MLflow Google Cloud Vertex AI integration package
Project description
MLflow plugin for Google Cloud Vertex AI
Note: The plugin is experimental and may be changed or removed in the future.
Installation
python3 -m pip install google_cloud_mlflow
Deployment plugin usage
Command-line
Create deployment
mlflow deployments create --target google_cloud --name "deployment name" --model-uri "models:/mymodel/mymodelversion" --config destination_image_uri="gcr.io/<repo>/<path>"
List deployments
mlflow deployments list --target google_cloud
Get deployment
mlflow deployments get --target google_cloud --name "deployment name"
Delete deployment
mlflow deployments delete --target google_cloud --name "deployment name"
Update deployment
mlflow deployments update --target google_cloud --name "deployment name" --model-uri "models:/mymodel/mymodelversion" --config destination_image_uri="gcr.io/<repo>/<path>"
Predict
mlflow deployments predict --target google_cloud --name "deployment name" --input-path "inputs.json" --output-path "outputs.json
Get help
mlflow deployments help --target google_cloud
Python
from mlflow import deployments
client = deployments.get_deploy_client("google_cloud")
# Create deployment
model_uri = "models:/mymodel/mymodelversion"
deployment = client.create_deployment(
name="deployment name",
model_uri=model_uri,
# Config is optional
config=dict(
# Deployed model config
machine_type="n1-standard-2",
min_replica_count=None,
max_replica_count=None,
accelerator_type=None,
accelerator_count=None,
service_account=None,
explanation_metadata=None, # JSON string
explanation_parameters=None, # JSON string
# Model container image building config
destination_image_uri=None,
timeout=None,
# Model deployment config
sync="true",
# Endpoint config
description=None,
# Vertex AI config
project=None,
location=None,
experiment=None,
experiment_description=None,
staging_bucket=None,
# List deployments
deployments = client.list_deployments()
# Get deployment
deployments = client.get_deployment(name="deployment name")
# Delete deployment
deployment = client.delete_deployment(name="deployment name")
# Update deployment
deployment = client.create_deployment(
name="deployment name",
model_uri=model_uri,
# Config is optional
config=dict(...),
)
# Predict
import pandas
df = pandas.DataFrame([
{"a": 1,"b": 2,"c": 3},
{"a": 4,"b": 5,"c": 6}
])
predictions = client.predict("deployment name", df)
Model Registry plugin usage
Set the MLflow Model Registry URI to a directory in some Google Cloud Storage bucket, then log models using mlflow.log_model
as usual.
mlflow.set_registry_uri("gs://<bucket>/models/")
Project details
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