Packaging Models

TensorFlow
Export your trained model and zip the model directory. An example is shown below (here is the complete example):
import tensorflow as tf
import shutil
import os
​
...
​
classifier = tf.estimator.Estimator(
    model_fn=my_model, model_dir="iris", params={"hidden_units": [10, 10], "n_classes": 3}
)
​
exporter = tf.estimator.FinalExporter("estimator", serving_input_fn, as_text=False)
train_spec = tf.estimator.TrainSpec(train_input_fn, max_steps=1000)
eval_spec = tf.estimator.EvalSpec(eval_input_fn, exporters=[exporter], name="estimator-eval")
​
tf.estimator.train_and_evaluate(classifier, train_spec, eval_spec)
​
# zip the estimator export dir (the exported path looks like iris/export/estimator/1562353043/)
shutil.make_archive("tensorflow", "zip", os.path.join("iris/export/estimator"))
Upload the zipped file to Amazon S3 using the AWS web console or CLI:
$ aws s3 cp model.zip s3://my-bucket/model.zip
Reference your model in an api:
- kind: api
  name: my-api
  model: s3://my-bucket/model.zip
ONNX
Export your trained model to an ONNX model format. An example of an sklearn model being exported to ONNX is shown below:
from sklearn.linear_model import LogisticRegression
from onnxmltools import convert_sklearn
from onnxconverter_common.data_types import FloatTensorType
​
...
​
logreg_model = LogisticRegression(solver="lbfgs", multi_class="multinomial")
logreg_model.fit(X_train, y_train)
​
# Convert to ONNX model format
onnx_model = convert_sklearn(logreg_model, initial_types=[("input", FloatTensorType([1, 4]))])
with open("sklearn.onnx", "wb") as f:
    f.write(onnx_model.SerializeToString())
Here are complete examples of converting models from some of the common ML frameworks to ONNX:
​PyTorch​
​Sklearn​
​XGBoost​
​Keras​
Upload your trained model in ONNX format to Amazon S3 using the AWS web console or CLI:
$ aws s3 cp model.onnx s3://my-bucket/model.onnx
Reference your model in an api:
- kind: api
  name: my-api
  model: s3://my-bucket/model.onnx
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