dpoulopoulos

def download_document(document_url: str) -> bytes:
    """Download a document from the given URL.

    Args:
        document_url (str): The URL of the document to download.
    """
    import requests
    import logging

    logging.basicConfig(level=logging.INFO)

dpoulopoulos

from kfp import dsl, compiler, kubernetes


@dsl.component(packages_to_install=['requests'])
def download_document(document_url: str, document_path: str):
    """Download a document from the given URL.

    Args:
        document_url (str): The URL of the document to download.
        document_path (str): The local path where the document will be saved.

dpoulopoulos

strategy = tf.distribute.MirroredStrategy()

BATCH_SIZE = 64
GLOBAL_BATCH_SIZE = BATCH_SIZE * strategy.num_replicas_in_sync

train_data = tf.data.Dataset(...).batch(GLOBAL_BATCH_SIZE)

with strategy.scope():
  model = tf.keras.Sequential(...)
  model.compile(...)

dpoulopoulos

BATCH_SIZE = 64

train_data = tf.data.Dataset(...).batch(BATCH_SIZE)

model = tf.keras.Sequential(...)
model.compile(...)

model.fit(train_data, epochs=4)

dpoulopoulos

class MyModel(keras.Model):
  def train_step(self, data):
    # Get the data batch
    inputs, targets = data
    # Get the model's weights
    trainable_vars = self.trainable_variables
    # Forward pass
    with tf.GradientTape() as tape:
      # Get the predictions
      preds = self(inputs, training=True)

dpoulopoulos

from PIL import Image
from torchvision.prototype import models as pm


img = Image.open("test/assets/encode_jpeg/grace_hopper_517x606.jpg")

# Step 1: Load a pre-trained model.
# In this step we will load a ResNet architecture.
weights = pm.ResNet50_Weights.ImageNet1K_V1
model = pm.resnet50(weights=weights)

dpoulopoulos

import torch
import torchvision.models as models
import torchvision.transforms as transforms

from PIL import Image


img = Image.open("test/assets/encode_jpeg/grace_hopper_517x606.jpg")

# Step 1: Load a pre-trained model.

dpoulopoulos

imp = package.PackageImporter(path)
loaded_model = imp.load_pickle(package_name, resource_name)

dpoulopoulos

with package.PackageExporter(path) as exp:
    exp.extern("numpy.**")
    exp.intern("models.**")
    exp.save_pickle(package_name, resource_name, model)

dpoulopoulos

with package.PackageExporter(path) as exp:
    exp.intern("models.**")
    exp.save_pickle(package_name, resource_name, model)