Python ML Environment Setup on Saudi Server

CLOUD|April 13, 2026|2 min read|264 views

Python 3.12 + PyTorch + TensorFlow + Jupyter + CUDA. Complete ML environment on your server. We explain the full setup.

Python ML Environment Setup: From Zero to Running

Setting up a Python ML environment on a server differs from setup on a personal machine. This guide covers the complete setup.

Why a Server?

Personal machine:
- Runs only when you open it
- Hard to collaborate on

كلاود VPS server:
- Runs 24/7
- Accessible from anywhere
- Supports immediate collaboration
- 6,900+ SAR/year

Basic Setup

# Install Python 3.11+
add-apt-repository ppa:deadsnakes/ppa
apt update && apt install python3.11 python3.11-venv python3.11-dev -y
curl https://bootstrap.pypa.io/get-pip.py | python3.11

# Virtual environment per project
python3.11 -m venv /opt/projects/myproject/venv
source /opt/projects/myproject/venv/bin/activate

Core ML Libraries

pip install numpy pandas scikit-learn matplotlib seaborn

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

# Hugging Face
pip install transformers datasets accelerate

# LangChain
pip install langchain langchain-community

JupyterLab on the Server

pip install jupyterlab
jupyter notebook password

jupyter lab --no-browser --port=8888 --ip=0.0.0.0 \
 --notebook-dir=/opt/notebooks
# Access: http://YOUR.SERVER.IP:8888

Auto-start Jupyter

cat > /etc/systemd/system/jupyter.service << EOF
[Unit]
Description=Jupyter Lab Server
After=network.target

[Service]
Type=simple
User=ubuntu
ExecStart=/opt/venv/bin/jupyter lab --no-browser --ip=0.0.0.0 --port=8888
Restart=always

[Install]
WantedBy=multi-user.target
EOF

systemctl enable jupyter && systemctl start jupyter

MLflow for Experiment Tracking

import mlflow

with mlflow.start_run:
 mlflow.log_param("learning_rate", 0.01)
 mlflow.log_metric("accuracy", 0.95)
 mlflow.sklearn.log_model(model, "model")

Conclusion

The Python ML Stack template on كلاود (cloud.net.sa) installs all of this automatically: Python 3.11, JupyterLab, PyTorch, MLflow — ready in under one minute.