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fix ConverterMasked.py,

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changing requirements
changing device.py ENV vars
This commit is contained in:
iperov 2019-03-07 19:48:12 +04:00
parent c3eea0cf98
commit 5587c93e01
7 changed files with 28 additions and 40 deletions
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20
nnlib/device.py
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@ -3,10 +3,9 @@ import json
import numpy as np
from .pynvml import *
#you can force_tf_min_req_cap 35, if your DFL is built for tf==1.5.0
#you can set DFL_TF_MIN_REQ_CAP manually for your build
#the reason why we cannot check tensorflow.version is it requires import tensorflow
tf_min_req_cap = int(os.environ.get("force_tf_min_req_cap", 37))
tf_min_req_cap = int(os.environ.get("DFL_TF_MIN_REQ_CAP", 35))
class device:
backend = None
@ -260,14 +259,15 @@ class device:
return result[0] * 10 + result[1]
force_plaidML = os.environ.get("force_plaidML", "0") == "1"
force_plaidML = os.environ.get("DFL_FORCE_PLAIDML", "0") == "1" #for OpenCL build , forcing using plaidML even if NVIDIA found
force_tf_cpu = os.environ.get("DFL_FORCE_TF_CPU", "0") == "1" #for OpenCL build , forcing using tf-cpu if plaidML failed
has_nvml = False
has_nvml_cap = False
#use force_has_nvidia_device=1 if
#use DFL_FORCE_HAS_NVIDIA_DEVICE=1 if
#- your NVIDIA cannot be seen by OpenCL
#- CUDA build of DFL
has_nvidia_device = os.environ.get("force_has_nvidia_device", "0") == "1"
has_nvidia_device = os.environ.get("DFL_FORCE_HAS_NVIDIA_DEVICE", "0") == "1"
plaidML_devices = []
@ -294,7 +294,7 @@ plaidML_devices_count = len(plaidML_devices)
#choosing backend
if device.backend is None:
if device.backend is None and not force_tf_cpu:
#first trying to load NVSMI and detect CUDA devices for tensorflow backend,
#even force_plaidML is choosed, because if plaidML will fail, we can choose tensorflow
try:
@ -320,13 +320,15 @@ if device.backend is None:
if not has_nvidia_device and (device.backend is None or force_plaidML):
#tensorflow backend was failed without has_nvidia_device , or forcing plaidML, trying to use plaidML backend
if plaidML_devices_count == 0:
print ("plaidML: No capable OpenCL devices found. Falling back to tensorflow backend.")
#print ("plaidML: No capable OpenCL devices found. Falling back to tensorflow backend.")
device.backend = None
else:
device.backend = "plaidML"
if device.backend is None:
if not has_nvml:
if force_tf_cpu:
device.backend = "tensorflow-cpu"
elif not has_nvml:
if has_nvidia_device:
#some notebook systems have NVIDIA card without NVSMI in official drivers
#in that case considering we have system with one capable GPU and let tensorflow to choose best GPU