Serving Alibi-Detect models

Out of the box, mlserver supports the deployment and serving of alibi_detect models. Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. In this example, we will cover how we can create a detector configuration to then serve it using mlserver.

Fetch reference data

The first step will be to fetch a reference data and other relevant metadata for an alibi-detect model.

For that, we will use the alibi library to get the adult dataset with demographic features from a 1996 US census.

Note

Install alibi library for dataset dependencies and alibi_detect library for detector configuration from Pypi

!pip install alibi alibi_detect
import alibi
import matplotlib.pyplot as plt
import numpy as np
adult = alibi.datasets.fetch_adult()
X, y = adult.data, adult.target
feature_names = adult.feature_names
category_map = adult.category_map
n_ref = 10000
n_test = 10000

X_ref, X_t0, X_t1 = X[:n_ref], X[n_ref:n_ref + n_test], X[n_ref + n_test:n_ref + 2 * n_test]
categories_per_feature = {f: None for f in list(category_map.keys())}

Drift Detector Configuration

This example is based on the Categorical and mixed type data drift detection on income prediction tabular data from the alibi-detect documentation.

Creating detector and saving configuration

from alibi_detect.cd import TabularDrift
cd_tabular = TabularDrift(X_ref, p_val=.05, categories_per_feature=categories_per_feature)
from alibi_detect.utils.saving import save_detector
filepath = "alibi-detector-artifacts"
save_detector(cd_tabular, filepath)

Detecting data drift directly

preds = cd_tabular.predict(X_t0,drift_type="feature")

labels = ['No!', 'Yes!']
print(f"Threshold {preds['data']['threshold']}")
for f in range(cd_tabular.n_features):
    fname = feature_names[f]
    is_drift = (preds['data']['p_val'][f] < preds['data']['threshold']).astype(int)
    stat_val, p_val = preds['data']['distance'][f], preds['data']['p_val'][f]
    print(f'{fname} -- Drift? {labels[is_drift]} -- Chi2 {stat_val:.3f} -- p-value {p_val:.3f}')
Threshold 0.05
Age -- Drift? No! -- Chi2 0.012 -- p-value 0.508
Workclass -- Drift? No! -- Chi2 8.487 -- p-value 0.387
Education -- Drift? No! -- Chi2 4.753 -- p-value 0.576
Marital Status -- Drift? No! -- Chi2 3.160 -- p-value 0.368
Occupation -- Drift? No! -- Chi2 8.194 -- p-value 0.415
Relationship -- Drift? No! -- Chi2 0.485 -- p-value 0.993
Race -- Drift? No! -- Chi2 0.587 -- p-value 0.965
Sex -- Drift? No! -- Chi2 0.217 -- p-value 0.641
Capital Gain -- Drift? No! -- Chi2 0.002 -- p-value 1.000
Capital Loss -- Drift? No! -- Chi2 0.002 -- p-value 1.000
Hours per week -- Drift? No! -- Chi2 0.012 -- p-value 0.508
Country -- Drift? No! -- Chi2 9.991 -- p-value 0.441

Serving

Now that we have the reference data and other configuration parameters, the next step will be to serve it using mlserver. For that, we will need to create 2 configuration files:

  • settings.json: holds the configuration of our server (e.g. ports, log level, etc.).

  • model-settings.json: holds the configuration of our model (e.g. input type, runtime to use, etc.).

settings.json

%%writefile settings.json
{
    "debug": "true"
}
Overwriting settings.json

model-settings.json

%%writefile model-settings.json
{
  "name": "income-tabular-drift",
  "implementation": "mlserver_alibi_detect.AlibiDetectRuntime",
  "parameters": {
    "uri": "./alibi-detector-artifacts",
    "version": "v0.1.0",
    "extra": {
      "predict_parameters":{
        "drift_type": "feature"
      }
    }
  }
}
Overwriting model-settings.json

Start serving our model

Now that we have our config in-place, we can start the server by running mlserver start command. This needs to either be ran from the same directory where our config files are or pointing to the folder where they are.

mlserver start .

Since this command will start the server and block the terminal, waiting for requests, this will need to be ran in the background on a separate terminal.

Send test inference request

We now have our alibi-detect model being served by mlserver. To make sure that everything is working as expected, let’s send a request from our test set.

For that, we can use the Python types that mlserver provides out of box, or we can build our request manually.

import requests

inference_request = {
    "inputs": [
        {
            "name": "predict",
            "shape": X_t0.shape,
            "datatype": "FP32",
            "data": X_t0.tolist(),
        }
    ]
}

endpoint = "http://localhost:8080/v2/models/income-tabular-drift/versions/v0.1.0/infer"
response = requests.post(endpoint, json=inference_request)

View model response

import json
response_dict = json.loads(response.text)

labels = ['No!', 'Yes!']
for f in range(cd_tabular.n_features):
    stat = 'Chi2' if f in list(categories_per_feature.keys()) else 'K-S'
    fname = feature_names[f]
    is_drift = response_dict['outputs'][0]['data'][f]
    stat_val, p_val = response_dict['outputs'][1]['data'][f], response_dict['outputs'][2]['data'][f]
    print(f'{fname} -- Drift? {labels[is_drift]} -- Chi2 {stat_val:.3f} -- p-value {p_val:.3f}')
Age -- Drift? No! -- Chi2 0.012 -- p-value 0.508
Workclass -- Drift? No! -- Chi2 8.487 -- p-value 0.387
Education -- Drift? No! -- Chi2 4.753 -- p-value 0.576
Marital Status -- Drift? No! -- Chi2 3.160 -- p-value 0.368
Occupation -- Drift? No! -- Chi2 8.194 -- p-value 0.415
Relationship -- Drift? No! -- Chi2 0.485 -- p-value 0.993
Race -- Drift? No! -- Chi2 0.587 -- p-value 0.965
Sex -- Drift? No! -- Chi2 0.217 -- p-value 0.641
Capital Gain -- Drift? No! -- Chi2 0.002 -- p-value 1.000
Capital Loss -- Drift? No! -- Chi2 0.002 -- p-value 1.000
Hours per week -- Drift? No! -- Chi2 0.012 -- p-value 0.508
Country -- Drift? No! -- Chi2 9.991 -- p-value 0.441