Menu
Reading : https://www.tensorflow.org/versions/r0.10/resources/faq.html it states :
Does TensorFlow make use of all the devices (GPUs and CPUs) available on my machine?
TensorFlow supports multiple GPUs and CPUs. See the how-to documentation on using GPUs with TensorFlow for details of how TensorFlow assigns operations to devices, and the CIFAR-10 tutorial for an example model that uses multiple GPUs.
Note that TensorFlow only uses GPU devices with a compute capability greater than 3.5.
Does this mean Tensorflow can automatically make use of all CPU's on given machine or does it ned to be explicitly configured ?
232
Running TensorFlow on multicore CPUs can be an attractive option, e.g., where a workflow is dominated by IO and faster computational hardware has less impact on runtime, or simply where no GPUs are available. This talk will discuss which TensorFlow package to choose, and how to optimise performance on multicore CPUs.
If a TensorFlow operation has both CPU and GPU implementations, by default, the GPU device is prioritized when the operation is assigned. For example, tf. matmul has both CPU and GPU kernels and on a system with devices CPU:0 and GPU:0 , the GPU:0 device is selected to run tf.
Strategy is a TensorFlow API to distribute training across multiple GPUs, multiple machines, or TPUs. Using this API, you can distribute your existing models and training code with minimal code changes.
CPUs are used via a "device" which is just a threadpool. You can control the number of threads if you feel like you need more:
sess = tf.Session(config=tf.ConfigProto(
intra_op_parallelism_threads=NUM_THREADS))
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
