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Importing the ecoinvent 3.8 cutoff dataset (~2.2GB) on a system with 4GB ram results in kernel death along with a non-descript error message. Importing the same dataset on a system with 32GB ram works fine. The associated error messages are likely related to out-of-memory conditions, as per VS Code issue #9741:
Visual Studio Code: "The Kernel crashed while executing code in the the current cell or a previous cell."
The VS Code Jupyter output reads:
(...)
info 15:33:0.573: Cell 2 executed with state Success
info 15:33:0.779: Cell 3 executed with state Success
info 15:33:0.896: Cell 4 executed with state Success
info 15:33:2.513: Execute Cell 5 /psi/home/weinol_m/Systematic bias in Monte Carlo medians.ipynb
error 15:34:28.932: Error in waiting for cell to complete [Error: Canceled future for execute_request message before replies were done
at a.KernelShellFutureHandler.dispose (/psi/home/weinol_m/.vscode-server/extensions/ms-toolsai.jupyter-2022.6.1201981810/out/node_modules/@jupyterlab/services.js:2:32353)
at /psi/home/weinol_m/.vscode-server/extensions/ms-toolsai.jupyter-2022.6.1201981810/out/node_modules/@jupyterlab/services.js:2:26572
at Map.forEach (<anonymous>)
at y._clearKernelState (/psi/home/weinol_m/.vscode-server/extensions/ms-toolsai.jupyter-2022.6.1201981810/out/node_modules/@jupyterlab/services.js:2:26557)
at /psi/home/weinol_m/.vscode-server/extensions/ms-toolsai.jupyter-2022.6.1201981810/out/node_modules/@jupyterlab/services.js:2:29000
at runMicrotasks (<anonymous>)
at processTicksAndRejections (node:internal/process/task_queues:96:5)]
warn 15:34:28.933: Cell completed with errors {
message: 'Canceled future for execute_request message before replies were done'
}
info 15:34:28.935: Cancel all remaining cells true || Error || undefined
info 15:34:28.935: Cancel pending cells
info 15:34:28.936: Cell 5 executed with state Error
Kernel: Linux x86_64 GNU/Linux
Distribution: Red Hat Enterprise Linux Server release 7.9 (Maipo)
Specs: 4GB RAM, 1 vCPU (Intel Xeon E5-2697Av4)
The text was updated successfully, but these errors were encountered:
Importing the ecoinvent 3.8 cutoff dataset (~2.2GB) on a system with 4GB ram results in kernel death along with a non-descript error message. Importing the same dataset on a system with 32GB ram works fine. The associated error messages are likely related to out-of-memory conditions, as per VS Code issue #9741:
Running
returns:
Jupyterhub: "connection failed"
Visual Studio Code: "The Kernel crashed while executing code in the the current cell or a previous cell."
The VS Code Jupyter output reads:
Kernel: Linux x86_64 GNU/Linux
Distribution: Red Hat Enterprise Linux Server release 7.9 (Maipo)
Specs: 4GB RAM, 1 vCPU (Intel Xeon E5-2697Av4)
The text was updated successfully, but these errors were encountered: