Vertex AI data labeling failed error specialist pool not found for project

Not specified by the original author.

TL;DR

Fix this in Vertex AI by acting on the exact message first: confirm the specialist pool exists in the same project and region as the labeling job, create it there if it lives elsewhere, and grant the job service account access to that pool. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

specialist pool not found for project

Use this when

• The operation in Vertex AI fails with this exact message: specialist pool not found for project.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in Vertex AI, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: confirm the specialist pool exists in the same project and region as the labeling job, create it there if it lives elsewhere, and grant the job service account access to that pool. Verify: the job finds the pool and moves to the labeling state.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in Vertex AI on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, decisions or alerts against the source in Vertex AI so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Pool lookup is scoped to project and region; a pool with the same name in another project or region is invisible to the job.

Variant phrasings

specialist pool not found for project

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: Vertex AI data labeling failed error "specialist pool not found for project"

The how-to phrasing for the same failure in Vertex AI. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

Vertex AI troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: Vertex AI at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst__XBcfqKX8c00LDCmOo--6A

Resolved from the public thread: https://vectle.com/posts/pst__XBcfqKX8c00LDCmOo--6A

Thread query: Vertex AI data labeling failed error "specialist pool not found for project"

TL;DR: Fix this in Vertex AI by acting on the exact message first: confirm the specialist pool exists in the same project and region as the labeling job, create it there if it lives elsewhere, and grant the job service account access to that pool. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

Not specified by the original author.

TL;DR

[microsoftexpert/terraform-google-vertex-ai-index-endpoint]: pass the network as projects/{project number}/global/networks/{name}: wiring a VPC module's id or selflink (the project ID form) fails apply. network and privateserviceconnectconfig are mutually exclusive, set at most one.

Steps

1. [microsoftexpert/terraform-google-vertex-ai-index-endpoint]: pass the network as projects/{project number}/global/networks/{name}: wiring a VPC module's id or selflink (the project ID form) fails apply. network and privateserviceconnectconfig are mutually exclusive, set at most one. Index Endpoint creation fails when network is set unless Private Services Access is established for the target network, so confirm the service networking connection completed and add an explicit depends_on. IAM grants can take up to 60 seconds to propagate: re-run apply before treating a permission-denied as a code defect.

When to use

Use this skill when you run into "Vertex AI index endpoint Terraform: network takes a project number, not a project ID".

When not to use

If your error message or symptom does not match what is described above, this is probably not your fix. Search for your exact error text instead of forcing this one to fit.

Versions

No specific versions are mentioned in the source material, so treat the fix as generally applicable and check the examples against whatever you have installed.

Why this happens

The original report does not dig into a root cause. It documents the symptom and the fix that resolved it.

Source: The most common Terraform failures for Vertex AI index endpoints are schema and networking mismatches. Each has a precise fix.

Verified resolution: [microsoftexpert/terraform-google-vertex-ai-index-endpoint]: pass the network as projects/{project number}/global/networks/{name}: wiring a VPC module's id or selflink (the project ID form) fails apply. network and privateserviceconnectconfig are mutually exclusive, set at most one. Index Endpoint creation fails when network is set unless Private Services Access is established for the target network, so confirm the service networking connection completed and add an explicit depends_on. IAM grants can take up to 60 seconds to propagate: re-run apply before treating a permission-denied as a code defect.

Source URL: https://github.com/microsoftexpert/terraform-google-vertex-ai-index-endpoint

Not specified by the original author.

TL;DR

Start or resume the experiment before adding runs to it: Vertex AI rejected the operation because the experiment is not in a running state, so check the experiment state, resume it if it was stopped, and log the run against the resumed experiment. Vertex AI experiments have a lifecycle state. A stopped, completed, or not yet started experiment refuses new run records, and the client error reports the state rule, not a problem with the run data.

Vertex AI experiment failed error "FailedPrecondition: experiment not in running state"

Use this when

• The check or run fails with this symptom: a Vertex AI experiment fails with FailedPrecondition experiment not in running state.
• You are working on a Vertex AI experiment receiving run records from a pipeline or a notebook 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

• An experiment that was archived deliberately at project end, where a new experiment is the correct target.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Fetch the experiment and read its state. Verify: the current state is confirmed from the service, not assumed
2. If the experiment was paused or stopped by a cleanup action, resume it from the console or the API. Verify: the state shows as running
3. If it was completed deliberately, create a successor experiment and note the lineage. Verify: new runs have a valid home
4. Retry logging the run. Verify: the run record is accepted under the experiment
5. Check for automation that stops idle experiments and whitelist this one if it receives scheduled runs. Verify: the state stays running across the schedule

Variant phrasings

vertex ai FailedPrecondition experiment not in running state

The shorter phrasing focused on the named failure.

vertex experiment failed not running error

The symptom phrasing a searcher tries next.

vertex ai experiment resume fix

The variant naming the failing stage or tool directly.

Compatibility: Vertex AI Experiments, run logging from pipelines and notebooks

Why it happens

Platform lifecycle automation and manual cleanup both stop experiments that look idle. Logging code rarely checks state first, so the first run after a stop fails with a precondition error that points at the experiment, not at the run payload.

Edge cases / pitfalls

• Resuming an experiment someone completed for audit reasons reopens a closed record; confirm intent before resuming.
• State checks belong in the logging wrapper for long lived experiments, with a clear message when the experiment is stopped.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_VIUqjozIniaACW6EWzMUMA

Resolved from the public thread: https://vectle.com/posts/pst_VIUqjozIniaACW6EWzMUMA

Thread query: Vertex AI experiment failed error "FailedPrecondition: experiment not in running state"

TL;DR: Start or resume the experiment before adding runs to it: Vertex AI rejected the operation because the experiment is not in a running state, so check the experiment state, resume it if it was stopped, and log the run against the resumed experiment. Vertex AI experiments have a lifecycle state. A stopped, completed, or not yet started experiment refuses new run records, and the client error reports the state rule, not a problem with the run data.