- at the start of the project, I built a python file to follow the CPU and Memory power,with a psutil modle that halps as with the monitoring.
import psutil
from flask import Flask
app = Flask(__name__)
@app.route("/")
def index():
cpu_percent = psutil.cpu_percent()
mem_percent = psutil.virtual_memory().percent
message = None
if cpu_percent > 80 or mem_percent > 80:
message = "High CPU or High Memory, Please scale up!"
return f"CPU Utilization: {cpu_percent}% and Memory Utilization: {mem_percent}%\n{message or ''}"
if __name__ == '__main__':
app.run(debug=True, host='0.0.0.0')2.after that I take a templeate that helps me to create a web more nice...
<!DOCTYPE html>
<html>
<head>
<title>System Monitoring</title>
<script src="https://cdn.plot.ly/plotly-latest.min.js"></script>
<style>
.plotly-graph-div {
margin: auto;
width: 50%;
background-color: rgba(151, 128, 128, 0.688);
padding: 20px;
}
</style>
</head>
<body>
<div class="container">
<h1>System Monitoring</h1>
<div id="cpu-gauge"></div>
<div id="mem-gauge"></div>
{% if message %}
<div class="alert alert-danger">{{ message }}</div>
{% endif %}
</div>
<script>
var cpuGauge = {
type: "indicator",
mode: "gauge+number",
value: {{ cpu_metric }},
gauge: {
axis: { range: [null, 100] },
bar: { color: "#1f77b4" },
bgcolor: "white",
borderwidth: 2,
bordercolor: "#ccc",
steps: [
{ range: [0, 50], color: "#d9f0a3" },
{ range: [50, 85], color: "#ffeb84" },
{ range: [85, 100], color: "#ff5f5f" }
],
threshold: {
line: { color: "red", width: 4 },
thickness: 0.75,
value: {{ cpu_metric }}
}
}
};
var memGauge = {
type: "indicator",
mode: "gauge+number",
value: {{ mem_metric }},
gauge: {
axis: { range: [null, 100] },
bar: { color: "#1f77b4" },
bgcolor: "white",
borderwidth: 2,
bordercolor: "#ccc",
steps: [
{ range: [0, 50], color: "#d9f0a3" },
{ range: [50, 85], color: "#ffeb84" },
{ range: [85, 100], color: "#ff5f5f" }
],
threshold: {
line: { color: "red", width: 4 },
thickness: 0.75,
value: {{ mem_metric }}
}
}
};
var cpuGaugeLayout = { title: "CPU Utilization" };
var memGaugeLayout = { title: "Memory Utilization" };
Plotly.newPlot('cpu-gauge', [cpuGauge], cpuGaugeLayout);
Plotly.newPlot('mem-gauge', [memGauge], memGaugeLayout);
</script>
</body>- after I finish with my UI and Python app, time to turn on my DevOps power! and to make the code to a container via Dockerfile.
FROM python:3.9-slim-buster
WORKDIR /app
COPY requirements.txt.
RUN pip3 install --upgrade pip --no-cache-dir -r /app/requirements.txt
COPY . .
ENV FLASK_RUN_HOST=0.0.0.0
EXPOSE 5000
CMD ["flask", "run"]via the requirements.txt I install all the things I need for the project, and then I copy all the files in the directory and start them on localhost ip in port 5000. 4. after I finished with my docker file, I write a Python file that gone build the repo in aws-:
import boto3
ecr_client = boto3.client('ecr')
repository_name = "my_project"
response = ecr_client.create_repository(repositoryName=repository_name)
repository_uri = response ['repository']['repositoryUri']
print(repository_uri)pls- play dose commend before to up the the boto3-
pip3 install boto3after we have a repo- I create a cluster and node group via gui, time to update the k8s servises,
#create deployment and service
from kubernetes import client, config
# Load Kubernetes configuration
config.load_kube_config()
# Create a Kubernetes API client
api_client = client.ApiClient()
# Define the deployment
deployment = client.V1Deployment(
metadata=client.V1ObjectMeta(name="my-flask-app"),
spec=client.V1DeploymentSpec(
replicas=1,
selector=client.V1LabelSelector(
match_labels={"app": "my-flask-app"}
),
template=client.V1PodTemplateSpec(
metadata=client.V1ObjectMeta(
labels={"app": "my-flask-app"}
),
spec=client.V1PodSpec(
containers=[
client.V1Container(
name="my-flask-container",
image="568373317874.dkr.ecr.us-east-1.amazonaws.com/my_monitoring_app_image:latest",
ports=[client.V1ContainerPort(container_port=5000)]
)
]
)
)
)
)
# Create the deployment
api_instance = client.AppsV1Api(api_client)
api_instance.create_namespaced_deployment(
namespace="default",
body=deployment
)
# Define the service
service = client.V1Service(
metadata=client.V1ObjectMeta(name="my-flask-service"),
spec=client.V1ServiceSpec(
selector={"app": "my-flask-app"},
ports=[client.V1ServicePort(port=5000)]
)
)
# Create the service
api_instance = client.CoreV1Api(api_client)
api_instance.create_namespaced_service(
namespace="default",
body=service
)
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