MLflow error: 'mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST' when fetching a run failed

TL;DR

Do not retry the fetch blindly: resolve the run id first with a search over the experiment, because a RESOURCEDOESNOTEXIST on getrun almost always means the run was deleted, the id was copied from a different tracking server, or the run lives under a different experiment than the client assumes. Confirm the id exists on the server you are actually connected to, then fetch it.

MLflow error: 'mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST' when fetching a run failed

Use this when

• The run or call fails with this symptom: mlflow.exceptions.RestException: RESOURCEDOESNOT_EXIST when fetching a run.
• You are working on an MLflow tracking setup where a run lookup fails and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A run that exists but belongs to an experiment the caller has no permission to read, where the fix is access rather than id repair.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Print the active tracking URI in the failing process and confirm it points at the server that recorded the run. Verify: the URI matches the server where the run was logged
2. Search the experiment with search_runs and look for the run id, or the run's start time and tags if the id was transcribed. Verify: the search shows whether the id exists on this server at all
3. Check the run's lifecycle stage; a deleted run sits in the trash stage and plain fetches behave as if it never existed. Verify: the run is found in deleted stage or confirmed absent
4. Restore the run if it was deleted, or correct the id or server in the caller, then fetch again. Verify: the fetch returns the run with its params and metrics

Variant phrasings

mlflow resource does not exist get run

The get-run phrasing.

mlflow run id not found tracking server

The wrong-server phrasing.

restexception resourcedoesnot_exist mlflow run deleted

The deleted-run phrasing.

Compatibility: MLflow 2.x and 3.x tracking servers and Python client

Why it happens

MLflow identifies runs only by id within one backend store, and the client raises the same not-exist error for a deleted run, a mistyped id, and an id from another server. Retrying the identical fetch cannot distinguish those causes, so the search-first check is what separates them.

Edge cases / pitfalls

• Ids copied from a UI URL can include the experiment path segment; strip everything except the 32 character run id.
• A run logged to a local file store will never appear on a remote server; check where the training job pointed its tracking URI.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_AjxT8fqiTwZMLTX-02wnFQ

Resolved from the public thread: https://vectle.com/posts/pst_AjxT8fqiTwZMLTX-02wnFQ

Thread query: MLflow error: 'mlflow.exceptions.RestException: RESOURCEDOESNOT_EXIST' when fetching a run failed

TL;DR: Do not retry the fetch blindly: resolve the run id first with a search over the experiment, because a RESOURCEDOESNOTEXIST on getrun almost always means the run was deleted, the id was copied from a different tracking server, or the run lives under a different experiment than the client assumes. Confirm the id exists on the server you are actually connected to, then fetch it.