मैं कैसे जांचूं कि कैरेस tensorflow के gpu संस्करण का उपयोग कर रहा है या नहीं?

Question

जब मैं एक keras स्क्रिप्ट चलाने, मैं निम्नलिखित उत्पादन प्राप्त करें:

Using TensorFlow backend. 
2017-06-14 17:40:44.621761: 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. 
2017-06-14 17:40:44.621783: 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. 
2017-06-14 17:40:44.621788: 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. 
2017-06-14 17:40:44.621791: W 
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow 
library wasn't compiled to use AVX2 instructions, but these are 
available on your machine and could speed up CPU computations. 
2017-06-14 17:40:44.621795: W 
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow 
library wasn't compiled to use FMA instructions, but these are 
available 
on your machine and could speed up CPU computations. 
2017-06-14 17:40:44.721911: I 
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful 
NUMA node read from SysFS had negative value (-1), but there must be 
at least one NUMA node, so returning NUMA node zero 
2017-06-14 17:40:44.722288: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 
with properties: 
name: GeForce GTX 850M 
major: 5 minor: 0 memoryClockRate (GHz) 0.9015 
pciBusID 0000:0a:00.0 
Total memory: 3.95GiB 
Free memory: 3.69GiB 
2017-06-14 17:40:44.722302: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
2017-06-14 17:40:44.722307: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y 
2017-06-14 17:40:44.722312: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating 
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, 
pci bus id: 0000:0a:00.0) 

इसका क्या मतलब है? क्या मैं tensorflow के जीपीयू या सीपीयू संस्करण का उपयोग कर रहा हूँ?

कैमरे स्थापित करने से पहले, मैं tensorflow के GPU संस्करण के साथ काम कर रहा था।

भी sudo pip3 listtensorflow-gpu(1.1.0) दिखाता है और tensorflow-cpu जैसा कुछ भी नहीं दिखाता है।

The TensorFlow library wasn't compiled to use SSE4.1 instructions, 
but these are available on your machine and could speed up CPU 
computations. 
2017-06-14 17:53:31.424793: 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. 
2017-06-14 17:53:31.424803: 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. 
2017-06-14 17:53:31.424812: W 
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow 
library wasn't compiled to use AVX2 instructions, but these are 
available on your machine and could speed up CPU computations. 
2017-06-14 17:53:31.424820: W 
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow 
library wasn't compiled to use FMA instructions, but these are 
available on your machine and could speed up CPU computations. 
2017-06-14 17:53:31.540959: I 
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful 
NUMA node read from SysFS had negative value (-1), but there must be 
at least one NUMA node, so returning NUMA node zero 
2017-06-14 17:53:31.541359: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 
with properties: 
name: GeForce GTX 850M 
major: 5 minor: 0 memoryClockRate (GHz) 0.9015 
pciBusID 0000:0a:00.0 
Total memory: 3.95GiB 
Free memory: 128.12MiB 
2017-06-14 17:53:31.541407: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
2017-06-14 17:53:31.541420: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y 
2017-06-14 17:53:31.541441: I 
tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating 
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, 
pci bus id: 0000:0a:00.0) 
2017-06-14 17:53:31.547902: E 
tensorflow/stream_executor/cuda/cuda_driver.cc:893] failed to 
allocate 128.12M (134348800 bytes) from device: 
CUDA_ERROR_OUT_OF_MEMORY 
Device mapping: 
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce 
GTX 850M, pci bus id: 0000:0a:00.0 
2017-06-14 17:53:31.549482: I 
tensorflow/core/common_runtime/direct_session.cc:257] Device 
mapping: 
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce 
GTX 850M, pci bus id: 0000:0a:00.0 

Answer 1

आप GPU संस्करण का उपयोग कर रहे हैं:

आदेश [इस stackoverflow सवाल] पर उल्लेख चल रहा है, निम्नलिखित देता है। आप के साथ (भी this प्रश्न की जाँच करें) उपलब्ध tensorflow उपकरणों सूचीबद्ध कर सकते हैं:

from tensorflow.python.client import device_lib 
print(device_lib.list_local_devices()) 

अपने मामले दोनों सीपीयू और GPU में उपलब्ध हैं, अगर आप tensorflow GPU सूचीबद्ध नहीं किया जाएगा की cpu संस्करण का उपयोग करें। आपके मामले में, अपने tensorflow डिवाइस (with tf.device("..")) को सेट किए बिना, tensorflow स्वचालित रूप से आपके जीपीयू को चुन देगा!

इसके अलावा, आपके sudo pip3 list स्पष्ट रूप से दिखाता है कि आप tensorflow-gpu का उपयोग कर रहे हैं। यदि आपके पास tensoflow cpu संस्करण होगा तो नाम tensorflow(1.1.0) जैसा होगा।

चेतावनियों के बारे में जानकारी के लिए this समस्या की जांच करें।