Python Model.get_model_path方法代码示例

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def init():
    global model

    try:
        model_path = Model.get_model_path('tacosandburritos')
    except:
        model_path = '../../data/model/latest.h5'

    print('Attempting to load model')
    model = tf.keras.models.load_model(model_path)
    model.summary()
    print('Done!')

    print('Initialized model "{}" at {}'.format(model_path, datetime.datetime.now())) 

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def init():
    # load the model from file into a global object
    global model

    # we assume that we have just one model
    # AZUREML_MODEL_DIR is an environment variable created during deployment.
    # It is the path to the model folder
    # (./azureml-models/$MODEL_NAME/$VERSION)
    model_path = Model.get_model_path(
        os.getenv("AZUREML_MODEL_DIR").split('/')[-2])

    model = joblib.load(model_path) 

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def init():
    global model
    global inputs_dc, prediction_dc

    inputs_dc = ModelDataCollector("torchcnn", identifier="inputs")
    prediction_dc = ModelDataCollector("torchcnn", identifier="predictions")

    model = CNN()
    # The line below loads the model from the AML Service
    model_path = Model.get_model_path(model_name="torchcnn")
    # It is also possible to load a local model file
    # model_path = '/temp/torchcnn.pth'
    model.load_state_dict(torch.load(model_path))
    model.eval() 

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def init():
    global model
    print("GPU USAGE: ", tf.test.is_gpu_available())
    model_path = Model.get_model_path(MODEL_FILE_NAME)
    dotenv.load_dotenv()
    print("model_path: ", model_path)
    # deserialize the model file back into a sklearn model
    model = keras.models.load_model(model_path)
    print("Model Loaded") 

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def init():
    global model
    model_path = Model.get_model_path(MODEL_FILE_NAME)
    #dotenv.load_dotenv()
    #model_path = os.getenv('AZUREML_MODEL_DIR')
    print("model_path: ", model_path)
    # deserialize the model file back into a sklearn model
    model = keras.models.load_model(model_path)
    print("Model Loaded") 

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def init():
    global model
    model_path = Model.get_model_path(MODEL_FILE_NAME)
    # deserialize the model file back into a sklearn model
    model = joblib.load(model_path) 

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def init():
    global model
    # retrieve the path to the model file using the model name
    model_path = Model.get_model_path(model_name='movielens_sar_model')
    model = joblib.load(model_path)

# Passes data to the model and returns the prediction 

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def load_prednet_model(name):
    nt = 10
    prednet_path = Model.get_model_path(name)

    print(prednet_path)
    # load json and create model
    with open(os.path.join(prednet_path, 'model.json'), 'r') as json_file:
        model_json = json_file.read()

    trained_model = model_from_json(
        model_json,
        custom_objects={"PredNet": PredNet})

    # load weights into new model
    trained_model.load_weights(os.path.join(prednet_path, "weights.hdf5"))

    # Create testing model (to output predictions)
    layer_config = trained_model.layers[1].get_config()
    layer_config['output_mode'] = 'prediction'

    test_prednet = PredNet(
        weights=trained_model.layers[1].get_weights(),
        **layer_config)
    input_shape = list(trained_model.layers[0].batch_input_shape[1:])
    input_shape[0] = nt
    inputs = Input(shape=tuple(input_shape))
    predictions = test_prednet(inputs)
    prednet_model = Model_keras(inputs=inputs, outputs=predictions)

    return prednet_model 

# 需要导入模块: from azureml.core.model import Model [as 别名]
# 或者: from azureml.core.model.Model import get_model_path [as 别名]
def init():

    global prednet_models, clf_models

    with open('deployment_assets/models.json', 'r') as f:
        models = json.load(f)

    prednet_models = {}
    for name in models['prednet_model_names']:
        prednet_models[name] = load_prednet_model(name)

    clf_models = {}
    for name in models['clf_model_names']:
        model_path = Model.get_model_path(name)
        clf_models[name] = joblib.load(model_path)