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This repository was archived by the owner on Mar 29, 2026. It is now read-only.
CPU Kernel Panic: After reducing the value for the -c option and allowing the process to run for around 6 minutes, my Mac shuts down with an error indicating a CPU kernel panic. Here's the related error message:
This panic effectively limits the number of images I can upload to around 5000.
Steps to Reproduce:
Run the command roboflow import ./"foldername"
Observe the Axios error, or, if that error does not occur,
Reduce the -c option value and observe the CPU kernel panic after approximately 6 minutes of operation.
Expected Results:
The CLI should successfully upload the dataset without causing any errors or system shutdowns.
Actual Results:
Two distinct errors are encountered: an Axios error that requires further investigation and a CPU kernel panic that causes a system shutdown.
Additional Context:
Understanding the best practices for uploading large datasets to the platform would be beneficial, as current limitations are posing challenges for clients with significant amounts of data.
Environment:
roboflow import ./"foldername"Description:
While attempting to upload a large dataset using the CLI, I encountered two distinct problems:
-coption and allowing the process to run for around 6 minutes, my Mac shuts down with an error indicating a CPU kernel panic. Here's the related error message:panic(cpu 1 caller 0xfffffe002a74da4c): Spinlock[0xfffffe24cb9b5be8]This panic effectively limits the number of images I can upload to around 5000.
Steps to Reproduce:
roboflow import ./"foldername"-coption value and observe the CPU kernel panic after approximately 6 minutes of operation.Expected Results:
The CLI should successfully upload the dataset without causing any errors or system shutdowns.
Actual Results:
Two distinct errors are encountered: an Axios error that requires further investigation and a CPU kernel panic that causes a system shutdown.
Additional Context:
Understanding the best practices for uploading large datasets to the platform would be beneficial, as current limitations are posing challenges for clients with significant amounts of data.
Attachments: