Update docs

Author: ospillinger

docs/apis/packaging-models.md

@@ -31,17 +31,18 @@ Reference your `model` in an API:
 Export your trained model to an ONNX model format. An example of an sklearn model being exported to ONNX is shown below:
 
 ```Python
+from sklearn.linear_model import LogisticRegression
+from onnxmltools import convert_sklearn
+from onnxconverter_common.data_types import FloatTensorType
+
 ...
-logreg_model = sklearn.linear_model.LogisticRegression(solver="lbfgs", multi_class="multinomial")
 
-# Train the model
+logreg_model = LogisticRegression(solver="lbfgs", multi_class="multinomial")
 logreg_model.fit(X_train, y_train)
 
 # Convert to ONNX model format
-onnx_model = onnxmltools.convert_sklearn(
-    logreg_model, initial_types=[("input", onnxconverter_common.data_types.FloatTensorType([1, 4]))]
-)
-with open("model.onnx", "wb") as f:
+onnx_model = convert_sklearn(logreg_model, initial_types=[("input", FloatTensorType([1, 4]))])
+with open("sklearn.onnx", "wb") as f:
     f.write(onnx_model.SerializeToString())
 ```
 
@@ -63,6 +64,6 @@ Reference your `model` in an API:
 ```yaml
 - kind: api
   name: my-api
-  model_format: onnx
   model: s3://my-bucket/model.onnx
+  model_format: onnx
 ```

docs/apis/python-packages.md

@@ -1,32 +1,26 @@
 # Python Packages
 
-Cortex allows you to install additional Python packages that can be made available to aggregators, transformers, and estimators.
+Cortex allows you to install additional Python packages to be used in any workload.
 
 ## PyPI Packages
 
-Cortex looks for a `requirements.txt` file in the top level cortex directory (in the same level as `cortex.yaml`). All packages listed in `requirements.txt` will be made available to aggregators, transformers, and estimators.
+Cortex looks for a `requirements.txt` file in the top level cortex directory (in the same level as `cortex.yaml`).
 
 ```text
 ./iris/
 ├── cortex.yaml
-├── requirements.txt
-├── samples.json
-├── implementations/
-└── resources/
+├── ...
+└── requirements.txt
 ```
 
 ## Custom Packages
 
 Cortex looks for your Python packages in the directory `./packages/<package name>`. The package must have a `setup.py` in the root of the package directory with the name set to your package name. Cortex will run `pip3 wheel -w wheelhouse ./packages/<package name>` to construct wheels for the Python Project.
 
-Cortex Directory
-
 ```text
 ./iris/
 ├── cortex.yaml
-├── samples.json
-├── implementations/
-├── resources/
+├── ...
 └── packages
     └── acme-util
         ├── acme-util/

examples/iris/keras/model.py

@@ -1,4 +1,3 @@
-import numpy as np
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 from keras.models import Sequential

examples/iris/sklearn/model.py

@@ -1,4 +1,3 @@
-import numpy as np
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LogisticRegression

examples/iris/xgboost/model.py

@@ -1,4 +1,3 @@
-import numpy as np
 import xgboost as xgb
 from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split