eileen-code4fun

import tensorflow as tf
import tensorflow_datasets as tfds

ds = tfds.load('cifar10', as_supervised=True)

def csvfy(dataset, filename):
  with open(filename, 'w') as f:
    for image, label in dataset:
      f.write('{},'.format(label.numpy()))
      f.write(','.join(str(x) for x in list(image.numpy().reshape(-1))))

eileen-code4fun

api_client.create_schedule_from_job_spec(
  job_spec_path='e2e-tutorial-automl-pipeline-schedule.json',
  # Start at the first minute of every hour.
  schedule='1 * * * *'
)

eileen-code4fun

from kfp.v2 import compiler
from google.cloud.aiplatform import AIPlatformClient

compiler.Compiler().compile(pipeline_func=e2e_tutorial_pipeline, package_path='e2e-tutorial-automl-pipeline.json')

api_client = AIPlatformClient(project_id='PROJECT_ID', region='us-central1')
api_client.create_run_from_job_spec(
  job_spec_path='e2e-tutorial-automl-pipeline.json'
)

eileen-code4fun

from kfp import dsl
from google_cloud_pipeline_components import aiplatform

@dsl.pipeline(
  name='e2e-tutorial-automl-pipeline',
  description='A simple automl training',
  pipeline_root='GCS_PATH_FOR_PIPELINE'
)
def e2e_tutorial_pipeline():
  job = aiplatform.AutoMLImageTrainingJobRunOp(

eileen-code4fun

{
  "displayName": "e2e-tutorial-automl",
  "inputDataConfig": {
    "datasetId": "DATASET_ID",
    "filterSplit": {
      "trainingFilter": "labels.aiplatform.googleapis.com/ml_use=training",
      "validationFilter": "labels.aiplatform.googleapis.com/ml_use=validation",
      "testFilter": "labels.aiplatform.googleapis.com/ml_use=test"
    }
  },

eileen-code4fun

import tensorflow as tf
import tensorflow_datasets as tfds

cifar10 = tfds.load('cifar10', as_supervised=True)
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']

def save_images_write_metadata(dataset, purpose, mf):
  i = 1
  for image, label in dataset:
    fn = '{}_{}.jpeg'.format(purpose, i)

eileen-code4fun

import base64
import io
attr_map = tf.io.decode_jpeg(base64.b64decode(BASE64_JPEG_DATA))

plt.subplot(1, 2, 1)
plt.xticks([])
plt.yticks([])
plt.title(class_names[test_labels[0][0]])
plt.imshow(test_images[0])
plt.xticks([])

eileen-code4fun

{
  "inputs": {
    "image": {
      "inputTensorName": "conv2d_input",
      "modality": "image"
    }
  },
  "outputs": {
    "explanation": {
      "outputTensorName": "dense_1"

eileen-code4fun

import json

data = {
    "instances": [
        (test_images[0]/255.0).tolist()
    ]
}
print(json.dumps(data),  file=open('prediction_request.json', 'w'))

eileen-code4fun

import tensorflow as tf
from tensorflow.keras import datasets
import matplotlib.pyplot as plt

_, (test_images, test_labels) = datasets.cifar10.load_data()

class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
plt.xticks([])
plt.yticks([])
plt.title(class_names[test_labels[0][0]])