prediction-try-java-python | Sample application illustrating use of the Google | GCP library

import pandas as pd

# gcp / google sheets URL
df_URL = "https://docs.google.com/spreadsheets/d/1wKtvNfWSjPNC1fNmTfUHm7sXiaPyOZMchjzQBt1y_f8/edit?usp=sharing"

raw_dataset = pd.read_csv(df_URL, na_values='?',sep=';'
                          , skipinitialspace=True, header=0, index_col=None)

import pandas as pd
from google.colab import auth
auth.authenticate_user()
import gspread
from oauth2client.client import GoogleCredentials


# gcp / google sheets url
df_URL = "https://docs.google.com/spreadsheets/d/1wKtvNfWSjPNC1fNmTfUHm7sXiaPyOZMchjzQBt1y_f8/edit?usp=sharing"

# importing the data from Google Drive  setup
gc = gspread.authorize(GoogleCredentials.get_application_default())

# read data and put it in dataframe
g_sheets = gc.open_by_url(df_URL) 

df = pd.DataFrame(g_sheets.get_worksheet(0).get_all_values())

  
# change first row to header
df = df.rename(columns=df.iloc[0]) 

# drop first row
df.drop(index=df.index[0], axis=0, inplace=True) 

try (SecretManagerServiceClient client = SecretManagerServiceClient.create()) {
  // Build the parent name.
  SecretName projectName = SecretName.of(projectId, secretId);

  // Get all versions.
  ListSecretVersionsPagedResponse pagedResponse = client.listSecretVersions(projectName);

  // List all versions and their state.
  pagedResponse
      .iterateAll()
      .forEach(
          version -> {
            System.out.printf("Secret version %s, %s\n", version.getName(), version.getState());
          });
}

> ####@1.0.1 prepare /workspace
> npm run gcp-build


> ####@1.0.1 gcp-build /workspace
> tsc -p .

sh: 1: tsc: not found
npm ERR! code ELIFECYCLE
npm ERR! syscall spawn
npm ERR! file sh
npm ERR! errno ENOENT
npm ERR! ####@1.0.1 gcp-build: `tsc -p .`
npm ERR! spawn ENOENT
npm ERR!
npm ERR! Failed at the ####@1.0.1 gcp-build script.
npm ERR! This is probably not a problem with npm. There is likely additional logging output above.

    "devDependencies": {
        "@google-cloud/nodejs-repo-tools": "^3.3.0",
        "@types/cors": "^2.8.12",
        "@types/express": "^4.17.13",
        "@types/pg": "^8.6.1",
        "@types/request": "^2.48.7",
        "tsc-watch": "^4.4.0",
        "tslint": "^6.1.3",
        "typescript": "^4.3.5"
    },

 // Configure the settings for the job controller client.
    JobControllerSettings jobControllerSettings =
        JobControllerSettings.newBuilder().setEndpoint(myEndpoint).build();

SEVERE: Servlet.service() for servlet [dispatcherServlet] in context with path [] threw exception [Handler dispatch failed; nested exception is java.lang.NoSuchMethodError: 'com.google.api.gax.core.GoogleCredentialsProvider$Builder com.google.api.gax.core.GoogleCredentialsProvider$Builder.setUseJwtAccessWithScope(boolean)'] with root cause
java.lang.NoSuchMethodError: 'com.google.api.gax.core.GoogleCredentialsProvider$Builder com.google.api.gax.core.GoogleCredentialsProvider$Builder.setUseJwtAccessWithScope(boolean)'

        
            
                com.google.cloud
                libraries-bom
                24.1.2
                pom
                import
            

            
                org.springframework.cloud
                spring-cloud-gcp-dependencies
                1.2.8.RELEASE
                pom
                import
            

        
    

            com.google.cloud
            google-cloud-dataproc
        

side_pipeline = (
    p
    | "periodic" >> PeriodicImpulse(fire_interval=3600, apply_windowing=True)
    | "map to read request" >>
        beam.Map(lambda x:beam.io.gcp.bigquery.ReadFromBigQueryRequest(table=side_table))
    | beam.io.ReadAllFromBigQuery()
)

def enrich_payload(payload, equipments):
    id = payload["id"]
    for equipment in equipments:
        if id == equipment["id"]:
            payload["type"] = equipment["type"]
            payload["brand"] = equipment["brand"]
            payload["year"] = equipment["year"]

            break

    return payload

main_pipeline = (
    p
    | "read" >> beam.io.ReadFromPubSub(topic="projects/my-project/topics/topiq")
    | "bytes to dict" >> beam.Map(lambda x: json.loads(x.decode("utf-8")))
    | "transform" >> beam.Map(transform_function)
    | "timestamping" >> beam.Map(lambda src: window.TimestampedValue(
        src,
        dt.datetime.fromisoformat(src["timestamp"]).timestamp()
    ))
    | "windowing" >> beam.WindowInto(window.FixedWindows(30))
)

final_pipeline = (
    main_pipeline
    | "enrich data" >> beam.Map(enrich_payload, equipments=beam.pvalue.AsIter(side_pipeline))
    | "store" >> beam.io.WriteToBigQuery(bq_table)
)

result = p.run()
result.wait_until_finish()

echo Give yearmonth "yyyyMM"
setlocal enabledelayedexpansion
SET /p yearmonth= 
SET ClientName[0]=abc
SET ClientName[1]=def

SET i = 0

:myLoop
if defined ClientName[%i%] (
    call bq query --use_legacy_sql=false "CREATE EXTERNAL TABLE `test.!ClientName[%%i]!.%yearmonth%` OPTIONS (format = 'CSV',skip_leading_rows = 1 uris = ['gs://test/!ClientName[%%i]!/AWS/%yearmonth%/Metrics/data/*.csv'])"
    set /a "i+=1"
    GOTO :myLoop

)

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()

   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/'
    )

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

@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

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'
    )

@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