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Data Engineering
Join discussions on data engineering best practices, architectures, and optimization strategies within the Databricks Community. Exchange insights and solutions with fellow data engineers.
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Alex_Persin
by • New Contributor II
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How can the shared memory size (/dev/shm) be increased on databricks worker nodes with custom docker images?

PyTorch uses shared memory to efficiently share tensors between its dataloader workers and its main process. However in a docker container the default size of the shared memory (a tmpfs file system mounted at /dev/shm) is 64MB, which is too small to ...

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Hugh_Ku
New Contributor II
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Hey folks, any follow-up on this, or alternative solution? thanks

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hvsk
by • New Contributor
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Using a Virtual environment

Hi All,We are working on training NHits/TFT (a Pytorch-forecasting implementation) for timeseries forecasting. However, we are having some issues with package dependency conflicts.Is there a way to consistently use a virtual environment across cells ...

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Anonymous
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Hi @Harsh Kalra​ Hope everything is going great.Just wanted to check in if you were able to resolve your issue. If yes, would you be happy to mark an answer as best so that other members can find the solution more quickly? If not, please tell us so w...

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Smu_Tan
by • New Contributor
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Resolved! Does Databricks supports the Pytorch Distributed Training for multiple devices?

Hi, Im trying to use the databricks platform to do the pytorch distributed training, but I didnt find any info about this. What I expected is using multiple clusters to run a common job using pytorch distributed data parallel (DDP) with the code belo...

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axb0
Esteemed Contributor III
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With Databricks MLR, HorovodRunner is provided which supports distributed training and inference with PyTorch. Here's an example notebook for your reference: PyTorchDistributedDeepLearningTraining - Databricks.

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