Question: This is my first time using Google's Vertex AI Pipelines. I checked this codelab as well as this post and this post, on top of some links derived from the official documentation. I decided to put all that knowledge to work, in some toy example: I was planning to build a pipeline consisting of 2 components: "get-data" (which reads some .csv file stored in Cloud Storage) and "report-data" (which basically returns the shape of the .csv data read in the previous component). Furthermore, I was cautious to include some suggestions provided in this forum. The code I currently have, goes as follows:  from kfp.v2 import compiler from kfp.v2.dsl import pipeline, component, Dataset, Input, Output from google.cloud import aiplatform # Components section @component( packages_to_install=[ "google-cloud-storage", "pandas", ], base_image="python:3.9", output_component_file="get_data.yaml" ) def get_data( bucket: str, url: str, dataset: Output[Dataset], ): import pandas as pd from google.cloud import storage storage_client = storage.Client("my-project") bucket = storage_client.get_bucket(bucket) blob = bucket.blob(url) blob.download_to_filename('localdf.csv') # path = "gs://my-bucket/program_grouping_data.zip" df = pd.read_csv('localdf.csv', compression='zip') df['new_skills'] = df['new_skills'].apply(ast.literal_eval) df.to_csv(dataset.path + ".csv" , index=False, encoding='utf-8-sig') @component( packages_to_install=["pandas"], base_image="python:3.9", output_component_file="report_data.yaml" ) def report_data( inputd: Input[Dataset], ): import pandas as pd df = pd.read_csv(inputd.path) return df.shape # Pipeline section @pipeline( # Default pipeline root. You can override it when submitting the pipeline. pipeline_root=PIPELINE_ROOT, # A name for the pipeline. name="my-pipeline", ) def my_pipeline( url: str = "test_vertex/pipeline_root/program_grouping_data.zip", bucket: str = "my-bucket" ): dataset_task = get_data(bucket, url) dimensions = report_data( dataset_task.output ) # Compilation section compiler.Compiler().compile( pipeline_func=my_pipeline, package_path="pipeline_job.json" ) # Running and submitting job from datetime import datetime TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S") run1 = aiplatform.PipelineJob( display_name="my-pipeline", template_path="pipeline_job.json", job_id="mlmd-pipeline-small-{0}".format(TIMESTAMP), parameter_values={"url": "test_vertex/pipeline_root/program_grouping_data.zip", "bucket": "my-bucket"}, enable_caching=True, ) run1.submit()  I was happy to see that the pipeline compiled with no errors, and managed to submit the job. However "my happiness lasted short", as when I went to Vertex AI Pipelines, I stumbled upon some "error", which goes like:  The DAG failed because some tasks failed. The failed tasks are: [get-data].; Job (project_id = my-project, job_id = 4290278978419163136) is failed due to the above error.; Failed to handle the job: {project_number = xxxxxxxx, job_id = 4290278978419163136}  I did not find any related info on the web, neither could I find any log or something similar, and I feel a bit overwhelmed that the solution to this (seemingly) easy example, is still eluding me. Quite obviously, I don't what or where I am mistaking. Any suggestion?

Answer: With some suggestions provided in the comments, I think I managed to make my demo pipeline work. I will first include the updated code: from kfp.v2 import compiler from kfp.v2.dsl import pipeline, component, Dataset, Input, Output from datetime import datetime from google.cloud import aiplatform from typing import NamedTuple # Importing 'COMPONENTS' of the 'PIPELINE' @component( packages_to_install=[ "google-cloud-storage", "pandas", ], base_image="python:3.9", output_component_file="get_data.yaml" ) def get_data( bucket: str, url: str, dataset: Output[Dataset], ): """Reads a csv file, from some location in Cloud Storage""" import ast import pandas as pd from google.cloud import storage # 'Pulling' demo .csv data from a know location in GCS storage_client = storage.Client("my-project") bucket = storage_client.get_bucket(bucket) blob = bucket.blob(url) blob.download_to_filename('localdf.csv') # Reading the pulled demo .csv data df = pd.read_csv('localdf.csv', compression='zip') df['new_skills'] = df['new_skills'].apply(ast.literal_eval) df.to_csv(dataset.path + ".csv" , index=False, encoding='utf-8-sig') @component( packages_to_install=["pandas"], base_image="python:3.9", output_component_file="report_data.yaml" ) def report_data( inputd: Input[Dataset], ) -> NamedTuple("output", [("rows", int), ("columns", int)]): """From a passed csv file existing in Cloud Storage, returns its dimensions""" import pandas as pd df = pd.read_csv(inputd.path+".csv") return df.shape # Building the 'PIPELINE' @pipeline( # i.e. in my case: PIPELINE_ROOT = 'gs://my-bucket/test_vertex/pipeline_root/' # Can be overriden when submitting the pipeline pipeline_root=PIPELINE_ROOT, name="readcsv-pipeline", # Your own naming for the pipeline. ) def my_pipeline( url: str = "test_vertex/pipeline_root/program_grouping_data.zip", bucket: str = "my-bucket" ): dataset_task = get_data(bucket, url) dimensions = report_data( dataset_task.output ) # Compiling the 'PIPELINE' compiler.Compiler().compile( pipeline_func=my_pipeline, package_path="pipeline_job.json" ) # Running the 'PIPELINE' TIMESTAMP = datetime.now().strftime("%Y%m%d%H%M%S") run1 = aiplatform.PipelineJob( display_name="my-pipeline", template_path="pipeline_job.json", job_id="mlmd-pipeline-small-{0}".format(TIMESTAMP), parameter_values={ "url": "test_vertex/pipeline_root/program_grouping_data.zip", "bucket": "my-bucket" }, enable_caching=True, ) # Submitting the 'PIPELINE' run1.submit()  Now, I will add some complementary comments, which in sum, managed to solve my problem:  First, having the "Logs Viewer" (roles/logging.viewer) enabled for your user, will greatly help to troubleshoot any existing error in your pipeline (Note: that role worked for me, however you might want to look for a better matching role for you own purposes here). Those errors will appear as "Logs", which can be accessed by clicking the corresponding button:    NOTE: In the picture above, when the "Logs" are displayed, it might be helpful to carefully check each log (close to the time when you created you pipeline), as generally each eof them corresponds with a single warning or error line:    Second, the output of my pipeline was a tuple. In my original approach, I just returned the plain tuple, but it is advised to return a NamedTuple instead. In general, if you need to input / output one or more "small values" (int or str, for any reason), pick a NamedTuple to do so. Third, when the connection between your pipelines is Input[Dataset] or Ouput[Dataset], adding the file extension is needed (and quite easy to forget). Take for instance the ouput of the get_data component, and notice how the data is recorded by specifically adding the file extension, i.e. dataset.path + ".csv".  Of course, this is a very tiny example, and projects can easily scale to huge projects, however as some sort of "Hello Vertex AI Pipelines" it will work well. Thank you.