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Inconsistent CLI parameters to specify resources requests and limits across training and inference

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Assessment

Difficulty
5/5
Estimated time
Over a week
Newbie friendliness
35/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Stale
Tech stack
python
Domain
cli

Research direction

Compare the training resource parameters with the custom-endpoint resources-requests and resources-limits parameters, then review issue 306 for the EFA and Neuron device requirements. Define a consistent CLI approach across training and inference, including device handling, and verify that both command paths expose the same resource concepts.

Written by the indexing model from the issue text.

Description

For training, the CLI parameters are:

  --accelerators INTEGER          Number of accelerators (GPUs/TPUs)
  --vcpu TEXT                     Number of vCPUs
  --memory TEXT                   Amount of memory in GiB
  --accelerators-limit INTEGER    Limit for the number of accelerators (GPUs/TPUs)
  --vcpu-limit TEXT               Limit for the number of vCPUs
  --memory-limit TEXT             Limit for the amount of memory in GiB

For inference (custom endpoints), the CLI parameters are:

  --resources-requests JSON       JSON object of resource requests, e.g. '{"cpu":"1","memory":"2Gi"}'
  --resources-limits JSON         JSON object of resource limits, e.g. '{"cpu":"2","memory":"4Gi"}'

Also, as mentioned in issue 306 (https://github.com/aws/sagemaker-hyperpod-cli/issues/306), the CLI should offer a consistent way to specify EFA (and Neuron) devices.

Dominant language
Python
Stars
41
Forks
95
Avg merge
14h 48m
Merged PRs (30d)
5

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