wehub-resource-sync
72 lines
1.9 KiB
Python
72 lines
1.9 KiB
Python
"""
|
|
Example of scoring images with MLflow model deployed to a REST API endpoint.
|
|
|
|
The MLflow model to be scored is expected to be an instance of KerasImageClassifierPyfunc
|
|
(e.g. produced by running this project) and deployed with MLflow prior to invoking this script.
|
|
"""
|
|
|
|
import base64
|
|
import os
|
|
|
|
import click
|
|
import pandas as pd
|
|
import requests
|
|
|
|
from mlflow.utils import cli_args
|
|
|
|
|
|
def score_model(path, host, port):
|
|
"""
|
|
Score images on the local path with MLflow model deployed at given uri and port.
|
|
|
|
Args:
|
|
path: Path to a single image file or a directory of images.
|
|
host: Host the model is deployed at.
|
|
port: Port the model is deployed at.
|
|
|
|
Returns:
|
|
Server response.
|
|
"""
|
|
if os.path.isdir(path):
|
|
filenames = [
|
|
os.path.join(path, x) for x in os.listdir(path) if os.path.isfile(os.path.join(path, x))
|
|
]
|
|
else:
|
|
filenames = [path]
|
|
|
|
def read_image(x):
|
|
with open(x, "rb") as f:
|
|
return f.read()
|
|
|
|
data = pd.DataFrame(
|
|
data=[base64.encodebytes(read_image(x)) for x in filenames], columns=["image"]
|
|
).to_json(orient="split")
|
|
|
|
response = requests.post(
|
|
url=f"{host}:{port}/invocations",
|
|
data={
|
|
"dataframe_split": data,
|
|
},
|
|
headers={"Content-Type": "application/json"},
|
|
)
|
|
|
|
if response.status_code != 200:
|
|
raise Exception(f"Status Code {response.status_code}. {response.text}")
|
|
return response
|
|
|
|
|
|
@click.command(help="Score images.")
|
|
@click.option("--port", type=click.INT, default=80, help="Port at which the model is deployed.")
|
|
@cli_args.HOST
|
|
@click.argument("data-path")
|
|
def run(data_path, host, port):
|
|
"""
|
|
Score images with MLflow deployed deployed at given uri and port and print out the response
|
|
to standard out.
|
|
"""
|
|
print(score_model(data_path, host, port).text)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
run()
|