我正在运行一个tensorflow工作，它总是在增加池大小时停滞不前，从不从那里开始。
这是输出：
2017-11-13 19:01:12.841317: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-13 19:01:12.841715: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2017-11-13 19:01:12.841729: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2017-11-13 19:01:17.941982: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 0 with properties:
name: Tesla K20m
major: 3 minor: 5 memoryClockRate (GHz) 0.7055
pciBusID 0000:09:00.0
Total memory: 4.63GiB
Free memory: 4.56GiB
2017-11-13 19:01:18.135538: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x6e48240 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-11-13 19:01:18.136394: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 1 with properties:
name: Tesla K20m
major: 3 minor: 5 memoryClockRate (GHz) 0.7055
pciBusID 0000:0a:00.0
Total memory: 4.63GiB
Free memory: 4.56GiB
2017-11-13 19:01:18.324134: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x6e4a680 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-11-13 19:01:18.325028: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 2 with properties:
name: Tesla K20m
major: 3 minor: 5 memoryClockRate (GHz) 0.7055
pciBusID 0000:0d:00.0
Total memory: 4.63GiB
Free memory: 4.56GiB
2017-11-13 19:01:18.519043: W tensorflow/stream_executor/cuda/cuda_driver.cc:523] A non-primary context 0x60ae510 exists before initializing the StreamExecutor. We haven't verified StreamExecutor works with that.
2017-11-13 19:01:18.519928: I tensorflow/core/common_runtime/gpu/gpu_device.cc:940] Found device 3 with properties:
name: Tesla K20m
major: 3 minor: 5 memoryClockRate (GHz) 0.7055
pciBusID 0000:0e:00.0
Total memory: 4.63GiB
Free memory: 4.56GiB
2017-11-13 19:01:18.521497: I tensorflow/core/common_runtime/gpu/gpu_device.cc:961] DMA: 0 1 2 3
2017-11-13 19:01:18.521514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0: Y Y Y Y
2017-11-13 19:01:18.521523: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 1: Y Y Y Y
2017-11-13 19:01:18.521530: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 2: Y Y Y Y
2017-11-13 19:01:18.521538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 3: Y Y Y Y
2017-11-13 19:01:18.521556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K20m, pci bus id: 0000:09:00.0)
2017-11-13 19:01:18.521566: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Tesla K20m, pci bus id: 0000:0a:00.0)
2017-11-13 19:01:18.521580: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:2) -> (device: 2, name: Tesla K20m, pci bus id: 0000:0d:00.0)
2017-11-13 19:01:18.521589: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Creating TensorFlow device (/gpu:3) -> (device: 3, name: Tesla K20m, pci bus id: 0000:0e:00.0)
2017-11-13 19:01:24.197527: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:247] PoolAllocator: After 2731 get requests, put_count=2675 evicted_count=1000 eviction_rate=0.373832 and unsatisfied allocation rate=0.423288
2017-11-13 19:01:24.197943: I tensorflow/core/common_runtime/gpu/pool_allocator.cc:259] Raising pool_size_limit_ from 100 to 110
当我获得GPU利用率时，内存几乎未被使用，在这里：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 384.81 Driver Version: 384.81 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 Tesla K20m Off | 00000000:09:00.0 Off | 0 |
| N/A 42C P0 93W / 225W | 646MiB / 4742MiB | 30% Default |
+-------------------------------+----------------------+----------------------+
| 1 Tesla K20m Off | 00000000:0A:00.0 Off | 0 |
| N/A 33C P0 43W / 225W | 72MiB / 4742MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 Tesla K20m Off | 00000000:0D:00.0 Off | 0 |
| N/A 35C P0 45W / 225W | 72MiB / 4742MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 3 Tesla K20m Off | 00000000:0E:00.0 Off | 0 |
| N/A 33C P0 43W / 225W | 72MiB / 4742MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 4 Tesla K20m Off | 00000000:28:00.0 Off | 0 |
| N/A 35C P0 45W / 225W | 0MiB / 4742MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 5 Tesla K20m Off | 00000000:2B:00.0 Off | 0 |
| N/A 37C P0 45W / 225W | 0MiB / 4742MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 6 Tesla K20m Off | 00000000:30:00.0 Off | 0 |
| N/A 38C P0 45W / 225W | 0MiB / 4742MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 7 Tesla K20m Off | 00000000:33:00.0 Off | 0 |
| N/A 32C P0 46W / 225W | 0MiB / 4742MiB | 96% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1554 C python 635MiB |
| 1 1554 C python 61MiB |
| 2 1554 C python 61MiB |
| 3 1554 C python 61MiB |
+-----------------------------------------------------------------------------+
这是我的会话配置：
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
config = tf.ConfigProto(allow_soft_placement=True, gpu_options=gpu_options)
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
我缺少什么？ 与它一起使用的CPU也非常大，即
8个CPU，每个都有30G
我无法看到或理解为什么会这样。