monitaur 0.4.0

Monitaur Client Library

Monitaur Client Library

Tested with the following versions of Python:

1. 3.8.1
2. 3.7.6
3. 3.6.10

Install

$ pip install monitaur

Methods

1. add_model: Adds metadata about the machine learning model to the system.
2. get_credentials: Retrieves AWS credentials.
3. record_training_tabular: Sends trained model and anchors data to S3.
4. record_training_image: Sends trained model to S3.
5. record_transaction: Sends transaction details to the server.
6. read_transactions: Retrieves transactions.

Client Library Examples

from monitaur import Monitaur
from monitaur.utils import hash_file


# create monitaur instance
monitaur = Monitaur(
    client_secret="changme",
    base_url="http://localhost:8008",
)

# train model
dataset = loadtxt("./_example/data.csv", delimiter=",")
seed = 7
test_size = 0.1
model_data = train_model(dataset, seed, test_size)
trained_model = model_data["trained_model"]
training_data = model_data["training_data"]
dump(trained_model, open(f"./_example/data.joblib", "wb"))


# add model to api
model_data = {
    "name": "Diabetes Classifier",
    "model_type": "xgboost",
    "model_class": "tabular",
    "library": "xg_boost",
    "trained_model_hash": hash_file("./_example/data.joblib"),  # trained model (None is allowed)
    "production_file_hash": hash_file("./_example/prediction.py"),  # production file used for running inputs through the trained model (None is allowed)
    "feature_number": 8,
    "owner": "Anthony Habayeb",
    "developer": "Andrew Clark",
    "python_version": "3.8.1",
    "ml_library_version": "1.0.2",
    "influences": True,
}
model_set_id = monitaur.add_model(**model_data)

# get aws credentials
credentials = monitaur.get_credentials(model_set_id)

# record training
record_training_data = {
    "credentials": credentials,
    "model_set_id": model_set_id,
    "trained_model": trained_model,
    "training_data": training_data,
    "feature_names": [
        "Pregnancies",
        "Glucose",
        "BloodPressure",
        "SkinThickness",
        "Insulin",
        "BMI",
        "DiabetesPedigreeF",
        "Age",
    ],
    # "re_train": True
}
monitaur.record_training_tabular(**record_training_data)

# record transaction
prediction = get_prediction([2, 84, 68, 27, 0, 26.7, 0.341, 32])
transaction_data = {
    "credentials": credentials,
    "model_set_id": model_set_id,
    "trained_model_hash": hash_file("./_example/data.joblib"),
    "production_file_hash": hash_file("./_example/prediction.py"),
    "prediction": prediction,
    "image": "cat.jpeg",  # required if 'model_class' is  'image'
    "features": {
        "Pregnancies": 2,
        "Glucose": 84,
        "BloodPressure": 68,
        "SkinThickness": 27,
        "Insulin": 0,
        "BMI": 26.7,
        "DiabetesPedigreeF": 0.341,
        "Age": 32,
    },
}
response = monitaur.record_transaction(**transaction_data)
print(response)

# read transactions by passing model_id and/or model_set_id
# both are optional arguments
transactions = monitaur.read_transactions(model_set_id=model_set_id)
print(transactions)

API Examples

requests:

import requests

API_ENDPOINT = "http://localhost:8000"
CLIENT_SECRET = "eaa74a3d715a36ed6d40af3fb9f5916d8205cf2c"
MODEL_SET_ID = "b7f60d02-06c9-418c-943e-cf74fe61d613"

# get access and refresh tokens
tokens = requests.post(
    f"{API_ENDPOINT}/api/auth/?grant_type=client_credentials",
    data={"client_secret": CLIENT_SECRET}
)
access_token = tokens.json()["access"]
refresh_token = tokens.json()["refresh"]

headers = {"Authorization": f"Token {access_token}"}

# get model metadata
model = requests.get(f"{API_ENDPOINT}/api/models/set/{MODEL_SET_ID}", headers=headers)
print(model.json())
model_id = model.json()["id"]

# get transactions
transactions = requests.get(f"{API_ENDPOINT}/api/transactions/?model={model_id}", headers=headers)
for transaction in transactions.json():
    print(f"\n{transaction}")

cURL:

$ curl -X POST http://localhost:8000/api/auth/\?grant_type=client_credentials \
    -d '{"client_secret": "eaa74a3d715a36ed6d40af3fb9f5916d8205cf2c"}' \
    -H 'Content-Type: application/json'

$ curl -X GET "http://localhost:8000/api/models/set/b7f60d02-06c9-418c-943e-cf74fe61d613/" \
    -H "Authorization: Token 54321"

httpie:

$ http --json POST http://localhost:8000/api/auth/\?grant_type=client_credentials \
    client_secret=eaa74a3d715a36ed6d40af3fb9f5916d8205cf2c

$ http GET http://localhost:8000/api/models/set/b7f60d02-06c9-418c-943e-cf74fe61d613/ Authorization:"Token 54321"

History

TBD