Build a ML Workflow For Scones Unlimited On Amazon SageMaker

Overview

In this project, using AWS Sagemaker to build an image classification model that can tell bicycles apart from motorcycles. You'll deploy your model, use AWS Lambda functions to build supporting services, and AWS Step Functions to compose your model and services into an event-driven application. At the end of this project, you will have created a portfolio-ready demo that showcases your ability to build and compose scalable, ML-enabled, AWS applications.

Source code: vnk8071/ml_workflow_for_scones_unlimited

Data

Link CIFAR100: https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz

Create bucket

Feature Engineering

Use label 8 and 48

df_train = df_train[(df_train['labels'] == 8) | (df_train['labels'] == 48)]
df_test = df_test[(df_test['labels'] == 8) | (df_test['labels'] == 48)]

Setup AWS Sagemaker

import sagemaker

session = sagemaker.Session()

bucket = "ml-workflow-khoivn"
print("Default Bucket: {}".format(bucket))

region = session.boto_region_name
print("AWS Region: {}".format(region))

role = sagemaker.get_execution_role()
print("RoleArn: {}".format(role))

Upload data to S3

import os

os.environ["DEFAULT_S3_BUCKET"] = bucket
!aws s3 sync ./train s3://${DEFAULT_S3_BUCKET}/train/
!aws s3 sync ./test s3://${DEFAULT_S3_BUCKET}/test/

Train model on Sagemaker

# Use the image_uris function to retrieve the latest 'image-classification' image
algo_image = sagemaker.image_uris.retrieve('image-classification', region, version='latest')
s3_output_location = f"s3://{bucket}/models/image_model"

img_classifier_model=sagemaker.estimator.Estimator(
    image_uri=algo_image,
    role=role,
    instance_count=1,
    instance_type='ml.p3.2xlarge',
    output_path=s3_output_location,
    sagemaker_session=sagemaker.Session()
)

img_classifier_model.set_hyperparameters(
    image_shape='3,32,32',
    num_classes=2,
    num_training_samples=len(df_train)
)

from sagemaker.debugger import Rule, rule_configs
from sagemaker.session import TrainingInput
model_inputs = {
        "train": sagemaker.inputs.TrainingInput(
            s3_data=f"s3://{bucket}/train/",
            content_type="application/x-image"
        ),
        "validation": sagemaker.inputs.TrainingInput(
            s3_data=f"s3://{bucket}/test/",
            content_type="application/x-image"
        ),
        "train_lst": sagemaker.inputs.TrainingInput(
            s3_data=f"s3://{bucket}/train.lst",
            content_type="application/x-image"
        ),
        "validation_lst": sagemaker.inputs.TrainingInput(
            s3_data=f"s3://{bucket}/test.lst",
            content_type="application/x-image"
        )
}
img_classifier_model.fit(model_inputs)

Result:

2023-09-03 16:20:57 Starting - Starting the training job...
2023-09-03 16:21:21 Starting - Preparing the instances for trainingProfilerReport-1693758056: InProgress
.........
2023-09-03 16:22:41 Downloading - Downloading input data...
2023-09-03 16:23:21 Training - Downloading the training image...............
2023-09-03 16:25:51 Training - Training image download completed. Training in progress....
...
[09/03/2023 16:29:28 INFO 139962714154816] Epoch[29] Batch [20]#011Speed: 191.406 samples/sec#011accuracy=0.976190
[09/03/2023 16:29:30 INFO 139962714154816] Epoch[29] Train-accuracy=0.980847
[09/03/2023 16:29:30 INFO 139962714154816] Epoch[29] Time cost=4.990
[09/03/2023 16:29:30 INFO 139962714154816] Epoch[29] Validation-accuracy=0.802083

Deploy model

from sagemaker.model_monitor import DataCaptureConfig

data_capture_config = DataCaptureConfig(
    enable_capture=True,
    sampling_percentage=100,
    destination_s3_uri=f"s3://{bucket}/data_capture"
)
deployment = img_classifier_model.deploy(
    initial_instance_count=1,
    instance_type='ml.m5.xlarge',
    data_capture_config=data_capture_config
    )

endpoint = deployment.endpoint_name
print(endpoint)

Inferencing

predictor = sagemaker.predictor.Predictor('image-classification-2023-09-03-16-30-39-872')

from sagemaker.serializers import IdentitySerializer
import base64

predictor.serializer = IdentitySerializer("image/png")
with open("./test/bicycle_s_001789.png", "rb") as f:
    payload = f.read()


inference = predictor.predict(payload)
print(inference)

Lambda function

Detail in lambda.py

Step function

Example input:

{
  "image_data": "",
  "s3_bucket": "ml-workflow-khoivn",
  "s3_key": "test/bicycle_s_001789.png"
}

Export defines to JSON: MyStateMachine-jpawniln9.asl.json

Testing and Evaluation

import random
import boto3
import json


def generate_test_case():
    # Setup s3 in boto3
    s3 = boto3.resource('s3')

    # Randomly pick from sfn or test folders in our bucket
    objects = s3.Bucket(bucket).objects.filter(Prefix="test")

    # Grab any random object key from that folder!
    obj = random.choice([x.key for x in objects])

    return json.dumps({
        "image_data": "",
        "s3_bucket": bucket,
        "s3_key": obj
    })
generate_test_case()

Plot report

Notebook

Pipeline: Jupyter

Export to HTML: project-KhoiVN.html