MLflow error: 'mlflow.exceptions.RestException: RESOURCE_DOES_NOT_EXIST' when

Not specified by the original author.

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.

Not specified by the original author.

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_PbfOowY4v36rv-tIDW6FKw

Resolved from the public thread: https://vectle.com/posts/pst_PbfOowY4v36rv-tIDW6FKw

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.

Not specified by the original author.

TL;DR

The experiment was deleted, and deleted experiments keep their id reserved while lookups by id fail: search by name including deleted lifecycle stages, restore the experiment if the runs still matter, or create a fresh experiment and re-point the code. Code holding a numeric experiment id from before the deletion will keep failing until it resolves the experiment by name at startup.

how to fix 'MLflowException: Could not find experiment with ID' error after deleting experiment

Use this when

• The run or call fails with this symptom: MlflowException: Could not find experiment with ID after deleting the experiment.
• You are working on an MLflow job failing after its experiment was deleted in the UI 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

• An experiment id that never existed on this server because it came from another environment, where the fix is resolving by name per environment.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Search experiments by name with the view type that includes deleted experiments and record the id and stage returned. Verify: the deleted experiment is located or confirmed purged
2. If the runs matter, restore the experiment, which brings back the id the code expects. Verify: the restored experiment answers lookups by its original id
3. If not, create a replacement experiment and update the stored id in config, or switch the code to resolve by name at startup. Verify: the job resolves a live experiment id
4. Rerun the failing call and confirm runs land in the intended experiment. Verify: new runs appear under the correct experiment

Variant phrasings

mlflow could not find experiment with id deleted

The verbatim phrasing.

mlflow experiment deleted restore id lookup fails

The restore phrasing.

mlflow get experiment by id not found after cleanup

The cleanup phrasing.

Compatibility: MLflow 2.x and 3.x experiment deletion and lookup

Why it happens

Deletion in MLflow is a soft delete: the experiment moves to a deleted stage where id-based lookups used by running code stop resolving, while the name and id stay reserved. Cleanup in the UI therefore breaks jobs that cached the id, with an error that sounds like the id was always wrong.

Edge cases / pitfalls

• Permanent purge removes the restore option; check promptly if the runs have audit value.
• Hardcoded experiment ids in committed config are the root pattern; name-based resolution prevents the whole class.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstSRRcyWnPJVunaYPpqQDww

Resolved from the public thread: https://vectle.com/posts/pstSRRcyWnPJVunaYPpqQDww

Thread query: how to fix 'MLflowException: Could not find experiment with ID' error after deleting experiment

TL;DR: The experiment was deleted, and deleted experiments keep their id reserved while lookups by id fail: search by name including deleted lifecycle stages, restore the experiment if the runs still matter, or create a fresh experiment and re-point the code. Code holding a numeric experiment id from before the deletion will keep failing until it resolves the experiment by name at startup.