How to deploy and scale an ML model in production using

Freelance Senior Data Scientist (currently at Servier)
+7 years experience in Consulting, Tech, and Startups
Interests in NLP, MLOps, and AI products
ML trainer
Content creator on Medium

Ahmed BESBES

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What we'll learn today

This presentation will cover:

APIs and production machine learning
BentoML: an Open Source Model Serving framework
Overview of ML-related features
Demo
Resources to go further

Don't hesitate to interrupt and ask questions!

Terminology

A model: a parametrized function whose weights (or parameters) are learned from data. Example: a logistic regression or ... a neural network

Model training: the step where the model weights are learned (or adjusted) from data by through the optimization of an cost function

Model inference: the step where the trained model predicts on new (unseen) data

1. APIs and production machine learning

Because there's a life after jupyter notebooks

What happens when your model is done training?

An after-life

The infra team needs a minimum of code packaging and dependency management to deploy your model
DevOps needs to know about resource consumption
Business and product teams need to stress-test the model (i.e. API needs to scale to multiple concurrent queries)
Developers need to access your API documentation to know how to consume it

API Requirements

As a data scientist, you want

Support for multiple frameworks (torch, TF, scikit learn)
Micro batching
Performance and scalability: parallelization, high throughput
Ability to use accelerated runtimes (GPUs)

As a thoughtful colleague, you want

Reproducibility
Dependency management
Documentation
Monitoring
Debugging
Data validation

2. BentoML

Open Source Model Serving

Simplifies model serving and deployment
Packages everything you need in a distribution format called a bento
Enables data science agility
- Integrates Pre/Post processing
- Supports many popular ML frameworks
- Automatically generates Docker container
- Deploy to any cloud infrastructure
Improves the inference performance

A Bento is like Docker container, but for ML

Bento is a file archive with all the source code, models, data files, and dependency configurations required for running a user-defined bentoml.Service, packaged into a standardized format.

A Bento is Self-contained and
deployable

Everywhere.

How to save a model with Bento, create an API and deploy it?

Step 1: save a model

import bentoml

from sklearn import svm
from sklearn import datasets

# Load training data set
iris = datasets.load_iris()
X, y = iris.data, iris.target

# Train the model
clf = svm.SVC(gamma='scale')
clf.fit(X, y)

# Save model to the BentoML local model store
saved_model = bentoml.sklearn.save_model("iris_clf", clf)
print(f"Model saved: {saved_model}")

# Model saved: Model(tag="iris_clf:hrcxybszzsm3khqa")

Step 2: Create a service

 import numpy as np
 import bentoml
 from bentoml.io import NumpyNdarray

 iris_clf_runner = bentoml.sklearn.get("iris_clf:latest").to_runner()

 svc = bentoml.Service("iris_classifier", runners=[iris_clf_runner])

 @svc.api(input=NumpyNdarray(), output=NumpyNdarray())
 def classify(input_series: np.ndarray) -> np.ndarray:
     result = iris_clf_runner.predict.run(input_series)
     return result

import requests

requests.post(
  "http://127.0.0.1:3000/classify",
  headers={"content-type": "application/json"},
  data="[[5.9, 3, 5.1, 1.8]]"
).text
  
'[2]'

Step 3: build a Bento 🍱

Define a bentofile.yaml

service: "service:svc"  # Same as the argument passed to `bentoml serve`
labels:
   owner: bentoml-team
   stage: dev
include:
- "*.py"  # A pattern for matching which files to include in the bento
python:
   packages:  # Additional pip packages required by the service
   - scikit-learn
   - pandas

Step 4: containerize

 bentoml containerize iris_classifier:latest

docker run -it --rm -p 3000:3000 iris_classifier:jclapisz2s6qyhqa serve --production

Step 5: deploy

We can do it in three ways

🐳 Generate container images from Bento for custom docker deployment
🚀 bentoctl: Fast model deployment on any cloud platform
🦄️ Yatai: Model Deployment at scale on Kubernetes

3. Super-charged ML features

to make your life easier

1. Micro Batching

Dynamically group prediction requests in real-time into batches for model inference

Increases performance of your app

Increases throughput leverages acceleration hardware

✅ Multiple input requests are run in parallel
✅ A proxy (i.e. a load balancer) distributes requests between workers (a worker is a running instance of an API server)
✅ Each worker distributes the requests to the model runners that are in charge of inference
✅ Each runner dynamically groups the requests in batches by finding a tradeoff between latency and throughput
✅ Runners make predictions on each batch
✅ Batch predictions are then split and released as individual responses

- How to enable batching?

bentoml.pytorch.save_model(
    name="mnist",
    model=model,
    signature={
        "__call__": {
            "batchable": True,
            "batch_dim": (0, 0),
        },
    },
)

send your parallel requests

Bentoml will take care of the rest

2. Parallel inference

Inference graph

Customizable control flows

Combine multiple models

gpt2_generator = (bentoml
                  .transformers
                  	.get("gpt2-generation:latest").to_runner())

distilgpt2_generator = (bentoml
                        .transformers
                        .get("distilgpt2-generation:latest").to_runner())

distilbegpt2_medium_generator = (bentoml
                                 .transformers
                                 .get("gpt2-medium-generation:latest").to_runner())

bert_base_uncased_classifier = (bentoml
                                .transformers
                                .get("bert-base-uncased-classification:latest").to_runner())

svc = bentoml.Service(
    "inference_graph",
    runners=[
        gpt2_generator,
        distilgpt2_generator,
        distilbegpt2_medium_generator,
        bert_base_uncased_classifier,
    ],
)

Load runners

Define service

Define inference workflow

@svc.api(input=Text(), output=JSON())
async def classify_generated_texts(original_sentence: str) -> dict:
    generated_sentences = [
        result[0]["generated_text"]
        for result in await asyncio.gather(
            gpt2_generator.async_run(
                original_sentence,
                max_length=MAX_LENGTH,
                num_return_sequences=NUM_RETURN_SEQUENCE,
            ),
            distilgpt2_generator.async_run(
                original_sentence,
                max_length=MAX_LENGTH,
                num_return_sequences=NUM_RETURN_SEQUENCE,
            ),
            distilbegpt2_medium_generator.async_run(
                original_sentence,
                max_length=MAX_LENGTH,
                num_return_sequences=NUM_RETURN_SEQUENCE,
            ),
        )
    ]

    results = []
    for sentence in generated_sentences:
        score = (await bert_base_uncased_classifier.async_run(sentence))[0]["score"]
        results.append(
            {
                "generated": sentence,
                "score": score,
            }
        )

    return results

3. Accelerated runtime

service: "service:svc"
include:
- "*.py"
python:
    packages:
    - torch
    - torchvision
    - torchaudio
    extra_index_url:
    - "https://download.pytorch.org/whl/cu113"
docker:
    distro: debian
    python_version: "3.8.12"
    cuda_version: "11.6.2"

Use GPU and declare it when building the Bento

4. Other cool features

API Documentation
Data validation
gRPC
Monitoring

https://docs.bentoml.org/en/latest/guides/index.html

How to deploy and scale an ML model in production using

Ahmed BESBES

What we'll learn today

Terminology

1. APIs and production machine learning

What happens when your model is done training?

An after-life

API Requirements

2. BentoML

Open Source Model Serving

A Bento is like Docker container, but for ML

A Bento is Self-contained and
deployable

How to save a model with Bento, create an API and deploy it?

Step 1: save a model

Step 2: Create a service

Step 3: build a Bento 🍱

Step 4: containerize

Step 5: deploy

3. Super-charged ML features

1. Micro Batching

- How to enable batching?

2. Parallel inference

3. Accelerated runtime

4. Other cool features

3. Demo

4. Resources

Interesting reads

[Groupbees] - How to deploy an ML model in production using BentoML

[Groupbees] - How to deploy an ML model in production using BentoML

Ahmed Besbes

How to deploy and scale an ML model in production using

Ahmed BESBES

What we'll learn today

Terminology

1. APIs and production machine learning

What happens when your model is done training?

An after-life

API Requirements

2. BentoML

Open Source Model Serving

A Bento is like Docker container, but for ML

A Bento is Self-contained and deployable

How to save a model with Bento, create an API and deploy it?

Step 1: save a model

Step 2: Create a service

Step 3: build a Bento 🍱

Step 4: containerize

Step 5: deploy

3. Super-charged ML features

1. Micro Batching

- How to enable batching?

2. Parallel inference

3. Accelerated runtime

4. Other cool features

3. Demo

4. Resources

Interesting reads

[Groupbees] - How to deploy an ML model in production using BentoML

More from Ahmed Besbes

A Bento is Self-contained and
deployable