jaeyow

def getLabelsOfImage(bucket_name, number_labels, image):
    # Use boto3 call detect_labels to get Amazon Rekognition labels
    client = boto3.client("rekognition")
    response = client.detect_labels(
        Image={"S3Object": {"Bucket": bucket_name, "Name": image}},
        MaxLabels=number_labels,
        MinConfidence=98
    )
    return response["Labels"]

jaeyow

@step
def model_training(self):
  """
  Model training
  - now training starts, first we specify the Docker image for the required algorithm, in this case linear learner
  - create an estimator with the specified parameters, 
  - set the static hyperparameters, and SageMaker will automatically calculate those set as 'auto'
  - calling fit() starts the training process, upto the specified number of epochs
  - the save the model name and location for the next steps
  - take note that we have to specify an instance for training, which may be different from the endpoint instance

jaeyow

from metaflow import FlowSpec, step, current
from comet_ml import API, Experiment
import os
import random

try:
    from dotenv import load_dotenv
    load_dotenv(verbose=True, dotenv_path='.env')
except:
    print("No dotenv package")

jaeyow

# Pre-process and model building
name: F1 Prediction MLOps

on:
  workflow_dispatch

jobs:
  build:

    runs-on: ubuntu-latest