GPU issue with multiple Inception V3 trained models
$begingroup$
While using multiple (three) trained tensorflow models on python run parallelly as 3 threads (or just 2); I get memory outage but no issue on running each individually on each GPU seperately (2X3 = 6 times) or as per code config below.
GPU config -
GeForce GTX 1060 6GB major totalMemory: 5.93GiB freeMemory: 5.69GiB memoryClockRate(GHz): 1.7715
GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392 =>ignored totalMemory: 3.94GiB freeMemory: 3.89GiB
Individual Model Files' relevant (GPU related) code-
1)d = '/gpu:0'
config=tf.ConfigProto()
#config.log_device_placement= True
print("SUNGLASSSSSSSSSSSSSSSSSSSS")
#config.gpu_options.per_process_gpu_memory_fraction = 0.3
config=tf.ConfigProto(gpu_options=tf.GPUOptions(visible_device_list='0'))
with tf.device(d):
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
2)
d = '/gpu:1'
config=tf.ConfigProto()
#config.log_device_placement= True
print("HATSSSSSSSSSSSSSSSSSS")
config.gpu_options.per_process_gpu_memory_fraction = 0.35
config=tf.ConfigProto(log_device_placement=False,gpu_options=tf.GPUOptions(allow_growth=True),allow_soft_placement = True)
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
3)
d = '/gpu:1'
config=tf.ConfigProto()
#config.log_device_placement= True
print("HANDSNEARFACEEEEEEEEEEEEE")
config.gpu_options.per_process_gpu_memory_fraction = 0.4
#config=tf.ConfigProto(log_device_placement=False,gpu_options=tf.GPUOptions(allow_growth=False),allow_soft_placement = True)
with tf.device(d):
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
tensorflow gpu inception
asked 9 mins ago
MikeMike
1
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$endgroup$
add a comment |
$begingroup$
While using multiple (three) trained tensorflow models on python run parallelly as 3 threads (or just 2); I get memory outage but no issue on running each individually on each GPU seperately (2X3 = 6 times) or as per code config below.
GPU config -
GeForce GTX 1060 6GB major totalMemory: 5.93GiB freeMemory: 5.69GiB memoryClockRate(GHz): 1.7715
GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392 =>ignored totalMemory: 3.94GiB freeMemory: 3.89GiB
Individual Model Files' relevant (GPU related) code-
1)d = '/gpu:0'
config=tf.ConfigProto()
#config.log_device_placement= True
print("SUNGLASSSSSSSSSSSSSSSSSSSS")
#config.gpu_options.per_process_gpu_memory_fraction = 0.3
config=tf.ConfigProto(gpu_options=tf.GPUOptions(visible_device_list='0'))
with tf.device(d):
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
2)
d = '/gpu:1'
config=tf.ConfigProto()
#config.log_device_placement= True
print("HATSSSSSSSSSSSSSSSSSS")
config.gpu_options.per_process_gpu_memory_fraction = 0.35
config=tf.ConfigProto(log_device_placement=False,gpu_options=tf.GPUOptions(allow_growth=True),allow_soft_placement = True)
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
3)
d = '/gpu:1'
config=tf.ConfigProto()
#config.log_device_placement= True
print("HANDSNEARFACEEEEEEEEEEEEE")
config.gpu_options.per_process_gpu_memory_fraction = 0.4
#config=tf.ConfigProto(log_device_placement=False,gpu_options=tf.GPUOptions(allow_growth=False),allow_soft_placement = True)
with tf.device(d):
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
tensorflow gpu inception
asked 9 mins ago
MikeMike
1
New contributor
Mike is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
While using multiple (three) trained tensorflow models on python run parallelly as 3 threads (or just 2); I get memory outage but no issue on running each individually on each GPU seperately (2X3 = 6 times) or as per code config below.
GPU config -
GeForce GTX 1060 6GB major totalMemory: 5.93GiB freeMemory: 5.69GiB memoryClockRate(GHz): 1.7715
GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392 =>ignored totalMemory: 3.94GiB freeMemory: 3.89GiB
Individual Model Files' relevant (GPU related) code-
1)d = '/gpu:0'
config=tf.ConfigProto()
#config.log_device_placement= True
print("SUNGLASSSSSSSSSSSSSSSSSSSS")
#config.gpu_options.per_process_gpu_memory_fraction = 0.3
config=tf.ConfigProto(gpu_options=tf.GPUOptions(visible_device_list='0'))
with tf.device(d):
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
2)
d = '/gpu:1'
config=tf.ConfigProto()
#config.log_device_placement= True
print("HATSSSSSSSSSSSSSSSSSS")
config.gpu_options.per_process_gpu_memory_fraction = 0.35
config=tf.ConfigProto(log_device_placement=False,gpu_options=tf.GPUOptions(allow_growth=True),allow_soft_placement = True)
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
3)
d = '/gpu:1'
config=tf.ConfigProto()
#config.log_device_placement= True
print("HANDSNEARFACEEEEEEEEEEEEE")
config.gpu_options.per_process_gpu_memory_fraction = 0.4
#config=tf.ConfigProto(log_device_placement=False,gpu_options=tf.GPUOptions(allow_growth=False),allow_soft_placement = True)
with tf.device(d):
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
tensorflow gpu inception
asked 9 mins ago
MikeMike
1
New contributor
Mike is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
While using multiple (three) trained tensorflow models on python run parallelly as 3 threads (or just 2); I get memory outage but no issue on running each individually on each GPU seperately (2X3 = 6 times) or as per code config below.
GPU config -
GeForce GTX 1060 6GB major totalMemory: 5.93GiB freeMemory: 5.69GiB memoryClockRate(GHz): 1.7715
GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392 =>ignored totalMemory: 3.94GiB freeMemory: 3.89GiB
Individual Model Files' relevant (GPU related) code-
1)d = '/gpu:0'
config=tf.ConfigProto()
#config.log_device_placement= True
print("SUNGLASSSSSSSSSSSSSSSSSSSS")
#config.gpu_options.per_process_gpu_memory_fraction = 0.3
config=tf.ConfigProto(gpu_options=tf.GPUOptions(visible_device_list='0'))
with tf.device(d):
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
2)
d = '/gpu:1'
config=tf.ConfigProto()
#config.log_device_placement= True
print("HATSSSSSSSSSSSSSSSSSS")
config.gpu_options.per_process_gpu_memory_fraction = 0.35
config=tf.ConfigProto(log_device_placement=False,gpu_options=tf.GPUOptions(allow_growth=True),allow_soft_placement = True)
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
3)
d = '/gpu:1'
config=tf.ConfigProto()
#config.log_device_placement= True
print("HANDSNEARFACEEEEEEEEEEEEE")
config.gpu_options.per_process_gpu_memory_fraction = 0.4
#config=tf.ConfigProto(log_device_placement=False,gpu_options=tf.GPUOptions(allow_growth=False),allow_soft_placement = True)
with tf.device(d):
with tf.Session(graph=graph, config=config) as sess:
results = sess.run(output_operation.outputs[0], {
input_operation.outputs[0]: t
})
results = np.squeeze(results)
tensorflow gpu inception
tensorflow gpu inception
asked 9 mins ago
MikeMike
1
New contributor
Mike is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 9 mins ago
MikeMike
1
New contributor
Mike is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 9 mins ago
MikeMike
1
New contributor
Mike is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 9 mins ago
MikeMike
1
asked 9 mins ago
MikeMike
1
1
New contributor
Mike is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Mike is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Mike is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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