CODEWITHSUNDEEP
memory-managementtensorflowobject-detection
Marcelo
Marcelo

Reputation: 59

TensorFlow object detection limiting memory and cpu usage

I manage to run tensorflow pet example from the tutorial. I decided to use the slowest model (because I want to use for my own data). However, when I start the training it gets killed after running a bit. It used all my cpus (4) and all my memory 8GB. Do you know anyway I can limit the number of CPUs to 2 and limit the amount of memory used ? If I reduce the batch size ? My batch size is already 1.

I managed to run by reducing the resize:

image_resizer { keep_aspect_ratio_resizer { min_dimension: 300 max_dimension: 612 }

Thanks in advance.

Upvotes: 0

Views: 1652

Answers (1)

Jonathan Huang
Jonathan Huang

Reputation: 1558

Another idea to reduce memory usage is to reduce the queue sizes for input data. Specifically, in the object_detection/protos/train.proto file, you will see entries for batch_queue_capacity and prefetch_queue_capacity --- consider setting these fields explicitly in your config file to smaller numbers.

Upvotes: 1

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