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[BUG]: outside of artifact store bounds #2986
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This seems like an issue with Windows paths. Can you just quickly verify whether the following code also fails for you on your local machine please: from zenml import pipeline, step
@step
def logging_step() -> None:
print("Some log message")
@pipeline
def p():
logging_step()
if __name__ == "__main__":
p() |
FileNotFoundError: File |
These are my environment variable HOMEDRIVE: C: ZENML_HOME: C:\Users\aadit\AppData\Local\zenml |
Are you using the default local artifact store? Or did you register a custom one? |
I was using custom in my previous stack but everything is on default now |
(PS) D:\Dir> zenml artifact-store describe default so the default artifact-store was not set properly so i change it to this (PS)D:\Dir> zenml artifact-store describe balance |
What do you mean by "the default artifact store was no set properly"? You ran into some issues when running pipelines with the default artifact store? I think with your custom artifact store, it's somehow messing up the volumes. Did you explicitly register it with |
when i was using default artifact store i was getting the same issue but the path were Failed to execute data ingestion pipeline: File so i change the artifact_store to custom one, updated its path to data/artifacts. I got the same issue but this time the paths were
PS D:\RAG-on-Balance_Sheet> zenml artifact-store register balance --flavor=local |
System Information
SYSTEM_INFO: {'os': 'windows', 'windows_version_release': '10', 'windows_version': '10.0.19045', 'windows_version_service_pack': 'SP0', 'windows_version_os_type':
'Multiprocessor Free'}
What happened?
Initiating a new run for the pipeline: data_ingestion_pipeline.
Executing a new run.
Caching is disabled by default for data_ingestion_pipeline.
Using user: default
Using stack: stack_1
orchestrator: default
artifact_store: local_artifact_store
You can visualize your pipeline runs in the ZenML Dashboard. In order to try it locally, please run zenml up.
Relevant log output
CURRENT STACK
Name: stack_1
ID: d1765057-c27f-4e5e-ac3e-e7cee6a45797
User: default / a8bf9397-512c-4f9a-9266-f24b3ea10921
Workspace: default / 5e1c6e98-5302-47d4-bbc7-b97d24d1def3
ORCHESTRATOR: default
Name: default
ID: 33520fcf-456f-438d-b894-a765938a6b5e
Type: orchestrator
Flavor: local
Configuration: {}
Workspace: default / 5e1c6e98-5302-47d4-bbc7-b97d24d1def3
ARTIFACT_STORE: local_artifact_store
Name: local_artifact_store
ID: abae7772-933e-40fb-8893-0eb1bd855a9e
Type: artifact_store
Flavor: local
Configuration: {'path': 'data/artifacts'}
User: default / a8bf9397-512c-4f9a-9266-f24b3ea10921
Workspace: default / 5e1c6e98-5302-47d4-bbc7-b97d24d1def3
Reproduction steps
@step
def data_ingestion_step(config_path: str) -> Annotated[Dict[str, str], "paths"]:
try:
# Load configuration
config = load_config(config_path)
pdf_path = config['balance_sheet_pdf']
@pipeline
def data_ingestion_pipeline(config_path: str):
data_ingestion_step(config_path=config_path)
if name == "main":
config_path = 'config.yml' # Adjust this path if needed
try:
# Run the pipeline
data_ingestion_pipeline = data_ingestion_pipeline.with_options(enable_cache=False)
data_ingestion_pipeline(config_path=config_path)
except Exception as e:
print(f"Failed to execute data ingestion pipeline: {e}")
Code of Conduct
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