I'm preparing a benchmark using kaggle-benchmarks and native tools. My verified Kaggle account receives ‘User location CN is not supported for this model/API’, while the same synthetic task runs successfully through a provider API available to me. May I use these external-model runs for this challenge, and how should I attach their accurate model IDs and results to a public Kaggle Benchmark? Would a public benchmark linking the reproducible task and external results qualify?
Update after testing the official CLI: authentication works and my private task now shows Completed. Using the SDK’s cache, I reused unchanged genuine Task/Run outputs from an external API run, preserving the actual model ID, original score and trajectories, with no new inference. However, the task page shows Results (0), and CLI status says ‘No runs yet’. I haven’t published an empty benchmark or relabelled the model. Is there a supported way to register these genuine external runs in a public Kaggle Benchmark, or must eligible scores come from the platform’s model scheduler? Would a public benchmark linking the reproducible task and clearly labelled external results qualify?
Thanks for the detailed testing and for keeping model IDs and results accurate. For this challenge, your benchmark must be built and run on Kaggle: eligible results need to come from runs on the Kaggle platform. External API runs, including cached outputs, can’t be registered as the benchmark’s results, and a public benchmark linking only to external results wouldn’t meet the requirement. For the “User location CN is not supported” error, please contact Kaggle support, as we can’t resolve platform access on our side.
For further actions, you may consider blocking this person and/or reporting abuse
We're a place where coders share, stay up-to-date and grow their careers.
I'm preparing a benchmark using kaggle-benchmarks and native tools. My verified Kaggle account receives ‘User location CN is not supported for this model/API’, while the same synthetic task runs successfully through a provider API available to me. May I use these external-model runs for this challenge, and how should I attach their accurate model IDs and results to a public Kaggle Benchmark? Would a public benchmark linking the reproducible task and external results qualify?
Update after testing the official CLI: authentication works and my private task now shows Completed. Using the SDK’s cache, I reused unchanged genuine Task/Run outputs from an external API run, preserving the actual model ID, original score and trajectories, with no new inference. However, the task page shows Results (0), and CLI status says ‘No runs yet’. I haven’t published an empty benchmark or relabelled the model. Is there a supported way to register these genuine external runs in a public Kaggle Benchmark, or must eligible scores come from the platform’s model scheduler? Would a public benchmark linking the reproducible task and clearly labelled external results qualify?
Thanks for the detailed testing and for keeping model IDs and results accurate. For this challenge, your benchmark must be built and run on Kaggle: eligible results need to come from runs on the Kaggle platform. External API runs, including cached outputs, can’t be registered as the benchmark’s results, and a public benchmark linking only to external results wouldn’t meet the requirement. For the “User location CN is not supported” error, please contact Kaggle support, as we can’t resolve platform access on our side.