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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Copyright 2021 The TensorFlow Authors.
Step1:  Simple TFX Pipeline for Vertex Pipelines
This notebook-based tutorial will create a simple TFX pipeline and run it using
Google Cloud Vertex Pipelines.  This notebook is based on the TFX pipeline
built in
Simple TFX Pipeline Tutorial.
Google Cloud Vertex Pipelines helps you to automate, monitor, and govern
your ML systems by orchestrating your ML workflow in a serverless manner. You
can define your ML pipelines using Python with TFX, and then execute your
pipelines on Google Cloud. See
Vertex Pipelines introduction
to learn more about Vertex Pipelines.
Setup
Install python packages
We will install required Python packages including TFX and KFP to author ML
pipelines and submit jobs to Vertex Pipelines.
Step2:  Restart the runtime
Restart the runtime to ensure the following cells use the updated versions.
You can restart the runtime with following cell
Step3:  Check the package versions.
Step4:  Set up variables
We will set up some variables used to customize the pipelines below. Following
information is required
Step5:  Set gcloud to use your project.
Step6:  Prepare example data
The dataset we are using is the
Palmer Penguins dataset.
There are four numeric features in this dataset
Step7:  Take a quick look at the CSV file.
Step11:  You should be able to see five values. species is one of 0, 1 or 2, and all other features should have values between 0 and 1.
Create a pipeline
TFX pipelines are defined using Python APIs. We will define a pipeline which
consists of three components
Step12:  Copy the module file to GCS which can be accessed from the pipeline components.
Because model training happens on GCP, we need to upload this model definition. 
Otherwise, you might want to build a container image including the module file
and use the image to run the pipeline.
Step14:  Write a pipeline definition
We will define a function to create a TFX pipeline.
Step15:  Run the pipeline on Vertex Pipelines.
TFX provides multiple orchestrators to run your pipeline. In this tutorial we
will use the Vertex Pipelines together with the Kubeflow V2 dag runner.
We need to define a runner to actually run the pipeline. You will compile
your pipeline into our pipeline definition format using TFX APIs.
Step16:  The generated definition file can be submitted using kfp client.
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Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Explanation: Copyright 2021 The TensorFlow Authors.
End of explanation
# Use the latest version of pip.
!pip install --upgrade pip
!pip install --upgrade "tfx[kfp]<2"
Explanation: Simple TFX Pipeline for Vertex Pipelines
This notebook-based tutorial will create a simple TFX pipeline and run it using
Google Cloud Vertex Pipelines.  This notebook is based on the TFX pipeline
built in
Simple TFX Pipeline Tutorial.
Google Cloud Vertex Pipelines helps you to automate, monitor, and govern
your ML systems by orchestrating your ML workflow in a serverless manner. You
can define your ML pipelines using Python with TFX, and then execute your
pipelines on Google Cloud. See
Vertex Pipelines introduction
to learn more about Vertex Pipelines.
Setup
Install python packages
We will install required Python packages including TFX and KFP to author ML
pipelines and submit jobs to Vertex Pipelines.
End of explanation
# docs_infra: no_execute
import sys
if not 'google.colab' in sys.modules:
  # Automatically restart kernel after installs
  import IPython
  app = IPython.Application.instance()
  app.kernel.do_shutdown(True)
Explanation: Restart the runtime
Restart the runtime to ensure the following cells use the updated versions.
You can restart the runtime with following cell:
End of explanation
import tensorflow as tf
print('TensorFlow version: {}'.format(tf.__version__))
from tfx import v1 as tfx
print('TFX version: {}'.format(tfx.__version__))
import kfp
print('KFP version: {}'.format(kfp.__version__))
Explanation: Check the package versions.
End of explanation
GOOGLE_CLOUD_PROJECT = ''     # <--- ENTER THIS
GOOGLE_CLOUD_REGION = 'us-central1'     
GCS_BUCKET_NAME = GOOGLE_CLOUD_PROJECT + '-gcs'
if not (GOOGLE_CLOUD_PROJECT and GOOGLE_CLOUD_REGION and GCS_BUCKET_NAME):
    from absl import logging
    logging.error('Please set all required parameters.')
Explanation: Set up variables
We will set up some variables used to customize the pipelines below. Following
information is required:
GCP Project id. You can find your Project ID in the panel with your lab instructions.
GCP Region to run pipelines. For more information about the regions that
Vertex Pipelines is available in, see the
Vertex AI locations guide.
Google Cloud Storage Bucket to store pipeline outputs.
Enter required values in the cell below before running it.
End of explanation
!gcloud config set project {GOOGLE_CLOUD_PROJECT}
PIPELINE_NAME = 'penguin-vertex-pipelines'
# Path to various pipeline artifact.
PIPELINE_ROOT = 'gs://{}/pipeline_root/{}'.format(
    GCS_BUCKET_NAME, PIPELINE_NAME)
# Paths for users' Python module.
MODULE_ROOT = 'gs://{}/pipeline_module/{}'.format(
    GCS_BUCKET_NAME, PIPELINE_NAME)
# Paths for input data.
DATA_ROOT = 'gs://{}/data/{}'.format(GCS_BUCKET_NAME, PIPELINE_NAME)
# This is the path where your model will be pushed for serving.
SERVING_MODEL_DIR = 'gs://{}/serving_model/{}'.format(
    GCS_BUCKET_NAME, PIPELINE_NAME)
print('PIPELINE_ROOT: {}'.format(PIPELINE_ROOT))
Explanation: Set gcloud to use your project.
End of explanation
!gsutil cp gs://download.tensorflow.org/data/palmer_penguins/penguins_processed.csv {DATA_ROOT}/
Explanation: Prepare example data
The dataset we are using is the
Palmer Penguins dataset.
There are four numeric features in this dataset:
culmen_length_mm
culmen_depth_mm
flipper_length_mm
body_mass_g
All features were already normalized
to have range [0,1]. We will build a classification model which predicts the
species of penguins.
We need to make our own copy of the dataset. Because TFX ExampleGen reads
inputs from a directory, we need to create a directory and copy dataset to it
on GCS.
End of explanation
!gsutil cat {DATA_ROOT}/penguins_processed.csv | head
Explanation: Take a quick look at the CSV file.
End of explanation
_trainer_module_file = 'penguin_trainer.py'
%%writefile {_trainer_module_file}
# Copied from https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple
from typing import List
from absl import logging
import tensorflow as tf
from tensorflow import keras
from tensorflow_transform.tf_metadata import schema_utils
from tfx import v1 as tfx
from tfx_bsl.public import tfxio
from tensorflow_metadata.proto.v0 import schema_pb2
_FEATURE_KEYS = [
    'culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm', 'body_mass_g'
]
_LABEL_KEY = 'species'
_TRAIN_BATCH_SIZE = 20
_EVAL_BATCH_SIZE = 10
# Since we're not generating or creating a schema, we will instead create
# a feature spec.  Since there are a fairly small number of features this is
# manageable for this dataset.
_FEATURE_SPEC = {
    **{
        feature: tf.io.FixedLenFeature(shape=[1], dtype=tf.float32)
           for feature in _FEATURE_KEYS
       },
    _LABEL_KEY: tf.io.FixedLenFeature(shape=[1], dtype=tf.int64)
}
def _input_fn(file_pattern: List[str],
              data_accessor: tfx.components.DataAccessor,
              schema: schema_pb2.Schema,
              batch_size: int) -> tf.data.Dataset:
  Generates features and label for training.
  Args:
    file_pattern: List of paths or patterns of input tfrecord files.
    data_accessor: DataAccessor for converting input to RecordBatch.
    schema: schema of the input data.
    batch_size: representing the number of consecutive elements of returned
      dataset to combine in a single batch
  Returns:
    A dataset that contains (features, indices) tuple where features is a
      dictionary of Tensors, and indices is a single Tensor of label indices.
  
  return data_accessor.tf_dataset_factory(
      file_pattern,
      tfxio.TensorFlowDatasetOptions(
          batch_size=batch_size, label_key=_LABEL_KEY),
      schema=schema).repeat()
def _make_keras_model() -> tf.keras.Model:
  Creates a DNN Keras model for classifying penguin data.
  Returns:
    A Keras Model.
  
  # The model below is built with Functional API, please refer to
  # https://www.tensorflow.org/guide/keras/overview for all API options.
  inputs = [keras.layers.Input(shape=(1,), name=f) for f in _FEATURE_KEYS]
  d = keras.layers.concatenate(inputs)
  for _ in range(2):
    d = keras.layers.Dense(8, activation='relu')(d)
  outputs = keras.layers.Dense(3)(d)
  model = keras.Model(inputs=inputs, outputs=outputs)
  model.compile(
      optimizer=keras.optimizers.Adam(1e-2),
      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
      metrics=[keras.metrics.SparseCategoricalAccuracy()])
  model.summary(print_fn=logging.info)
  return model
# TFX Trainer will call this function.
def run_fn(fn_args: tfx.components.FnArgs):
  Train the model based on given args.
  Args:
    fn_args: Holds args used to train the model as name/value pairs.
  
  # This schema is usually either an output of SchemaGen or a manually-curated
  # version provided by pipeline author. A schema can also derived from TFT
  # graph if a Transform component is used. In the case when either is missing,
  # `schema_from_feature_spec` could be used to generate schema from very simple
  # feature_spec, but the schema returned would be very primitive.
  schema = schema_utils.schema_from_feature_spec(_FEATURE_SPEC)
  train_dataset = _input_fn(
      fn_args.train_files,
      fn_args.data_accessor,
      schema,
      batch_size=_TRAIN_BATCH_SIZE)
  eval_dataset = _input_fn(
      fn_args.eval_files,
      fn_args.data_accessor,
      schema,
      batch_size=_EVAL_BATCH_SIZE)
  model = _make_keras_model()
  model.fit(
      train_dataset,
      steps_per_epoch=fn_args.train_steps,
      validation_data=eval_dataset,
      validation_steps=fn_args.eval_steps)
  # The result of the training should be saved in `fn_args.serving_model_dir`
  # directory.
  model.save(fn_args.serving_model_dir, save_format='tf')
Explanation: You should be able to see five values. species is one of 0, 1 or 2, and all other features should have values between 0 and 1.
Create a pipeline
TFX pipelines are defined using Python APIs. We will define a pipeline which
consists of three components:
CsvExampleGen: Reads in data files and convert them to TFX internal format for further processing. There are multiple ExampleGens for various formats. In this tutorial, we will use CsvExampleGen which takes CSV file input.
Trainer: Trains an ML model. Trainer component requires a model definition code from users. You can use TensorFlow APIs to specify how to train a model and save it in a _savedmodel format.
Pusher: Copies the trained model outside of the TFX pipeline. Pusher component can be thought of an deployment process of the trained ML model.
Our pipeline will be almost identical to a basic TFX pipeline.
The only difference is that we don't need to set metadata_connection_config
which is used to locate
ML Metadata database. Because
Vertex Pipelines uses a managed metadata service, users don't need to care
of it, and we don't need to specify the parameter.
Before actually define the pipeline, we need to write a model code for the
Trainer component first.
Write model code.
We will create a simple DNN model for classification using TensorFlow Keras API. This model training code will be saved to a separate file.
In this tutorial we will use Generic Trainer of TFX which support Keras-based models. You need to write a Python file containing run_fn function, which is the entrypoint for the Trainer component.
End of explanation
!gsutil cp {_trainer_module_file} {MODULE_ROOT}/
Explanation: Copy the module file to GCS which can be accessed from the pipeline components.
Because model training happens on GCP, we need to upload this model definition. 
Otherwise, you might want to build a container image including the module file
and use the image to run the pipeline.
End of explanation
# Copied from https://www.tensorflow.org/tfx/tutorials/tfx/penguin_simple and
# slightly modified because we don't need `metadata_path` argument.
def _create_pipeline(pipeline_name: str, pipeline_root: str, data_root: str,
                     module_file: str, serving_model_dir: str,
                     ) -> tfx.dsl.Pipeline:
  Creates a three component penguin pipeline with TFX.
  # Brings data into the pipeline.
  example_gen = tfx.components.CsvExampleGen(input_base=data_root)
  # Uses user-provided Python function that trains a model.
  trainer = tfx.components.Trainer(
      module_file=module_file,
      examples=example_gen.outputs['examples'],
      train_args=tfx.proto.TrainArgs(num_steps=100),
      eval_args=tfx.proto.EvalArgs(num_steps=5))
  # Pushes the model to a filesystem destination.
  pusher = tfx.components.Pusher(
      model=trainer.outputs['model'],
      push_destination=tfx.proto.PushDestination(
          filesystem=tfx.proto.PushDestination.Filesystem(
              base_directory=serving_model_dir)))
  # Following three components will be included in the pipeline.
  components = [
      example_gen,
      trainer,
      pusher,
  ]
  return tfx.dsl.Pipeline(
      pipeline_name=pipeline_name,
      pipeline_root=pipeline_root,
      components=components)
Explanation: Write a pipeline definition
We will define a function to create a TFX pipeline.
End of explanation
import os
PIPELINE_DEFINITION_FILE = PIPELINE_NAME + '_pipeline.json'
runner = tfx.orchestration.experimental.KubeflowV2DagRunner(
    config=tfx.orchestration.experimental.KubeflowV2DagRunnerConfig(),
    output_filename=PIPELINE_DEFINITION_FILE)
# Following function will write the pipeline definition to PIPELINE_DEFINITION_FILE.
_ = runner.run(
    _create_pipeline(
        pipeline_name=PIPELINE_NAME,
        pipeline_root=PIPELINE_ROOT,
        data_root=DATA_ROOT,
        module_file=os.path.join(MODULE_ROOT, _trainer_module_file),
        serving_model_dir=SERVING_MODEL_DIR))
Explanation: Run the pipeline on Vertex Pipelines.
TFX provides multiple orchestrators to run your pipeline. In this tutorial we
will use the Vertex Pipelines together with the Kubeflow V2 dag runner.
We need to define a runner to actually run the pipeline. You will compile
your pipeline into our pipeline definition format using TFX APIs.
End of explanation
# docs_infra: no_execute
from google.cloud import aiplatform
from google.cloud.aiplatform import pipeline_jobs
aiplatform.init(project=GOOGLE_CLOUD_PROJECT, location=GOOGLE_CLOUD_REGION)
job = pipeline_jobs.PipelineJob(template_path=PIPELINE_DEFINITION_FILE,
                                display_name=PIPELINE_NAME)
job.run(sync=False)
Explanation: The generated definition file can be submitted using kfp client.
End of explanation
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