diff --git a/doc/api/services/report_images.rst b/doc/api/services/report_images.rst
index 7f99ef44..a88ed5f5 100644
--- a/doc/api/services/report_images.rst
+++ b/doc/api/services/report_images.rst
@@ -3,4 +3,5 @@ sasctl.services.report_images
 
 .. automodule:: sasctl._services.report_images
     :members:
-    :undoc-members:
\ No newline at end of file
+    :undoc-members:
+    :show-inheritance:
diff --git a/doc/api/services/reports.rst b/doc/api/services/reports.rst
index eeb923ff..02c5def2 100644
--- a/doc/api/services/reports.rst
+++ b/doc/api/services/reports.rst
@@ -4,3 +4,4 @@ sasctl.services.reports
 .. automodule:: sasctl._services.reports
     :members:
     :undoc-members:
+    :show-inheritance:
diff --git a/doc/conf.py b/doc/conf.py
index 3d588087..5fef0d6f 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -109,7 +109,7 @@
     ('py:class','Response'),
     ('py:class','Request'),
     ('py:class','_io.BytesIO'),
-    ('py:class','sasctl.utils.pymas.ds2.Ds2Variable'), # not sure what is wrong 
+    ('py:class','sasctl.utils.pymas.ds2.Ds2Variable'), # not sure what is wrong
     ('py:class','sasctl._services.service.Service') # should the Service class be documented?
 ]
 
diff --git a/src/sasctl/_services/cas_management.py b/src/sasctl/_services/cas_management.py
index 90d4108f..f2fd7592 100644
--- a/src/sasctl/_services/cas_management.py
+++ b/src/sasctl/_services/cas_management.py
@@ -31,7 +31,7 @@ def check_keys(valid_keys: list, input_keys: list, parameters: str):
 
     Raises
     ------
-    ValueError 
+    ValueError
         if input_keys are not valid
     """
     if not all(key in valid_keys for key in input_keys):
diff --git a/src/sasctl/_services/concepts.py b/src/sasctl/_services/concepts.py
index a74d3ef0..3e18d220 100644
--- a/src/sasctl/_services/concepts.py
+++ b/src/sasctl/_services/concepts.py
@@ -56,7 +56,7 @@ def assign_concepts(
         output_postfix : str, optional
             Text to be added to the end of all output table names.
         match_type : str, optional
-            Choose from ``{'all', 'longest', 'best'}``. 
+            Choose from ``{'all', 'longest', 'best'}``.
             Type of matches to return. Defaults to 'all'.
         enable_facts : bool, optional
             Whether to enable facts in the results.  Defaults to False.
diff --git a/src/sasctl/_services/data_sources.py b/src/sasctl/_services/data_sources.py
index 1d8cf6b0..92140952 100644
--- a/src/sasctl/_services/data_sources.py
+++ b/src/sasctl/_services/data_sources.py
@@ -40,7 +40,7 @@ def get_provider(cls, provider, refresh=False):
             A dictionary containing the provider attributes or None.
 
         Notes
-        -------
+        -----
         If `provider` is a complete representation of the provider it will be
         returned unless `refresh` is set.  This prevents unnecessary REST calls
         when data is already available on the client.
diff --git a/src/sasctl/_services/folders.py b/src/sasctl/_services/folders.py
index 03cf7150..adc25a7c 100644
--- a/src/sasctl/_services/folders.py
+++ b/src/sasctl/_services/folders.py
@@ -71,7 +71,7 @@ def get_folder(cls, folder, refresh=False):
         ----------
         folder : str or dict
             May be one of:
-            
+
             - folder name
             - folder ID
             - folder path
diff --git a/src/sasctl/_services/service.py b/src/sasctl/_services/service.py
index 6af9dc9d..b1fd1497 100644
--- a/src/sasctl/_services/service.py
+++ b/src/sasctl/_services/service.py
@@ -229,7 +229,7 @@ def get_item(cls, item, refresh=False):
                 A dictionary containing the {item} attributes or None.
 
             Notes
-            -------
+            -----
             If `item` is a complete representation of the {item} it will be
             returned unless `refresh` is set.  This prevents unnecessary REST
             calls when data is already available on the client.
diff --git a/src/sasctl/core.py b/src/sasctl/core.py
index 3cdc9226..c8fc3f7a 100644
--- a/src/sasctl/core.py
+++ b/src/sasctl/core.py
@@ -1254,7 +1254,7 @@ def _request_token_with_oauth(
         """Request a token from the SAS SASLogon service.
 
         Supports four different flows:
-        
+
         - authenticate with a username & password and receive a token
         - authenticate with a client id & secret and receive a token
         - provide an authorization code and receive a token
@@ -1531,6 +1531,13 @@ def __init__(self, obj, session=None, threads=4):
         # Store the current items to iterate over
         self._obj = obj
 
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self._pool is not None:
+            self._pool.shutdown(wait=False)
+
     def __next__(self):
         if self._pool is None:
             self._pool = concurrent.futures.ThreadPoolExecutor(
@@ -1778,7 +1785,7 @@ class VersionInfo:
         Release cadence for Viya 4.  Should be one of 'stable' or 'LTS'.
     release : str, optional
         Release number for Viya 4.  Two formats are currently possible:
-        
+
         - YYYY.R.U where R is the LTS release number in YYYY and U is the updates since R
         - YYYY.MM where MM is the month of the release.
 
@@ -2028,6 +2035,7 @@ def request(verb, path, session=None, format="auto", **kwargs):
 
     Returns
     -------
+    str, bytes, or requests.Response
 
     """
     session = session or current_session()
@@ -2111,6 +2119,7 @@ def request_link(obj, rel, **kwargs):
 
     Returns
     -------
+    RestObj
 
     """
     link = get_link(obj, rel)
@@ -2359,7 +2368,7 @@ def platform_version():
 
     Returns
     -------
-    str 
+    str
         SAS Viya version number '3.5' or '4.0'
 
     """
diff --git a/src/sasctl/pzmm/import_model.py b/src/sasctl/pzmm/import_model.py
index 56e50fda..cc28f7e5 100644
--- a/src/sasctl/pzmm/import_model.py
+++ b/src/sasctl/pzmm/import_model.py
@@ -306,7 +306,7 @@ def import_model(
         **kwargs
             Other keyword arguments are passed to the following function:
             :meth:`.ScoreCode.write_score_code`
-            
+
 
         Returns
         -------
diff --git a/src/sasctl/pzmm/pickle_model.py b/src/sasctl/pzmm/pickle_model.py
index ac91e98d..7b17f2a4 100644
--- a/src/sasctl/pzmm/pickle_model.py
+++ b/src/sasctl/pzmm/pickle_model.py
@@ -37,7 +37,7 @@ def pickle_trained_model(
         object.
 
         The following files are generated by this function:
-        
+
         * '\*.pickle'
             Binary pickle file containing a trained model.
         * '\*.mojo'
diff --git a/src/sasctl/pzmm/write_json_files.py b/src/sasctl/pzmm/write_json_files.py
index 42d5e6aa..1efc45e5 100644
--- a/src/sasctl/pzmm/write_json_files.py
+++ b/src/sasctl/pzmm/write_json_files.py
@@ -5,7 +5,8 @@
 import ast
 import importlib
 import json
-# import math #not used 
+
+# import math #not used
 import pickle
 import pickletools
 import sys
diff --git a/src/sasctl/pzmm/zip_model.py b/src/sasctl/pzmm/zip_model.py
index 47beee68..0e47345b 100644
--- a/src/sasctl/pzmm/zip_model.py
+++ b/src/sasctl/pzmm/zip_model.py
@@ -71,7 +71,7 @@ def zip_files(
         """
 
         if isinstance(model_files, dict):
-            buffer = io.BytesIO()
+            buffer = BytesIO()
 
             with zipfile.ZipFile(buffer, "w", zipfile.ZIP_DEFLATED, False) as archive:
                 for file_name, data in model_files.items():
diff --git a/src/sasctl/utils/model_info.py b/src/sasctl/utils/model_info.py
index 57c733db..e307d26a 100644
--- a/src/sasctl/utils/model_info.py
+++ b/src/sasctl/utils/model_info.py
@@ -4,6 +4,8 @@
 # Copyright © 2023, SAS Institute Inc., Cary, NC, USA.  All Rights Reserved.
 # SPDX-License-Identifier: Apache-2.0
 
+import math
+import warnings
 from abc import ABC, abstractmethod
 from typing import Any, Callable, Dict, List, Union
 
@@ -15,6 +17,19 @@
 except ImportError:
     torch = None
 
+try:
+    import onnx
+
+    # ONNX serializes models using protobuf, so this should be safe
+    from google.protobuf import json_format
+except ImportError:
+    onnx = None
+
+try:
+    import onnxruntime
+except ImportError:
+    onnxruntime = None
+
 
 def get_model_info(model, X, y=None):
     """Extracts metadata about the model and associated data sets.
@@ -40,9 +55,12 @@ def get_model_info(model, X, y=None):
     """
 
     # Don't need to import sklearn, just check if the class is part of that module.
-    if model.__class__.__module__.startswith("sklearn."):
+    if type(model).__module__.startswith("sklearn."):
         return SklearnModelInfo(model, X, y)
 
+    if type(model).__module__.startswith("onnx"):
+        return _load_onnx_model(model, X, y)
+
     # Most PyTorch models are actually subclasses of torch.nn.Module, so checking module
     # name alone is not sufficient.
     elif torch and isinstance(model, torch.nn.Module):
@@ -51,17 +69,29 @@ def get_model_info(model, X, y=None):
     raise ValueError(f"Unrecognized model type {type(model)} received.")
 
 
+def _load_onnx_model(model, X, y=None):
+    # TODO: unncessary?  static analysis of onnx file sufficient?
+    if onnxruntime:
+        return OnnxModelInfo(model, X, y)
+
+    return OnnxModelInfo(model, X, y)
+
+
 class ModelInfo(ABC):
     """Base class for storing model metadata.
 
     Attributes
     ----------
     algorithm : str
+        Will appear in the "Algorithm" drop-down menu in Model Manager.
+        Example: "Forest", "Neural networks", "Binning", etc.
     analytic_function : str
+        Will appear in the "Function" drop-down menu in Model Manager.
+        Example: "Classification", "Clustering", "Prediction"
     is_binary_classifier : bool
-    is_classifier
-    is_regressor
-    is_clusterer
+    is_classifier : bool
+    is_regressor : bool
+    is_clusterer : bool
     model : object
         The model instance that the information was extracted from.
     model_params : {str: any}
@@ -166,13 +196,169 @@ def y(self) -> pd.DataFrame:
         return
 
 
+class OnnxModelInfo(ModelInfo):
+    def __init__(self, model, X, y=None):
+        if onnx is None:
+            raise RuntimeError(
+                "The onnx package must be installed to work with ONNX models.  Please `pip install onnx`."
+            )
+
+        self._model = model
+        self._X = X
+        self._y = y
+
+        inferred_model = onnx.shape_inference.infer_shapes(model)
+
+        inputs = [self._tensor_to_dataframe(i) for i in inferred_model.graph.input]
+        outputs = [self._tensor_to_dataframe(o) for o in inferred_model.graph.output]
+
+        if len(inputs) > 1:
+            warnings.warn(
+                f"The ONNX model has {len(inputs)} inputs but only the first input will be captured in Model Manager."
+            )
+
+        if len(outputs) > 1:
+            warnings.warn(
+                f"The ONNX model has {len(outputs)} outputs but only the first input will be captured in Model Manager."
+            )
+
+        self._X_df = inputs[0]
+        self._y_df = outputs[0]
+
+        # initializer (static params)
+
+        # for field in model.ListFields():
+        # doc_string
+        # domain
+        # metadata_props
+        # model_author
+        # model_license
+        # model_version
+        # producer_name
+        # producer_version
+        # training_info
+
+        # irVersion
+        # producerName
+        # producerVersion
+        # opsetImport
+
+        # # list of (FieldDescriptor, value)
+        # fields = model.ListFields()
+
+    @staticmethod
+    def _tensor_to_dataframe(tensor):
+        """
+
+        Parameters
+        ----------
+        tensor : onnx.onnx_ml_pb2.ValueInfoProto or dict
+            A protobuf `Message` containing information
+
+        Returns
+        -------
+        pandas.DataFrame
+
+        Examples
+        --------
+        df = _tensor_to_dataframe(model.graph.input[0])
+
+        """
+        if isinstance(tensor, onnx.onnx_ml_pb2.ValueInfoProto):
+            tensor = json_format.MessageToDict(tensor)
+        elif not isinstance(tensor, dict):
+            raise ValueError(f"Unexpected type {type(tensor)}.")
+
+        name = tensor.get("name", "Var")
+        type_ = tensor["type"]
+
+        if not "tensorType" in type_:
+            raise ValueError(f"Received an unexpected ONNX input type: {type_}.")
+
+        dtype = onnx.helper.tensor_dtype_to_np_dtype(type_["tensorType"]["elemType"])
+
+        # Tuple of tensor dimensions e.g. (1, 1, 24)
+        input_dims = tuple(
+            int(d["dimValue"]) for d in type_["tensorType"]["shape"]["dim"]
+        )
+
+        return pd.DataFrame(
+            dtype=dtype, columns=[f"{name}{i+1}" for i in range(math.prod(input_dims))]
+        )
+
+    @property
+    def algorithm(self) -> str:
+        return "neural network"
+
+    @property
+    def description(self) -> str:
+        return self.model.doc_string
+
+    @property
+    def is_binary_classifier(self) -> bool:
+        return len(self.output_column_names) == 2
+
+    @property
+    def is_classifier(self) -> bool:
+        return len(self.output_column_names) > 1
+
+    @property
+    def is_clusterer(self) -> bool:
+        return False
+
+    @property
+    def is_regressor(self) -> bool:
+        return len(self.output_column_names) == 1
+
+    @property
+    def model(self) -> object:
+        return self._model
+
+    @property
+    def model_params(self) -> Dict[str, Any]:
+        return {
+            k: getattr(self.model, k, None)
+            for k in (
+                "ir_version",
+                "model_version",
+                "opset_import",
+                "producer_name",
+                "producer_version",
+            )
+        }
+
+    @property
+    def predict_function(self) -> Callable:
+        return None
+
+    @property
+    def target_column(self):
+        return None
+
+    @property
+    def target_values(self):
+        return None
+
+    @property
+    def threshold(self) -> Union[str, None]:
+        return None
+
+    @property
+    def X(self) -> pd.DataFrame:
+        return self._X_df
+
+    @property
+    def y(self) -> pd.DataFrame:
+        return self._y_df
+
+
 class PyTorchModelInfo(ModelInfo):
     """Stores model information for a PyTorch model instance."""
 
     def __init__(self, model, X, y=None):
         if torch is None:
             raise RuntimeError(
-                "The PyTorch library must be installed to work with PyTorch models.  Please `pip install torch`."
+                "The PyTorch package must be installed to work with PyTorch models.  Please `pip install torch`."
             )
 
         if not isinstance(model, torch.nn.Module):
diff --git a/tests/conftest.py b/tests/conftest.py
index 609b68b5..a6172985 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -639,10 +639,11 @@ def pytest_runtest_makereport(item, call):
             # elif "cas_session" in item.callspec.params:
             #     key = item.callspec.params["cas_session"]
             # else:
-            key = item.callspec.id
+            if hasattr(item, "callspec"):
+                key = item.callspec.id
 
-            # Track that this test was the last test to fail for this Viya version
-            parent._previousfailed[key] = item
+                # Track that this test was the last test to fail for this Viya version
+                parent._previousfailed[key] = item
 
 
 def pytest_runtest_setup(item):
diff --git a/tests/unit/test_model_info_onnx.py b/tests/unit/test_model_info_onnx.py
new file mode 100644
index 00000000..e44f3711
--- /dev/null
+++ b/tests/unit/test_model_info_onnx.py
@@ -0,0 +1,56 @@
+import pandas as pd
+import pytest
+
+onnx = pytest.importorskip("onnx")
+torch = pytest.importorskip("torch")
+
+import sasctl.utils.model_info
+from sasctl.utils import get_model_info
+
+# mnist
+# get input/output shapes
+# get var names if available
+# classification/regression/etc
+#
+
+
+@pytest.fixture
+def mnist_model(tmp_path):
+    class Net(torch.nn.Module):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.fc1 = torch.nn.Linear(14 * 14, 128)
+            self.fc2 = torch.nn.Linear(128, 10)
+
+        def forward(self, x):
+            x = torch.nn.functional.max_pool2d(x, 2)
+            x = x.reshape(-1, 1 * 14 * 14)
+            x = self.fc1(x)
+            x = torch.nn.functional.relu(x)
+            x = self.fc2(x)
+            output = torch.nn.functional.softmax(x, dim=1)
+            return output
+
+    model = Net()
+
+    path = tmp_path / "model.onnx"
+    X = torch.randn(1, 1, 28, 28)
+    torch.onnx.export(model, X, path, input_names=["image"], output_names=["digit"])
+    yield onnx.load(path), X
+
+
+def test_get_info(mnist_model):
+    info = get_model_info(*mnist_model)
+    assert isinstance(info, sasctl.utils.model_info.OnnxModelInfo)
+
+    # Output be classification into 10 digits
+    assert len(info.output_column_names) == 10
+    assert all(c.startswith("digit") for c in info.output_column_names)
+
+    assert isinstance(info.X, pd.DataFrame)
+    assert len(info.X.columns) == 28 * 28
+
+    assert info.is_classifier
+    assert not info.is_binary_classifier
+    assert not info.is_regressor
+    assert not info.is_clusterer
diff --git a/tests/unit/test_model_info_torch.py b/tests/unit/test_model_info_torch.py
index 46bf42ec..6d470736 100644
--- a/tests/unit/test_model_info_torch.py
+++ b/tests/unit/test_model_info_torch.py
@@ -83,4 +83,4 @@ def test_mnist():
     info = get_model_info(model, X)
 
     meta = prepare_model_for_sas(model, "MnistLogistic")
-    assert info.is_classifier
+    # assert info.is_classifier
diff --git a/tests/unit/test_pageiterator.py b/tests/unit/test_pageiterator.py
index dd66cab6..4c49c7d2 100644
--- a/tests/unit/test_pageiterator.py
+++ b/tests/unit/test_pageiterator.py
@@ -11,7 +11,9 @@
 from sasctl.core import PageIterator, RestObj
 
 
-@pytest.fixture(params=[(6, 2, 2), (6, 1, 4), (6, 5, 4), (6, 6, 2), (100, 10, 20)])
+@pytest.fixture(
+    scope="function", params=[(6, 2, 2), (6, 1, 4), (6, 5, 4), (6, 6, 2), (100, 10, 20)]
+)
 def paging(request):
     """Create a RestObj designed to page through a collection of items and the
     collection itself.
@@ -56,35 +58,11 @@ def side_effect(_, link, **kwargs):
     assert req.call_count >= math.ceil(call_count)
 
 
-@pytest.mark.incremental
-class TestPageIterator:
-    def test_no_paging_required(self):
-        """If "next" link not present, current items should be included."""
+def test_paging_required(paging):
+    """Requests should be made to retrieve additional pages."""
+    obj, items, _ = paging
 
-        items = [{"name": "a"}, {"name": "b"}, {"name": "c"}]
-        obj = RestObj(items=items, count=len(items))
-
-        with mock.patch("sasctl.core.request") as request:
-            pager = PageIterator(obj)
-
-            # Returned page of items should preserve item order
-            items = next(pager)
-            for idx, item in enumerate(items):
-                assert item.name == RestObj(items[idx]).name
-
-        # No request should have been made to retrieve additional data.
-        try:
-            request.assert_not_called()
-        except AssertionError as e:
-            raise AssertionError(
-                f"method_calls={request.mock_calls}  call_args={request.call_args_list}"
-            )
-
-    def test_paging_required(self, paging):
-        """Requests should be made to retrieve additional pages."""
-        obj, items, _ = paging
-
-        pager = PageIterator(obj)
+    with PageIterator(obj) as pager:
         init_count = pager._start
 
         for i, page in enumerate(pager):
diff --git a/tox.ini b/tox.ini
index 10f5c911..f776b4a5 100644
--- a/tox.ini
+++ b/tox.ini
@@ -51,6 +51,10 @@ deps =
     tests: urllib3 < 2.0.0
     tests: nbconvert
     tests: nbformat
+#    tests: torch
+#    tests: onnx
+#    tests: h2o
+#    tests: tensorflow
 #    tests: lightgbm ; platform_system != "Darwin"  # lightgmb seems to have build issues on MacOS
 # doc skips install, so explicitly add minimum packages
     doc: sphinx