README.md

MLflow Scoring Server Benchmarks

Benchmarks for the MLflow scoring server.

Overview

• Launch one of the variants of the MLflow scoring server.
  • Web server - no container. See Deploy MLflow models.
  • SageMaker docker container. See mlflow.sagemaker.
  • Plain docker container. See mlflow CLI - build-docker.

The container servers support the same interface (POST a JSON-serialized pandas DataFrame or CSV) as the webserver (and I assume the same implementation).

However, there are some discrepancies in loading models. Only the SageMaker container supports both SparkML and MLeap scoring. The other two do not support MLeap scoring and will return the following error.

Exception: No suitable flavor backend was found for the model.

Here's a breakdown of flavor support for the server variants.

Server Type	SparkML	MLeap
webserver	OK	Error
docker	OK	Error
SageMaker	OK	OK

Launch scoring server

Launch the scoring server on port 5001. For examples, see PySpark Real-time Predictions.

Simple benchmark

python -u benchmark.py --host localhost --port 5001

Calls:
  0/4898: 0.087 - [5.470]
  100/4898: 0.083 - [5.474]
  .  . .
  400/4898: 0.065 - [6.626]

Results (seconds):
  mean:    0.126
  max:     0.413
  min:     0.081
  total:   2.529
  records: 4198

Multi-threaded benchmark

Launches a number of threads that concurrently call the scoring server.

python -u threaded_benchmark.py --uri http://localhost:5001/invocations --num_threads 5

Summary
  Thread Mean  Max   Min
       0 0.028 0.032 0.025
       1 0.027 0.033 0.016
       2 0.027 0.033 0.021
       3 0.028 0.036 0.025
       4 0.027 0.032 0.025
  Total  0.0275 0.036 0.016