我已经在Anaconda上使用以下命令安装了Cuda
conda install -c anaconda cudatoolkit
之前，我还使用以下命令安装Tensorflow GPU版本
conda install -c anaconda tensorflow-gpu
但是，当我运行以下脚本时，Tensorflow-gpu未激活：
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
名称：“ / device：CPU：0”
device_type：“ CPU”
memory_limit：268435456
地区 { } 化身：12015853371339101357 ]
答案 0 :(得分：4)
如果通过anaconda安装numba，则可以运行async function _readSourceDataFromCache (slug_name) {
aerospikeClient = aerospikeConf.AerospikeClient;
console.log('In async function')
console.log(aerospikeClient);
return new Promise ( function (resolve, reject) {
aerospikeClient.get(aerospikeConf.AerospikeKey, function (error, record) {
if (error) {
switch (error.code) {
case aerospikeConf.Aerospike.status.AEROSPIKE_ERR_RECORD_NOT_FOUND:
console.log('NOT_FOUND -', aerospikeConf.AerospikeKey)
break
default:
console.log('ERR - ', error, aerospikeConf.AerospikeKey)
}
resolve(false)
}
else{
resp = record['value']
aerospikeClient.close();
return resolve(resp);
}
});
})
}
，这将确认您是否具有正常运行的CUDA系统。在具有CUDA的Linux系统上：
numba -s在没有运行CUDA GPU的Windows系统上：
$ numba -s
System info:
--------------------------------------------------------------------------------
__Time Stamp__
2018-08-27 09:16:49.622828
__Hardware Information__
Machine : x86_64
CPU Name : ivybridge
CPU Features :
aes avx cmov cx16 f16c fsgsbase mmx pclmul popcnt rdrnd sse sse2 sse3 sse4.1
sse4.2 ssse3 xsave xsaveopt
__OS Information__
Platform : Linux-4.4.0-57-generic-x86_64-with-debian-jessie-sid
Release : 4.4.0-57-generic
System Name : Linux
Version : #78~14.04.1-Ubuntu SMP Sat Dec 10 00:14:47 UTC 2016
OS specific info : debianjessie/sid
glibc info : glibc 2.2.5
__Python Information__
Python Compiler : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)
Python Implementation : CPython
Python Version : 3.6.0
Python Locale : en_US UTF-8
__LLVM information__
LLVM version : 6.0.0
__CUDA Information__
Found 1 CUDA devices
id 0 b'GeForce GTX 970' [SUPPORTED]
compute capability: 5.2
pci device id: 0
pci bus id: 1
Summary:
1/1 devices are supported
CUDA driver version : 9020
CUDA libraries:
Finding cublas
named libcublas.so.8.0.88
trying to open library... ok
Finding cusparse
named libcusparse.so.8.0.61
trying to open library... ok
Finding cufft
named libcufft.so.8.0.61
trying to open library... ok
Finding curand
named libcurand.so.8.0.61
trying to open library... ok
Finding nvvm
named libnvvm.so.3.1.0
trying to open library... ok
finding libdevice for compute_20... ok
finding libdevice for compute_30... ok
finding libdevice for compute_35... ok
finding libdevice for compute_50... ok
__Conda Information__
conda_build_version : not installed
conda_env_version : 4.5.4
platform : linux-64
python_version : 3.6.0.final.0
root_writable : False
__Current Conda Env__
absl-py 0.1.10 py36_0
accelerate_cudalib 2.0 0
bleach 1.5.0 py36_0
ca-certificates 2018.03.07 0
certifi 2018.4.16 py36_0
cffi 1.9.1 py36_0
conda 4.5.4 py36_0
conda-env 2.6.0 h36134e3_1
cryptography 1.7.1 py36_0
cudatoolkit 8.0 3
cudnn 7.0.5 cuda8.0_0
decorator 4.0.11 py36_0
html5lib 0.9999999 py36_0
idna 2.2 py36_0
intel-openmp 2018.0.0 hc7b2577_8
ipython 5.3.0 py36_0
ipython_genutils 0.2.0 py36_0
libffi 3.2.1 1
libgcc-ng 7.2.0 h7cc24e2_2
libgfortran 3.0.0 1
libgfortran-ng 7.2.0 hdf63c60_3
libprotobuf 3.5.1 h6f1eeef_0
libstdcxx-ng 7.2.0 hdf63c60_3
llvmlite 0.23.2 py36hdbcaa40_0
markdown 2.6.11 py36_0
mkl 2018.0.1 h19d6760_4
mpmath 0.19 py36_1
nccl 1.3.4 cuda8.0_1
numba 0.38.1 py36h04863e7_0
numpy 1.12.1 py36he24570b_1
openssl 1.0.2o h20670df_0
path.py 10.1 py36_0
pexpect 4.2.1 py36_0
pickleshare 0.7.4 py36_0
pip 9.0.1 py36_1
prompt_toolkit 1.0.13 py36_0
protobuf 3.5.1 py36hf484d3e_0
ptyprocess 0.5.1 py36_0
pyasn1 0.1.9 py36_0
pycosat 0.6.3 py36h0a5515d_0
pycparser 2.17 py36_0
pyculib 1.0.2 np112py36_2
pyculib_sorting 1.0.0 8
pygments 2.2.0 py36_0
pyopenssl 16.2.0 py36_0
python 3.6.0 0
pytorch 0.3.0 py36cuda8.0cudnn7.0_0
readline 6.2 2
requests 2.12.4 py36_0
ruamel_yaml 0.11.14 py36_1
scipy 1.0.0 py36hbf646e7_0
setuptools 38.5.1 py36_0
simplegeneric 0.8.1 py36_1
six 1.10.0 py36_0
sqlite 3.13.0 0
sympy 1.1.1 py36_0
tensorflow 1.4.1 0
tensorflow-base 1.4.1 py36hd00c003_2
tensorflow-tensorboard 1.5.1 py36hf484d3e_0
tk 8.5.18 0
traitlets 4.3.2 py36_0
wcwidth 0.1.7 py36_0
werkzeug 0.14.1 py36_0
wheel 0.29.0 py36_0
xz 5.2.2 1
yaml 0.1.6 0
zlib 1.2.11 ha838bed_2
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