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I tried accessing an NVIDIA Quadro M2200 GPU I have in my system with tensorflow-gpu 1.8.0-h7b35bdc_0; however, it doesn't seem to be detected when I run the following code:
2018-07-11 23:47:49.707014: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-07-11 23:47:49.707880: I tensorflow/core/common_runtime/direct_session.cc:284] Device mapping: Device mapping: no known devices.
Expected Behavior
The above Python code should return something similar to the following (which is what I get when tensorflow-gpu 1.7.0 was installed):
2018-07-11 23:47:14.807787: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-07-11 23:47:15.329984: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-07-11 23:47:15.330454: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1344] Found device 0 with properties:
name: Quadro M2200 major: 5 minor: 2 memoryClockRate(GHz): 1.036
pciBusID: 0000:01:00.0
totalMemory: 3.95GiB freeMemory: 3.90GiB
2018-07-11 23:47:15.330468: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1423] Adding visible gpu devices: 0
2018-07-11 23:47:15.560098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:911] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-07-11 23:47:15.560124: I tensorflow/core/common_runtime/gpu/gpu_device.cc:917] 0
2018-07-11 23:47:15.560130: I tensorflow/core/common_runtime/gpu/gpu_device.cc:930] 0: N
2018-07-11 23:47:15.560284: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1041] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3639 MB memory) -> physical GPU (device: 0, name: Quadro M2200, pci bus id: 0000:01:00.0, compute capability: 5.2)
2018-07-11 23:47:15.600253: I tensorflow/core/common_runtime/direct_session.cc:297] Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Quadro M2200, pci bus id: 0000:01:00.0, compute capability: 5.2
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: Quadro M2200, pci bus id: 0000:01:00.0, compute capability: 5.2
Your environment is a mixture of packages from conda-forge and defaults and some pip installed packages. These two channels are not always compatible. Can you try creating an environment using just the packages in defaults and see if the issue persists? conda create -n tflow_test -c defaults python=3.6 tensorflow-gpu=1.8.0 should create such an environment.
Yes - it appears that the conda-forge and default packages are clashing and the former probably don't support GPUs because this issue is still open; using the default channel only, I can see my GPU with TensorFlow 1.8.0; in contrast to what I posted earlier, the environment created with default-only tensorflow packages contains
conda list t.*flow
# packages in environment at /home/lebedov/miniconda3/envs/tflow_test:
#
# Name Version Build Channel
_tflow_180_select 1.0 gpu
tensorflow 1.8.0 hb11d968_0
tensorflow-base 1.8.0 py36hc1a7637_0
tensorflow-gpu 1.8.0 h7b35bdc_0
Actual Behavior
I tried accessing an NVIDIA Quadro M2200 GPU I have in my system with tensorflow-gpu 1.8.0-h7b35bdc_0; however, it doesn't seem to be detected when I run the following code:
Expected Behavior
The above Python code should return something similar to the following (which is what I get when tensorflow-gpu 1.7.0 was installed):
Steps to Reproduce
Anaconda or Miniconda version:
Miniconda3
Operating System:
Ubuntu 16.04.4
conda info
conda list --show-channel-urls
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