def predict_onnx(model_uri, data):
print(f"\nmlflow.onnx.load_model\nModel URI: {model_uri}")
import mlflow.onnx
import onnx_utils
model = mlflow.onnx.load_model(model_uri)
print("model.type:", type(model))
data = data.to_numpy()
predictions = onnx_utils.score_model(model, data)
display(predictions)
def main(model_uri, data_path):
print("Options:")
for k, v in locals().items():
print(f" {k}: {v}")
data = pd.read_csv(data_path).to_numpy()
model = mlflow.onnx.load_model(model_uri)
print("model.type:", type(model))
predictions = onnx_utils.score_model(model, data)
print("predictions.type:", type(predictions))
print("predictions.shape:", predictions.shape)
print("predictions:", predictions)
def main(model_uri):
print("model_uri:", model_uri)
data = utils.get_prediction_data()
print("\n**** mlflow.onnx.load_model\n")
model = mlflow.onnx.load_model(model_uri)
print("model.type:", type(model))
predictions = onnx_utils.score_model(model, data)
print("predictions.type:", type(predictions))
print("predictions.shape:", predictions.shape)
print("predictions:", predictions)
utils.predict_pyfunc(model_uri, data)
print("\n**** mlflow.pyfunc.load_model\n")
model = mlflow.pyfunc.load_model(model_uri)
print("model.type:", type(model))
data = pd.DataFrame(data)
predictions = model.predict(data)
print("predictions.type:", type(predictions))
print("predictions.shape:", predictions.shape)
print("predictions:", predictions)
from argparse import ArgumentParser
import mlflow
import mlflow.onnx
import utils
import onnx_utils
print("MLflow Version:", mlflow.__version__)
print("Tracking URI:", mlflow.tracking.get_tracking_uri())
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--model_uri", dest="model_uri", help="model_uri", default="../../data/train/wine-quality-white.csv")
args = parser.parse_args()
print("Arguments:")
for arg in vars(args):
print(f" {arg}: {getattr(args, arg)}")
_,_,data,_ = utils.build_data()
model = mlflow.onnx.load_model(args.model_uri)
print("model.type:", type(model))
predictions = onnx_utils.score_model(model, data)
print("predictions.type:",type(predictions))
print("predictions.shape:",predictions.shape)
print("predictions:",predictions)
# TODO: convert tensor to Pyfunc scoring format
if args.score_as_pyfunc:
print("\n**** pyfunc.load_model")
model = mlflow.pyfunc.load_model(model_uri)
print("model.type:", type(model))
data_pd = pd.DataFrame(
data.numpy()) # TODO: ValueError: Must pass 2-d input
outputs = model.predict(data_pd)
print("outputs.type:", type(outputs))
if args.score_as_onnx:
print("\n**** onnx.load_model - onnx\n")
import mlflow.onnx
import onnx
import onnx_utils
print("ONNX Version:", onnx.__version__)
model_uri = f"runs:/{args.run_id}/onnx-model"
model = mlflow.onnx.load_model(model_uri)
print("model.type:", type(model))
# TODO: convert tensor to ONNX scoring format
# INVALID_ARGUMENT : Got invalid dimensions for input: input.1 for the following indices
# index: 0 Got: 10000 Expected: 64
data = data.numpy()
outputs = onnx_utils.score_model(model, data)
print("outputs.type:", type(outputs))
print("outputs:\n", pd.DataFrame(outputs))
outputs = model(data)
print("outputs.type:", type(outputs))
outputs = outputs.detach().numpy()
outputs = pd.DataFrame(outputs)
print("outputs:\n", outputs)
print("\n==== pyfunc.load_model - pytorch\n")
model = mlflow.pyfunc.load_model(model_uri)
print("model.type:", type(model))
outputs = model.predict(data_pd)
print("outputs.type:", type(outputs))
print("outputs:\n", outputs)
artifacts = client.list_artifacts(args.run_id, "onnx-model")
if len(artifacts) > 0:
model_uri = f"runs:/{args.run_id}/onnx-model"
print("\n==== onnx.load_model - onnx\n")
model = mlflow.onnx.load_model(model_uri)
print("model.type:", type(model))
outputs = onnx_utils.score_model(model, data_pd.to_numpy())
print("outputs.type:", type(outputs))
print("outputs:\n", pd.DataFrame(outputs))
print("\n==== pyfunc.load_model - onnx\n")
print("model_uri:", model_uri)
model = mlflow.pyfunc.load_model(model_uri)
print("model.type:", type(model))
outputs = model.predict(data_pd)
print("outputs.type:", type(outputs))
print("outputs:\n", outputs)
