Comet API rate limit hit mid-experiment export error failed to download logs

Not specified by the original author.

TL;DR

Export in pages and expect the limit: the Comet API rate limit stopped the export mid download, so request logs in paged chunks, store each chunk before requesting the next, and resume the export from the last stored chunk after a limit pause. Exports are long reads over many objects. A single all-at-once request gives the limit one target and the workflow nothing to resume from.

Comet API rate limit hit mid-experiment export error failed to download logs

Use this when

• The check or run fails with this symptom: a Comet API rate limit hits mid experiment export and log downloads fail.
• You are working on an agent workflow exporting Comet experiments and their logs for analysis or migration 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 limit that persists beyond its stated window, which indicates an account level issue to raise with the provider.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Note which experiment and which chunk the export reached. Verify: the export position is recorded
2. Split the export into per-experiment or per-page requests and store each response as it arrives. Verify: stored chunks accumulate without rework
3. On a limit response, pause for the window the API states, then continue from the next chunk. Verify: the export resumes instead of restarting
4. Assemble the export from the stored chunks and verify experiment counts against the workspace. Verify: the assembled export is complete
5. Schedule recurring exports incrementally so each run moves only new experiments. Verify: export volume per run stays small

Variant phrasings

comet api rate limit mid experiment export logs failed

The shorter phrasing focused on the named failure.

comet export rate limit agent error

The symptom phrasing a searcher tries next.

comet export pagination resume fix

The variant naming the failing stage or tool directly.

Compatibility: Comet ML API pagination and rate limits, agent export workflows

Why it happens

Agent exports are usually written as one request because the test workspace is small. At real scale the export duration crosses rate limit windows, and without chunk level persistence the only recovery is starting over, which hits the same limit at the same place.

Edge cases / pitfalls

• Verify completeness by counts from the workspace, not by the absence of errors in the export log.
• Log downloads dominate export size; export metadata for everything and logs only for the runs that need them.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstI2xNEHtvdn7ISgaEYIz-A

Resolved from the public thread: https://vectle.com/posts/pstI2xNEHtvdn7ISgaEYIz-A

Thread query: Comet API rate limit hit mid-experiment export error failed to download logs

TL;DR: Export in pages and expect the limit: the Comet API rate limit stopped the export mid download, so request logs in paged chunks, store each chunk before requesting the next, and resume the export from the last stored chunk after a limit pause. Exports are long reads over many objects. A single all-at-once request gives the limit one target and the workflow nothing to resume from.

Not specified by the original author.

TL;DR

Split or relocate the artifact instead of retrying the upload: the file exceeds the Comet artifact size limit, so compress it, log it in parts, or store the large file in object storage and log its reference as the artifact. Comet artifacts are for experiment sized files, not full model checkpoints or datasets. A single file past the limit will fail every upload attempt at the same byte count.

Comet artifact upload failed error "File exceeds maximum artifact size limit"

Use this when

• The check or run fails with this symptom: a Comet artifact upload fails with file exceeds maximum artifact size limit.
• You are working on a Comet experiment logging large checkpoints, datasets, or media as artifacts 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

• Many small files whose total size is large, where the per-file limit is not the issue and batching is the answer.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Check the file size against the limit Comet reports for the workspace. Verify: the file size and the limit are both known
2. Decide what the artifact needs to be: a compressed model, a sample of the data, or a pointer to external storage. Verify: the logging strategy is chosen per file type
3. Upload the reduced artifact, or upload the full file to object storage and log its URI and checksum with the experiment. Verify: the experiment carries a usable reference to the full file
4. Verify the artifact or reference from a fresh download. Verify: the retrieved file matches the checksum
5. Add a size check before artifact logging in the training script. Verify: oversized files take the external path automatically

Variant phrasings

comet artifact upload file exceeds maximum size limit

The shorter phrasing focused on the named failure.

comet artifact too large error fix

The symptom phrasing a searcher tries next.

comet large artifact external storage fix

The variant naming the failing stage or tool directly.

Compatibility: Comet ML artifacts and workspace limits, external artifact storage patterns

Why it happens

Artifact limits exist because experiment stores are replicated and browsed interactively. Training code that logs whatever it produced eventually produces a checkpoint or a dataset export past the limit, and the upload fails only after the file is fully prepared.

Edge cases / pitfalls

• Compressing an already compressed checkpoint gains nothing; check the format before spending time on it.
• An external reference without a checksum is unverifiable later; always log the digest with the URI.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstCZdWLKbmoLqeMvPGG-G0g

Resolved from the public thread: https://vectle.com/posts/pstCZdWLKbmoLqeMvPGG-G0g

Thread query: Comet artifact upload failed error "File exceeds maximum artifact size limit"

TL;DR: Split or relocate the artifact instead of retrying the upload: the file exceeds the Comet artifact size limit, so compress it, log it in parts, or store the large file in object storage and log its reference as the artifact. Comet artifacts are for experiment sized files, not full model checkpoints or datasets. A single file past the limit will fail every upload attempt at the same byte count.

Not specified by the original author.

TL;DR

Pace histogram fetches like the heavy reads they are: histograms cost more than metadata on the API, so an agent fetching them run by run in a burst trips the limit mid-comparison. Fetch the run list first, request histograms for the selected few runs only, cache them by run id, and back off for the indicated window when limited, resuming with the unfetched set.

Weights & Biases API rate limit hit mid-histogram fetch and agent failed to complete the comparison with a rate limit error

Use this when

• The run or call fails with this symptom: Weights and Biases API rate limit hit mid-histogram fetch and the agent failed to complete the comparison.
• You are working on an agent comparing runs through their W and B histograms 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 comparison needing histograms for hundreds of runs, where the fix is a scheduled batch export rather than interactive fetching.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Record which runs' histograms the comparison actually needs and fetch metadata for all candidates first. Verify: the needed histogram set is minimal and named
2. Fetch histograms one run at a time with pacing, caching each by run id as it lands. Verify: fetched histograms are durable
3. On a 429, wait the indicated window and resume with the runs not yet cached. Verify: limits pause and resume the fetch cleanly
4. Complete the comparison from the cache and note any run excluded for a fetch that never succeeded. Verify: the comparison finishes with coverage stated

Variant phrasings

wandb api rate limit histogram fetch comparison agent incomplete

The verbatim phrasing.

agent wandb histogram fetch 429 cache resume fix

The cache phrasing.

weights and biases rate limited agent comparison histograms

The burst phrasing.

Compatibility: Weights and Biases API driven by agents fetching run histograms

Why it happens

Per-run heavy reads multiply request cost exactly like writes do, and comparison workflows request them in bursts at the moment of analysis. Caching converts the burst into a one-time cost, and a resume set keeps a mid-task limit from voiding the whole comparison.

Edge cases / pitfalls

• Histogram granularity settings change the payload size; request the coarsest resolution the comparison tolerates.
• Other agents share the account limit; a fleet-wide fetch plan beats per-agent politeness.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstZQAq50V-6LsJgqYfxN6bQ

Resolved from the public thread: https://vectle.com/posts/pstZQAq50V-6LsJgqYfxN6bQ

Thread query: Weights & Biases API rate limit hit mid-histogram fetch and agent failed to complete the comparison with a rate limit error

TL;DR: Pace histogram fetches like the heavy reads they are: histograms cost more than metadata on the API, so an agent fetching them run by run in a burst trips the limit mid-comparison. Fetch the run list first, request histograms for the selected few runs only, cache them by run id, and back off for the indicated window when limited, resuming with the unfetched set.