miniconda3/miniconda.sh -b -u -p /path/to/miniconda3

   Delete miniconda.sh, which you will no longer use.

   rm -rf  /path/to/miniconda3/miniconda.sh

5. Optional: Initialize Miniconda.

   /path/to/miniconda3/bin/conda init bash 
   /path/to/miniconda3/bin/conda init zsh

   After the installation is complete, the conda directory (/path/to/miniconda3/condabin) is added to the PATH environment variable.

6. Close the window of connecting to the server and reconnect to the server. The temporary proxy needs to be reconfigured. For details, see Method 1: Configuring a Temporary Network Proxy.
7. Check whether Miniconda is installed.

   conda -V

   

   If the command output shows that the version is 24.3.0, the installation is successful.

8. Configure the conda proxy environment.
   1. Create a .condarc file.

      vi /root/.condarc

   2. Press i to enter the insert mode and add the following content to the .condarc file.

      Set the IP address and port as required.

      channels:
        - defaults
      
      proxy_servers:
      
        http: http://Proxy_IP_address:Proxy_port
      
        https: http://Proxy_IP_address:Proxy_port
      
      
      ssl_verify: false
      
      show_channel_urls: true
      
      allow_other_channels: true
      

   3. Press Esc, type :wq!, and press Enter to save the file and exit.