1. Overview
The focus of this demo is you can use Vertex AI to train and deploy a ML model. It assumes that you are familiar with Machine Learning even though the machine learning code for training is provided to you. You will use Datasets for dataset creation and managemet, and custom model for training a Scikit Learn model. Finally you will deploy the trained model and get online predictions. The dataset you will use for this demo is the Titanic Dataset.
2. Setup your environment
Create a project
To complete this codelab you need a Google Cloud Platform project. If you do not aleady have a project, follow these instructions to create one.
Load data in BigQuery
In order to train a Machine Learning model you need access to data. BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse and it is the perfect service for keeping your data.
Create dataset
To create a BigQuery dataset, navigate to BigQuery on Google Cloud Console
The below image marks 3 steps:
- Make sure that you select the right project from the top of console page
- Select the project you want to create the Dataset in
- Click Create Dataset
A popup will appear. Enter the dataset id: titanic and then click Create Dataset
You have now created the dataset.
Create table
You need a table to load your data. First download the titanic dataset locally.
Then from the UI :
- Select the titanic dataset you have created in the previous step
- Click CREATE TABLE
From the Sidebar select the following:
- Create table from: Upload
- Select file: Use the downloaded titanic dataset
- File Format: CSV
- Table Name: survivors
- Auto-detect: Select auto-detect checkbox - Schema and input parameters
Click Create Table
You have now created and populated the table with the titanic dataset! You can explore the table contencts, run queries and analyse your data.
3. Create a dataset
Datasets in Vertex AI allow you to create datasets for your Machine Learning workloads. You can create datasets for structured data (CSV files or BigQuery tables) or unstructured data such as Images and Text. It is important to notice that Vertex AI Datasets just reference your original data and there is no duplication.
Create ML dataset
Find Vertex AI on the GCP side menu, under Artificial Intelligence. If this is the first time visiting Vertex AI, you will get a notification to Enable Vertex AI API. Please do so!
Once you select Vertex AI you can select a region you want your resources to use. Thus tutorial is using europe-west4 as a reagion. If you need to use a different regions you can. Just replace europe-west4 with the region of your choice for the rest of this tutorial.
Select europe-west4 and click on CREATE A DATASET
Give your dataset a name. How about titanic
You can create datasets for images, text or videos as well as tabular data. The titanic dataset is tabular so you should click the Tabular tab
For region selection select europe-west4 and click CREATE . We did not yet connect to the datasource yet. We just created a placeholder. Your will connect the datasource on the following step.
Select datasource
As you have already loaded the titanic dataset in BigQuery, we can connect our ML dataset to our BigQuery table as shown in the image. To make it easy to find your table you can click Browse. Once you select the dataset click on CONTINUE
Generate statistics
Under the ANALYZE tab you can generate statistics regarding your data. This gives you the ability to quickly have a peek at the data and check for distributions, missing values etc.
In order to run the statistical analysis click GENERATE STATISTICS. It can take a couple of minutes to execute. You can continue with the lab and came back later to see the results.
4. Custom training package using Notebooks
It is a good practice to package and parameterise your code so that it becomes a portable asset.
In this section you will create a training package with custom code using Notebooks. A fundamental step using the service is to be able to create a python source distribution, AKA a distribution package. This is not much more than creating folders and files within. The next section will explain how a package is structured.
Application Structure
The basic structured of a python package can be seen in the image below.
Let's see what those folders and files are for:
- titanic-package: This is your working directory. Inside this folder we will have our package and code related to the titanic survivor classifier.
- setup.py: The setup file specifies how to build your distribution package. It includes information such as the package name, version as well as any other packages that you might need for your training job and are not included by default in GCP's pre-built training containers.
- trainer: The folder that contains the training code. This is also a python package. What makes it a package is the empty __init__.py file that is inside the folder.
- __init__.py: Empty file called __init__.py. It signifies that the folder that it belongs to is a package.
- task.py: The task.py is a package module. Here is the entry point of your code and it also accepts CLI parameters for model training. You can include your training code in this module as well or you can create additional modules inside your package. This is entirely up to you and how you want to structure your code.
Now that you have an understanding of the structure, let me clarify that the names used for the package and module do not have to be trainer and task.py. We only use this name convention so that it aligns with our online documentations but you can in fact pick the names that suit you.
Create your notebook instance
How about creating a notebook instance and try training a custom model? From the Vertex AI navigate to notebooks and start an instance with Python 3, which includes scikit-learn as shown in the image below. We will use a scikit learn model for our classifier.
A pop-up will appear. Here you can change settings like the region your notebook instance will be created at and the compute power you require. As we are not dealing with a lot of data and the we only need the instance for development purposes please do not change any of the settings and simply click Create
The instance will be up and running in no more than a couple of minutes. Once the instance is ready go ahead and OPEN JUPYTERLAB
Create your package
Now that the notebook is up and running you can start building your training assets.
For this task is easier to use the terminal. From the Launcher, click on Terminal to create a new terminal session (marks #1 and #2 in the image below)
Now in the terminal execute the following commands to create the folder structure with the required files
mkdir -p /home/jupyter/titanic/trainer
touch /home/jupyter/titanic/setup.py /home/jupyter/titanic/trainer/__init__.py /home/jupyter/titanic/trainer/task.py
Once you run the commands click the refresh button (#3 in below image) to see the newly created folder and files
Copy-paste the following code in titanic/trainer/task.py The code contains comments. Spend few minutes going through the file to better understand it:
from google.cloud import bigquery, bigquery_storage, storage
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, OrdinalEncoder
from sklearn.model_selection import cross_val_score
from sklearn.svm import SVC
from sklearn.metrics import classification_report, f1_score
from typing import Union, List
import os, logging, json, pickle, argparse
import dask.dataframe as dd
import pandas as pd
import numpy as np
# feature selection. The FEATURE list defines what features are needed from the training data.
# as well as the types of those features. We will perform different feature engineering depending on the type
# List all column names for binary features: 0,1 or True,False or Male,Female etc
BINARY_FEATURES = [
'sex']
# List all column names for numeric features
NUMERIC_FEATURES = [
'age',
'fare']
# List all column names for categorical features
CATEGORICAL_FEATURES = [
'pclass',
'embarked',
'home_dest',
'parch',
'sibsp']
ALL_COLUMNS = BINARY_FEATURES+NUMERIC_FEATURES+CATEGORICAL_FEATURES
# define the column name for label
LABEL = 'survived'
# Define the index position of each feature. This is needed for processing a
# numpy array (instead of pandas) which has no column names.
BINARY_FEATURES_IDX = list(range(0,len(BINARY_FEATURES)))
NUMERIC_FEATURES_IDX = list(range(len(BINARY_FEATURES), len(BINARY_FEATURES)+len(NUMERIC_FEATURES)))
CATEGORICAL_FEATURES_IDX = list(range(len(BINARY_FEATURES+NUMERIC_FEATURES), len(ALL_COLUMNS)))
def load_data_from_gcs(data_gcs_path: str) -> pd.DataFrame:
'''
Loads data from Google Cloud Storage (GCS) to a dataframe
Parameters:
data_gcs_path (str): gs path for the location of the data. Wildcards are also supported. i.e gs://example_bucket/data/training-*.csv
Returns:
pandas.DataFrame: a dataframe with the data from GCP loaded
'''
# using dask that supports wildcards to read multiple files. Then with dd.read_csv().compute we create a pandas dataframe
# Additionally I have noticed that some values for TotalCharges are missing and this creates confusion regarding TotalCharges the data types.
# to overcome this we manually define TotalCharges as object.
# We will later fix this upnormality
logging.info("reading gs data: {}".format(data_gcs_path))
return dd.read_csv(data_gcs_path, dtype={'TotalCharges': 'object'}).compute()
def load_data_from_bq(bq_uri: str) -> pd.DataFrame:
'''
Loads data from BigQuery table (BQ) to a dataframe
Parameters:
bq_uri (str): bq table uri. i.e: example_project.example_dataset.example_table
Returns:
pandas.DataFrame: a dataframe with the data from GCP loaded
'''
if not bq_uri.startswith('bq://'):
raise Exception("uri is not a BQ uri. It should be bq://project_id.dataset.table")
logging.info("reading bq data: {}".format(bq_uri))
project,dataset,table = bq_uri.split(".")
bqclient = bigquery.Client(project=project[5:])
bqstorageclient = bigquery_storage.BigQueryReadClient()
query_string = """
SELECT * from {ds}.{tbl}
""".format(ds=dataset, tbl=table)
return (
bqclient.query(query_string)
.result()
.to_dataframe(bqstorage_client=bqstorageclient)
)
def clean_missing_numerics(df: pd.DataFrame, numeric_columns):
'''
removes invalid values in the numeric columns
Parameters:
df (pandas.DataFrame): The Pandas Dataframe to alter
numeric_columns (List[str]): List of column names that are numberic from the DataFrame
Returns:
pandas.DataFrame: a dataframe with the numeric columns fixed
'''
for n in numeric_columns:
df[n] = pd.to_numeric(df[n], errors='coerce')
df = df.fillna(df.mean())
return df
def data_selection(df: pd.DataFrame, selected_columns: List[str], label_column: str) -> (pd.DataFrame, pd.Series):
'''
From a dataframe it creates a new dataframe with only selected columns and returns it.
Additionally it splits the label column into a pandas Series.
Parameters:
df (pandas.DataFrame): The Pandas Dataframe to drop columns and extract label
selected_columns (List[str]): List of strings with the selected columns. i,e ['col_1', 'col_2', ..., 'col_n' ]
label_column (str): The name of the label column
Returns:
tuple(pandas.DataFrame, pandas.Series): Tuble with the new pandas DataFrame containing only selected columns and lablel pandas Series
'''
# We create a series with the prediciton label
labels = df[label_column]
data = df.loc[:, selected_columns]
return data, labels
def pipeline_builder(params_svm: dict, bin_ftr_idx: List[int], num_ftr_idx: List[int], cat_ftr_idx: List[int]) -> Pipeline:
'''
Builds a sklearn pipeline with preprocessing and model configuration.
Preprocessing steps are:
* OrdinalEncoder - used for binary features
* StandardScaler - used for numerical features
* OneHotEncoder - used for categorical features
Model used is SVC
Parameters:
params_svm (dict): List of parameters for the sklearn.svm.SVC classifier
bin_ftr_idx (List[str]): List of ints that mark the column indexes with binary columns. i.e [0, 2, ... , X ]
num_ftr_idx (List[str]): List of ints that mark the column indexes with numerica columns. i.e [6, 3, ... , X ]
cat_ftr_idx (List[str]): List of ints that mark the column indexes with categorical columns. i.e [5, 10, ... , X ]
label_column (str): The name of the label column
Returns:
Pipeline: sklearn.pipelines.Pipeline with preprocessing and model training
'''
# Definining a preprocessing step for our pipeline.
# it specifies how the features are going to be transformed
preprocessor = ColumnTransformer(
transformers=[
('bin', OrdinalEncoder(), bin_ftr_idx),
('num', StandardScaler(), num_ftr_idx),
('cat', OneHotEncoder(handle_unknown='ignore'), cat_ftr_idx)], n_jobs=-1)
# We now create a full pipeline, for preprocessing and training.
# for training we selected a linear SVM classifier
clf = SVC()
clf.set_params(**params_svm)
return Pipeline(steps=[ ('preprocessor', preprocessor),
('classifier', clf)])
def train_pipeline(clf: Pipeline, X: Union[pd.DataFrame, np.ndarray], y: Union[pd.DataFrame, np.ndarray]) -> float:
'''
Trains a sklearn pipeline by fiting training data an labels and returns the accuracy f1 score
Parameters:
clf (sklearn.pipelines.Pipeline): the Pipeline object to fit the data
X: (pd.DataFrame OR np.ndarray): Training vectors of shape n_samples x n_features, where n_samples is the number of samples and n_features is the number of features.
y: (pd.DataFrame OR np.ndarray): Labels of shape n_samples. Order should mathc Training Vectors X
Returns:
score (float): Average F1 score from all cross validations
'''
# run cross validation to get training score. we can use this score to optimise training
score = cross_val_score(clf, X, y, cv=10, n_jobs=-1).mean()
# Now we fit all our data to the classifier.
clf.fit(X, y)
return score
def process_gcs_uri(uri: str) -> (str, str, str, str):
'''
Receives a Google Cloud Storage (GCS) uri and breaks it down to the scheme, bucket, path and file
Parameters:
uri (str): GCS uri
Returns:
scheme (str): uri scheme
bucket (str): uri bucket
path (str): uri path
file (str): uri file
'''
url_arr = uri.split("/")
if "." not in url_arr[-1]:
file = ""
else:
file = url_arr.pop()
scheme = url_arr[0]
bucket = url_arr[2]
path = "/".join(url_arr[3:])
path = path[:-1] if path.endswith("/") else path
return scheme, bucket, path, file
def pipeline_export_gcs(fitted_pipeline: Pipeline, model_dir: str) -> str:
'''
Exports trained pipeline to GCS
Parameters:
fitted_pipeline (sklearn.pipelines.Pipeline): the Pipeline object with data already fitted (trained pipeline object)
model_dir (str): GCS path to store the trained pipeline. i.e gs://example_bucket/training-job
Returns:
export_path (str): Model GCS location
'''
scheme, bucket, path, file = process_gcs_uri(model_dir)
if scheme != "gs:":
raise ValueError("URI scheme must be gs")
# Upload the model to GCS
b = storage.Client().bucket(bucket)
export_path = os.path.join(path, 'model.pkl')
blob = b.blob(export_path)
blob.upload_from_string(pickle.dumps(fitted_pipeline))
return scheme + "//" + os.path.join(bucket, export_path)
def prepare_report(cv_score: float, model_params: dict, classification_report: str, columns: List[str], example_data: np.ndarray) -> str:
'''
Prepares a training report in Text
Parameters:
cv_score (float): score of the training job during cross validation of training data
model_params (dict): dictonary containing the parameters the model was trained with
classification_report (str): Model classification report with test data
columns (List[str]): List of columns that where used in training.
example_data (np.array): Sample of data (2-3 rows are enough). This is used to include what the prediciton payload should look like for the model
Returns:
report (str): Full report in text
'''
buffer_example_data = '['
for r in example_data:
buffer_example_data+='['
for c in r:
if(isinstance(c,str)):
buffer_example_data+="'"+c+"', "
else:
buffer_example_data+=str(c)+", "
buffer_example_data= buffer_example_data[:-2]+"], \n"
buffer_example_data= buffer_example_data[:-3]+"]"
report = """
Training Job Report
Cross Validation Score: {cv_score}
Training Model Parameters: {model_params}
Test Data Classification Report:
{classification_report}
Example of data array for prediciton:
Order of columns:
{columns}
Example for clf.predict()
{predict_example}
Example of GCP API request body:
{{
"instances": {json_example}
}}
""".format(
cv_score=cv_score,
model_params=json.dumps(model_params),
classification_report=classification_report,
columns = columns,
predict_example = buffer_example_data,
json_example = json.dumps(example_data.tolist()))
return report
def report_export_gcs(report: str, report_dir: str) -> None:
'''
Exports training job report to GCS
Parameters:
report (str): Full report in text to sent to GCS
report_dir (str): GCS path to store the report model. i.e gs://example_bucket/training-job
Returns:
export_path (str): Report GCS location
'''
scheme, bucket, path, file = process_gcs_uri(report_dir)
if scheme != "gs:":
raise ValueError("URI scheme must be gs")
# Upload the model to GCS
b = storage.Client().bucket(bucket)
export_path = os.path.join(path, 'report.txt')
blob = b.blob(export_path)
blob.upload_from_string(report)
return scheme + "//" + os.path.join(bucket, export_path)
# Define all the command line arguments your model can accept for training
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# Input Arguments
parser.add_argument(
'--model_param_kernel',
help = 'SVC model parameter- kernel',
choices=['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'],
type = str,
default = 'linear'
)
parser.add_argument(
'--model_param_degree',
help = 'SVC model parameter- Degree. Only applies for poly kernel',
type = int,
default = 3
)
parser.add_argument(
'--model_param_C',
help = 'SVC model parameter- C (regularization)',
type = float,
default = 1.0
)
parser.add_argument(
'--model_param_probability',
help = 'Whether to enable probability estimates',
type = bool,
default = True
)
'''
Vertex AI automatically populates a set of environment varialbes in the container that executes
your training job. those variables include:
* AIP_MODEL_DIR - Directory selected as model dir
* AIP_DATA_FORMAT - Type of dataset selected for training (can be csv or bigquery)
Vertex AI will automatically split selected dataset into training,validation and testing
and 3 more environment variables will reflect the locaiton of the data:
* AIP_TRAINING_DATA_URI - URI of Training data
* AIP_VALIDATION_DATA_URI - URI of Validation data
* AIP_TEST_DATA_URI - URI of Test data
Notice that those environment varialbes are default. If the user provides a value using CLI argument,
the environment variable will be ignored. If the user does not provide anything as CLI argument
the program will try and use the environemnt variables if those exist. otherwise will leave empty.
'''
parser.add_argument(
'--model_dir',
help = 'Directory to output model and artifacts',
type = str,
default = os.environ['AIP_MODEL_DIR'] if 'AIP_MODEL_DIR' in os.environ else ""
)
parser.add_argument(
'--data_format',
choices=['csv', 'bigquery'],
help = 'format of data uri csv for gs:// paths and bigquery for project.dataset.table formats',
type = str,
default = os.environ['AIP_DATA_FORMAT'] if 'AIP_DATA_FORMAT' in os.environ else "csv"
)
parser.add_argument(
'--training_data_uri',
help = 'location of training data in either gs:// uri or bigquery uri',
type = str,
default = os.environ['AIP_TRAINING_DATA_URI'] if 'AIP_TRAINING_DATA_URI' in os.environ else ""
)
parser.add_argument(
'--validation_data_uri',
help = 'location of validation data in either gs:// uri or bigquery uri',
type = str,
default = os.environ['AIP_VALIDATION_DATA_URI'] if 'AIP_VALIDATION_DATA_URI' in os.environ else ""
)
parser.add_argument(
'--test_data_uri',
help = 'location of test data in either gs:// uri or bigquery uri',
type = str,
default = os.environ['AIP_TEST_DATA_URI'] if 'AIP_TEST_DATA_URI' in os.environ else ""
)
parser.add_argument("-v", "--verbose", help="increase output verbosity",
action="store_true")
args = parser.parse_args()
arguments = args.__dict__
if args.verbose:
logging.basicConfig(level=logging.INFO)
logging.info('Model artifacts will be exported here: {}'.format(arguments['model_dir']))
logging.info('Data format: {}'.format(arguments["data_format"]))
logging.info('Training data uri: {}'.format(arguments['training_data_uri']) )
logging.info('Validation data uri: {}'.format(arguments['validation_data_uri']))
logging.info('Test data uri: {}'.format(arguments['test_data_uri']))
'''
We have 2 different ways to load our data to pandas. One is from cloud storage by loading csv files and
the other is by connecting to BigQuery. Vertex AI supports both and
here we created a code that depelnding on the dataset provided, we will select the appropriated loading method.
'''
logging.info('Loading {} data'.format(arguments["data_format"]))
if(arguments['data_format']=='csv'):
df_train = load_data_from_gcs(arguments['training_data_uri'])
df_test = load_data_from_bq(arguments['test_data_uri'])
df_valid = load_data_from_gcs(arguments['validation_data_uri'])
elif(arguments['data_format']=='bigquery'):
print(arguments['training_data_uri'])
df_train = load_data_from_bq(arguments['training_data_uri'])
df_test = load_data_from_bq(arguments['test_data_uri'])
df_valid = load_data_from_bq(arguments['validation_data_uri'])
else:
raise ValueError("Invalid data type ")
#as we will be using cross validation, we will have just a training set and a single test set.
# we ill merge the test and validation to achieve an 80%-20% split
df_test = pd.concat([df_test,df_valid])
logging.info('Defining model parameters')
model_params = dict()
model_params['kernel'] = arguments['model_param_kernel']
model_params['degree'] = arguments['model_param_degree']
model_params['C'] = arguments['model_param_C']
model_params['probability'] = arguments['model_param_probability']
df_train = clean_missing_numerics(df_train, NUMERIC_FEATURES)
df_test = clean_missing_numerics(df_test, NUMERIC_FEATURES)
logging.info('Running feature selection')
X_train, y_train = data_selection(df_train, ALL_COLUMNS, LABEL)
X_test, y_test = data_selection(df_test, ALL_COLUMNS, LABEL)
logging.info('Training pipelines in CV')
clf = pipeline_builder(model_params, BINARY_FEATURES_IDX, NUMERIC_FEATURES_IDX, CATEGORICAL_FEATURES_IDX)
cv_score = train_pipeline(clf, X_train, y_train)
logging.info('Export trained pipeline and report')
pipeline_export_gcs(clf, arguments['model_dir'])
y_pred = clf.predict(X_test)
test_score = f1_score(y_test, y_pred, average='weighted')
logging.info('f1score: '+ str(test_score))
report = prepare_report(cv_score,
model_params,
classification_report(y_test,y_pred),
ALL_COLUMNS,
X_test.to_numpy()[0:2])
report_export_gcs(report, arguments['model_dir'])
logging.info('Training job completed. Exiting...')
Build your package
Now it is time to build your package so that you can use it with the training service.
Copy-paste the following code in titanic/setup.py
from setuptools import find_packages
from setuptools import setup
REQUIRED_PACKAGES = [
'gcsfs==0.7.1',
'dask[dataframe]==2021.2.0',
'google-cloud-bigquery-storage==1.0.0',
'six==1.15.0'
]
setup(
name='trainer',
version='0.1',
install_requires=REQUIRED_PACKAGES,
packages=find_packages(), # Automatically find packages within this directory or below.
include_package_data=True, # if packages include any data files, those will be packed together.
description='Classification training titanic survivors prediction model'
)
Return to your terminal and test if you can train a model using task.py.
First create the following environment variables but remember to ensure you have selected the right GCP project from the console:
- PROJECT_ID Will be set to the selected project id
- BUCKET_NAME Will be the PROJECT_ID and "-bucket" attached to it
export REGION="europe-west4"
export PROJECT_ID=$(gcloud config list --format 'value(core.project)')
export BUCKET_NAME=$PROJECT_ID"-bucket"
First create a bucket where you want to export your trained model.
gsutil mb -l $REGION "gs://"$BUCKET_NAME
Now run the the following commands. We are using all of our training data to test. Also the same dataset for test, validation and training is used. Here you want to ensure that the code executes and that it is free of bugs. In reality we want to use different test and validation data. We will leave that for Vertex AI training service to handle.
First install the required libraries
cd /home/jupyter/titanic
pip install setuptools
python setup.py install
Now run your training code to verify that it executes without issues
python -m trainer.task -v \
--model_param_kernel=linear \
--model_dir="gs://"$BUCKET_NAME"/titanic/trial" \
--data_format=bigquery \
--training_data_uri="bq://"$PROJECT_ID".titanic.survivors" \
--test_data_uri="bq://"$PROJECT_ID".titanic.survivors" \
--validation_data_uri="bq://"$PROJECT_ID".titanic.survivors"
If the code executed successfully you will be able to see INFO logs printed. The two lines indicate the f1 score which should be around 0.85 and the last line idicating that the training job completed successfully:
INFO:root:f1score: 0.85
INFO:root:Training job completed. Exiting...
Cogratulations! You are ready to create your training python package!
The following command does exactly that:
cd /home/jupyter/titanic
python setup.py sdist
After the command executes you will see a new folder called dist that contains a tar.gz file. This is your python package
You should copy the package to GCS so that the training service can use it to train a new model when you need to
gsutil cp dist/trainer-0.1.tar.gz "gs://"$BUCKET_NAME"/titanic/dist/trainer-0.1.tar.gz"
5. Model Training
In this section you will train a model on Vertex AI. You are going to use the GUI for that. There is also a programmatic way using a python SDK, however using the GUI helps to better understand the process.
From the Google Cloud console navigate to Vertex AI -> Training
Step 0:
Select the region as europe-west4 and click create as the picture below:
Step 1: Training method
In this step select the dataset and define the objective for the training job.
- Dataset: The dataset we created few steps back. The name should be titanic
- Objective: The model predicts if an individual is likely to survive the titanic tragedy or not. This is a Classification problem
- Custom Training: You want to use your custom training package.
Click CONTINUE
Step 2: Model details
Now define the model name. The default name should be the name of the dataset and a timestamp. You can leave it as is. If you click show more you will see the option to define the split of data into training, test and validation sets. Random assignment will randomly split the data into training, validation and testing. This seems like a good option.
Click CONTINUE
Step 3: Training container
Define your training environment.
- Pre-built container: Google cloud offers a set of prebuilt containers that make it easy to train your models. Those containers support frameworks such as Scikit-Learn, Tensorflow and XGBoost. If your training job is using something exotic you will need to prepare and provide a container for training(custom container). Your model is based on scikit-learn and prebuilt container already exists.
- Model framework:
Scikit-learn. This is the library you used for model training. - Model framework version: Your code is compatible with
0.23. - Package location: You can browse to the location of your training package. This is the location where you uploaded training-0.1.tar.gz. If you followed the previous steps correctly the location should be
gs://YOUR-BUCKET-NAME/titanic/dist/trainer-0.1.tar.gzand YOUR-BUCKET-NAME is the name of the bucket you used under the Build your package section - Python Module: The python module you created in Notebooks. It will correspond to the folder that has your training code/module and the name of the entry file. This should be
trainer.task - BigQuery project for exporting data: In step 1 you selected the dataset and defined automatic split. A new dataset and tables for train/test/validate sets will be created under the selected project. Select the same project you are running the lab.
Additionally training/test/validation datasets URIs will be set as environment variables in the training container so You can automatically use those variables to load your data. The environment variable names for the datasets will beAIP_TRAINING_DATA_URI,AIP_TEST_DATA_URI,AIP_VALIDATION_DATA_URI. An additional variable will beAIP_DATA_FORMATwhich will be eithercsvorbigquery, depending on the type of the selected dataset in Step 1.
You have already built this logic in task.py . Observe this example code (taken from task.py):... parser.add_argument( '--training_data_uri ', help = 'Directory to output model and artifacts', type = str, default = os.environ['AIP_TRAINING_DATA_URI'] if 'AIP_TRAINING_DATA_URI' in os.environ else "" ) ... - Model output directory: The location the model will be exported to. This is going to be an environment variable in the training container called
AIP_MODEL_DIR. In our task.py there is an input parameters to capture this:
You can use the environment variable to know where to export the training job artifacts. Let's select:... parser.add_argument( '--model_dir', help = 'Directory to output model and artifacts', type = str, default = os.environ['AIP_MODEL_DIR'] if 'AIP_MODEL_DIR' in os.environ else "" ) ...gs://YOUR-BUCKET-NAME/training/assets
Click CONTINUE
Step 4: Hyperparameter tuning
Hyperparameter tuning section allows you to define a set of model parameters that you would like to tune your model with. Different values will be explored in order to produce the model with the best parameters. In our code we did not implement the hyperparameter tuner functionality. It's only a few lines of code (about 5) but we did not want to add this complexity now. Let's skip this step by pressing CONTINUE
Step 5: Compute and pricing
Where do we want our training job to run and what type of server do we want to use? Your model training process is not hungry for resources. We were able to run the training job inside a relatively small notebook instance and the execution finishes quite fast. With that in mind we choose:
- Region:
europe-west4 - Machine type:
n1-standard-4
Click CONTINUE
Step 6: Prediction container
In this step you can decide if you want to just train the model or also add add settings for the prediction service used to productionise your model.
You will be using a pre-built container in this lab, however keep in mind that Vertex AI gives you a few options for model serving:
- No Prediction Container: Just train the model and worry about productionizing the model later
- Pre-built container: Train the model and define the prebuilt container to be used for deployment
- Custom container: Train the model and define a custom container to be used for deployment
You should choose a Pre-built container since Google Cloud already offers a Scikit-Learn container. You will deploy the model after the training job is completed.
- Model framework:
scikit-learn - Model framework version:
0.23 - Model directory:
gs://YOUR-BUCKET-NAME/training/assetsThis should be the same as the model output directory you defined in step 3
Click START TRAINING
The new training job will show under the TRAINING PIPELINE tab. The training will take about 17 minutes to complete
6. Model Evaluation
After the training job completion artifacts will be exported under gs://YOUR-BUCKET-NAME/training/assets You can inspect the report.txt file which contains evaluation metrics and classification report of the model.
7. Model Deployment
Last step is model deployment! After the model training job is completed (just under 20 minutes), select the trained model and deploy it to an endpoint.
Click on the trained model and DEPLOY TO ENDPOINT
On the popup you can define the required resources for model deployment:
- Endpoint name: Endpoint URL where the model is served. A reasonable name for that would be
titanic-endpoint - Traffic split: Defines the percentage of traffic that you want to direct to this model. An endpoint can have multiple models and you can despite how to split the traffic among them. In this case you are deploying a singe model so the traffic has to be
100percent. - Minimum number of compute nodes: The minimum number of nodes required to serve model predictions. Start with
1. Additionally the prediction service will autoscale in case there is traffic - Maximum number of compute nodes: In case of autoscaling, this variable defines the upper limit of nodes. It helps protecting against unwanted costs that autoscaling might result in. Set this variable to
2 - Machine type: Google cloud offers a set of machine types you can deploy your model to. Each machine has its own memory and vcpu specs. Your model is simple so serving on an
n1-standard-4instance will do the job
Click CONTINUE and DEPLOY
8. Model Prediction
Under Models test the model prediction endpoint. The GUI provides a form to send a json request payload and responds back with the predictions as well as the model id used for the prediction. That is because you can deploy more than one model to an endpoint and split the traffic.
Try the following payload and perhaps change some of the values to see how the predictions change: The sequence of the input features is [‘sex', ‘age', ‘fare', ‘pclass', ‘embarked', ‘home_dest', ‘parch', ‘sibsp']
{
"instances": [
["male", 29.8811345124283, 26.0, 1, "S", "New York, NY", 0, 0],
["female", 48.0, 39.6, 1, "C", "London / Paris", 0, 1]]
}
The endpoint responds with a list of Zeros or Ones in the same order as your input. 0 means it is more likely that the individual will not survive the titanic accident and 1 means the individual is likely to survive it.
9. Cleaning up
Congratulations! You have created a dataset, packaged your training code and run a custom training job using Vertex AI. Furthermore you deployed the trained model and sent some data for predictions.
Given that you do not need the created resourses, it is a good idea to delete them in order to avoid unwanted charges.
Delete ML Dataset
Navigate to Datasets console page, click the three dots on the dataset you want to delete and click Delete.
Delete Notebook
Navigate to Notebooks console page, select only the notebook you want to delete and click Delete from the top menu.
Delete Endpoint
To delete the created end point you need to first remove the deployed model from that endpoint. Navigate to Endpoints console page end click on the titanic-endpoint like in the image below.
Now click the trash can icon from the model row and Undeploy
Finally to delete the endpoint navigate back to Endpoints Click on the three dots at the right of the endpoint row and select Remove endpoint
Delete Model
Navigate to Models console page, click the three dots on the mode you want to delete and click Delete Model.
Delete Objects
!Be careful not to delete buckets or objects that you need!
To delete objects created for this codelab, like the actual model and report and the training package, navigate to Storage and find the bucket you used for this codelab. Inside the bucket you will find the titanic object. Select the object and click Delete from the top menu.
Delete BigQuery dataset
To delete the BigQuery dataset, perform the following steps:
- Navigate to BigQuery console
- Select the titanic dataset from the left side navigation panel in BigQuery .
- In the details panel, select Delete dataset.
- In the Delete dataset dialog, enter titanic and select Delete to confirm that you want to delete the dataset.
Delete the project
If you created a poject just for this lab, perform the following steps for deletion:
- In the GCP navigation menu, select IAM & Admin.
- In the navigation panel, select Settings.
- In the details panel, confirm that your current project is the project you created for this codelab and select Shut down.
- In the Shut down project dialog, enter the project ID (not project name) for your project and select Shut down to confirm.