To display the running time of the Linux system in a human-readable format, you can use the following command.

uptime -p

To kill a process by its PID, first use htop to obtain the PID. (ref)

kill -9 PID

To obtain an IPv4 address, if the IPv4 address is unavailable, run the following code :

dhclient -v

To use conda to create new environment with a specific name and Python version, run the following code:

conda create -n py3.10 python==3.10

To import a file from another directory in python, using the following approach: Suppose you have two folders at the same level:

/project/some_folder/some_file.py
/project/another_floder/another.py

You can make one module visible to the other using the following code:

import sys
sys.path.append('../')

To resolve the issue where the Visual Studio Code terminal shows multiple Conda environments on the server, follows these steps:

(py3.10)(base)future@lif323:~/work$
  1. Delete the ~/.vscode-server directory on the server.
  2. Connect to the server again.

To run .notebook on a remote server throught vs code, you need to following these steps:

  • Install jupyter extension on vs code on the remote server side.
  • Open .notebook file and Select Kernel
  • Install jupyter python package through pip install jupyter on the python environment associate with the Kernel.

To add a new user in ubuntu, use the following command:

sudo adduser username

To grant the user with administrative privileges, use:

sudo usermod -aG sudo username

Running Hugging Face Models Offline ref1

Model

  1. load the pre-trained model and save to locally:
model_saved = "./model-distilbert-base-uncased-finetuned-sst-2-english/"
model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
model.save_pretrained(model_saved)
  1. Transfer the model_saved directory to offline server (e.g. to ./model_path). Then, load the model from the offline path:
model = AutoModelForSequenceClassification.from_pretrained('./model_path')

Dataset

  1. Load the dataset and save it locally:
from datasets import load_dataset, load_from_disk
dataset = load_dataset('glue', 'sst2')
dataset.save_to_disk('./dataset_saved')
  1. Transfer the ./dataset_saved directory to the offline server (e.g. ./dataset_path/). Then, load the dataset from the offline path:
dataset = load_from_disk('./dataset_path')

Problem When multiple Conda environments are activated simultaneously (e.g., (pytorch) (base) shown in the prompt), it may cause confusion or unexpected behavior. ref

(pytorch) (base) db@server

Resolution To resolve this issue:

  1. Disable the auto-activation of the base environment:
conda config --set auto_activate_base False
  1. Restart your shell (close and reopen the terminal).
  2. If needed, re-enable base activation later with:
conda config --set auto_activate_base True