app.py

from datetime import datetime
import hashlib
import hmac
import json
import textwrap

from dateutil.tz import tzutc
import requests
from yarl import URL
from config import configurations

# for API
from flask import Flask, request, jsonify
import logging


# == Configurations
cfg = configurations[1]

# Manager's endpoint
MANAGER_SERVICE_URL = URL(cfg['MANAGER_ENDPOINT'])
# Access key of a user (use superadmin's key to query every sessions)
ACCESS_KEY = cfg['ADMIN_ACCESS_KEY']
# Secret key of a user
SECRET_KEY = cfg['ADMIN_SECRET_KEY']
# API version. Refer to https://github.com/lablup/backend.ai-manager/blob/05f21a09bc6eff8678747f1199c48ed221be974a/src/ai/backend/manager/server.py#L82.
API_VERSION = cfg['API_VERSION']
# Hash algorithm to generate Backend.AI request signature
HASH_TYPE = cfg['HASH_TYPE']


def generate_signature(
    *,
    method,
    version,
    endpoint,
    date,
    rel_url,
    content_type,
    access_key,
    secret_key,
    hash_type,
):
   
    hash_type = hash_type
    hostname = endpoint._val.netloc
    body_hash = hashlib.new(hash_type, b'').hexdigest()

    sign_str = '{}\n{}\n{}\nhost:{}\ncontent-type:{}\nx-backendai-version:{}\n{}'.format(
        method.upper(),
        rel_url,
        date.isoformat(),
        hostname,
        content_type.lower(),
        version,
        body_hash
    )
    sign_bytes = sign_str.encode()

    sign_key = hmac.new(secret_key.encode(),
                        date.strftime('%Y%m%d').encode(), hash_type).digest()
    sign_key = hmac.new(sign_key, hostname.encode(), hash_type).digest()

    signature = hmac.new(sign_key, sign_bytes, hash_type).hexdigest()
    headers = {
        'Authorization': 'BackendAI signMethod=HMAC-{}, credential={}:{}'.format(
            hash_type.upper(),
            access_key,
            signature
        ),
    }
    return headers, signature



def query_agent_list():
    # == Generate authorization header.
    # HTTP method to use: POST, GET, HEAD, etc.
    method = 'POST'
    # Request date time. Should be UTC.
    date = datetime.now(tzutc())
    # Request url. To fetch compute session list, a client have to use
    # a GraphQL request. Manager serves every GraphQl requests by a single
    # URL, `/admin/graphql`.
    rel_url = '/admin/graphql'
    # Content type of the request.
    content_type = 'application/json'
    hdrs, _ = generate_signature(
        method=method,
        version=API_VERSION,
        endpoint=MANAGER_SERVICE_URL,
        date=date,
        rel_url=str(rel_url),
        content_type=content_type,
        access_key=ACCESS_KEY,
        secret_key=SECRET_KEY,
        hash_type=HASH_TYPE,
    )

    # == Build request headers
    headers = {
        # Client can set User-Agent string, which will be displayed in
        # Manager's log. This is optional, but good to know to categorize
        # the origin of the request.
        'User-Agent': 'Backed.AI Session Query Test',
        # Following three headers are mandatory.
        'Content-Type': content_type,
        'X-BackendAI-Version': API_VERSION,
        'Date': date.isoformat(),
    }
    headers.update(hdrs)

    # == Prepare session query (using GraphQL)
    # Fields to fetch. Refer following link to see possible fields.
    # https://github.com/lablup/backend.ai-manager/blob/main/src/ai/backend/manager/models/kernel.py#L687
    fields = [
        'id',
        'occupied_slots',
        'available_slots',
        'live_stat',
    ]
    # Query definition for compute_session_list can be found at
    # https://github.com/lablup/backend.ai-manager/blob/05f21a09bc6eff8678747f1199c48ed221be974a/src/ai/backend/manager/models/gql.py#L403
    query = '''
        query($limit:Int!, $offset:Int!) {
            agent_list(limit:$limit, offset:$offset) {
                items { $fields }
                total_count
            }
        }
    '''
    query = query.replace('$fields', ' '.join(fields))
    query = textwrap.dedent(query).strip()
    variables = {
      'limit': 50,
      'offset': 0,
      #'sess_id': '91edc3fd-8c23-45e1-93b6-d15a2a0cd888',
     
    }
    data = {
        'query': query,
        'variables': variables,
    }

    # Send requests and return response after parsing into JSON.
    r = requests.post(MANAGER_SERVICE_URL / rel_url[1:],
                      headers=headers, data=json.dumps(data))
    result = r.json()
    return result



def extract_utilization():

    #agents's type is dict
    agents=query_agent_list()

    result_dict={}

    for agent in agents['agent_list']['items']:


        if agent['id'] == 'i-gs-gpu-1080ti-01':
            break


        #live's type is dict
        live = json.loads(agent['live_stat'])

        #6400% 중에 pct %.
        cpu=live['node']['cpu_util']['pct']
        cpu=float(cpu)
        #100%기준으로 표시하며 소수점 둘째 자리까지만 사용 
        cpu=round(int(cpu)/64, 2)

        mem=live['node']['mem']['pct']

        #800% 중에 current %.
        cuda=live['node']['cuda_util']['current']
        cuda=float(cuda)
        #100%기준으로 표시하며 소수점 둘째 자리까지만 사용 
        cuda=round(int(cuda)/8, 2)

        disk =live['node']['disk']['pct']

        result_dict[agent['id']] ={'cpu':cpu, 'cuda':cuda, 'mem':mem, 'disk':disk}

        #print("cpu_util : ",cpu/64,"%")
        #print("memory_util : ", mem,"%")
        #print("gpu_util : ", cuda/8,"%")
        #print("disk_util : ", disk, "%")

    # adding root key of the result_dict
    result_dict ={"agent":result_dict}

    # convert type dict to json
    result_json=json.dumps(result_dict)

    print(result_json)
    return result_json


# == API Logger ==
logging.basicConfig(filename = "../logs/monitoringAPI.log", level = logging.DEBUG)


# == restAPI ==
app = Flask(__name__)  
 
@app.route('/api/getMonitoring', methods=['GET'])
def myfunction_api():
    
    result = extract_utilization()
    return result


if __name__ == "__main__":
    app.run(host='0.0.0.0', port=31000, debug=True)

# api url 
# http://10.231.238.231:31000/api/getMonitoring

# api url 결과값값 예시
'''

{"agent": {"i-ai-1": {"cpu": 4.09, "cuda": 38.38, "mem": "4.54", "disk": "25.02"}, "i-ai-2": {"cpu": 5.22, "cuda": 99.62, "mem": "7.25", "disk": "8.44"}, "i-ai-3": {"cpu": 1.05, "cuda": 12.5, "mem": "1.93", "disk": "8.27"}, "i-ai-4": {"cpu": 2.92, "cuda": 87.62, "mem": "5.54", "disk": "8.35"}, "i-ai-5": {"cpu": 1.72, "cuda": 50.0, "mem": "8.65", "disk": "40.45"}, "i-ai-6": {"cpu": 0.09, "cuda": 0.0, "mem": "1.23", "disk": "87.7"}}}

''