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This is being tracked in the GitHub issue linked below.
A possible workaround is to use a regular Notebook Instance instead of a Studio Notebook Instance. On a regular Notebook Instance of the same size (ml.g4dn.xlarge), /dev/shm is 7.7G
df -h | grep -E 'shm|File'
Filesystem Size Used Avail Use% Mounted on
tmpfs 7.7G 0 7.7G 0% /dev/shm
Contenus pertinents
- demandé il y a un an
- demandé il y a 5 mois
- demandé il y a 3 mois
- AWS OFFICIELA mis à jour il y a un an
- AWS OFFICIELA mis à jour il y a un an
- AWS OFFICIELA mis à jour il y a un an
- AWS OFFICIELA mis à jour il y a 5 mois
Are you using PyTorch with the SageMaker Python SDK (as described here: https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html) or are you bringing your own code to the SageMaker environment (as described here: https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#bring-your-own-model)?