mlflow.log_artifact failed upload error for large checkpoint file

Not specified by the original author.

TL;DR

Find where the upload actually stops before changing code: large checkpoints fail on artifact store limits, multipart timeouts, or a proxied tracking server, not in log_artifact itself. Check the file size against the store's single-object and proxy limits, confirm free space or quota on the destination, and for server-proxied uploads raise the server's upload handling or write the artifact directly to the store path.

mlflow.log_artifact failed upload error for large checkpoint file

Use this when

• The run or call fails with this symptom: mlflow.log_artifact upload fails for a large checkpoint file.
• You are working on an MLflow run logging a large model checkpoint as an artifact 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 small artifact failing with a permissions error, where the fix is store credentials rather than size handling.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Note the checkpoint size and reproduce with a scratch file of the same size to separate size from content. Verify: a same-size test file fails or succeeds the same way
2. Check the artifact destination's limits and free capacity: bucket quota, filesystem space, and any proxy body-size limit in front of the tracking server. Verify: the binding limit is identified by name
3. Watch the upload error detail for multipart or timeout wording and note how many bytes or parts complete before failure. Verify: the failure point is consistent across attempts
4. Apply the matching fix: raise the proxy or server limit, switch to direct store access, or split the checkpoint, then re-upload. Verify: the full checkpoint lands in the run's artifact listing

Variant phrasings

mlflow log_artifact large file upload failed

The large-file phrasing.

mlflow checkpoint artifact upload error size limit

The size-limit phrasing.

mlflow artifact store upload fails big model file

The store phrasing.

Compatibility: MLflow 2.x and 3.x with local, S3, or other artifact stores

Why it happens

log_artifact streams the file through whatever path the tracking setup uses, and each hop, server, proxy, and store, enforces its own size and timeout rules. The client surfaces a generic upload failure, so the hop that gives up has to be found by size testing and limit checking rather than from the error text.

Edge cases / pitfalls

• Server-proxied artifact access is the usual bottleneck; a direct store URI in the run's artifact location bypasses it.
• Compress or shard genuinely huge checkpoints; some stores cap single objects regardless of proxy settings.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst-vpWxDEkUpwRGZwLMbwOA

Resolved from the public thread: https://vectle.com/posts/pst-vpWxDEkUpwRGZwLMbwOA

Thread query: mlflow.log_artifact failed upload error for large checkpoint file

TL;DR: Find where the upload actually stops before changing code: large checkpoints fail on artifact store limits, multipart timeouts, or a proxied tracking server, not in log_artifact itself. Check the file size against the store's single-object and proxy limits, confirm free space or quota on the destination, and for server-proxied uploads raise the server's upload handling or write the artifact directly to the store path.