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Remote Server Configuration

Karahan Sarıtaş edited this page Mar 11, 2023 · 9 revisions

Introduction

To train better word embedding models, we have to use huge datasets consisting of millions of words. In order to minimize the amount of time required for training, we decided to use a remote server. In this guide, we will be explaining the initial steps to start working on our project easily.

Step-by-Step Guide: To run Python code on a remote server using Putty, follow these simple steps:

  • First, you need to download and install PuTTY from the official website. The installation process is straightforward, and you can accept all the default options during the installation process.
  • Once you have installed Putty, launch it and enter the IP address or hostname of the remote server you want to connect to. You can also specify the port number if it is different from the default SSH port (22). Click on the "Open" button to initiate the connection. Once you have established the connection, you will be prompted to enter your password.
  • Navigate to your working folder and clone the repository:
git clone https://github.com/Turkish-Word-Embeddings/Word-Embeddings-Repository-for-Turkish.git 
  • Open the repository and create a folder called corpus. This folder will be ignored by the .gitignore. Open the folder and install bounwebcorpus.txt with the following commands.
curl --remote-name-all https://tulap.cmpe.boun.edu.tr/repository/xmlui/bitstream/handle/20.500.12913/16{/bounwebcorpus.txt.zip}
unzip bounwebcorpus.txt.zip
rm bounwebcorpus.txt.zip
  • Install Miniconda with Python 3.9 with the following command:
wget https://repo.anaconda.com/miniconda/Miniconda3-py39_23.1.0-1-Linux-x86_64.sh
bash <filename>

You have to follow the instructions to complete the installation. You can capture the filename using Miniconda*. After installation, you have to restart the terminal and type conda list to make sure that it is successfully installed. You can activate the current environment with conda activate if it's not automatically activated. You can run conda info --envs to list all environments. The active environment is the one with an asterisk (*).

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