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We are using WMLA to run GPU based jobs fine and also able to scale across the cluster. However, we are trying to run a model only on CPU (not on GPU) using the WMLA framework. We have prepared a model which runs only on CPU and not at all sending it to GPU.
Also, while submitting a job, we did not define “gpuPerWorker” parameter. However, what we see is that the model is getting GPU slots allocated first, based on availability, and then CPU slot allocation starts once the GPU slots are over.
For Example: Our current configuration: Total CPU slots: 12*96 Total GPU slots: 48
Resource available before submitting the WMLA job: Total CPU slots: 10*96 Total GPU slots: 35
We submitted a job with below params: 'maxParalleJobNum': 120
Did not define --gpuPerWorker param while submitting the job.
Our observation: The job gets allocated to 35 GPU slots first and then starts occupying CPU slots for remaining 85 parallel tasks, the job stays in WAITING state for some time here for CPU slot allocation.
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