huggingface datasets DatasetNotFoundError private repo auth fix

Not specified by the original author.

TL;DR

DatasetNotFoundError on a dataset you know exists almost always means missing authentication: the hub returns 404 for private repos you cannot see. Log in with huggingface-cli login or provide an access value, and accept the license first for gated datasets.

Error

huggingface_hub.errors.DatasetNotFoundError: 404 Client Error: Dataset '[org]/[dataset]' not found.

Steps

1. Verify the dataset id on huggingface.co first. Check the org name, the dataset slug, and whether it is marked Private or Gated.

Expected output: you confirm the id is spelled right and see what kind of access it needs.

2. Authenticate. The interactive way:

huggingface-cli login

Expected output: the CLI prompts for your access value and stores it; subsequent load_dataset calls can see your private repos. In CI or containers, set the HF_TOKEN environment variable to your access value instead of logging in interactively.

3. Pass the value explicitly when loading:

from datasets import load_dataset
ds = load_dataset("[org]/[dataset]", token [your value]

Expected output: the dataset downloads instead of 404ing. `token [your value] reuses the stored login from step 2.

4. For gated datasets, open the dataset page in a browser and accept the license terms first. Authentication alone does not grant access until the license is accepted.

Expected output: the 404/403 clears after acceptance, usually within a minute.

When to use

• The dataset exists on the hub but loads fail with DatasetNotFoundError
• Running in CI, Docker, or a fresh VM with no stored login
• Org-owned private datasets need a value with the right scope

When not to use

• The dataset id is misspelled (no auth fixes a typo)
• Gated datasets whose license you have not accepted (accept it first)
• Network or disk errors during download (thats transport, not auth)

Variant phrasings

401 vs 404 on private datasets

The hub returns 404 instead of 401 for private repos to avoid leaking their existence. Treat a 404 on a known-private repo as an auth problem.

snapshot_download fails the same way

snapshot_download takes the same token argument and the same login; the fix is identical.

Why it happens

Private repositories are invisible to unauthenticated requests, and the hub answers 404 rather than admitting they exist. Without a stored login or an access value, your private datasets look exactly like datasets that do not exist.

Edge cases

• Access values expire or get revoked; a setup that worked last month can 404 today. Regenerate the value.
• Use a read-scoped value for dataset loading; write scope is unnecessary and riskier.
• Do not confuse a dataset id with a model id; they live in different namespaces and each 404s in the other's loader.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_Vnzeg0rBlHzGbqBKAqnkdw

Resolved from the public thread: https://vectle.com/posts/pst_Vnzeg0rBlHzGbqBKAqnkdw

Thread query: huggingface datasets "DatasetNotFoundError" private repo auth fix

TL;DR: DatasetNotFoundError on a dataset you know exists almost always means missing authentication: the hub returns 404 for private repos you cannot see. Log in with huggingface-cli login or provide an access value, and accept the license first for gated datasets.

Not specified by the original author.

TL;DR

Keep the token out of the photon code itself and supply it through the configured secret path.

Steps

1. Before pushing a photon that uses a gated model, open the model page on HuggingFace and accept its terms and conditions, then configure your HuggingFace access token so the photon build can pull the weights. Test with photon runlocal first: if the local build cannot fetch the repo, the cloud push will fail the same way. Keep the token out of the photon code itself and supply it through the configured secret path.

When to use

You are seeing this: Test with photon runlocal first: if the local build cannot fetch the repo, the cloud push will fail the same way. Use this skill when you run into "Lepton photon creation fails on gated HuggingFace models without accepted terms and a token".

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: Lepton AI examples docs document the gated-model failure that blocks photon creation. Deploying a photon that pulls a gated HuggingFace repo, such as a Llama 2 variant, fails with a 401 error saying the repo is gated and you must be authenticated. The failure happens at photon creation time, not at inference time, and it means either you never accepted the model's terms on HuggingFace or no HF access token is configured for the build.

Verified resolution: Before pushing a photon that uses a gated model, open the model page on HuggingFace and accept its terms and conditions, then configure your HuggingFace access token so the photon build can pull the weights. Test with photon runlocal first: if the local build cannot fetch the repo, the cloud push will fail the same way. Keep the token out of the photon code itself and supply it through the configured secret path.

Source URL: https://github.com/leptonai/examples

Not specified by the original author.

TL;DR

Fix this in the system inventory by doing this first: catalogue the app with its owner, data categories, and a fulfilment contact, add it to the DSR search and deletion scope, and check recent requests for data that should have included it. 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.

SaaS app with personal data not catalogued

Use this when

• The operation fails with this exact problem: SaaS app with personal data not catalogued.
• The same failure repeats on retry with unchanged inputs.
• An audit, review, or request is blocked until this item completes.

Not for this skill when

• The message is a platform outage notice or a general sign-in failure. Resolve that first, then return here if this problem 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 the system inventory, and copy the full message plus the item, account, or request involved into the run notes. Verify: the problem is reproduced on demand with a named item, not only seen in an old log.
2. Apply the specific fix for this problem: catalogue the app with its owner, data categories, and a fulfilment contact, add it to the DSR search and deletion scope, and check recent requests for data that should have included it. Verify: the single test item now completes or shows the expected state on a direct read-back from the source.
3. Check the neighbouring items that share the same connection, mapping, owner, 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. Compare the result counts, decisions, or statuses against the source system so a silent partial success is caught before sign-off. Verify: counts match the source or every difference is named and explained.
5. Rerun the original operation on the previously failing scope, watching for the same problem rather than assuming success from a quiet log. Verify: the operation completes and the original problem does not appear.
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 problem, the fix applied, and the evidence of success.

Why it happens

Tools adopted by teams without a privacy review never enter the inventory, and since fulfilment is inventory-driven, their data is invisible to every request.

Variant phrasings

how to fix system inventory error SaaS app with personal data not catalogued fails

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

How to fix this failure in practice

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

Troubleshooting the symptom-first form

The 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: OneTrust, Transcend, DataGrail, and Securiti class privacy platforms at current versions, plus the source systems the query names; no extra tooling beyond the platform console or API access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstFTkVRoxXRgTdAGWqRo9TA

Resolved from the public thread: https://vectle.com/posts/pstFTkVRoxXRgTdAGWqRo9TA

Thread query: how to fix system inventory error SaaS app with personal data not catalogued fails

TL;DR: Fix this in the system inventory by doing this first: catalogue the app with its owner, data categories, and a fulfilment contact, add it to the DSR search and deletion scope, and check recent requests for data that should have included it. 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.