'How to Build a Training Pipeline on Multiple GPUs ' machinelearning mlopspipelines
DDP stands for “Distributed Data-Parallel”. The idea behind it is that the model is replicated on every worker while each replica worker on a different set of data samples which enables scalability.
Furthermore, the gradients involved are computed independently on each worker to later accumulate via communication between them.The main advantage of Pytorch Lightning compared to Pytorch, is that there is no need to write a lot of boilerplate code.dstack is a framework to automate machine learning pipelines.
Now that we understand the tools involved, let's have a look into the steps required to build our pipeline.Once you’ve signed in to your account at dstack.ai, click the Settings tab on the left-hand side, and further click on the AWS tab. Here, you have to provide your AWS credentials and specify which AWS instance types dstack is allowed to use.instance type. Make sure to select the region where you have GPU quotes to use this type of instance.. After you’re done, the user interface should look like the image provided below.WandB Configuration, we’ll have to specify our WandB API key as a secret in the Settings of dstack. Your WandB API key can be found in “Settings” as shown below.
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