<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Vertex SDK
Step1:  Install the latest GA version of google-cloud-storage library as well.
Step2:  Restart the kernel
Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages.
Step3:  Before you begin
GPU runtime
This tutorial does not require a GPU runtime.
Set up your Google Cloud project
The following steps are required, regardless of your notebook environment.
Select or create a Google Cloud project. When you first create an account, you get a $300 free credit towards your compute/storage costs.
Make sure that billing is enabled for your project.
Enable the following APIs
Step4:  Region
You can also change the REGION variable, which is used for operations
throughout the rest of this notebook.  Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.
Americas
Step5:  Timestamp
If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial.
Step6:  Authenticate your Google Cloud account
If you are using Google Cloud Notebook, your environment is already authenticated. Skip this step.
If you are using Colab, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.
Otherwise, follow these steps
Step7:  Create a Cloud Storage bucket
The following steps are required, regardless of your notebook environment.
When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.
Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization.
Step8:  Only if your bucket doesn't already exist
Step9:  Finally, validate access to your Cloud Storage bucket by examining its contents
Step10:  Set up variables
Next, set up some variables used throughout the tutorial.
Import libraries and define constants
Step11:  Initialize Vertex SDK for Python
Initialize the Vertex SDK for Python for your project and corresponding bucket.
Step12:  Set hardware accelerators
You can set hardware accelerators for training and prediction.
Set the variables TRAIN_GPU/TRAIN_NGPU and DEPLOY_GPU/DEPLOY_NGPU to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify
Step13:  Set pre-built containers
Set the pre-built Docker container image for training and prediction.
For the latest list, see Pre-built containers for training.
For the latest list, see Pre-built containers for prediction.
Step14:  Set machine type
Next, set the machine type to use for training and prediction.
Set the variables TRAIN_COMPUTE and DEPLOY_COMPUTE to configure  the compute resources for the VMs you will use for for training and prediction.
machine type
n1-standard
Step15:  Tutorial
Now you are ready to start creating your own custom model and training for Boston Housing.
Examine the training package
Package layout
Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.
PKG-INFO
README.md
setup.cfg
setup.py
trainer
__init__.py
task.py
The files setup.cfg and setup.py are the instructions for installing the package into the operating environment of the Docker image.
The file trainer/task.py is the Python script for executing the custom training job. Note, when we referred to it in the worker pool specification, we replace the directory slash with a dot (trainer.task) and dropped the file suffix (.py).
Package Assembly
In the following cells, you will assemble the training package.
Step16:  Task.py contents
In the next cell, you write the contents of the training script task.py. I won't go into detail, it's just there for you to browse. In summary
Step17:  Store training script on your Cloud Storage bucket
Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket.
Step18:  Create and run custom training job
To train a custom model, you perform two steps
Step19:  Prepare your command-line arguments
Now define the command-line arguments for your custom training container
Step20:  Run the custom training job
Next, you run the custom job to start the training job by invoking the method run, with the following parameters
Step21:  Load the saved model
Your model is stored in a TensorFlow SavedModel format in a Cloud Storage bucket. Now load it from the Cloud Storage bucket, and then you can do some things, like evaluate the model, and do a prediction.
To load, you use the TF.Keras model.load_model() method passing it the Cloud Storage path where the model is saved -- specified by MODEL_DIR.
Step22:  Evaluate the model
Now let's find out how good the model is.
Load evaluation data
You will load the Boston Housing test (holdout) data from tf.keras.datasets, using the method load_data(). This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements
Step23:  Perform the model evaluation
Now evaluate how well the model in the custom job did.
Step24:  Get the serving function signature
You can get the signatures of your model's input and output layers by reloading the model into memory, and querying it for the signatures corresponding to each layer.
When making a prediction request, you need to route the request to the serving function instead of the model, so you need to know the input layer name of the serving function -- which you will use later when you make a prediction request.
You also need to know the name of the serving function's input and output layer for constructing the explanation metadata -- which is discussed subsequently.
Step25:  Explanation Specification
To get explanations when doing a prediction, you must enable the explanation capability and set corresponding settings when you upload your custom model to an Vertex Model resource. These settings are referred to as the explanation metadata, which consists of
Step26:  Explanation Metadata
Let's first dive deeper into the explanation metadata, which consists of
Step27:  Upload the model
Next, upload your model to a Model resource using Model.upload() method, with the following parameters
Step28:  Send a batch prediction request
Send a batch prediction to your deployed model.
Make test items
You will use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction.
Make the batch input file
Now make a batch input file, which you will store in your local Cloud Storage bucket. Unlike image, video and text, the batch input file for tabular is only supported for CSV. For CSV file, you make
Step29:  Make the batch explanation request
Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters
Step30:  Wait for completion of batch prediction job
Next, wait for the batch job to complete. Alternatively, one can set the parameter sync to True in the batch_predict() method to block until the batch prediction job is completed.
Step31:  Get the explanations
Next, get the explanation results from the completed batch prediction job.
The results are written to the Cloud Storage output bucket you specified in the batch prediction request. You call the method iter_outputs() to get a list of each Cloud Storage file generated with the results. Each file contains one or more explanation requests in a CSV format
Step32:  Cleaning up
To clean up all Google Cloud resources used in this project, you can delete the Google Cloud
project you used for the tutorial.
Otherwise, you can delete the individual resources you created in this tutorial
<ASSISTANT_TASK:>
Python Code:
import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
    USER_FLAG = "--user"
else:
    USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
Explanation: Vertex SDK: Custom training tabular regression model for batch prediction with explainabilty
<table align="left">
  <td>
    <a href="https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_custom_tabular_regression_batch_explain.ipynb">
      <img src="https://cloud.google.com/ml-engine/images/colab-logo-32px.png" alt="Colab logo"> Run in Colab
    </a>
  </td>
  <td>
    <a href="https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_custom_tabular_regression_batch_explain.ipynb">
      <img src="https://cloud.google.com/ml-engine/images/github-logo-32px.png" alt="GitHub logo">
      View on GitHub
    </a>
  </td>
  <td>
    <a href="https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_custom_tabular_regression_batch_explain.ipynb">
      Open in Vertex AI Workbench
    </a>
  </td>
</table>
<br/><br/><br/>
Overview
This tutorial demonstrates how to use the Vertex SDK to train and deploy a custom tabular regression model for batch prediction with explanation.
Dataset
The dataset used for this tutorial is the Boston Housing Prices dataset. The version of the dataset you will use in this tutorial is built into TensorFlow. The trained model predicts the median price of a house in units of 1K USD.
Objective
In this tutorial, you create a custom model, with a training pipeline, from a Python script in a Google prebuilt Docker container using the Vertex SDK, and then do a batch prediction with explanations on the uploaded model.  You can alternatively create custom models using gcloud command-line tool or online using Cloud Console.
The steps performed include:
Create a Vertex custom job for training a model.
Train the TensorFlow model.
Retrieve and load the model artifacts.
View the model evaluation.
Set explanation parameters.
Upload the model as a Vertex Model resource.
Make a batch prediction with explanations.
Costs
This tutorial uses billable components of Google Cloud:
Vertex AI
Cloud Storage
Learn about Vertex AI
pricing and Cloud Storage
pricing, and use the Pricing
Calculator
to generate a cost estimate based on your projected usage.
Set up your local development environment
If you are using Colab or Google Cloud Notebook, your environment already meets all the requirements to run this notebook. You can skip this step.
Otherwise, make sure your environment meets this notebook's requirements. You need the following:
The Cloud Storage SDK
Git
Python 3
virtualenv
Jupyter notebook running in a virtual environment with Python 3
The Cloud Storage guide to Setting up a Python development environment and the Jupyter installation guide provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:
Install and initialize the SDK.
Install Python 3.
Install virtualenv and create a virtual environment that uses Python 3.
Activate that environment and run pip3 install Jupyter in a terminal shell to install Jupyter.
Run jupyter notebook on the command line in a terminal shell to launch Jupyter.
Open this notebook in the Jupyter Notebook Dashboard.
Installation
Install the latest version of Vertex SDK for Python.
End of explanation
! pip3 install -U google-cloud-storage $USER_FLAG
if os.getenv("IS_TESTING"):
    ! pip3 install --upgrade tensorflow $USER_FLAG
Explanation: Install the latest GA version of google-cloud-storage library as well.
End of explanation
import os
if not os.getenv("IS_TESTING"):
    # Automatically restart kernel after installs
    import IPython
    app = IPython.Application.instance()
    app.kernel.do_shutdown(True)
Explanation: Restart the kernel
Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages.
End of explanation
PROJECT_ID = "[your-project-id]"  # @param {type:"string"}
if PROJECT_ID == "" or PROJECT_ID is None or PROJECT_ID == "[your-project-id]":
    # Get your GCP project id from gcloud
    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null
    PROJECT_ID = shell_output[0]
    print("Project ID:", PROJECT_ID)
! gcloud config set project $PROJECT_ID
Explanation: Before you begin
GPU runtime
This tutorial does not require a GPU runtime.
Set up your Google Cloud project
The following steps are required, regardless of your notebook environment.
Select or create a Google Cloud project. When you first create an account, you get a $300 free credit towards your compute/storage costs.
Make sure that billing is enabled for your project.
Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.
The Google Cloud SDK is already installed in Google Cloud Notebook.
Enter your project ID in the cell below. Then run the  cell to make sure the
Cloud SDK uses the right project for all the commands in this notebook.
Note: Jupyter runs lines prefixed with ! as shell commands, and it interpolates Python variables prefixed with $.
End of explanation
REGION = "us-central1"  # @param {type: "string"}
Explanation: Region
You can also change the REGION variable, which is used for operations
throughout the rest of this notebook.  Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.
Americas: us-central1
Europe: europe-west4
Asia Pacific: asia-east1
You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.
Learn more about Vertex AI regions
End of explanation
from datetime import datetime
TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S")
Explanation: Timestamp
If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial.
End of explanation
# If you are running this notebook in Colab, run this cell and follow the
# instructions to authenticate your GCP account. This provides access to your
# Cloud Storage bucket and lets you submit training jobs and prediction
# requests.
import os
import sys
# If on Google Cloud Notebook, then don't execute this code
if not os.path.exists("/opt/deeplearning/metadata/env_version"):
    if "google.colab" in sys.modules:
        from google.colab import auth as google_auth
        google_auth.authenticate_user()
    # If you are running this notebook locally, replace the string below with the
    # path to your service account key and run this cell to authenticate your GCP
    # account.
    elif not os.getenv("IS_TESTING"):
        %env GOOGLE_APPLICATION_CREDENTIALS ''
Explanation: Authenticate your Google Cloud account
If you are using Google Cloud Notebook, your environment is already authenticated. Skip this step.
If you are using Colab, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.
Otherwise, follow these steps:
In the Cloud Console, go to the Create service account key page.
Click Create service account.
In the Service account name field, enter a name, and click Create.
In the Grant this service account access to project section, click the Role drop-down list. Type "Vertex" into the filter box, and select Vertex Administrator. Type "Storage Object Admin" into the filter box, and select Storage Object Admin.
Click Create. A JSON file that contains your key downloads to your local environment.
Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell.
End of explanation
BUCKET_NAME = "gs://[your-bucket-name]"  # @param {type:"string"}
if BUCKET_NAME == "" or BUCKET_NAME is None or BUCKET_NAME == "gs://[your-bucket-name]":
    BUCKET_NAME = "gs://" + PROJECT_ID + "aip-" + TIMESTAMP
Explanation: Create a Cloud Storage bucket
The following steps are required, regardless of your notebook environment.
When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.
Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization.
End of explanation
! gsutil mb -l $REGION $BUCKET_NAME
Explanation: Only if your bucket doesn't already exist: Run the following cell to create your Cloud Storage bucket.
End of explanation
! gsutil ls -al $BUCKET_NAME
Explanation: Finally, validate access to your Cloud Storage bucket by examining its contents:
End of explanation
import google.cloud.aiplatform as aip
Explanation: Set up variables
Next, set up some variables used throughout the tutorial.
Import libraries and define constants
End of explanation
aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)
Explanation: Initialize Vertex SDK for Python
Initialize the Vertex SDK for Python for your project and corresponding bucket.
End of explanation
if os.getenv("IS_TESTING_TRAIN_GPU"):
    TRAIN_GPU, TRAIN_NGPU = (
        aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,
        int(os.getenv("IS_TESTING_TRAIN_GPU")),
    )
else:
    TRAIN_GPU, TRAIN_NGPU = (None, None)
if os.getenv("IS_TESTING_DEPLOY_GPU"):
    DEPLOY_GPU, DEPLOY_NGPU = (
        aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,
        int(os.getenv("IS_TESTING_DEPLOY_GPU")),
    )
else:
    DEPLOY_GPU, DEPLOY_NGPU = (None, None)
Explanation: Set hardware accelerators
You can set hardware accelerators for training and prediction.
Set the variables TRAIN_GPU/TRAIN_NGPU and DEPLOY_GPU/DEPLOY_NGPU to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:
(aip.AcceleratorType.NVIDIA_TESLA_K80, 4)
Otherwise specify (None, None) to use a container image to run on a CPU.
Learn more here hardware accelerator support for your region
Note: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3 -- which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support.
End of explanation
if os.getenv("IS_TESTING_TF"):
    TF = os.getenv("IS_TESTING_TF")
else:
    TF = "2-1"
if TF[0] == "2":
    if TRAIN_GPU:
        TRAIN_VERSION = "tf-gpu.{}".format(TF)
    else:
        TRAIN_VERSION = "tf-cpu.{}".format(TF)
    if DEPLOY_GPU:
        DEPLOY_VERSION = "tf2-gpu.{}".format(TF)
    else:
        DEPLOY_VERSION = "tf2-cpu.{}".format(TF)
else:
    if TRAIN_GPU:
        TRAIN_VERSION = "tf-gpu.{}".format(TF)
    else:
        TRAIN_VERSION = "tf-cpu.{}".format(TF)
    if DEPLOY_GPU:
        DEPLOY_VERSION = "tf-gpu.{}".format(TF)
    else:
        DEPLOY_VERSION = "tf-cpu.{}".format(TF)
TRAIN_IMAGE = "gcr.io/cloud-aiplatform/training/{}:latest".format(TRAIN_VERSION)
DEPLOY_IMAGE = "gcr.io/cloud-aiplatform/prediction/{}:latest".format(DEPLOY_VERSION)
print("Training:", TRAIN_IMAGE, TRAIN_GPU, TRAIN_NGPU)
print("Deployment:", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)
Explanation: Set pre-built containers
Set the pre-built Docker container image for training and prediction.
For the latest list, see Pre-built containers for training.
For the latest list, see Pre-built containers for prediction.
End of explanation
if os.getenv("IS_TESTING_TRAIN_MACHINE"):
    MACHINE_TYPE = os.getenv("IS_TESTING_TRAIN_MACHINE")
else:
    MACHINE_TYPE = "n1-standard"
VCPU = "4"
TRAIN_COMPUTE = MACHINE_TYPE + "-" + VCPU
print("Train machine type", TRAIN_COMPUTE)
if os.getenv("IS_TESTING_DEPLOY_MACHINE"):
    MACHINE_TYPE = os.getenv("IS_TESTING_DEPLOY_MACHINE")
else:
    MACHINE_TYPE = "n1-standard"
VCPU = "4"
DEPLOY_COMPUTE = MACHINE_TYPE + "-" + VCPU
print("Deploy machine type", DEPLOY_COMPUTE)
Explanation: Set machine type
Next, set the machine type to use for training and prediction.
Set the variables TRAIN_COMPUTE and DEPLOY_COMPUTE to configure  the compute resources for the VMs you will use for for training and prediction.
machine type
n1-standard: 3.75GB of memory per vCPU.
n1-highmem: 6.5GB of memory per vCPU
n1-highcpu: 0.9 GB of memory per vCPU
vCPUs: number of [2, 4, 8, 16, 32, 64, 96 ]
Note: The following is not supported for training:
standard: 2 vCPUs
highcpu: 2, 4 and 8 vCPUs
Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs.
End of explanation
# Make folder for Python training script
! rm -rf custom
! mkdir custom
# Add package information
! touch custom/README.md
setup_cfg = "[egg_info]\n\ntag_build =\n\ntag_date = 0"
! echo "$setup_cfg" > custom/setup.cfg
setup_py = "import setuptools\n\nsetuptools.setup(\n\n    install_requires=[\n\n        'tensorflow_datasets==1.3.0',\n\n    ],\n\n    packages=setuptools.find_packages())"
! echo "$setup_py" > custom/setup.py
pkg_info = "Metadata-Version: 1.0\n\nName: Boston Housing tabular regression\n\nVersion: 0.0.0\n\nSummary: Demostration training script\n\nHome-page: www.google.com\n\nAuthor: Google\n\nAuthor-email: aferlitsch@google.com\n\nLicense: Public\n\nDescription: Demo\n\nPlatform: Vertex"
! echo "$pkg_info" > custom/PKG-INFO
# Make the training subfolder
! mkdir custom/trainer
! touch custom/trainer/__init__.py
Explanation: Tutorial
Now you are ready to start creating your own custom model and training for Boston Housing.
Examine the training package
Package layout
Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.
PKG-INFO
README.md
setup.cfg
setup.py
trainer
__init__.py
task.py
The files setup.cfg and setup.py are the instructions for installing the package into the operating environment of the Docker image.
The file trainer/task.py is the Python script for executing the custom training job. Note, when we referred to it in the worker pool specification, we replace the directory slash with a dot (trainer.task) and dropped the file suffix (.py).
Package Assembly
In the following cells, you will assemble the training package.
End of explanation
%%writefile custom/trainer/task.py
# Single, Mirror and Multi-Machine Distributed Training for Boston Housing
import tensorflow_datasets as tfds
import tensorflow as tf
from tensorflow.python.client import device_lib
import numpy as np
import argparse
import os
import sys
tfds.disable_progress_bar()
parser = argparse.ArgumentParser()
parser.add_argument('--model-dir', dest='model_dir',
                    default=os.getenv('AIP_MODEL_DIR'), type=str, help='Model dir.')
parser.add_argument('--lr', dest='lr',
                    default=0.001, type=float,
                    help='Learning rate.')
parser.add_argument('--epochs', dest='epochs',
                    default=20, type=int,
                    help='Number of epochs.')
parser.add_argument('--steps', dest='steps',
                    default=100, type=int,
                    help='Number of steps per epoch.')
parser.add_argument('--distribute', dest='distribute', type=str, default='single',
                    help='distributed training strategy')
parser.add_argument('--param-file', dest='param_file',
                    default='/tmp/param.txt', type=str,
                    help='Output file for parameters')
args = parser.parse_args()
print('Python Version = {}'.format(sys.version))
print('TensorFlow Version = {}'.format(tf.__version__))
print('TF_CONFIG = {}'.format(os.environ.get('TF_CONFIG', 'Not found')))
# Single Machine, single compute device
if args.distribute == 'single':
    if tf.test.is_gpu_available():
        strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
    else:
        strategy = tf.distribute.OneDeviceStrategy(device="/cpu:0")
# Single Machine, multiple compute device
elif args.distribute == 'mirror':
    strategy = tf.distribute.MirroredStrategy()
# Multiple Machine, multiple compute device
elif args.distribute == 'multi':
    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
# Multi-worker configuration
print('num_replicas_in_sync = {}'.format(strategy.num_replicas_in_sync))
def make_dataset():
  # Scaling Boston Housing data features
  def scale(feature):
    max = np.max(feature)
    feature = (feature / max).astype(np.float)
    return feature, max
  (x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data(
    path="boston_housing.npz", test_split=0.2, seed=113
  )
  params = []
  for _ in range(13):
    x_train[_], max = scale(x_train[_])
    x_test[_], _ = scale(x_test[_])
    params.append(max)
  # store the normalization (max) value for each feature
  with tf.io.gfile.GFile(args.param_file, 'w') as f:
    f.write(str(params))
  return (x_train, y_train), (x_test, y_test)
# Build the Keras model
def build_and_compile_dnn_model():
  model = tf.keras.Sequential([
      tf.keras.layers.Dense(128, activation='relu', input_shape=(13,)),
      tf.keras.layers.Dense(128, activation='relu'),
      tf.keras.layers.Dense(1, activation='linear')
  ])
  model.compile(
      loss='mse',
      optimizer=tf.keras.optimizers.RMSprop(learning_rate=args.lr))
  return model
NUM_WORKERS = strategy.num_replicas_in_sync
# Here the batch size scales up by number of workers since
# `tf.data.Dataset.batch` expects the global batch size.
BATCH_SIZE = 16
GLOBAL_BATCH_SIZE = BATCH_SIZE * NUM_WORKERS
with strategy.scope():
  # Creation of dataset, and model building/compiling need to be within
  # `strategy.scope()`.
  model = build_and_compile_dnn_model()
# Train the model
(x_train, y_train), (x_test, y_test) = make_dataset()
model.fit(x_train, y_train, epochs=args.epochs, batch_size=GLOBAL_BATCH_SIZE)
model.save(args.model_dir)
Explanation: Task.py contents
In the next cell, you write the contents of the training script task.py. I won't go into detail, it's just there for you to browse. In summary:
Get the directory where to save the model artifacts from the command line (--model_dir), and if not specified, then from the environment variable AIP_MODEL_DIR.
Loads Boston Housing dataset from TF.Keras builtin datasets
Builds a simple deep neural network model using TF.Keras model API.
Compiles the model (compile()).
Sets a training distribution strategy according to the argument args.distribute.
Trains the model (fit()) with epochs specified by args.epochs.
Saves the trained model (save(args.model_dir)) to the specified model directory.
Saves the maximum value for each feature f.write(str(params)) to the specified parameters file.
End of explanation
! rm -f custom.tar custom.tar.gz
! tar cvf custom.tar custom
! gzip custom.tar
! gsutil cp custom.tar.gz $BUCKET_NAME/trainer_boston.tar.gz
Explanation: Store training script on your Cloud Storage bucket
Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket.
End of explanation
job = aip.CustomTrainingJob(
    display_name="boston_" + TIMESTAMP,
    script_path="custom/trainer/task.py",
    container_uri=TRAIN_IMAGE,
    requirements=["gcsfs==0.7.1", "tensorflow-datasets==4.4"],
)
print(job)
Explanation: Create and run custom training job
To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.
Create custom training job
A custom training job is created with the CustomTrainingJob class, with the following parameters:
display_name: The human readable name for the custom training job.
container_uri: The training container image.
requirements: Package requirements for the training container image (e.g., pandas).
script_path: The relative path to the training script.
End of explanation
MODEL_DIR = "{}/{}".format(BUCKET_NAME, TIMESTAMP)
EPOCHS = 20
STEPS = 100
DIRECT = True
if DIRECT:
    CMDARGS = [
        "--model-dir=" + MODEL_DIR,
        "--epochs=" + str(EPOCHS),
        "--steps=" + str(STEPS),
    ]
else:
    CMDARGS = [
        "--epochs=" + str(EPOCHS),
        "--steps=" + str(STEPS),
    ]
Explanation: Prepare your command-line arguments
Now define the command-line arguments for your custom training container:
args: The command-line arguments to pass to the executable that is set as the entry point into the container.
--model-dir : For our demonstrations, we use this command-line argument to specify where to store the model artifacts.
direct: You pass the Cloud Storage location as a command line argument to your training script (set variable DIRECT = True), or
indirect: The service passes the Cloud Storage location as the environment variable AIP_MODEL_DIR to your training script (set variable DIRECT = False). In this case, you tell the service the model artifact location in the job specification.
"--epochs=" + EPOCHS: The number of epochs for training.
"--steps=" + STEPS: The number of steps per epoch.
End of explanation
if TRAIN_GPU:
    job.run(
        args=CMDARGS,
        replica_count=1,
        machine_type=TRAIN_COMPUTE,
        accelerator_type=TRAIN_GPU.name,
        accelerator_count=TRAIN_NGPU,
        base_output_dir=MODEL_DIR,
        sync=True,
    )
else:
    job.run(
        args=CMDARGS,
        replica_count=1,
        machine_type=TRAIN_COMPUTE,
        base_output_dir=MODEL_DIR,
        sync=True,
    )
model_path_to_deploy = MODEL_DIR
Explanation: Run the custom training job
Next, you run the custom job to start the training job by invoking the method run, with the following parameters:
args: The command-line arguments to pass to the training script.
replica_count: The number of compute instances for training (replica_count = 1 is single node training).
machine_type: The machine type for the compute instances.
accelerator_type: The hardware accelerator type.
accelerator_count: The number of accelerators to attach to a worker replica.
base_output_dir: The Cloud Storage location to write the model artifacts to.
sync: Whether to block until completion of the job.
End of explanation
import tensorflow as tf
local_model = tf.keras.models.load_model(MODEL_DIR)
Explanation: Load the saved model
Your model is stored in a TensorFlow SavedModel format in a Cloud Storage bucket. Now load it from the Cloud Storage bucket, and then you can do some things, like evaluate the model, and do a prediction.
To load, you use the TF.Keras model.load_model() method passing it the Cloud Storage path where the model is saved -- specified by MODEL_DIR.
End of explanation
import numpy as np
from tensorflow.keras.datasets import boston_housing
(_, _), (x_test, y_test) = boston_housing.load_data(
    path="boston_housing.npz", test_split=0.2, seed=113
)
def scale(feature):
    max = np.max(feature)
    feature = (feature / max).astype(np.float32)
    return feature
# Let's save one data item that has not been scaled
x_test_notscaled = x_test[0:1].copy()
for _ in range(13):
    x_test[_] = scale(x_test[_])
x_test = x_test.astype(np.float32)
print(x_test.shape, x_test.dtype, y_test.shape)
print("scaled", x_test[0])
print("unscaled", x_test_notscaled)
Explanation: Evaluate the model
Now let's find out how good the model is.
Load evaluation data
You will load the Boston Housing test (holdout) data from tf.keras.datasets, using the method load_data(). This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements: the feature data, and the corresponding labels (median value of owner-occupied home).
You don't need the training data, and hence why we loaded it as (_, _).
Before you can run the data through evaluation, you need to preprocess it:
x_test:
1. Normalize (rescale) the data in each column by dividing each value by the maximum value of that column. This replaces each single value with a 32-bit floating point number between 0 and 1.
End of explanation
local_model.evaluate(x_test, y_test)
Explanation: Perform the model evaluation
Now evaluate how well the model in the custom job did.
End of explanation
loaded = tf.saved_model.load(model_path_to_deploy)
serving_input = list(
    loaded.signatures["serving_default"].structured_input_signature[1].keys()
)[0]
print("Serving function input:", serving_input)
serving_output = list(loaded.signatures["serving_default"].structured_outputs.keys())[0]
print("Serving function output:", serving_output)
input_name = local_model.input.name
print("Model input name:", input_name)
output_name = local_model.output.name
print("Model output name:", output_name)
Explanation: Get the serving function signature
You can get the signatures of your model's input and output layers by reloading the model into memory, and querying it for the signatures corresponding to each layer.
When making a prediction request, you need to route the request to the serving function instead of the model, so you need to know the input layer name of the serving function -- which you will use later when you make a prediction request.
You also need to know the name of the serving function's input and output layer for constructing the explanation metadata -- which is discussed subsequently.
End of explanation
XAI = "ig"  # [ shapley, ig, xrai ]
if XAI == "shapley":
    PARAMETERS = {"sampled_shapley_attribution": {"path_count": 10}}
elif XAI == "ig":
    PARAMETERS = {"integrated_gradients_attribution": {"step_count": 50}}
elif XAI == "xrai":
    PARAMETERS = {"xrai_attribution": {"step_count": 50}}
parameters = aip.explain.ExplanationParameters(PARAMETERS)
Explanation: Explanation Specification
To get explanations when doing a prediction, you must enable the explanation capability and set corresponding settings when you upload your custom model to an Vertex Model resource. These settings are referred to as the explanation metadata, which consists of:
parameters: This is the specification for the explainability algorithm to use for explanations on your model. You can choose between:
Shapley - Note, not recommended for image data -- can be very long running
XRAI
Integrated Gradients
metadata: This is the specification for how the algoithm is applied on your custom model.
Explanation Parameters
Let's first dive deeper into the settings for the explainability algorithm.
Shapley
Assigns credit for the outcome to each feature, and considers different permutations of the features. This method provides a sampling approximation of exact Shapley values.
Use Cases:
  - Classification and regression on tabular data.
Parameters:
path_count: This is the number of paths over the features that will be processed by the algorithm. An exact approximation of the Shapley values requires M! paths, where M is the number of features. For the CIFAR10 dataset, this would be 784 (28*28).
For any non-trival number of features, this is too compute expensive. You can reduce the number of paths over the features to M * path_count.
Integrated Gradients
A gradients-based method to efficiently compute feature attributions with the same axiomatic properties as the Shapley value.
Use Cases:
  - Classification and regression on tabular data.
  - Classification on image data.
Parameters:
step_count: This is the number of steps to approximate the remaining sum. The more steps, the more accurate the integral approximation. The general rule of thumb is 50 steps, but as you increase so does the compute time.
XRAI
Based on the integrated gradients method, XRAI assesses overlapping regions of the image to create a saliency map, which highlights relevant regions of the image rather than pixels.
Use Cases:
Classification on image data.
Parameters:
step_count: This is the number of steps to approximate the remaining sum. The more steps, the more accurate the integral approximation. The general rule of thumb is 50 steps, but as you increase so does the compute time.
In the next code cell, set the variable XAI to which explainabilty algorithm you will use on your custom model.
End of explanation
INPUT_METADATA = {
    "input_tensor_name": serving_input,
    "encoding": "BAG_OF_FEATURES",
    "modality": "numeric",
    "index_feature_mapping": [
        "crim",
        "zn",
        "indus",
        "chas",
        "nox",
        "rm",
        "age",
        "dis",
        "rad",
        "tax",
        "ptratio",
        "b",
        "lstat",
    ],
}
OUTPUT_METADATA = {"output_tensor_name": serving_output}
input_metadata = aip.explain.ExplanationMetadata.InputMetadata(INPUT_METADATA)
output_metadata = aip.explain.ExplanationMetadata.OutputMetadata(OUTPUT_METADATA)
metadata = aip.explain.ExplanationMetadata(
    inputs={"features": input_metadata}, outputs={"medv": output_metadata}
)
Explanation: Explanation Metadata
Let's first dive deeper into the explanation metadata, which consists of:
outputs: A scalar value in the output to attribute -- what to explain. For example, in a probability output [0.1, 0.2, 0.7] for classification, one wants an explanation for 0.7. Consider the following formulae, where the output is y and that is what we want to explain.
y = f(x)
Consider the following formulae, where the outputs are y and z. Since we can only do attribution for one scalar value, we have to pick whether we want to explain the output y or z. Assume in this example the model is object detection and y and z are the bounding box and the object classification. You would want to pick which of the two outputs to explain.
y, z = f(x)
The dictionary format for outputs is:
{ "outputs": { "[your_display_name]":
               "output_tensor_name": [layer]
             }
}
<blockquote>
 -  [your_display_name]: A human readable name you assign to the output to explain. A common example is "probability".<br/>
 -  "output_tensor_name": The key/value field to identify the output layer to explain. <br/>
 -  [layer]: The output layer to explain. In a single task model, like a tabular regressor, it is the last (topmost) layer in the model.
</blockquote>
inputs: The features for attribution -- how they contributed to the output. Consider the following formulae, where a and b are the features. We have to pick which features to explain how the contributed. Assume that this model is deployed for A/B testing, where a are the data_items for the prediction and b identifies whether the model instance is A or B. You would want to pick a (or some subset of) for the features, and not b since it does not contribute to the prediction.
y = f(a,b)
The minimum dictionary format for inputs is:
{ "inputs": { "[your_display_name]":
              "input_tensor_name": [layer]
             }
}
<blockquote>
 -  [your_display_name]: A human readable name you assign to the input to explain. A common example is "features".<br/>
 -  "input_tensor_name": The key/value field to identify the input layer for the feature attribution. <br/>
 -  [layer]: The input layer for feature attribution. In a single input tensor model, it is the first (bottom-most) layer in the model.
</blockquote>
Since the inputs to the model are tabular, you can specify the following two additional fields as reporting/visualization aids:
<blockquote>
 - "modality": "image": Indicates the field values are image data.
</blockquote>
Since the inputs to the model are tabular, you can specify the following two additional fields as reporting/visualization aids:
<blockquote>
 - "encoding": "BAG_OF_FEATURES" : Indicates that the inputs are set of tabular features.<br/>
 - "index_feature_mapping": [ feature-names ] : A list of human readable names for each feature. For this example, we use the feature names specified in the dataset.<br/>
 - "modality": "numeric": Indicates the field values are numeric.
</blockquote>
End of explanation
model = aip.Model.upload(
    display_name="boston_" + TIMESTAMP,
    artifact_uri=MODEL_DIR,
    serving_container_image_uri=DEPLOY_IMAGE,
    explanation_parameters=parameters,
    explanation_metadata=metadata,
    sync=False,
)
model.wait()
Explanation: Upload the model
Next, upload your model to a Model resource using Model.upload() method, with the following parameters:
display_name: The human readable name for the Model resource.
artifact: The Cloud Storage location of the trained model artifacts.
serving_container_image_uri: The serving container image.
sync: Whether to execute the upload asynchronously or synchronously.
explanation_parameters: Parameters to configure explaining for Model's predictions.
explanation_metadata: Metadata describing the Model's input and output for explanation.
If the upload() method is run asynchronously, you can subsequently block until completion with the wait() method.
End of explanation
! gsutil cat $IMPORT_FILE | head -n 1 > tmp.csv
! gsutil cat $IMPORT_FILE | tail -n 10 >> tmp.csv
! cut -d, -f1-16 tmp.csv > batch.csv
gcs_input_uri = BUCKET_NAME + "/test.csv"
! gsutil cp batch.csv $gcs_input_uri
Explanation: Send a batch prediction request
Send a batch prediction to your deployed model.
Make test items
You will use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction.
Make the batch input file
Now make a batch input file, which you will store in your local Cloud Storage bucket. Unlike image, video and text, the batch input file for tabular is only supported for CSV. For CSV file, you make:
The first line is the heading with the feature (fields) heading names.
Each remaining line is a separate prediction request with the corresponding feature values.
For example:
"feature_1", "feature_2". ...
value_1, value_2, ...
End of explanation
MIN_NODES = 1
MAX_NODES = 1
batch_predict_job = model.batch_predict(
    job_display_name="boston_" + TIMESTAMP,
    gcs_source=gcs_input_uri,
    gcs_destination_prefix=BUCKET_NAME,
    instances_format="csv",
    predictions_format="jsonl",
    machine_type=DEPLOY_COMPUTE,
    starting_replica_count=MIN_NODES,
    max_replica_count=MAX_NODES,
    generate_explanation=True,
    sync=False,
)
print(batch_predict_job)
Explanation: Make the batch explanation request
Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters:
job_display_name: The human readable name for the batch prediction job.
gcs_source: A list of one or more batch request input files.
gcs_destination_prefix: The Cloud Storage location for storing the batch prediction resuls.
instances_format: The format for the input instances, either 'csv' or 'jsonl'. Defaults to 'jsonl'.
predictions_format: The format for the output predictions, either 'csv' or 'jsonl'. Defaults to 'jsonl'.
generate_explanations: Set to True to generate explanations.
sync: If set to True, the call will block while waiting for the asynchronous batch job to complete.
End of explanation
if not os.getenv("IS_TESTING"):
    batch_predict_job.wait()
Explanation: Wait for completion of batch prediction job
Next, wait for the batch job to complete. Alternatively, one can set the parameter sync to True in the batch_predict() method to block until the batch prediction job is completed.
End of explanation
if not os.getenv("IS_TESTING"):
    import tensorflow as tf
    bp_iter_outputs = batch_predict_job.iter_outputs()
    explanation_results = list()
    for blob in bp_iter_outputs:
        if blob.name.split("/")[-1].startswith("explanation"):
            explanation_results.append(blob.name)
    tags = list()
    for explanation_result in explanation_results:
        gfile_name = f"gs://{bp_iter_outputs.bucket.name}/{explanation_result}"
        with tf.io.gfile.GFile(name=gfile_name, mode="r") as gfile:
            for line in gfile.readlines():
                print(line)
Explanation: Get the explanations
Next, get the explanation results from the completed batch prediction job.
The results are written to the Cloud Storage output bucket you specified in the batch prediction request. You call the method iter_outputs() to get a list of each Cloud Storage file generated with the results. Each file contains one or more explanation requests in a CSV format:
CSV header + predicted_label
CSV row + explanation, per prediction request
End of explanation
delete_all = True
if delete_all:
    # Delete the dataset using the Vertex dataset object
    try:
        if "dataset" in globals():
            dataset.delete()
    except Exception as e:
        print(e)
    # Delete the model using the Vertex model object
    try:
        if "model" in globals():
            model.delete()
    except Exception as e:
        print(e)
    # Delete the endpoint using the Vertex endpoint object
    try:
        if "endpoint" in globals():
            endpoint.delete()
    except Exception as e:
        print(e)
    # Delete the AutoML or Pipeline trainig job
    try:
        if "dag" in globals():
            dag.delete()
    except Exception as e:
        print(e)
    # Delete the custom trainig job
    try:
        if "job" in globals():
            job.delete()
    except Exception as e:
        print(e)
    # Delete the batch prediction job using the Vertex batch prediction object
    try:
        if "batch_predict_job" in globals():
            batch_predict_job.delete()
    except Exception as e:
        print(e)
    # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object
    try:
        if "hpt_job" in globals():
            hpt_job.delete()
    except Exception as e:
        print(e)
    if "BUCKET_NAME" in globals():
        ! gsutil rm -r $BUCKET_NAME
Explanation: Cleaning up
To clean up all Google Cloud resources used in this project, you can delete the Google Cloud
project you used for the tutorial.
Otherwise, you can delete the individual resources you created in this tutorial:
Dataset
Pipeline
Model
Endpoint
AutoML Training Job
Batch Job
Custom Job
Hyperparameter Tuning Job
Cloud Storage Bucket
End of explanation
<END_TASK>