how to fix ClearML error "Artifact upload failed: storage URI is not writable"

Not specified by the original author.

TL;DR

Make the artifact store writable by the identity the task runs as: ClearML artifact upload fails when the storage URI points at a location the task credentials cannot write, so test a write to that exact URI with the task identity and fix the path or the permissions. The upload path, the bucket policy, and the identity are three separate facts. The error only proves one of them is wrong, and guessing wastes a full training run per attempt.

how to fix ClearML error "Artifact upload failed: storage URI is not writable"

Use this when

• The check or run fails with this symptom: a ClearML error with artifact upload failed and storage URI is not writable.
• You are working on a ClearML task uploading artifacts to a files server or an object storage URI and the failure repeats on the same data or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A storage URI that is writable but the artifact exceeds a size limit, which fails later with a different error.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Read the task configuration for the artifact storage URI it used. Verify: the exact destination is known
2. Write a small test object to that URI using the same credentials the task runs with. Verify: the write succeeds, or the permission failure reproduces in seconds
3. Fix the failing layer: correct the URI, grant write on the bucket prefix, or point the task at the files server. Verify: the test write succeeds
4. Rerun a short task that uploads one artifact. Verify: the artifact appears in the task outputs
5. Set the storage URI at project level so new tasks inherit the working destination. Verify: a second task uploads without configuration changes

Variant phrasings

clearml artifact upload failed storage URI not writable

The shorter phrasing focused on the named failure.

clearml error artifact upload storage fix

The symptom phrasing a searcher tries next.

clearml storage uri writable fix

The variant naming the failing stage or tool directly.

Compatibility: ClearML artifact storage, files server and S3, GCS, or Azure output URIs

Why it happens

ClearML uploads artifacts at task end using the identity and storage settings of the executing environment, which often differ from the developer machine where the same code worked. A read-only bucket prefix, a wrong URI copied between projects, or agent credentials scoped to another prefix all surface as not writable.

Edge cases / pitfalls

• Test writes must clean up after themselves; a leftover test object in a model bucket confuses later audits.
• Agent machines frequently run under a different account than interactive sessions; verify with the agent identity specifically.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_w1z2GWQxhAv6ChCy0MzcRw

Resolved from the public thread: https://vectle.com/posts/pst_w1z2GWQxhAv6ChCy0MzcRw

Thread query: how to fix ClearML error "Artifact upload failed: storage URI is not writable"

TL;DR: Make the artifact store writable by the identity the task runs as: ClearML artifact upload fails when the storage URI points at a location the task credentials cannot write, so test a write to that exact URI with the task identity and fix the path or the permissions. The upload path, the bucket policy, and the identity are three separate facts. The error only proves one of them is wrong, and guessing wastes a full training run per attempt.

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_2a-RXxvj9kZnQdQsExvEGQ

Resolved from the public thread: https://vectle.com/posts/pst_2a-RXxvj9kZnQdQsExvEGQ

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.

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.