How To Install Jitsi On Ubuntu 24.04 LTS

 Jitsi is an open-source project that allows you to build and deploy secure video conferencing solutions. This tutorial will teach us how to install Jitsi on Ubuntu 24.04 LTS.
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Testing Environment: Ubuntu 24.04.1 LTS       Hostname -          ip Address -
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apt update ; apt install build-essential net-tools curl git software-properties-common

sudo apt update -y && sudo apt ugprade -y
sudo hostnamectl set-hostname jitsi.tutorial.com
curl https://download.jitsi.org/jitsi-key.gpg.key | sudo sh -c 'gpg --dearmor > /usr/share/keyrings/jitsi-keyring.gpg' && \
echo 'deb [signed-by=/usr/share/keyrings/jitsi-keyring.gpg] https://download.jitsi.org stable/' | sudo tee /etc/apt/sources.list.d/jitsi-stable.list > /dev/null && \
sudo apt update

sudo apt remove --purge apache2
sudo apt install nginx-full

sudo systemctl status nginx ; sudo systemctl start nginx ; sudo systemctl enable nginx
sudo apt install jitsi-meet -y
admin@jitsi.tutorial.com
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How To Install Odoo 18 on Ubuntu 24.04 LTS

 Odoo is an open-source Python and Javascript software used to manage business processes. It includes various features such as customer relationship management, e-commerce, accounting, billing, manufacturing, project management, etc. Odoo uses the PostgreSQL database management system to store its data.
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Testing Environment: Ubuntu 24.04.1 LTS       Hostname -          ip Address -
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apt update ; apt install build-essential net-tools curl git software-properties-common

sudo apt install build-essential wget git python3-pip python3-dev python3-venv python3-wheel python3 libfreetype6-dev libxml2-dev libzip-dev libsasl2-dev python3-setuptools libjpeg-dev zlib1g-dev libpq-dev libxslt1-dev libldap2-dev libtiff5-dev libopenjp2-7-dev -y

sudo apt-get install npm node-less wkhtmltopdf -y
npm install -g less less-plugin-clean-css
python3 -V
sudo ln -s /usr/local/bin/wkhtmltopdf /usr/bin

sudo apt-get install postgresql -y
sudo systemctl start postgresql && sudo systemctl enable postgresql ; sudo systemctl status postgresql
sudo useradd -m -U -r -d /opt/odoo18 -s /bin/bash odoo18
sudo su - postgres -c "createuser -s odoo18"

su - odoo18
pwd
git clone https://www.github.com/odoo/odoo --depth 1 --branch 18.0 /opt/odoo18/odoo18
python3.12 -m venv odoo18-venv
source odoo18-venv/bin/activate
pip install --upgrade pip
pip3 install wheel
pip3 install -r odoo18/requirements.txt
deactivate

su -
mkdir /opt/odoo18/odoo18-custom-addons
chown -R odoo18:odoo18 /opt/odoo18/odoo18-custom-addons
mkdir -p /var/log/odoo18/ && touch /var/log/odoo18/odoo18.log
chown -R odoo18:odoo18 /var/log/odoo18/

touch /etc/odoo18.conf
nano /etc/odoo18.conf
[options]

admin_passwd = StrongPasswordHere
db_host = False
db_port = False
db_user = odoo18
db_password = False
xmlrpc_port = 8069
logfile = /var/log/odoo18/odoo18.log
addons_path = /opt/odoo18/odoo18/addons,/opt/odoo18/odoo18-custom-addons

touch /etc/systemd/system/odoo18.service
nano /etc/systemd/system/odoo18.service
[Unit]
Description=odoo18

[Service]
Type=simple
SyslogIdentifier=odoo18
PermissionsStartOnly=true
User=odoo18
Group=odoo18
ExecStart=/opt/odoo18/odoo18-venv/bin/python3 /opt/odoo18/odoo18/odoo-bin -c /etc/odoo18.conf
StandardOutput=journal+console

[Install]
WantedBy=multi-user.target

sudo systemctl start odoo18 && sudo systemctl enable odoo18 ; sudo systemctl status odoo18
http://127.0.0.1:8069
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" How to Deploy a Machine Learning Model Using Flask on Ubuntu 24.04 LTS "

Deploying a machine learning model is a crucial step to making it accessible to end-users. Flask, a lightweight web framework for Python, is popular for deploying machine learning models due to its simplicity and flexibility. When deploying on an Ubuntu 24.04, you can leverage the power of GPUs to accelerate inference, especially for deep learning models.

Flask is a micro web framework written in Python.
In this Tutorial you will Learn  How to Deploy a Machine Learning Model Using Flask on Ubuntu 24.04
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Testing Environment: Ubuntu 24.04.1 LTS       Hostname -          ip Address -
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apt update ; apt install build-essential net-tools curl git software-properties-common

apt install python3 python3-pip python3-virtualenv python3.12-venv -y
python3 -m venv ml-env
source ml-env/bin/activate
pip install --upgrade pip
pip install flask gunicorn torch torchvision tensorflow numpy pandas
nano train_model.py
import torch
import torch.nn as nn
import torch.optim as optim

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc1 = nn.Linear(4, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, 1)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        # Using sigmoid for a binary classification output
        x = torch.sigmoid(self.fc3(x))
        return x

# Create the model
model = SimpleModel()

# Define a binary cross-entropy loss and Adam optimizer
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# Dummy training data:
data = torch.rand(100, 4)
labels = torch.randint(0, 2, (100, 1), dtype=torch.float32)

# Train for 10 epochs
for epoch in range(10):
    optimizer.zero_grad()
    outputs = model(data)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()
    if (epoch+1) % 2 == 0:
        print(f"Epoch [{epoch+1}/10], Loss: {loss.item():.4f}")

# Switch model to evaluation mode (optional but good practice)
model.eval()

# Save the model's state dict only (recommended approach)
torch.save(model.state_dict(), "model_weights.pth")
print("Model weights saved to 'model_weights.pth'")

python3 train_model.py
nano app.py
import torch
import torch.nn as nn
import numpy as np
from flask import Flask, request, jsonify

app = Flask(__name__)

###############################################
# 1. Define the EXACT SAME PyTorch architecture
###############################################
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        # Must match your training script's layers:
        self.fc1 = nn.Linear(4, 64)
        self.fc2 = nn.Linear(64, 64)
        self.fc3 = nn.Linear(64, 1)  # Single output (e.g. for binary classification)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = torch.sigmoid(self.fc3(x))
        return x

###########################################################
# 2. Load the PyTorch model state dict (must match architecture)
###########################################################
pytorch_model = SimpleModel()
pytorch_model.load_state_dict(torch.load("model_weights.pth"))
pytorch_model.eval()

###########################################################
# 3. Flask endpoint for PyTorch inference
###########################################################
@app.route("/predict", methods=["POST"])
def predict():
    data = request.get_json()

    # Convert input to a numpy array of shape [1, 4] since the model expects 4 features
    input_data = np.array(data["input"]).reshape(1, 4)
    
    # PyTorch inference
    tensor_input = torch.tensor(input_data, dtype=torch.float32)
    pytorch_output = pytorch_model(tensor_input).detach().numpy().tolist()

    return jsonify({
        "pytorch": pytorch_output
    })

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000, debug=True)

python3 app.py
curl -X POST -H "Content-Type: application/json" -d '{"input":[0.1, 0.2, 0.3, 0.4]}' http://localhost:5000/predict