Vertex AI SDK for Python
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Vertex AI_: Google Vertex AI is an integrated suite of machine learning tools and services for building and using ML models with AutoML or custom code. It offers both novices and experts the best workbench for the entire machine learning development lifecycle.
Client Library Documentation_Product Documentation_
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.. _Vertex AI: https://cloud.google.com/vertex-ai/docs
.. _Client Library Documentation: https://cloud.google.com/python/docs/reference/aiplatform/latest
.. _Product Documentation: https://cloud.google.com/vertex-ai/docs
Quick Start
In order to use this library, you first need to go through the following steps:
Select or create a Cloud Platform project._Enable billing for your project._Enable the Vertex AI API._Setup Authentication._
.. _Select or create a Cloud Platform project.: https://console.cloud.google.com/project
.. _Enable billing for your project.: https://cloud.google.com/billing/docs/how-to/modify-project#enable_billing_for_a_project
.. _Enable the Vertex AI API.: https://cloud.google.com/ai-platform/docs
.. _Setup Authentication.: https://googleapis.dev/python/google-api-core/latest/auth.html
Installation
Install this library in a `virtualenv`_ using pip. `virtualenv`_ is a tool to
create isolated Python environments. The basic problem it addresses is one of
dependencies and versions, and indirectly permissions.
With `virtualenv`_, it's possible to install this library without needing system
install permissions, and without clashing with the installed system
dependencies.
.. _virtualenv: https://virtualenv.pypa.io/en/latest/
Mac/Linux
^^^^^^^^^
.. code-block:: console
pip install virtualenv
virtualenv <your-env>
source <your-env>/bin/activate
<your-env>/bin/pip install google-cloud-aiplatform
Windows
^^^^^^^
.. code-block:: console
pip install virtualenv
virtualenv <your-env>
<your-env>\Scripts\activate
<your-env>\Scripts\pip.exe install google-cloud-aiplatform
Overview
~~~~~~~~
This section provides a brief overview of the Vertex AI SDK for Python. You can also reference the notebooks in `vertex-ai-samples`_ for examples.
.. _vertex-ai-samples: https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/community/sdk
Importing
^^^^^^^^^
SDK functionality can be used from the root of the package:
.. code-block:: Python
from google.cloud import aiplatform
Initialization
^^^^^^^^^^^^^^
Initialize the SDK to store common configurations that you use with the SDK.
.. code-block:: Python
aiplatform.init(
# your Google Cloud Project ID or number
# environment default used is not set
project='my-project',
# the Vertex AI region you will use
# defaults to us-central1
location='us-central1',
# Google Cloud Storage bucket in same region as location
# used to stage artifacts
staging_bucket='gs://my_staging_bucket',
# custom google.auth.credentials.Credentials
# environment default credentials used if not set
credentials=my_credentials,
# customer managed encryption key resource name
# will be applied to all Vertex AI resources if set
encryption_spec_key_name=my_encryption_key_name,
# the name of the experiment to use to track
# logged metrics and parameters
experiment='my-experiment',
# description of the experiment above
experiment_description='my experiment description'
)
Datasets
^^^^^^^^
Vertex AI provides managed tabular, text, image, and video datasets. In the SDK, datasets can be used downstream to
train models.
To create a tabular dataset:
.. code-block:: Python
my_dataset = aiplatform.TabularDataset.create(
display_name="my-dataset", gcs_source=['gs://path/to/my/dataset.csv'])
You can also create and import a dataset in separate steps:
.. code-block:: Python
from google.cloud import aiplatform
my_dataset = aiplatform.TextDataset.create(
display_name="my-dataset")
my_dataset.import(
gcs_source=['gs://path/to/my/dataset.csv']
import_schema_uri=aiplatform.schema.dataset.ioformat.text.multi_label_classification
)
To get a previously created Dataset:
.. code-block:: Python
dataset = aiplatform.ImageDataset('projects/my-project/location/us-central1/datasets/{DATASET_ID}')
Vertex AI supports a variety of dataset schemas. References to these schemas are available under the
:code:`aiplatform.schema.dataset` namespace. For more information on the supported dataset schemas please refer to the
`Preparing data docs`_.
.. _Preparing data docs: https://cloud.google.com/ai-platform-unified/docs/datasets/prepare
Training
^^^^^^^^
The Vertex AI SDK for Python allows you train Custom and AutoML Models.
You can train custom models using a custom Python script, custom Python package, or container.
**Preparing Your Custom Code**
Vertex AI custom training enables you to train on Vertex AI datasets and produce Vertex AI models. To do so your
script must adhere to the following contract:
It must read datasets from the environment variables populated by the training service:
.. code-block:: Python
os.environ['AIP_DATA_FORMAT'] # provides format of data
os.environ['AIP_TRAINING_DATA_URI'] # uri to training split
os.environ['AIP_VALIDATION_DATA_URI'] # uri to validation split
os.environ['AIP_TEST_DATA_URI'] # uri to test split
Please visit `Using a managed dataset in a custom training application`_ for a detailed overview.
.. _Using a managed dataset in a custom training application: https://cloud.google.com/vertex-ai/docs/training/using-managed-datasets
It must write the model artifact to the environment variable populated by the training service:
.. code-block:: Python
os.environ['AIP_MODEL_DIR']
**Running Training**
.. code-block:: Python
job = aiplatform.CustomTrainingJob(
display_name="my-training-job",
script_path="training_script.py",
container_uri="us-docker.pkg.dev/vertex-ai/training/tf-cpu.2-2:latest",
requirements=["gcsfs==0.7.1"],
model_serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-2:latest",
)
model = job.run(my_dataset,
replica_count=1,
machine_type="n1-standard-4",
accelerator_type='NVIDIA_TESLA_K80',
accelerator_count=1)
In the code block above `my_dataset` is managed dataset created in the `Dataset` section above. The `model` variable is a managed Vertex AI model that can be deployed or exported.
AutoMLs
-------
The Vertex AI SDK for Python supports AutoML tabular, image, text, video, and forecasting.
To train an AutoML tabular model:
.. code-block:: Python
dataset = aiplatform.TabularDataset('projects/my-project/location/us-central1/datasets/{DATASET_ID}')
job = aiplatform.AutoMLTabularTrainingJob(
display_name="train-automl",
optimization_prediction_type="regression",
optimization_objective="minimize-rmse",
)
model = job.run(
dataset=dataset,
target_column="target_column_name",
training_fraction_split=0.6,
validation_fraction_split=0.2,
test_fraction_split=0.2,
budget_milli_node_hours=1000,
model_display_name="my-automl-model",
disable_early_stopping=False,
)
Models
------
To get a model:
.. code-block:: Python
model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
To upload a model:
.. code-block:: Python
model = aiplatform.Model.upload(
display_name='my-model',
artifact_uri="gs://python/to/my/model/dir",
serving_container_image_uri="us-docker.pkg.dev/vertex-ai/prediction/tf2-cpu.2-2:latest",
)
To deploy a model:
.. code-block:: Python
endpoint = model.deploy(machine_type="n1-standard-4",
min_replica_count=1,
max_replica_count=5
machine_type='n1-standard-4',
accelerator_type='NVIDIA_TESLA_K80',
accelerator_count=1)
Please visit `Importing models to Vertex AI`_ for a detailed overview:
.. _Importing models to Vertex AI: https://cloud.google.com/vertex-ai/docs/general/import-model
Batch Prediction
----------------
To create a batch prediction job:
.. code-block:: Python
model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
batch_prediction_job = model.batch_predict(
job_display_name='my-batch-prediction-job',
instances_format='csv'
machine_type='n1-standard-4',
gcs_source=['gs://path/to/my/file.csv']
gcs_destination_prefix='gs://path/to/by/batch_prediction/results/'
)
You can also create a batch prediction job asynchronously by including the `sync=False` argument:
.. code-block:: Python
batch_prediction_job = model.batch_predict(..., sync=False)
# wait for resource to be created
batch_prediction_job.wait_for_resource_creation()
# get the state
batch_prediction_job.state
# block until job is complete
batch_prediction_job.wait()
Endpoints
---------
To create an endpoint:
.. code-block:: Python
endpoint = aiplatform.Endpoint.create(display_name='my-endpoint')
To deploy a model to a created endpoint:
.. code-block:: Python
model = aiplatform.Model('/projects/my-project/locations/us-central1/models/{MODEL_ID}')
endpoint.deploy(model,
min_replica_count=1,
max_replica_count=5
machine_type='n1-standard-4',
accelerator_type='NVIDIA_TESLA_K80',
accelerator_count=1)
To get predictions from endpoints:
.. code-block:: Python
endpoint.predict(instances=[[6.7, 3.1, 4.7, 1.5], [4.6, 3.1, 1.5, 0.2]])
To undeploy models from an endpoint:
.. code-block:: Python
endpoint.undeploy_all()
To delete an endpoint:
.. code-block:: Python
endpoint.delete()
Pipelines
---------
To create a Vertex AI Pipeline run and monitor until completion:
.. code-block:: Python
# Instantiate PipelineJob object
pl = PipelineJob(
display_name="My first pipeline",
# Whether or not to enable caching
# True = always cache pipeline step result
# False = never cache pipeline step result
# None = defer to cache option for each pipeline component in the pipeline definition
enable_caching=False,
# Local or GCS path to a compiled pipeline definition
template_path="pipeline.json",
# Dictionary containing input parameters for your pipeline
parameter_values=parameter_values,
# GCS path to act as the pipeline root
pipeline_root=pipeline_root,
)
# Execute pipeline in Vertex AI and monitor until completion
pl.run(
# Email address of service account to use for the pipeline run
# You must have iam.serviceAccounts.actAs permission on the service account to use it
service_account=service_account,
# Whether this function call should be synchronous (wait for pipeline run to finish before terminating)
# or asynchronous (return immediately)
sync=True
)
To create a Vertex AI Pipeline without monitoring until completion, use `submit` instead of `run`:
.. code-block:: Python
# Instantiate PipelineJob object
pl = PipelineJob(
display_name="My first pipeline",
# Whether or not to enable caching
# True = always cache pipeline step result
# False = never cache pipeline step result
# None = defer to cache option for each pipeline component in the pipeline definition
enable_caching=False,
# Local or GCS path to a compiled pipeline definition
template_path="pipeline.json",
# Dictionary containing input parameters for your pipeline
parameter_values=parameter_values,
# GCS path to act as the pipeline root
pipeline_root=pipeline_root,
)
# Submit the Pipeline to Vertex AI
pl.submit(
# Email address of service account to use for the pipeline run
# You must have iam.serviceAccounts.actAs permission on the service account to use it
service_account=service_account,
)
Explainable AI: Get Metadata
----------------------------
To get metadata in dictionary format from TensorFlow 1 models:
.. code-block:: Python
from google.cloud.aiplatform.explain.metadata.tf.v1 import saved_model_metadata_builder
builder = saved_model_metadata_builder.SavedModelMetadataBuilder(
'gs://python/to/my/model/dir', tags=[tf.saved_model.tag_constants.SERVING]
)
generated_md = builder.get_metadata()
To get metadata in dictionary format from TensorFlow 2 models:
.. code-block:: Python
from google.cloud.aiplatform.explain.metadata.tf.v2 import saved_model_metadata_builder
builder = saved_model_metadata_builder.SavedModelMetadataBuilder('gs://python/to/my/model/dir')
generated_md = builder.get_metadata()
To use Explanation Metadata in endpoint deployment and model upload:
.. code-block:: Python
explanation_metadata = builder.get_metadata_protobuf()
# To deploy a model to an endpoint with explanation
model.deploy(..., explanation_metadata=explanation_metadata)
# To deploy a model to a created endpoint with explanation
endpoint.deploy(..., explanation_metadata=explanation_metadata)
# To upload a model with explanation
aiplatform.Model.upload(..., explanation_metadata=explanation_metadata)
Cloud Profiler
----------------------------
Cloud Profiler allows you to profile your remote Vertex AI Training jobs on demand and visualize the results in Vertex AI Tensorboard.
To start using the profiler with TensorFlow, update your training script to include the following:
.. code-block:: Python
from google.cloud.aiplatform.training_utils import cloud_profiler
...
cloud_profiler.init()
Next, run the job with with a Vertex AI TensorBoard instance. For full details on how to do this, visit https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview
Finally, visit your TensorBoard in your Google Cloud Console, navigate to the "Profile" tab, and click the `Capture Profile` button. This will allow users to capture profiling statistics for the running jobs.
Next Steps
~~~~~~~~~~
- Read the `Client Library Documentation`_ for Vertex AI
API to see other available methods on the client.
- Read the `Vertex AI API Product documentation`_ to learn
more about the product and see How-to Guides.
- View this `README`_ to see the full list of Cloud
APIs that we cover.
.. _Vertex AI API Product documentation: https://cloud.google.com/vertex-ai/docs
.. _README: https://github.com/googleapis/google-cloud-python/blob/main/README.rst