image-classification/code/inference.py [42:74]:
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physical_gpus = tf.config.experimental.list_physical_devices('GPU')
if physical_gpus:
  try:
    # Currently, memory growth needs to be the same across GPUs
    for gpu in physical_gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(physical_gpus), 'Physical GPUs,', len(logical_gpus), 'Logical GPUs')
  except RuntimeError as e:
    # Memory growth must be set before GPUs have been initialized
    print(e)
else:
    print('**** NO physical GPUs')


num_inferences = 0
print(f'num_inferences: {num_inferences}')

Context = namedtuple('Context',
                     'model_name, model_version, method, rest_uri, grpc_uri, '
                     'custom_attributes, request_content_type, accept_header')

def handler(data, context):

    global num_inferences
    num_inferences += 1
    
    print(f'\n************ inference #: {num_inferences}')
    if context.request_content_type == 'application/x-image':
        stream = io.BytesIO(data.read())
        img = Image.open(stream).convert('RGB')
        img = img.resize((WIDTH, HEIGHT))
        img_array = image.img_to_array(img) #, data_format = "channels_first")
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object-detection/code/inference.py [39:71]:
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physical_gpus = tf.config.experimental.list_physical_devices('GPU')
if physical_gpus:
  try:
    # Currently, memory growth needs to be the same across GPUs
    for gpu in physical_gpus:
        tf.config.experimental.set_memory_growth(gpu, True)
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(physical_gpus), 'Physical GPUs,', len(logical_gpus), 'Logical GPUs')
  except RuntimeError as e:
    # Memory growth must be set before GPUs have been initialized
    print(e)
else:
    print('**** NO physical GPUs')


num_inferences = 0
print(f'num_inferences: {num_inferences}')

Context = namedtuple('Context',
                     'model_name, model_version, method, rest_uri, grpc_uri, '
                     'custom_attributes, request_content_type, accept_header')

def handler(data, context):

    global num_inferences
    num_inferences += 1
    
    print(f'\n************ inference #: {num_inferences}')
    if context.request_content_type == 'application/x-image':
        stream = io.BytesIO(data.read())
        img = Image.open(stream).convert('RGB')
        img = img.resize((WIDTH, HEIGHT))
        img_array = image.img_to_array(img)
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