I'm struggling to correctly set Vertex AI pipeline which does the following:
from google_cloud_pipeline_components import aiplatform as gcc_aip
from kfp.v2 import dsl
from kfp.v2.dsl import component
from kfp.v2.dsl import (
Output,
Artifact,
Model,
)
PROJECT_ID = 'my-gcp-project'
BUCKET_NAME = "mybucket"
PIPELINE_ROOT = "{}/pipeline_root".format(BUCKET_NAME)
@component
def get_input_data() -> str:
# getting data from API, save to Cloud Storage
# return GS URI
gcs_batch_input_path = 'gs://somebucket/file'
return gcs_batch_input_path
@component(
base_image="python:3.9",
packages_to_install=['google-cloud-aiplatform==1.8.0']
)
def load_ml_model(project_id: str, model: Output[Artifact]):
"""Load existing Vertex model"""
import google.cloud.aiplatform as aip
model_id = '1234'
model = aip.Model(model_name=model_id, project=project_id, location='us-central1')
@dsl.pipeline(
name="batch-pipeline", pipeline_root=PIPELINE_ROOT,
)
def pipeline(gcp_project: str):
input_data = get_input_data()
ml_model = load_ml_model(gcp_project)
gcc_aip.ModelBatchPredictOp(
project=PROJECT_ID,
job_display_name=f'test-prediction',
model=ml_model.output,
gcs_source_uris=[input_data.output], # this doesn't work
# gcs_source_uris=['gs://mybucket/output/'], # hardcoded gs uri works
gcs_destination_output_uri_prefix=f'gs://{PIPELINE_ROOT}/prediction_output/'
)
if __name__ == '__main__':
from kfp.v2 import compiler
import google.cloud.aiplatform as aip
pipeline_export_filepath = 'test-pipeline.json'
compiler.Compiler().compile(pipeline_func=pipeline,
package_path=pipeline_export_filepath)
# pipeline_params = {
# 'gcp_project': PROJECT_ID,
# }
# job = aip.PipelineJob(
# display_name='test-pipeline',
# template_path=pipeline_export_filepath,
# pipeline_root=f'gs://{PIPELINE_ROOT}',
# project=PROJECT_ID,
# parameter_values=pipeline_params,
# )
# job.run()
When running the pipeline it throws this exception when running Batch prediction:
details = "List of found errors: 1.Field: batch_prediction_job.model; Message: Invalid Model resource name.
so I'm not sure what could be wrong. I tried to load model in the notebook (outside of component) and it correctly returns.
Second issue I'm having is referencing GCS URI as output from component to batch job input.
input_data = get_input_data2()
gcc_aip.ModelBatchPredictOp(
project=PROJECT_ID,
job_display_name=f'test-prediction',
model=ml_model.output,
gcs_source_uris=[input_data.output], # this doesn't work
# gcs_source_uris=['gs://mybucket/output/'], # hardcoded gs uri works
gcs_destination_output_uri_prefix=f'gs://{PIPELINE_ROOT}/prediction_output/'
)
During compilation, I get following exception TypeError: Object of type PipelineParam is not JSON serializable, though I think this could be issue of ModelBatchPredictOp component.
Again any help/advice appreciated, I'm dealing with this from yesterday, so maybe I missed something obvious.
libraries I'm using:
google-cloud-aiplatform==1.8.0
google-cloud-pipeline-components==0.2.0
kfp==1.8.10
kfp-pipeline-spec==0.1.13
kfp-server-api==1.7.1
UPDATE After comments, some research and tuning, for referencing model this works:
@component
def load_ml_model(project_id: str, model: Output[Artifact]):
region = 'us-central1'
model_id = '1234'
model_uid = f'projects/{project_id}/locations/{region}/models/{model_id}'
model.uri = model_uid
model.metadata['resourceName'] = model_uid
and then I can use it as intended:
batch_predict_op = gcc_aip.ModelBatchPredictOp(
project=gcp_project,
job_display_name=f'batch-prediction-test',
model=ml_model.outputs['model'],
gcs_source_uris=[input_batch_gcs_path],
gcs_destination_output_uri_prefix=f'gs://{BUCKET_NAME}/prediction_output/test'
)
UPDATE 2 regarding GCS path, a workaround is to define path outside of the component and pass it as an input parameter, for example (abbreviated):
@dsl.pipeline(
name="my-pipeline",
pipeline_root=PIPELINE_ROOT,
)
def pipeline(
gcp_project: str,
region: str,
bucket: str
):
ts = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
gcs_prediction_input_path = f'gs://{BUCKET_NAME}/prediction_input/video_batch_prediction_input_{ts}.jsonl'
batch_input_data_op = get_input_data(gcs_prediction_input_path) # this loads input data to GCS path
batch_predict_op = gcc_aip.ModelBatchPredictOp(
project=gcp_project,
model=training_job_run_op.outputs["model"],
job_display_name='batch-prediction',
# gcs_source_uris=[batch_input_data_op.output],
gcs_source_uris=[gcs_prediction_input_path],
gcs_destination_output_uri_prefix=f'gs://{BUCKET_NAME}/prediction_output/',
).after(batch_input_data_op) # we need to add 'after' so it runs after input data is prepared since get_input_data doesn't returns anything
still not sure, why it doesn't work/compile when I return GCS path from get_input_data component
I'm glad you solved most of your main issues and found a workaround for model declaration.
For your input.output observation on gcs_source_uris, the reason behind it is because the way the function/class returns the value. If you dig inside the class/methods of google_cloud_pipeline_components you will find that it implements a structure that will allow you to use .outputs from the returned value of the function called.
If you go to the implementation of one of the components of the pipeline you will find that it returns an output array from convert_method_to_component function. So, in order to have that implemented in your custom class/function your function should return a value which can be called as an attribute. Below is a basic implementation of it.
class CustomClass():
def __init__(self):
self.return_val = {'path':'custompath','desc':'a desc'}
@property
def output(self):
return self.return_val
hello = CustomClass()
print(hello.output['path'])
If you want to dig more about it you can go to the following pages:
convert_method_to_component, which is the implementation of convert_method_to_component
Properties, basics of property in python.