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tensorflowgpucpu-usagecpu-coresvram
Karan Shah
Karan Shah

Reputation: 457

TensorFlow GPU and CPU offloaded ops segregation

Assuming TensorFlow GPU library being used in computation, which operations are offloaded to GPU (and how often)? What is the performance impact of:

  1. CPU Core count (because it is now not actively involved in computation)
  2. RAM size.
  3. GPU VRAM (What benefit of owning a higher memory GPU)

Say I'd like to decide upon particular(s) of these hardware choices. Can someone explain with an example, which aspect of a Machine Learning model will impact the particular hardware constraint?

(I need a little elaboration on what exact ops are offloaded to GPU and CPU, based on TensorFlow GPU lib for example.)

Upvotes: 1

Views: 230

Answers (1)

NiallJG
NiallJG

Reputation: 1961

One way of using tensorflow to efficiently spread work between CPUs and GPUs is to use estimators.

For example :

 model = tf.estimator.Estimator(model_fn=model_fn,
                              params=params,
                              model_dir="./models/model-v0-0")


 model.train(lambda:input_fn(train_data_path), steps=1000)

In the function 'input_fn' the data batch loading and batch preparation will be offloaded to the CPU while the GPU is working on the model as declared in the function 'model_fn'.

If you are concerned about RAM constraints then you should look at using the tfrecord format as this avoids loading up the whole dataset in RAM

see tensorflow.org/tutorials/load_data/tf_records

Upvotes: 1

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