...
If a kernel is constantly crashing when running a specific notebook, it usually means that the kernel is trying to use more memory than the system has access to. If applicable, you can try to verify this by running the same code with fewer datapoints. We can increase the amount of allocated memory for very special cases, however please be aware it is often likely that your use-case will not fit into that category. That is not to say the specific use-case is not important, but the point of the hub is to serve as many users as possible.
File size too large
If you encounter an error with the error code 413, it generally means that the file that you are working on is too large. The error can occur both when uploading files and when saving files within the Jupyter interface. The default file size limit is very strict, and it is strict for a reason. However, it is possible for us to increase the limit - but only for a very good reason.