问题
Even with
inter_op_parallelism_threads = 1
intra_op_parallelism_threads = 1
values set, TensorFlow 1.5 process is not single-threaded. Why? Is there a way to completely disable unexpected thread spawning?
回答1:
First of all, TensorFlow is a multi-level software stack, and each layer tries to be smart and introduces some worker threads of its own:
- One thread is created by Python runtime
- Two more threads are created by NVIDIA CUDA runtime
Next, there are threads originating from the way how TensorFlow administers internal compute jobs:
- Threads are created/joined all the time to poll on job completion (GRPC engine)
Thus, TensorFlow cannot be single-threaded, even with all options set to 1. Perhaps, this design is intended to reduce latencies for async jobs. Yet, there is a certain drawback: multicore compute libraries, such as linear algebra, do cache-intensive operations best with static symmetric core-thread mapping. And dangling callback threads produced by TensorFlow will disturb this symmetry all the time.
来源：https://stackoverflow.com/questions/48696900/why-tensorflow-creates-so-many-cpu-threads