ult

ACTIVE_STACK: default

ACTIVE_USER: some_userTELEMETRY_STATUS: disabled

ANALYTICS_CLIENT_ID: xxxxxxx-xxxxxxx-xxxxxxx

ANALYTICS_USER_ID: xxxxxxx-xxxxxxx-xxxxxxx

ANALYTICS_SERVER_ID: xxxxxxx-xxxxxxx-xxxxxxx

INTEGRATIONS: ['airflow', 'aws', 'azure', 'dash', 'evidently', 'facets', 'feast', 'gcp', 'github',

'graphviz', 'huggingface', 'kaniko', 'kubeflow', 'kubernetes', 'lightgbm', 'mlflow',

'neptune', 'neural_prophet', 'pillow', 'plotly', 'pytorch', 'pytorch_lightning', 's3', 'scipy',

'sklearn', 'slack', 'spark', 'tensorboard', 'tensorflow', 'vault', 'wandb', 'whylogs', 'xgboost']

System information provides more context to your issue and also eliminates the need for anyone to ask when they're trying to help. This increases the chances of your question getting answered and saves everyone's time.

2. What happened?

Tell us briefly:

What were you trying to achieve?

What did you expect to happen?

What actually happened?

3. How to reproduce the error?

Walk us through how to reproduce the same error you had step-by-step, whenever possible. Use the format you prefer. Write it in text or record a video, whichever lets you get the issue at hand across to us!

4. Relevant log output

As a general rule of thumb, always attach relevant log outputs and the full error traceback to help us understand what happened under the hood. If the full error traceback does not fit into a text message, attach a file or use a service like Pastebin or Github's Gist.

Along with the error traceback, we recommend to always share the output of the following commands:

zenml status

zenml stack describe

When applicable, also attach logs of the orchestrator. For example, if you're using the Kubeflow orchestrator, include the logs of the pod that was running the step that failed.