I am using Databricks AutoML ( Python SDK) to forecast bed occupancy. (Actually, Databricks used MLflow experiments for AutoML run). After training with different iterations, I registered the best model in the Databricks Model registry. Now I am trying to serve the register model and I have seen that it is always in the "Pending" stage and in the deployment log has an attached error message.
Can anyone please help me here?
Note: Model artifacts has been created automatically as its from AutoML run. I think I don't have control to add pip dependency and package version.
Warning: you have pip-installed dependencies in your environment file, but you do not list pip itself as one of your conda dependencies. Conda may not use the correct pip to install your packages, and they may end up in the wrong place. Please add an explicit pip dependency. I'm adding one for you, but still nagging you.
Collecting package metadata (repodata.json): ...working... done
Solving environment: ...working... done
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done
Pip subprocess error:
error: subprocess-exited-with-error
ร python setup.py egg_info did not run successfully.
โ exit code: 1
โฐโ> [8 lines of output]
Traceback (most recent call last):
File "<string>", line 2, in <module>
File "<pip-setuptools-caller>", line 34, in <module>
File "/tmp/pip-install-_4rq4zju/numba_f054bd7eb658421087c86678b088efff/setup.py", line 51, in <module>
_guard_py_ver()
File "/tmp/pip-install-_4rq4zju/numba_f054bd7eb658421087c86678b088efff/setup.py", line 48, in _guard_py_ver
raise RuntimeError(msg.format(cur_py, min_py, max_py))
RuntimeError: Cannot install on Python version 3.11.0; only versions >=3.7,<3.11 are supported.
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed
ร Encountered error while generating package metadata.
โฐโ> See above for output.
note: This is an issue with the package mentioned above, not pip.
hint: See above for details.
This issue I am facing only for Databricks AutoML forecasting, not getting this issue for Databricks AutoML for Regression and Classification.