leapy 0.0.3

Real-time inference pipelines

Just give me the dataz!

Welcome! Leapy is a library for real-time, sub-millisecond inference; it provides customizable machine learning pipeline export for fast model serving. These pipelines are targeted for using Dask's scalable machine learning, which follows the Scikit-Learn API. However, you can use this framework directly with Scikit-Learn as well.

Leapy is inspired by MLeap.

Benefits

Dask is a Python distributed computing environment in Python with a burgeoning machine learning component, compatible with Scikit-Learn's API. Using Leapy's framework, we can serve these pipelines in real-time!

This means:

• No JVM: No reliance on JVM from using Spark.
• Python: All Python development and custom transformers -- no Scala & Java needed!
• Scale: Scikit-Learn logic and pipelines scaled to clusters.
• Fast: You're in control of how fast your transformers are!
• Simple: Easily build and deploy models with Docker.
• Reliable: Encourages a test-driven approach.

Examples

• Simple -- Super simple example of creating, testing, and using custom transformers
• XGBoost -- Advanced example of using XGBoost with Dask, saving, and serving the model.

Benchmarks

A simple example of what we're going for -- computing a one-hot-encoding, with ~200K labels, of a single data point (dask array x_da and numpy array x = x_da.compute()):

Where ohe_dml (from dask_ml) and ohe (from leapy) are essentially the same; ohe_sk is from scikit-learn and ohe_runtime is from ohe.to_runtime(). And, running compute() on Dask transforms above bumps the time up to about 1 second.

With the time we save using ohe_runtime, we can squeeze in many more transformations and an estimator to still come in under 1ms.

Example Usage

Start with a dataset in dask arrays, X and y, and create a Scikit-Learn pipeline:

from dask_ml.linear_model import LogisticRegression
from leapy.dask.transformers import OneHotEncoder
from leapy.dask.pipeline import FeaturePipeline

pipe = Pipeline([
        ('fp', FeaturePipeline([('ohe',
                                 OneHotEncoder(sparse=False),
                                 [0, 1])])),
        ('clf', LogisticRegression())
])

pipe.fit(X, y)

Then we export to a runtime pipeline, and save:

pipe_runtime = pipe.to_runtime()

with open('pipe_runtime.pkl', 'wb') as f:
    pickle.dump(pipe_runtime, f)

This model is ready to be served! Docker