Remove Java dependencies to tensorflow

72 files changed, 2 insertions, 104097 deletions

diff --git a/model-integration/README b/model-integration/README
index 7b29ac16e34..a58d88dc311 100644
--- a/model-integration/README
+++ b/model-integration/README
@@ -4,5 +4,3 @@ This has two purposes
 - Make converters (importers) available to config models while loading them in just a single instance even when
   there are multiple config models.
 - Make third party models directly available to the container.
-
-TensorFlow depends on JNI code which necessitates using a separate bundle to achieve the above.
\ No newline at end of file
diff --git a/model-integration/pom.xml b/model-integration/pom.xml
index 62014ef174a..549cd07594f 100644
--- a/model-integration/pom.xml
+++ b/model-integration/pom.xml
@@ -60,14 +60,6 @@
       <groupId>com.google.protobuf</groupId>
       <artifactId>protobuf-java</artifactId>
     </dependency>
-    <dependency>
-      <groupId>org.tensorflow</groupId>
-      <artifactId>proto</artifactId>
-    </dependency>
-    <dependency>
-      <groupId>org.tensorflow</groupId>
-      <artifactId>tensorflow</artifactId>
-    </dependency>
   </dependencies>
   <build>
     <plugins>
diff --git a/model-integration/src/main/config/model-integration.xml b/model-integration/src/main/config/model-integration.xml
index 8b2fa28b9df..675f1e13456 100644
--- a/model-integration/src/main/config/model-integration.xml
+++ b/model-integration/src/main/config/model-integration.xml
@@ -4,7 +4,7 @@
      model pseudo features in ranking expressions during config model building.
      The importers are order by the cost of their probing method, from cheap to expensive.
      It is provided as separate bundles instead of being included in the config model
-     because some of these (TensorFlow) includes
+     because some of these includes
      JNI code, and so can only exist in one instance in the server. -->
 <component id="ai.vespa.rankingexpression.importer.vespa.VespaImporter" bundle="model-integration" />
 <component id="ai.vespa.rankingexpression.importer.onnx.OnnxImporter" bundle="model-integration" />
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/AttributeConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/AttributeConverter.java
deleted file mode 100644
index f2c6dfd9069..00000000000
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/AttributeConverter.java
+++ /dev/null
@@ -1,87 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import com.yahoo.searchlib.rankingexpression.evaluation.BooleanValue;
-import com.yahoo.searchlib.rankingexpression.evaluation.DoubleValue;
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import com.yahoo.searchlib.rankingexpression.evaluation.Value;
-import ai.vespa.rankingexpression.importer.OrderedTensorType;
-import ai.vespa.rankingexpression.importer.operations.IntermediateOperation;
-import org.tensorflow.framework.AttrValue;
-import org.tensorflow.framework.NodeDef;
-
-import java.util.List;
-import java.util.Map;
-import java.util.Optional;
-import java.util.stream.Collectors;
-
-/**
- * Converts TensorFlow node attributes to Vespa attribute values.
- *
- * @author lesters
- */
-class AttributeConverter implements IntermediateOperation.AttributeMap {
-
-    private final Map<String, AttrValue> attributeMap;
-
-    private AttributeConverter(NodeDef node) {
-        attributeMap = node.getAttrMap();
-    }
-
-    static AttributeConverter convert(NodeDef node) {
-        return new AttributeConverter(node);
-    }
-
-    @Override
-    public Optional<Value> get(String key) {
-        if (attributeMap.containsKey(key)) {
-            AttrValue attrValue = attributeMap.get(key);
-            if (attrValue.getValueCase() == AttrValue.ValueCase.TENSOR) {
-                return Optional.empty();  // requires type
-            }
-            if (attrValue.getValueCase() == AttrValue.ValueCase.B) {
-                return Optional.of(new BooleanValue(attrValue.getB()));
-            }
-            if (attrValue.getValueCase() == AttrValue.ValueCase.I) {
-                return Optional.of(new DoubleValue(attrValue.getI()));
-            }
-            if (attrValue.getValueCase() == AttrValue.ValueCase.F) {
-                return Optional.of(new DoubleValue(attrValue.getF()));
-            }
-        }
-        return Optional.empty();
-    }
-
-    @Override
-    public Optional<Value> get(String key, OrderedTensorType type) {
-        if (attributeMap.containsKey(key)) {
-            AttrValue attrValue = attributeMap.get(key);
-            if (attrValue.getValueCase() == AttrValue.ValueCase.TENSOR) {
-                return Optional.of(new TensorValue(TensorConverter.toVespaTensor(attrValue.getTensor(), type)));
-            }
-        }
-        return get(key);
-    }
-
-    @Override
-    public Optional<List<Value>> getList(String key) {
-        if (attributeMap.containsKey(key)) {
-            AttrValue attrValue = attributeMap.get(key);
-            if (attrValue.getValueCase() == AttrValue.ValueCase.LIST) {
-                AttrValue.ListValue listValue = attrValue.getList();
-                if ( ! listValue.getBList().isEmpty()) {
-                    return Optional.of(listValue.getBList().stream().map(BooleanValue::new).collect(Collectors.toList()));
-                }
-                if ( ! listValue.getIList().isEmpty()) {
-                    return Optional.of(listValue.getIList().stream().map(DoubleValue::new).collect(Collectors.toList()));
-                }
-                if ( ! listValue.getFList().isEmpty()) {
-                    return Optional.of(listValue.getFList().stream().map(DoubleValue::new).collect(Collectors.toList()));
-                }
-                // add the rest
-            }
-        }
-        return Optional.empty();
-    }
-
-}
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/GraphImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/GraphImporter.java
deleted file mode 100644
index 0d2ba0cc714..00000000000
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/GraphImporter.java
+++ /dev/null
@@ -1,254 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.operations.Softmax;
-import ai.vespa.rankingexpression.importer.operations.Sum;
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import ai.vespa.rankingexpression.importer.IntermediateGraph;
-import ai.vespa.rankingexpression.importer.OrderedTensorType;
-import ai.vespa.rankingexpression.importer.operations.Argument;
-import ai.vespa.rankingexpression.importer.operations.ConcatV2;
-import ai.vespa.rankingexpression.importer.operations.Const;
-import ai.vespa.rankingexpression.importer.operations.Constant;
-import ai.vespa.rankingexpression.importer.operations.ExpandDims;
-import ai.vespa.rankingexpression.importer.operations.Identity;
-import ai.vespa.rankingexpression.importer.operations.IntermediateOperation;
-import ai.vespa.rankingexpression.importer.operations.Join;
-import ai.vespa.rankingexpression.importer.operations.Map;
-import ai.vespa.rankingexpression.importer.operations.MatMul;
-import ai.vespa.rankingexpression.importer.operations.Mean;
-import ai.vespa.rankingexpression.importer.operations.Merge;
-import ai.vespa.rankingexpression.importer.operations.NoOp;
-import ai.vespa.rankingexpression.importer.operations.PlaceholderWithDefault;
-import ai.vespa.rankingexpression.importer.operations.Reshape;
-import ai.vespa.rankingexpression.importer.operations.Select;
-import ai.vespa.rankingexpression.importer.operations.Shape;
-import ai.vespa.rankingexpression.importer.operations.Squeeze;
-import ai.vespa.rankingexpression.importer.operations.Switch;
-import com.yahoo.tensor.functions.ScalarFunctions;
-import org.tensorflow.SavedModelBundle;
-import org.tensorflow.Session;
-import org.tensorflow.framework.GraphDef;
-import org.tensorflow.framework.MetaGraphDef;
-import org.tensorflow.framework.NodeDef;
-import org.tensorflow.framework.SignatureDef;
-import org.tensorflow.framework.TensorInfo;
-
-import java.io.IOException;
-import java.util.List;
-import java.util.stream.Collectors;
-
-/**
- * Converts a TensorFlow graph to a Vespa IntermediateGraph which is the basis
- * for generating Vespa ranking expressions.
- *
- * @author lesters
- */
-class GraphImporter {
-
-    private static IntermediateOperation mapOperation(NodeDef node,
-                                                     List<IntermediateOperation> inputs,
-                                                     IntermediateGraph graph) {
-        String nodeName = node.getName();
-        String modelName = graph.name();
-        int nodePort = IntermediateOperation.indexPartOf(nodeName);
-        OrderedTensorType nodeType = TypeConverter.typeFrom(node);
-        AttributeConverter attributes = AttributeConverter.convert(node);
-
-        switch (node.getOp().toLowerCase()) {
-            // array ops
-            case "concatv2":    return new ConcatV2(modelName, nodeName, inputs);
-            case "const":       return new Const(modelName, nodeName, inputs, attributes, nodeType);
-            case "expanddims":  return new ExpandDims(modelName, nodeName, inputs);
-            case "identity":    return new Identity(modelName, nodeName, inputs);
-            case "placeholder": return new Argument(modelName, nodeName, nodeType);
-            case "placeholderwithdefault": return new PlaceholderWithDefault(modelName, nodeName, inputs);
-            case "reshape":     return new Reshape(modelName, nodeName, inputs, attributes);
-            case "shape":       return new Shape(modelName, nodeName, inputs);
-            case "squeeze":     return new Squeeze(modelName, nodeName, inputs, attributes);
-
-            // control flow
-            case "merge":       return new Merge(modelName, nodeName, inputs);
-            case "switch":      return new Switch(modelName, nodeName, inputs, nodePort);
-
-            // math ops
-            case "abs":         return new Map(modelName, nodeName, inputs, ScalarFunctions.abs());
-            case "acos":        return new Map(modelName, nodeName, inputs, ScalarFunctions.acos());
-            case "add":         return new Join(modelName, nodeName, inputs, ScalarFunctions.add());
-            case "add_n":       return new Join(modelName, nodeName, inputs, ScalarFunctions.add());
-            case "asin":        return new Map(modelName, nodeName, inputs, ScalarFunctions.asin());
-            case "atan":        return new Map(modelName, nodeName, inputs, ScalarFunctions.atan());
-            case "ceil":        return new Map(modelName, nodeName, inputs, ScalarFunctions.ceil());
-            case "cos":         return new Map(modelName, nodeName, inputs, ScalarFunctions.cos());
-            case "div":         return new Join(modelName, nodeName, inputs, ScalarFunctions.divide());
-            case "exp":         return new Map(modelName, nodeName, inputs, ScalarFunctions.exp());
-            case "realdiv":     return new Join(modelName, nodeName, inputs, ScalarFunctions.divide());
-            case "floor":       return new Map(modelName, nodeName, inputs, ScalarFunctions.floor());
-            case "log":         return new Map(modelName, nodeName, inputs, ScalarFunctions.log());
-            case "matmul":      return new MatMul(modelName, nodeName, inputs);
-            case "maximum":     return new Join(modelName, nodeName, inputs, ScalarFunctions.max());
-            case "mean":        return new Mean(modelName, nodeName, inputs, attributes);
-            case "reducemean":  return new Mean(modelName, nodeName, inputs, attributes);
-            case "mul":         return new Join(modelName, nodeName, inputs, ScalarFunctions.multiply());
-            case "multiply":    return new Join(modelName, nodeName, inputs, ScalarFunctions.multiply());
-            case "negate":      return new Map(modelName, nodeName, inputs, ScalarFunctions.neg());
-            case "reciprocal":  return new Map(modelName, nodeName, inputs, ScalarFunctions.reciprocal());
-            case "rsqrt":       return new Map(modelName, nodeName, inputs, ScalarFunctions.rsqrt());
-            case "select":      return new Select(modelName, nodeName, inputs);
-            case "where3":      return new Select(modelName, nodeName, inputs);
-            case "sigmoid":     return new Map(modelName, nodeName, inputs, ScalarFunctions.sigmoid());
-            case "sin":         return new Map(modelName, nodeName, inputs, ScalarFunctions.sin());
-            case "squareddifference": return new Join(modelName, nodeName, inputs, ScalarFunctions.squareddifference());
-            case "sub":         return new Join(modelName, nodeName, inputs, ScalarFunctions.subtract());
-            case "subtract":    return new Join(modelName, nodeName, inputs, ScalarFunctions.subtract());
-            case "sum":         return new Sum(modelName, nodeName, inputs, attributes);
-            case "square":      return new Map(modelName, nodeName, inputs, ScalarFunctions.square());
-            case "sqrt":        return new Map(modelName, nodeName, inputs, ScalarFunctions.sqrt());
-            case "tan":         return new Map(modelName, nodeName, inputs, ScalarFunctions.tan());
-            case "tanh":        return new Map(modelName, nodeName, inputs, ScalarFunctions.tanh());
-
-            // nn ops
-            case "biasadd":     return new Join(modelName, nodeName, inputs, ScalarFunctions.add());
-            case "elu":         return new Map(modelName, nodeName, inputs, ScalarFunctions.elu());
-            case "relu":        return new Map(modelName, nodeName, inputs, ScalarFunctions.relu());
-            case "selu":        return new Map(modelName, nodeName, inputs, ScalarFunctions.selu());
-            case "softmax":     return new Softmax(modelName, nodeName, inputs, attributes);
-
-            // state ops
-            case "variable":    return new Constant(modelName, nodeName, nodeType);
-            case "variablev2":  return new Constant(modelName, nodeName, nodeType);
-            case "varhandleop": return new Constant(modelName, nodeName, nodeType);
-            case "readvariableop":return new Identity(modelName, nodeName, inputs);
-
-            // evaluation no-ops
-            case "stopgradient":return new Identity(modelName, nodeName, inputs);
-            case "noop":        return new NoOp(modelName, nodeName, inputs);
-
-        }
-
-        IntermediateOperation op = new NoOp(modelName, node.getName(), inputs);
-        op.warning("Operation '" + node.getOp() + "' is currently not implemented");
-        return op;
-    }
-
-    static IntermediateGraph importGraph(String modelName, SavedModelBundle bundle) throws IOException {
-        MetaGraphDef tfGraph = MetaGraphDef.parseFrom(bundle.metaGraphDef());
-
-        IntermediateGraph intermediateGraph = new IntermediateGraph(modelName);
-        importSignatures(tfGraph, intermediateGraph);
-        importOperations(tfGraph, intermediateGraph, bundle);
-        verifyOutputTypes(tfGraph, intermediateGraph);
-
-        return intermediateGraph;
-    }
-
-    private static void importSignatures(MetaGraphDef tfGraph, IntermediateGraph intermediateGraph) {
-        for (java.util.Map.Entry<String, SignatureDef> signatureEntry : tfGraph.getSignatureDefMap().entrySet()) {
-            String signatureName = signatureEntry.getKey();
-            java.util.Map<String, TensorInfo> inputInfoMap = signatureEntry.getValue().getInputsMap();
-            for (java.util.Map.Entry<String, TensorInfo> input : inputInfoMap.entrySet()) {
-                String inputName = input.getKey();
-                String nodeName = input.getValue().getName();
-                intermediateGraph.inputs(signatureName).put(inputName, IntermediateOperation.namePartOf(nodeName));
-            }
-            java.util.Map<String, TensorInfo> outputInfoMap = signatureEntry.getValue().getOutputsMap();
-            for (java.util.Map.Entry<String, TensorInfo> output : outputInfoMap.entrySet()) {
-                String outputName = output.getKey();
-                String nodeName = output.getValue().getName();
-                intermediateGraph.outputs(signatureName).put(outputName, IntermediateOperation.namePartOf(nodeName));
-            }
-        }
-    }
-
-    private static void importOperations(MetaGraphDef tfGraph,
-                                         IntermediateGraph intermediateGraph,
-                                         SavedModelBundle bundle) {
-        for (String signatureName : intermediateGraph.signatures()) {
-            for (String outputName : intermediateGraph.outputs(signatureName).values()) {
-                importOperation(outputName, tfGraph.getGraphDef(), intermediateGraph, bundle);
-            }
-        }
-    }
-
-    private static IntermediateOperation importOperation(String nodeName,
-                                                         GraphDef tfGraph,
-                                                         IntermediateGraph intermediateGraph,
-                                                         SavedModelBundle bundle) {
-        if (intermediateGraph.alreadyImported(nodeName)) {
-            return intermediateGraph.get(nodeName);
-        }
-        NodeDef node = getTensorFlowNodeFromGraph(IntermediateOperation.namePartOf(nodeName), tfGraph);
-        List<IntermediateOperation> inputs = importOperationInputs(node, tfGraph, intermediateGraph, bundle);
-        IntermediateOperation operation = mapOperation(node, inputs, intermediateGraph);
-        intermediateGraph.put(nodeName, operation);
-
-        List<IntermediateOperation> controlInputs = importControlInputs(node, tfGraph, intermediateGraph, bundle);
-        if (controlInputs.size() > 0) {
-            operation.setControlInputs(controlInputs);
-        }
-
-        if (operation.isConstant()) {
-            operation.setConstantValueFunction(
-                    type -> new TensorValue(TensorConverter.toVespaTensor(readVariable(nodeName, bundle), type)));
-        }
-
-        return operation;
-    }
-
-    private static List<IntermediateOperation> importOperationInputs(NodeDef node,
-                                                                     GraphDef tfGraph,
-                                                                     IntermediateGraph intermediateGraph,
-                                                                     SavedModelBundle bundle) {
-        return node.getInputList().stream()
-                .filter(name -> ! isControlDependency(name))
-                .map(nodeName -> importOperation(nodeName, tfGraph, intermediateGraph, bundle))
-                .collect(Collectors.toList());
-    }
-
-    private static List<IntermediateOperation> importControlInputs(NodeDef node,
-                                                                   GraphDef tfGraph,
-                                                                   IntermediateGraph intermediateGraph,
-                                                                   SavedModelBundle bundle) {
-        return node.getInputList().stream()
-                .filter(nodeName -> isControlDependency(nodeName))
-                .map(nodeName -> importOperation(nodeName, tfGraph, intermediateGraph, bundle))
-                .collect(Collectors.toList());
-    }
-
-    private static boolean isControlDependency(String name) {
-        return name.startsWith("^");
-    }
-
-    private static NodeDef getTensorFlowNodeFromGraph(String name, GraphDef tfGraph) {
-        for (NodeDef node : tfGraph.getNodeList()) {
-            if (node.getName().equals(name)) {
-                return node;
-            }
-        }
-        throw new IllegalArgumentException("Could not find node '" + name + "'");
-    }
-
-    static org.tensorflow.Tensor<?> readVariable(String name, SavedModelBundle bundle) {
-        Session.Runner fetched = bundle.session().runner().fetch(name);
-        List<org.tensorflow.Tensor<?>> importedTensors = fetched.run();
-        if (importedTensors.size() != 1)
-            throw new IllegalStateException("Expected 1 tensor from fetching " + name +
-                                            ", but got " + importedTensors.size());
-        return importedTensors.get(0);
-    }
-
-    private static void verifyOutputTypes(MetaGraphDef tfGraph, IntermediateGraph intermediateGraph) {
-        for (String signatureName : intermediateGraph.signatures()) {
-            for (String outputName : intermediateGraph.outputs(signatureName).values()) {
-                IntermediateOperation operation = intermediateGraph.get(outputName);
-                NodeDef node = getTensorFlowNodeFromGraph(IntermediateOperation.namePartOf(operation.name()), tfGraph.getGraphDef());
-                OrderedTensorType type = operation.type().orElseThrow(
-                        () -> new IllegalArgumentException("Output of '" + outputName + "' has no type."));
-                TypeConverter.verifyType(node, type);
-            }
-        }
-
-    }
-
-}
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorConverter.java
deleted file mode 100644
index 95727acb5b4..00000000000
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorConverter.java
+++ /dev/null
@@ -1,238 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.OrderedTensorType;
-import com.yahoo.tensor.IndexedTensor;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import org.tensorflow.framework.DataType;
-import org.tensorflow.framework.TensorProto;
-import org.tensorflow.framework.TensorShapeProto;
-
-import java.nio.ByteBuffer;
-import java.nio.DoubleBuffer;
-import java.nio.FloatBuffer;
-import java.nio.IntBuffer;
-import java.nio.LongBuffer;
-import java.util.List;
-
-/**
- * Converts TensorFlow tensors into Vespa tensors.
- *
- * @author bratseth
- * @author lesters
- */
-public class TensorConverter {
-
-    public static Tensor toVespaTensor(org.tensorflow.Tensor<?> tfTensor) {
-        return toVespaTensor(tfTensor, "d");
-    }
-
-    private static Tensor toVespaTensor(org.tensorflow.Tensor<?> tfTensor, String dimensionPrefix) {
-        TensorType type = TypeConverter.typeFrom(tfTensor, dimensionPrefix);
-        Values values = readValuesOf(tfTensor);
-        IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder)Tensor.Builder.of(type);
-        for (int i = 0; i < values.size(); i++)
-            builder.cellByDirectIndex(i, values.get(i));
-        return builder.build();
-    }
-
-    static Tensor toVespaTensor(org.tensorflow.Tensor<?> tfTensor, OrderedTensorType type) {
-        Values values = readValuesOf(tfTensor);
-        IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder) Tensor.Builder.of(type.type());
-        for (int i = 0; i < values.size(); i++) {
-            builder.cellByDirectIndex(type.toDirectIndex(i), values.get(i));
-        }
-        return builder.build();
-    }
-
-    static Tensor toVespaTensor(TensorProto tensorProto, OrderedTensorType type) {
-        Values values = readValuesOf(tensorProto);
-        if (values.size() == 0) { // Might be stored as "tensor_content" instead
-            return toVespaTensor(readTensorContentOf(tensorProto), type);
-        }
-        IndexedTensor.BoundBuilder builder = (IndexedTensor.BoundBuilder)Tensor.Builder.of(type.type());
-        for (int i = 0; i < values.size(); ++i)
-            builder.cellByDirectIndex(i, values.get(i));
-        return builder.build();
-    }
-
-    public static Long tensorSize(TensorType type) {
-        Long size = 1L;
-        for (TensorType.Dimension dimension : type.dimensions()) {
-            size *= dimensionSize(dimension);
-        }
-        return size;
-    }
-
-    private static Long dimensionSize(TensorType.Dimension dim) {
-        return dim.size().orElseThrow(() -> new IllegalArgumentException("Dimension has no size"));
-    }
-
-    private static Values readValuesOf(org.tensorflow.Tensor<?> tfTensor) {
-        switch (tfTensor.dataType()) {
-            case DOUBLE: return new DoubleValues(tfTensor);
-            case FLOAT: return new FloatValues(tfTensor);
-            case BOOL: return new BoolValues(tfTensor);
-            case UINT8: return new IntValues(tfTensor);
-            case INT32: return new IntValues(tfTensor);
-            case INT64: return new LongValues(tfTensor);
-            default: throw new IllegalArgumentException("Cannot convert a tensor with elements of type " +
-                                                        tfTensor.dataType() + " to a Vespa tensor");
-        }
-    }
-
-    private static Values readValuesOf(TensorProto tensorProto) {
-        switch (tensorProto.getDtype()) {
-            case DT_BOOL: return new ProtoBoolValues(tensorProto);
-            case DT_HALF: return new ProtoHalfValues(tensorProto);
-            case DT_INT16: case DT_INT32: return new ProtoIntValues(tensorProto);
-            case DT_INT64: return new ProtoInt64Values(tensorProto);
-            case DT_FLOAT: return new ProtoFloatValues(tensorProto);
-            case DT_DOUBLE: return new ProtoDoubleValues(tensorProto);
-            default: throw new IllegalArgumentException("Unsupported data type in attribute tensor import");
-        }
-    }
-
-    private static Class dataTypeToClass(DataType dataType) {
-        switch (dataType) {
-            case DT_BOOL: return Boolean.class;
-            case DT_INT16: return Short.class;
-            case DT_INT32: return Integer.class;
-            case DT_INT64: return Long.class;
-            case DT_HALF: return Float.class;
-            case DT_FLOAT: return Float.class;
-            case DT_DOUBLE: return Double.class;
-            default: throw new IllegalArgumentException("Unsupported data type in attribute tensor import");
-        }
-    }
-
-    private static org.tensorflow.Tensor readTensorContentOf(TensorProto tensorProto) {
-        return org.tensorflow.Tensor.create(dataTypeToClass(tensorProto.getDtype()),
-                                            asSizeArray(tensorProto.getTensorShape().getDimList()),
-                                            tensorProto.getTensorContent().asReadOnlyByteBuffer());
-    }
-
-    private static long[] asSizeArray(List<TensorShapeProto.Dim> dimensions) {
-        long[] sizes = new long[dimensions.size()];
-        for (int i = 0; i < dimensions.size(); i++)
-            sizes[i] = dimensions.get(i).getSize();
-        return sizes;
-    }
-
-    /** Allows reading values from buffers of various numeric types as bytes */
-    private static abstract class Values {
-        abstract double get(int i);
-        abstract int size();
-    }
-
-    private static abstract class TensorFlowValues extends Values {
-        private final int size;
-        TensorFlowValues(int size) {
-            this.size = size;
-        }
-        @Override int size() { return this.size; }
-    }
-
-    private static class DoubleValues extends TensorFlowValues {
-        private final DoubleBuffer values;
-        DoubleValues(org.tensorflow.Tensor<?> tfTensor) {
-            super(tfTensor.numElements());
-            values = DoubleBuffer.allocate(tfTensor.numElements());
-            tfTensor.writeTo(values);
-        }
-        @Override double get(int i) {
-            return values.get(i);
-        }
-    }
-
-    private static class FloatValues extends TensorFlowValues {
-        private final FloatBuffer values;
-        FloatValues(org.tensorflow.Tensor<?> tfTensor) {
-            super(tfTensor.numElements());
-            values = FloatBuffer.allocate(tfTensor.numElements());
-            tfTensor.writeTo(values);
-        }
-        @Override double get(int i) {
-            return values.get(i);
-        }
-    }
-
-    private static class BoolValues extends TensorFlowValues {
-        private final ByteBuffer values;
-        BoolValues(org.tensorflow.Tensor<?> tfTensor) {
-            super(tfTensor.numElements());
-            values = ByteBuffer.allocate(tfTensor.numElements());
-            tfTensor.writeTo(values);
-        }
-        @Override double get(int i) {
-            return values.get(i);
-        }
-    }
-
-    private static class IntValues extends TensorFlowValues {
-        private final IntBuffer values;
-        IntValues(org.tensorflow.Tensor<?> tfTensor) {
-            super(tfTensor.numElements());
-            values = IntBuffer.allocate(tfTensor.numElements());
-            tfTensor.writeTo(values);
-        }
-        @Override double get(int i) {
-            return values.get(i);
-        }
-    }
-
-    private static class LongValues extends TensorFlowValues {
-        private final LongBuffer values;
-        LongValues(org.tensorflow.Tensor<?> tfTensor) {
-            super(tfTensor.numElements());
-            values = LongBuffer.allocate(tfTensor.numElements());
-            tfTensor.writeTo(values);
-        }
-        @Override double get(int i) {
-            return values.get(i);
-        }
-    }
-
-    private static abstract class ProtoValues extends Values {
-        final TensorProto tensorProto;
-        ProtoValues(TensorProto tensorProto) { this.tensorProto = tensorProto; }
-    }
-
-    private static class ProtoBoolValues extends ProtoValues {
-        ProtoBoolValues(TensorProto tensorProto) { super(tensorProto); }
-        @Override double get(int i) { return tensorProto.getBoolVal(i) ? 1.0 : 0.0; }
-        @Override int size() { return tensorProto.getBoolValCount(); }
-    }
-
-    private static class ProtoHalfValues extends ProtoValues {
-        ProtoHalfValues(TensorProto tensorProto) { super(tensorProto); }
-        @Override double get(int i) { return tensorProto.getHalfVal(i); }
-        @Override int size() { return tensorProto.getHalfValCount(); }
-    }
-
-    private static class ProtoIntValues extends ProtoValues {
-        ProtoIntValues(TensorProto tensorProto) { super(tensorProto); }
-        @Override double get(int i) { return tensorProto.getIntVal(i); }
-        @Override int size() { return tensorProto.getIntValCount(); }
-    }
-
-    private static class ProtoInt64Values extends ProtoValues {
-        ProtoInt64Values(TensorProto tensorProto) { super(tensorProto); }
-        @Override double get(int i) { return tensorProto.getInt64Val(i); }
-        @Override int size() { return tensorProto.getInt64ValCount(); }
-    }
-
-    private static class ProtoFloatValues extends ProtoValues {
-        ProtoFloatValues(TensorProto tensorProto) { super(tensorProto); }
-        @Override double get(int i) { return tensorProto.getFloatVal(i); }
-        @Override int size() { return tensorProto.getFloatValCount(); }
-    }
-
-    private static class ProtoDoubleValues extends ProtoValues {
-        ProtoDoubleValues(TensorProto tensorProto) { super(tensorProto); }
-        @Override double get(int i) { return tensorProto.getDoubleVal(i); }
-        @Override int size() { return tensorProto.getDoubleValCount(); }
-    }
-
-}
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java
index 0e307992143..04ddb48e859 100644
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java
+++ b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowImporter.java
@@ -2,14 +2,12 @@
 package ai.vespa.rankingexpression.importer.tensorflow;
 
 import ai.vespa.rankingexpression.importer.ImportedModel;
-import ai.vespa.rankingexpression.importer.IntermediateGraph;
 import ai.vespa.rankingexpression.importer.ModelImporter;
 import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlModel;
 import ai.vespa.rankingexpression.importer.onnx.OnnxImporter;
 import com.yahoo.collections.Pair;
 import com.yahoo.io.IOUtils;
 import com.yahoo.system.ProcessExecuter;
-import org.tensorflow.SavedModelBundle;
 
 import java.io.File;
 import java.io.IOException;
@@ -27,7 +25,7 @@ public class TensorFlowImporter extends ModelImporter {
 
     private static final Logger log = Logger.getLogger(TensorFlowImporter.class.getName());
 
-    private final static int[] onnxOpsetsToTry = {8, 10, 12};
+    private final static int[] onnxOpsetsToTry = {12, 10, 8};
 
     private final OnnxImporter onnxImporter = new OnnxImporter();
 
@@ -56,17 +54,6 @@ public class TensorFlowImporter extends ModelImporter {
         return convertToOnnxAndImport(modelName, modelDir);
     }
 
-    /** Imports a TensorFlow model - DEPRECATED */
-    public ImportedModel importModel(String modelName, String modelDir, SavedModelBundle model) {
-        try {
-            IntermediateGraph graph = GraphImporter.importGraph(modelName, model);
-            return convertIntermediateGraphToModel(graph, modelDir, ImportedMlModel.ModelType.TENSORFLOW);
-        }
-        catch (IOException e) {
-            throw new IllegalArgumentException("Could not import TensorFlow model '" + model + "'", e);
-        }
-    }
-
     private ImportedModel convertToOnnxAndImport(String modelName, String modelDir) {
         Path tempDir = null;
         try {
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java
deleted file mode 100644
index 3102d5431d4..00000000000
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/TypeConverter.java
+++ /dev/null
@@ -1,128 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.OrderedTensorType;
-import com.yahoo.tensor.TensorType;
-import org.tensorflow.framework.AttrValue;
-import org.tensorflow.framework.DataType;
-import org.tensorflow.framework.NodeDef;
-import org.tensorflow.framework.TensorShapeProto;
-
-/**
- * Converts and verifies TensorFlow tensor types into Vespa tensor types.
- *
- * @author lesters
- */
-class TypeConverter {
-
-    static void verifyType(NodeDef node, OrderedTensorType type) {
-        TensorShapeProto shape = tensorFlowShape(node);
-        if (shape != null) {
-            if (shape.getDimCount() != type.rank()) {
-                throw new IllegalArgumentException("TensorFlow shape of '" + node.getName() + "' " +
-                                                   "does not match Vespa shape");
-            }
-            for (int tensorFlowIndex = 0; tensorFlowIndex < type.dimensions().size(); ++tensorFlowIndex) {
-                int vespaIndex = type.dimensionMap(tensorFlowIndex);
-                TensorShapeProto.Dim tensorFlowDimension = shape.getDim(tensorFlowIndex);
-                TensorType.Dimension vespaDimension = type.type().dimensions().get(vespaIndex);
-                if (tensorFlowDimension.getSize() != vespaDimension.size().orElse(-1L)) {
-                    throw new IllegalArgumentException("TensorFlow dimensions of '" + node.getName() + "' " +
-                                                       "does not match Vespa dimensions");
-                }
-            }
-        }
-    }
-
-    static OrderedTensorType typeFrom(NodeDef node) {
-        String dimensionPrefix = "d"; // standard naming convention: d0, d1, ...
-        TensorShapeProto shape = tensorFlowShape(node);
-        OrderedTensorType.Builder builder = new OrderedTensorType.Builder(toValueType(tensorFlowValueType(node)));
-        for (int i = 0; i < shape.getDimCount(); ++ i) {
-            String dimensionName = dimensionPrefix + i;
-            TensorShapeProto.Dim tensorFlowDimension = shape.getDim(i);
-            if (tensorFlowDimension.getSize() >= 0) {
-                builder.add(TensorType.Dimension.indexed(dimensionName, tensorFlowDimension.getSize()));
-            } else {
-                builder.add(TensorType.Dimension.indexed(dimensionName));
-            }
-        }
-        return builder.build();
-    }
-
-    static TensorType typeFrom(org.tensorflow.Tensor<?> tfTensor, String dimensionPrefix) {
-        TensorType.Builder b = new TensorType.Builder(toValueType(tfTensor.dataType()));
-        int dimensionIndex = 0;
-        for (long dimensionSize : tfTensor.shape()) {
-            if (dimensionSize == 0) dimensionSize = 1; // TensorFlow ...
-            b.indexed(dimensionPrefix + (dimensionIndex++), dimensionSize);
-        }
-        return b.build();
-    }
-
-    private static TensorShapeProto tensorFlowShape(NodeDef node) {
-        // Use specific shape if available...
-        AttrValue attrShape = node.getAttrMap().get("shape");
-        if (attrShape != null && attrShape.getValueCase() == AttrValue.ValueCase.SHAPE) {
-            return attrShape.getShape();
-        }
-
-        // ... else use inferred shape
-        AttrValue attrOutputShapes = node.getAttrMap().get("_output_shapes");
-        if (attrOutputShapes == null)
-            throw new IllegalArgumentException("_output_shapes attribute of '" + node.getName() + "' " +
-                                               "does not exist");
-        if (attrOutputShapes.getValueCase() != AttrValue.ValueCase.LIST)
-            throw new IllegalArgumentException("_output_shapes attribute of '" + node.getName() + "' " +
-                                               "is not of expected type");
-
-        return attrOutputShapes.getList().getShape(0); // support multiple outputs?
-    }
-
-    private static DataType tensorFlowValueType(NodeDef node) {
-        AttrValue attrValueList = node.getAttrMap().get("dtypes");
-        if (attrValueList == null)
-            return DataType.DT_DOUBLE; // default. This will usually (always?) be used. TODO: How can we do better?
-        if (attrValueList.getValueCase() != AttrValue.ValueCase.LIST)
-            return DataType.DT_DOUBLE; // default
-
-        return attrValueList.getList().getType(0); // support multiple outputs?
-    }
-
-    private static TensorType.Value toValueType(DataType dataType) {
-        switch (dataType) {
-            case DT_FLOAT: return TensorType.Value.FLOAT;
-            case DT_DOUBLE: return TensorType.Value.DOUBLE;
-            // Imperfect conversion, for now:
-            case DT_BOOL: return TensorType.Value.FLOAT;
-            case DT_BFLOAT16: return TensorType.Value.FLOAT;
-            case DT_HALF: return TensorType.Value.FLOAT;
-            case DT_INT8: return TensorType.Value.FLOAT;
-            case DT_INT16: return TensorType.Value.DOUBLE;
-            case DT_INT32: return TensorType.Value.DOUBLE;
-            case DT_INT64: return TensorType.Value.DOUBLE;
-            case DT_UINT8: return TensorType.Value.FLOAT;
-            case DT_UINT16: return TensorType.Value.DOUBLE;
-            case DT_UINT32: return TensorType.Value.DOUBLE;
-            case DT_UINT64: return TensorType.Value.DOUBLE;
-            default: throw new IllegalArgumentException("A TensorFlow tensor with data type " + dataType +
-                                                        " cannot be converted to a Vespa tensor type");
-        }
-    }
-
-    private static TensorType.Value toValueType(org.tensorflow.DataType dataType) {
-        switch (dataType) {
-            case FLOAT: return TensorType.Value.FLOAT;
-            case DOUBLE: return TensorType.Value.DOUBLE;
-            // Imperfect conversion, for now:
-            case BOOL: return TensorType.Value.FLOAT;
-            case INT32: return TensorType.Value.DOUBLE;
-            case UINT8: return TensorType.Value.FLOAT;
-            case INT64: return TensorType.Value.DOUBLE;
-            default: throw new IllegalArgumentException("A TensorFlow tensor with data type " + dataType +
-                                                        " cannot be converted to a Vespa tensor type");
-        }
-    }
-
-}
diff --git a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverter.java b/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverter.java
deleted file mode 100644
index 85ae5238bae..00000000000
--- a/model-integration/src/main/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverter.java
+++ /dev/null
@@ -1,59 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.OrderedTensorType;
-import com.yahoo.tensor.serialization.JsonFormat;
-import com.yahoo.yolean.Exceptions;
-import org.tensorflow.SavedModelBundle;
-
-import java.nio.charset.StandardCharsets;
-
-/**
- * Converts TensorFlow Variables to the Vespa document format.
- * Intended to be used from the command line to convert trained tensors to document form.
- *
- * @author bratseth
- */
-class VariableConverter {
-
-    /**
-     * Reads the tensor with the given TensorFlow name at the given model location,
-     * and encodes it as UTF-8 Vespa document tensor JSON having the given ordered tensor type.
-     * Note that order of dimensions in the tensor type does matter as the TensorFlow tensor
-     * tensor dimensions are implicitly ordered.
-     */
-    static byte[] importVariable(String modelDir, String tensorFlowVariableName, String orderedTypeSpec) {
-        try (SavedModelBundle bundle = SavedModelBundle.load(modelDir, "serve")) {
-            return JsonFormat.encode(TensorConverter.toVespaTensor(GraphImporter.readVariable(tensorFlowVariableName,
-                                                                                                   bundle),
-                                                                   OrderedTensorType.fromSpec(orderedTypeSpec)));
-        }
-        catch (IllegalArgumentException e) {
-            throw new IllegalArgumentException("Could not import TensorFlow model from directory '" + modelDir + "'", e);
-        }
-    }
-
-    public static void main(String[] args) {
-        if ( args.length != 3) {
-            System.out.println("Converts a TensorFlow variable into Vespa tensor document field value JSON:");
-            System.out.println("A JSON map containing a 'cells' array, see");
-            System.out.println("https://docs.vespa.ai/en/reference/document-json-format.html#tensor");
-            System.out.println("");
-            System.out.println("Arguments: modelDirectory tensorFlowVariableName orderedTypeSpec");
-            System.out.println(" - modelDirectory: The directory of the TensorFlow SavedModel");
-            System.out.println(" - tensorFlowVariableName: The name of the TensorFlow variable to convert");
-            System.out.println(" - orderedTypeSpec: The tensor type, e.g tensor(b[],a[10]), where dimensions are ");
-            System.out.println("                    ordered as given in the deployment log message starting by ");
-            System.out.println("                    'Importing TensorFlow variable'");
-            return;
-        }
-
-        try {
-            System.out.println(new String(importVariable(args[0], args[1], args[2]), StandardCharsets.UTF_8));
-        }
-        catch (Exception e) {
-            System.err.println("Import failed: " + Exceptions.toMessageString(e));
-        }
-    }
-
-}
diff --git a/model-integration/src/main/java/org/tensorflow/package-info.java b/model-integration/src/main/java/org/tensorflow/package-info.java
deleted file mode 100644
index 237cb36a77a..00000000000
--- a/model-integration/src/main/java/org/tensorflow/package-info.java
+++ /dev/null
@@ -1,7 +0,0 @@
-// Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-@ExportPackage
-@PublicApi
-package org.tensorflow;
-
-import com.yahoo.api.annotations.PublicApi;
-import com.yahoo.osgi.annotation.ExportPackage;
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/TestableModel.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/TestableModel.java
index c5355ebdf6f..fc9785f8cc0 100644
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/TestableModel.java
+++ b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/TestableModel.java
@@ -2,7 +2,6 @@
 package ai.vespa.rankingexpression.importer.onnx;
 
 import ai.vespa.rankingexpression.importer.ImportedModel;
-import ai.vespa.rankingexpression.importer.tensorflow.TensorConverter;
 import com.yahoo.searchlib.rankingexpression.RankingExpression;
 import com.yahoo.searchlib.rankingexpression.evaluation.Context;
 import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex;
@@ -14,16 +13,9 @@ import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
 import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
 import com.yahoo.tensor.Tensor;
 import com.yahoo.tensor.TensorType;
-import org.tensorflow.SavedModelBundle;
-import org.tensorflow.Session;
 
-import java.nio.DoubleBuffer;
-import java.nio.FloatBuffer;
-import java.util.List;
 import java.util.Map;
 
-import static org.junit.Assert.assertEquals;
-
 public class TestableModel {
 
     Tensor evaluateVespa(ImportedModel model, String operationName, Map<String, TensorType> inputs) {
@@ -39,38 +31,6 @@ public class TestableModel {
         return expression.evaluate(context).asTensor();
     }
 
-    Tensor evaluateTF(SavedModelBundle tensorFlowModel, String operationName, Map<String, TensorType> inputs) {
-        Session.Runner runner = tensorFlowModel.session().runner();
-        for (Map.Entry<String, TensorType> entry : inputs.entrySet()) {
-            try {
-                runner.feed(entry.getKey(), tensorFlowFloatInputArgument(1, entry.getValue().dimensions().get(1).size().get().intValue()));
-            } catch (Exception e) {
-                runner.feed(entry.getKey(), tensorFlowDoubleInputArgument(1, entry.getValue().dimensions().get(1).size().get().intValue()));
-            }
-        }
-        List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run();
-        assertEquals(1, results.size());
-        return TensorConverter.toVespaTensor(results.get(0));
-    }
-
-    private org.tensorflow.Tensor<?> tensorFlowFloatInputArgument(int d0Size, int d1Size) {
-        FloatBuffer fb1 = FloatBuffer.allocate(d0Size * d1Size);
-        int i = 0;
-        for (int d0 = 0; d0 < d0Size; d0++)
-            for (int d1 = 0; d1 < d1Size; ++d1)
-                fb1.put(i++, (float)(d1 * 1.0 / d1Size));
-        return org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb1);
-    }
-
-    private org.tensorflow.Tensor<?> tensorFlowDoubleInputArgument(int d0Size, int d1Size) {
-        DoubleBuffer fb1 = DoubleBuffer.allocate(d0Size * d1Size);
-        int i = 0;
-        for (int d0 = 0; d0 < d0Size; d0++)
-            for (int d1 = 0; d1 < d1Size; ++d1)
-                fb1.put(i++, (float)(d1 * 1.0 / d1Size));
-        return org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb1);
-    }
-
     private Tensor vespaInputArgument(int d0Size, int d1Size) {
         Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", d0Size).indexed("d1", d1Size).build());
         for (int d0 = 0; d0 < d0Size; d0++)
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/Tf2OnnxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/Tf2OnnxImportTestCase.java
deleted file mode 100644
index c7245fe53e8..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/onnx/Tf2OnnxImportTestCase.java
+++ /dev/null
@@ -1,153 +0,0 @@
-// Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.onnx;
-
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import ai.vespa.rankingexpression.importer.tensorflow.TensorFlowImporter;
-import com.fasterxml.jackson.core.JsonProcessingException;
-import com.fasterxml.jackson.databind.ObjectMapper;
-import com.fasterxml.jackson.databind.node.ArrayNode;
-import com.fasterxml.jackson.databind.node.ObjectNode;
-import com.yahoo.collections.Pair;
-import com.yahoo.system.ProcessExecuter;
-import com.yahoo.tensor.Tensor;
-import org.junit.Ignore;
-import org.junit.Rule;
-import org.junit.Test;
-import org.junit.rules.TemporaryFolder;
-import org.tensorflow.SavedModelBundle;
-
-import java.io.IOException;
-import java.nio.file.Paths;
-import java.util.HashMap;
-import java.util.Map;
-
-/**
- * Tries to convert a set of TensorFlow models to ONNX using the tf2onnx tool,
- * and asserts that the result when evaluated on TensorFlow, the imported
- * TensorFlow model and the imported ONNX model are equal.
- *
- * Requires the tf2onnx tool to be installed so the test itself should be ignored.
- *
- * @author lesters
- */
-public class Tf2OnnxImportTestCase extends TestableModel {
-
-    @Rule
-    public TemporaryFolder testFolder = new TemporaryFolder();
-
-    @Test
-    @Ignore
-    public void testOnnxConversionAndImport() {
-        Report report = new Report();
-        for (int i = 1; i < 12; ++i) {
-            testModelsWithOpset(report, i);
-        }
-        System.out.println(report);
-    }
-
-    private void testModelsWithOpset(Report report, int opset) {
-        String [] models = {
-            "tensorflow/mnist/saved/",
-            "tensorflow/mnist_softmax/saved/",
-            "tensorflow/9662/",
-            "tensorflow/regression/test1/",
-            "tensorflow/regression/test2/",
-            "tensorflow/softmax/saved/",
-            "tensorflow/blog/saved/",
-            "tensorflow/batch_norm/saved/",
-            "tensorflow/dropout/saved/",
-            "tensorflow/external/Model_A/optimized_v2/",
-            "tensorflow/external/Model_B/factorization_machine_v1/export/optimized/",
-            "tensorflow/external/Model_B/factorization_machine_v1/export/standard/",
-            "tensorflow/external/Model_C/factorization_machine_v1/export/optimized/",
-            "tensorflow/external/Model_C/factorization_machine_v1/export/standard/",
-            "tensorflow/external/modelv1/",
-            "tensorflow/external/modelv2/"
-        };
-        for (String model : models) {
-            try {
-                testModelWithOpset(report, opset, "src/test/models/" + model);
-            } catch (Exception e) {
-                report.add(model, opset, false, "Exception: " + e.getMessage());
-            }
-        }
-    }
-
-    private boolean testModelWithOpset(Report report, int opset, String tfModel) throws IOException {
-        String onnxModel = Paths.get(testFolder.getRoot().getAbsolutePath(), "converted.onnx").toString();
-
-        var res = tf2onnxConvert(tfModel, onnxModel, opset);
-        if (res.getFirst() != 0) {
-            return reportAndFail(report, opset, tfModel, "tf2onnx conversion failed: " + res.getSecond());
-        }
-
-        SavedModelBundle tensorFlowModel = SavedModelBundle.load(tfModel, "serve");
-        ImportedModel model = new TensorFlowImporter().importModel("test", tfModel, tensorFlowModel);
-        ImportedModel onnxImportedModel = new OnnxImporter().importModel("test", onnxModel);
-
-        if (model.signature("serving_default").skippedOutputs().size() > 0) {
-            return reportAndFail(report, opset, tfModel, "Failed to import model from TensorFlow due to skipped outputs");
-        }
-        if (onnxImportedModel.signature("default").skippedOutputs().size() > 0) {
-            return reportAndFail(report, opset, tfModel, "Failed to import model from ONNX due to skipped outputs");
-        }
-
-        ImportedModel.Signature sig = model.signatures().values().iterator().next();
-        String output = sig.outputs().values().iterator().next();
-        String onnxOutput = onnxImportedModel.signatures().values().iterator().next().outputs().values().iterator().next();
-
-        Tensor tfResult = evaluateTF(tensorFlowModel, output, model.inputs());
-        Tensor vespaResult = evaluateVespa(model, output, model.inputs());
-        Tensor onnxResult = evaluateVespa(onnxImportedModel, onnxOutput, model.inputs());
-
-        if ( ! tfResult.equals(vespaResult) ) {
-            return reportAndFail(report, opset, tfModel, "Diff between tf and imported tf evaluation:\n\t" + tfResult + "\n\t" + vespaResult);
-        }
-        if ( ! vespaResult.equals(onnxResult) ) {
-            return reportAndFail(report, opset, tfModel, "Diff between imported tf eval and onnx eval:\n\t" + vespaResult + "\n\t" + onnxResult);
-        }
-
-        return reportAndSucceed(report, opset, tfModel, "Ok");
-    }
-
-    private Pair<Integer, String> tf2onnxConvert(String savedModel, String output, int opset) throws IOException {
-        ProcessExecuter executer = new ProcessExecuter();
-        String job = "python3 -m tf2onnx.convert --saved-model " + savedModel + " --output " + output + " --opset " + opset;
-        return executer.exec(job);
-    }
-
-    private static class Report {
-        final ObjectMapper mapper = new ObjectMapper();
-        final Map<String, ArrayNode> results = new HashMap<>();
-
-        public boolean add(String model, int opset, boolean ok, String desc) {
-            ObjectNode obj = mapper.createObjectNode().
-                    put("opset", opset).
-                    put("ok", ok).
-                    put("desc", desc);
-            results.computeIfAbsent(model, r -> mapper.createArrayNode()).add(obj);
-            return ok;
-        }
-
-        public String toString() {
-            ArrayNode array = mapper.createArrayNode();
-            results.forEach((key, value) -> array.add(mapper.createObjectNode().
-                    put("model", key).
-                    set("tests", value)));
-            try {
-                return mapper.writerWithDefaultPrettyPrinter().writeValueAsString(array);
-            } catch (JsonProcessingException e) {
-                return e.getMessage();
-            }
-        }
-    }
-
-    private static boolean reportAndFail(Report report, int opset, String model, String desc) {
-        return report.add(model, opset, false, desc);
-    }
-
-    private static boolean reportAndSucceed(Report report, int opset, String model, String desc) {
-        return report.add(model, opset, true, desc);
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BatchNormImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BatchNormImportTestCase.java
deleted file mode 100644
index e75c7fd4da3..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BatchNormImportTestCase.java
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlFunction;
-import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import org.junit.Test;
-
-import java.util.List;
-
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-
-/**
- * @author lesters
- */
-public class BatchNormImportTestCase {
-
-    @Test
-    public void testBatchNormImport() {
-        TestableTensorFlowModel model = new TestableTensorFlowModel("test",
-                                                                    "src/test/models/tensorflow/batch_norm/saved");
-        ImportedModel.Signature signature = model.get().signature("serving_default");
-
-        assertEquals("Should have no skipped outputs",
-                     0, model.get().signature("serving_default").skippedOutputs().size());
-
-
-        // Test signature
-        ImportedMlFunction function = signature.outputFunction("y", "y");
-        assertNotNull(function);
-        assertEquals("{X=tensor(d0[],d1[784])}", function.argumentTypes().toString());
-
-        // Test outputs
-        List<ImportedMlFunction> outputs = model.get().outputExpressions();
-        assertEquals(1, outputs.size());
-        assertEquals("serving_default.y", outputs.get(0).name());
-        assertEquals("{X=tensor(d0[],d1[784])}", function.argumentTypes().toString());
-        model.assertEqualResult("X", "dnn/batch_normalization_3/batchnorm/add_1");
-    }
-
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java
deleted file mode 100644
index 37104ab43db..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationBenchmark.java
+++ /dev/null
@@ -1,116 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import com.yahoo.searchlib.rankingexpression.RankingExpression;
-import com.yahoo.searchlib.rankingexpression.evaluation.Context;
-import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex;
-import com.yahoo.searchlib.rankingexpression.evaluation.ExpressionOptimizer;
-import com.yahoo.searchlib.rankingexpression.evaluation.OptimizationReport;
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import com.yahoo.searchlib.rankingexpression.parser.ParseException;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import org.tensorflow.SavedModelBundle;
-import org.tensorflow.Session;
-
-import java.nio.FloatBuffer;
-import java.util.List;
-
-/**
- * Microbenchmark of imported ML model evaluation.
- *
- * @author lesters
- */
-public class BlogEvaluationBenchmark {
-
-    static final String modelDir = "src/test/models/tensorflow/blog/saved";
-
-    public static void main(String[] args) throws ParseException {
-        SavedModelBundle tensorFlowModel = SavedModelBundle.load(modelDir, "serve");
-        ImportedModel model = new TensorFlowImporter().importModel("blog", modelDir, tensorFlowModel);
-
-        Context context = TestableTensorFlowModel.contextFrom(model);
-        Tensor u = generateInputTensor();
-        Tensor d = generateInputTensor();
-        context.put("input_u", new TensorValue(u));
-        context.put("input_d", new TensorValue(d));
-
-        // Parse the ranking expression from imported string to force primitive tensor functions.
-        RankingExpression expression = new RankingExpression(model.expressions().get("y").getRoot().toString());
-        benchmarkJava(expression, context, 20, 200);
-
-        System.out.println("*** Optimizing expression ***");
-        ExpressionOptimizer optimizer = new ExpressionOptimizer();
-        OptimizationReport report = optimizer.optimize(expression, (ContextIndex)context);
-        System.out.println(report.toString());
-
-        benchmarkJava(expression, context, 2000, 20000);
-        benchmarkTensorFlow(tensorFlowModel, 2000, 20000);
-    }
-
-    private static void benchmarkJava(RankingExpression expression, Context context, int warmup, int iterations) {
-        System.out.println("*** Java evaluation - warmup ***");
-        evaluate(expression, context, warmup);
-        System.gc();
-        System.out.println("*** Java evaluation - " + iterations + " iterations ***");
-        double startTime = System.nanoTime();
-        evaluate(expression, context, iterations);
-        double endTime = System.nanoTime();
-        System.out.println("Model evaluation time is " + ((endTime-startTime) / (1000*1000)) + " ms");
-        System.out.println("Average model evaluation time is " + ((endTime-startTime) / (1000*1000)) / iterations + " ms");
-    }
-
-    private static double evaluate(RankingExpression expression, Context context, int iterations) {
-        double result = 0;
-        for (int i = 0 ; i < iterations; i++) {
-            result = expression.evaluate(context).asTensor().sum().asDouble();
-        }
-        return result;
-    }
-
-    private static Tensor generateInputTensor() {
-        Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", 128).build());
-        for (int d0 = 0; d0 < 1; d0++)
-            for (int d1 = 0; d1 < 128; d1++)
-                b.cell(d1 * 1.0 / 128, d0, d1);
-        return b.build();
-    }
-
-    private static void benchmarkTensorFlow(SavedModelBundle tensorFlowModel, int warmup, int iterations) {
-        org.tensorflow.Tensor<?> u = generateInputTensorFlow();
-        org.tensorflow.Tensor<?> d = generateInputTensorFlow();
-
-        System.out.println("*** TensorFlow evaluation - warmup ***");
-        evaluateTensorflow(tensorFlowModel, u, d, warmup);
-
-        System.gc();
-        System.out.println("*** TensorFlow evaluation - " + iterations + " iterations ***");
-        double startTime = System.nanoTime();
-        evaluateTensorflow(tensorFlowModel, u, d, iterations);
-        double endTime = System.nanoTime();
-        System.out.println("Model evaluation time is " + ((endTime-startTime) / (1000*1000) + " ms"));
-        System.out.println("Average model evaluation time is " + ((endTime-startTime) / (1000*1000)) / iterations + " ms");
-    }
-
-    private static double evaluateTensorflow(SavedModelBundle tensorFlowModel, org.tensorflow.Tensor<?> u, org.tensorflow.Tensor<?> d, int iterations) {
-        double result = 0;
-        for (int i = 0 ; i < iterations; i++) {
-            Session.Runner runner = tensorFlowModel.session().runner();
-            runner.feed("input_u", u);
-            runner.feed("input_d", d);
-            List<org.tensorflow.Tensor<?>> results = runner.fetch("y").run();
-            result = TensorConverter.toVespaTensor(results.get(0)).sum().asDouble();
-        }
-        return result;
-    }
-
-    private static org.tensorflow.Tensor<?> generateInputTensorFlow() {
-        FloatBuffer fb = FloatBuffer.allocate(1 * 128);
-        for (int i = 0; i < 128; ++i) {
-            fb.put(i, (float)(i * 1.0 / 128));
-        }
-        return org.tensorflow.Tensor.create(new long[]{ 1, 128 }, fb);
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationTestCase.java
deleted file mode 100644
index 3a35efed2c0..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/BlogEvaluationTestCase.java
+++ /dev/null
@@ -1,28 +0,0 @@
-// Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import org.junit.Test;
-import org.tensorflow.SavedModelBundle;
-
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-
-/**
- * @author bratseth
- */
-public class BlogEvaluationTestCase {
-
-    static final String modelDir = "src/test/models/tensorflow/blog/saved";
-
-    @Test
-    public void testImport() {
-        SavedModelBundle tensorFlowModel = SavedModelBundle.load(modelDir, "serve");
-        ImportedModel model = new TensorFlowImporter().importModel("blog", modelDir, tensorFlowModel);
-
-        ImportedModel.Signature y = model.signature("serving_default.y");
-        assertNotNull(y);
-        assertEquals(0, y.inputs().size());
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/DropoutImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/DropoutImportTestCase.java
deleted file mode 100644
index 25f8acf1f6d..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/DropoutImportTestCase.java
+++ /dev/null
@@ -1,43 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlFunction;
-import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import com.yahoo.tensor.TensorType;
-import org.junit.Assert;
-import org.junit.Test;
-
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-import static org.junit.Assert.assertTrue;
-
-/**
- * @author lesters
- */
-public class DropoutImportTestCase {
-
-    @Test
-    public void testDropoutImport() {
-        TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/models/tensorflow/dropout/saved");
-
-        // Check required functions
-        Assert.assertEquals(1, model.get().inputs().size());
-        assertTrue(model.get().inputs().containsKey("X"));
-        Assert.assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(),
-                            model.get().inputs().get("X"));
-
-        ImportedModel.Signature signature = model.get().signature("serving_default");
-
-        Assert.assertEquals("Should have no skipped outputs",
-                            0, model.get().signature("serving_default").skippedOutputs().size());
-
-        ImportedMlFunction function = signature.outputFunction("y", "y");
-        assertNotNull(function);
-        assertEquals("join(join(reduce(constant(test_outputs_Const), sum, d1), imported_ml_function_test_outputs_BiasAdd, f(a,b)(a * b)), imported_ml_function_test_outputs_BiasAdd, f(a,b)(max(a,b)))",
-                     function.expression());
-        model.assertEqualResult("X", "outputs/Maximum");
-        assertEquals("{X=tensor(d0[],d1[784])}", function.argumentTypes().toString());
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Issue9662TestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Issue9662TestCase.java
deleted file mode 100644
index 25b36535b50..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Issue9662TestCase.java
+++ /dev/null
@@ -1,28 +0,0 @@
-// Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlFunction;
-import org.junit.Assert;
-import org.junit.Test;
-
-import static org.junit.Assert.assertNotNull;
-
-/**
- * @author bratseth
- */
-public class Issue9662TestCase {
-
-    @Test
-    public void testImporting() {
-        TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/models/tensorflow/9662");
-        ImportedModel.Signature signature = model.get().signature("serving_default");
-        Assert.assertEquals("Should have no skipped outputs",
-                            0, model.get().signature("serving_default").skippedOutputs().size());
-
-        ImportedMlFunction output = signature.outputFunction("output", "output");
-        assertNotNull(output);
-        model.assertEqualResultSum("input_embedding_user_guid", "dense_out/Sigmoid", 0.0001);
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/MnistImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/MnistImportTestCase.java
deleted file mode 100644
index 56f31f6f788..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/MnistImportTestCase.java
+++ /dev/null
@@ -1,30 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlFunction;
-import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import org.junit.Assert;
-import org.junit.Test;
-
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-
-/**
- * @author bratseth
- */
-public class MnistImportTestCase {
-
-    @Test
-    public void testMnistImport() {
-        TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/models/tensorflow/mnist/saved");
-        ImportedModel.Signature signature = model.get().signature("serving_default");
-        Assert.assertEquals("Should have no skipped outputs",
-                            0, model.get().signature("serving_default").skippedOutputs().size());
-
-        ImportedMlFunction output = signature.outputFunction("y", "y");
-        assertNotNull(output);
-        model.assertEqualResultSum("input", "dnn/outputs/add", 0.0001);
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/RegressionTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/RegressionTestCase.java
deleted file mode 100644
index 46ced6f42ad..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/RegressionTestCase.java
+++ /dev/null
@@ -1,79 +0,0 @@
-// Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import org.junit.Assert;
-import org.junit.Test;
-
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-
-/**
- * @author bratseth
- */
-public class RegressionTestCase {
-
-    @Test
-    public void testRegressionModel1() {
-        TestableTensorFlowModel model = new TestableTensorFlowModel("test",
-                                                                    "src/test/models/tensorflow/regression/test1",
-                                                                    14,
-                                                                    1536);
-
-        // Check constants
-        Assert.assertEquals(2, model.get().largeConstants().size());
-
-        Tensor constant0 = Tensor.from(model.get().largeConstants().get("test_Variable_read"));
-        assertNotNull(constant0);
-        assertEquals(new TensorType.Builder().indexed("d2", 1536).indexed("d1", 14).build(),
-                     constant0.type());
-        assertEquals(21504, constant0.size());
-
-        Tensor constant1 = Tensor.from(model.get().largeConstants().get("test_Variable_1_read"));
-        assertNotNull(constant1);
-        assertEquals(new TensorType.Builder().indexed("d1", 14).build(), constant1.type());
-        assertEquals(14, constant1.size());
-
-        // Check (provided) functions
-        Assert.assertEquals(0, model.get().functions().size());
-
-        // Check signatures
-        Assert.assertEquals(1, model.get().signatures().size());
-        ImportedModel.Signature signature = model.get().signatures().get("serving_default");
-        assertNotNull(signature);
-
-        // Test execution
-        model.assertEqualResult("input", "MatMul");
-        model.assertEqualResult("input", "logits");
-        model.assertEqualResult("input", "Sigmoid");
-        model.assertEqualResult("input", "add");
-    }
-
-    @Test
-    public void testRegressionModel2() {
-        TestableTensorFlowModel model = new TestableTensorFlowModel("test",
-                                                                    "src/test/models/tensorflow/regression/test2",
-                                                                    14,
-                                                                    1536,
-                                                                    false);
-
-        // Check constants
-        Assert.assertEquals(2, model.get().largeConstants().size());
-
-        // Check (provided) functions
-        Assert.assertEquals(0, model.get().functions().size());
-
-        // Check signatures
-        Assert.assertEquals(1, model.get().signatures().size());
-        ImportedModel.Signature signature = model.get().signatures().get("serving_default");
-        assertNotNull(signature);
-
-        // Test execution
-        model.assertEqualResult("input", "MatMul");
-        model.assertEqualResult("input", "add");
-        model.assertEqualResult("input", "predict");
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/SoftmaxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/SoftmaxImportTestCase.java
deleted file mode 100644
index 525f915b252..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/SoftmaxImportTestCase.java
+++ /dev/null
@@ -1,29 +0,0 @@
-// Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlFunction;
-import org.junit.Assert;
-import org.junit.Test;
-
-
-import static org.junit.Assert.assertNotNull;
-
-/**
- * @author lesters
- */
-public class SoftmaxImportTestCase {
-
-    @Test
-    public void testSoftmaxImport() {
-        TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/models/tensorflow/softmax/saved", 1, 5);
-        ImportedModel.Signature signature = model.get().signature("serving_default");
-        Assert.assertEquals("Should have no skipped outputs",
-                0, model.get().signature("serving_default").skippedOutputs().size());
-
-        ImportedMlFunction output = signature.outputFunction("y", "y");
-        assertNotNull(output);
-        model.assertEqualResult("input", "output");
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowMnistSoftmaxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowMnistSoftmaxImportTestCase.java
deleted file mode 100644
index 50e24f20972..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TensorFlowMnistSoftmaxImportTestCase.java
+++ /dev/null
@@ -1,73 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.configmodelview.ImportedMlFunction;
-import com.yahoo.searchlib.rankingexpression.ExpressionFunction;
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import org.junit.Assert;
-import org.junit.Test;
-
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNotNull;
-import static org.junit.Assert.assertTrue;
-
-/**
- * @author bratseth
- */
-public class TensorFlowMnistSoftmaxImportTestCase {
-
-    @Test
-    public void testMnistSoftmaxImport() {
-        TestableTensorFlowModel model = new TestableTensorFlowModel("test", "src/test/models/tensorflow/mnist_softmax/saved");
-
-        // Check constants
-        Assert.assertEquals(2, model.get().largeConstants().size());
-
-        Tensor constant0 = Tensor.from(model.get().largeConstants().get("test_Variable_read"));
-        assertNotNull(constant0);
-        assertEquals(new TensorType.Builder().indexed("d2", 784).indexed("d1", 10).build(),
-                     constant0.type());
-        assertEquals(7840, constant0.size());
-
-        Tensor constant1 = Tensor.from(model.get().largeConstants().get("test_Variable_1_read"));
-        assertNotNull(constant1);
-        assertEquals(new TensorType.Builder().indexed("d1", 10).build(),
-                     constant1.type());
-        assertEquals(10, constant1.size());
-
-        // Check (provided) functions
-        Assert.assertEquals(0, model.get().functions().size());
-
-        // Check required functions
-        Assert.assertEquals(1, model.get().inputs().size());
-        assertTrue(model.get().inputs().containsKey("Placeholder"));
-        Assert.assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(),
-                            model.get().inputs().get("Placeholder"));
-
-        // Check signatures
-        Assert.assertEquals(1, model.get().signatures().size());
-        ImportedModel.Signature signature = model.get().signatures().get("serving_default");
-        assertNotNull(signature);
-
-        // ... signature inputs
-        assertEquals(1, signature.inputs().size());
-        TensorType argument0 = signature.inputArgument("x");
-        assertNotNull(argument0);
-        assertEquals(new TensorType.Builder().indexed("d0").indexed("d1", 784).build(), argument0);
-
-        // ... signature outputs
-        assertEquals(1, signature.outputs().size());
-        ImportedMlFunction output = signature.outputFunction("y", "y");
-        assertNotNull(output);
-        assertEquals("join(reduce(join(rename(Placeholder, (d0, d1), (d0, d2)), constant(test_Variable_read), f(a,b)(a * b)), sum, d2), constant(test_Variable_1_read), f(a,b)(a + b))",
-                     output.expression());
-        assertEquals("{Placeholder=tensor(d0[],d1[784])}", output.argumentTypes().toString());
-
-        // Test execution
-        model.assertEqualResult("Placeholder", "MatMul");
-        model.assertEqualResult("Placeholder", "add");
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TestableTensorFlowModel.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TestableTensorFlowModel.java
deleted file mode 100644
index 41f343dbdaa..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/TestableTensorFlowModel.java
+++ /dev/null
@@ -1,173 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import com.yahoo.searchlib.rankingexpression.RankingExpression;
-import com.yahoo.searchlib.rankingexpression.evaluation.Context;
-import com.yahoo.searchlib.rankingexpression.evaluation.ContextIndex;
-import com.yahoo.searchlib.rankingexpression.evaluation.ExpressionOptimizer;
-import com.yahoo.searchlib.rankingexpression.evaluation.MapContext;
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import com.yahoo.searchlib.rankingexpression.rule.CompositeNode;
-import com.yahoo.searchlib.rankingexpression.rule.ExpressionNode;
-import com.yahoo.searchlib.rankingexpression.rule.ReferenceNode;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import org.tensorflow.SavedModelBundle;
-import org.tensorflow.Session;
-
-import java.nio.DoubleBuffer;
-import java.nio.FloatBuffer;
-import java.util.List;
-
-import static org.junit.Assert.assertEquals;
-
-/**
- * Helper for TensorFlow import tests: Imports a model and provides asserts on it.
- *
- * @author bratseth
- */
-public class TestableTensorFlowModel {
-
-    private SavedModelBundle tensorFlowModel;
-    private ImportedModel model;
-
-    // Spec of the input vector
-    private final boolean floatInput; // false: double
-    private final int d0Size;
-    private final int d1Size;
-
-
-    public TestableTensorFlowModel(String modelName, String modelDir) {
-        this(modelName, modelDir, 1, 784);
-    }
-
-    public TestableTensorFlowModel(String modelName, String modelDir, int d0Size, int d1Size) {
-        this(modelName, modelDir, d0Size, d1Size, true);
-    }
-
-    public TestableTensorFlowModel(String modelName, String modelDir, int d0Size, int d1Size, boolean floatInput) {
-        tensorFlowModel = SavedModelBundle.load(modelDir, "serve");
-        model = new TensorFlowImporter().importModel(modelName, modelDir, tensorFlowModel);
-        this.d0Size = d0Size;
-        this.d1Size = d1Size;
-        this.floatInput = floatInput;
-    }
-
-    public ImportedModel get() { return model; }
-
-    /** Compare that computing the expressions produce the same result to within some tolerance delta */
-    public void assertEqualResultSum(String inputName, String operationName, double delta) {
-        Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
-        Context context = contextFrom(model);
-        Tensor placeholder = vespaInputArgument();
-        context.put(inputName, new TensorValue(placeholder));
-
-        model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
-
-        RankingExpression expression = model.expressions().get(operationName);
-        ExpressionOptimizer optimizer = new ExpressionOptimizer();
-        optimizer.optimize(expression, (ContextIndex)context);
-
-        Tensor vespaResult = expression.evaluate(context).asTensor();
-        assertEquals("Operation '" + operationName + "' produces equal results",
-                     tfResult.sum().asDouble(), vespaResult.sum().asDouble(), delta);
-    }
-
-    /** Compare tensors 100% exactly */
-    public void assertEqualResult(String inputName, String operationName) {
-        Tensor tfResult = tensorFlowExecute(tensorFlowModel, inputName, operationName);
-        Context context = contextFrom(model);
-        Tensor inputValue = vespaInputArgument();
-        context.put(inputName, new TensorValue(inputValue));
-
-        model.functions().forEach((k, v) -> evaluateFunction(context, model, k));
-
-        RankingExpression expression = model.expressions().get(operationName);
-        ExpressionOptimizer optimizer = new ExpressionOptimizer();
-        optimizer.optimize(expression, (ContextIndex)context);
-
-        Tensor vespaResult = expression.evaluate(context).asTensor();
-        assertEquals("Operation '" + operationName + "': Actual value from Vespa equals expected value from TensorFlow",
-                     tfResult, vespaResult);
-    }
-
-    private Tensor tensorFlowExecute(SavedModelBundle model, String inputName, String operationName) {
-        Session.Runner runner = model.session().runner();
-        org.tensorflow.Tensor<?> input = floatInput ? tensorFlowFloatInputArgument() : tensorFlowDoubleInputArgument();
-        runner.feed(inputName, input);
-        List<org.tensorflow.Tensor<?>> results = runner.fetch(operationName).run();
-        assertEquals(1, results.size());
-        return TensorConverter.toVespaTensor(results.get(0));
-    }
-
-    static Context contextFrom(ImportedModel result) {
-        TestableModelContext context = new TestableModelContext();
-        result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor))));
-        result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor))));
-        return context;
-    }
-
-    /** Must be the same as vespaInputArgument() */
-    private org.tensorflow.Tensor<?> tensorFlowDoubleInputArgument() {
-        DoubleBuffer fb = DoubleBuffer.allocate(d0Size * d1Size);
-        int i = 0;
-        for (int d0 = 0; d0 < d0Size; d0++)
-            for (int d1 = 0; d1 < d1Size; ++d1)
-                fb.put(i++, (d1 * 1.0 / d1Size));
-        return org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb);
-    }
-
-    /** Must be the same as vespaInputArgument() */
-    private org.tensorflow.Tensor<?> tensorFlowFloatInputArgument() {
-        FloatBuffer fb = FloatBuffer.allocate(d0Size * d1Size);
-        int i = 0;
-        for (int d0 = 0; d0 < d0Size; d0++)
-            for (int d1 = 0; d1 < d1Size; ++d1)
-                fb.put(i++, (float)(d1 * 1.0 / d1Size));
-        return org.tensorflow.Tensor.create(new long[]{ d0Size, d1Size }, fb);
-    }
-
-    /** Must be the same as tensorFlowFloatInputArgument() */
-    private Tensor vespaInputArgument() {
-        Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", d0Size).indexed("d1", d1Size).build());
-        for (int d0 = 0; d0 < d0Size; d0++)
-            for (int d1 = 0; d1 < d1Size; d1++)
-                b.cell(d1 * 1.0 / d1Size, d0, d1);
-        return b.build();
-    }
-
-    private void evaluateFunction(Context context, ImportedModel model, String functionName) {
-        if (!context.names().contains(functionName)) {
-            RankingExpression e = RankingExpression.from(model.functions().get(functionName));
-            evaluateFunctionDependencies(context, model, e.getRoot());
-            context.put(functionName, new TensorValue(e.evaluate(context).asTensor()));
-        }
-    }
-
-    private void evaluateFunctionDependencies(Context context, ImportedModel model, ExpressionNode node) {
-        if (node instanceof ReferenceNode) {
-            String name = node.toString();
-            if (model.functions().containsKey(name)) {
-                evaluateFunction(context, model, name);
-            }
-        }
-        else if (node instanceof CompositeNode) {
-            for (ExpressionNode child : ((CompositeNode)node).children()) {
-                evaluateFunctionDependencies(context, model, child);
-            }
-        }
-    }
-
-    private static class TestableModelContext extends MapContext implements ContextIndex {
-        @Override
-        public int size() {
-            return bindings().size();
-        }
-        @Override
-        public int getIndex(String name) {
-            throw new UnsupportedOperationException(this + " does not support index lookup by name");
-        }
-    }
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Tf2OnnxImportTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Tf2OnnxImportTestCase.java
deleted file mode 100644
index 0510a433dd9..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/Tf2OnnxImportTestCase.java
+++ /dev/null
@@ -1,61 +0,0 @@
-// Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import ai.vespa.rankingexpression.importer.ImportedModel;
-import com.yahoo.searchlib.rankingexpression.RankingExpression;
-import com.yahoo.searchlib.rankingexpression.evaluation.Context;
-import com.yahoo.searchlib.rankingexpression.evaluation.MapContext;
-import com.yahoo.searchlib.rankingexpression.evaluation.TensorValue;
-import com.yahoo.tensor.Tensor;
-import com.yahoo.tensor.TensorType;
-import org.junit.Ignore;
-import org.junit.Test;
-
-import static org.junit.Assert.assertEquals;
-
-/**
- * @author lesters
- */
-public class Tf2OnnxImportTestCase {
-
-    @Ignore  // Ignored because conversion requires python tf2onnx dependencies - tested in system test
-    @Test
-    public void testConversionFromTensorFlowToOnnx() {
-        String modelPath = "src/test/models/tensorflow/mnist_softmax/saved";
-        String modelPathToConvert = "src/test/models/tensorflow/mnist_softmax/tf_2_onnx";
-
-        Tensor argument = placeholderArgument();
-        Tensor tensorFlowResult = evaluateTensorFlowModel(modelPath, argument, "Placeholder", "add");
-        Tensor tf2OnnxResult = evaluateTensorFlowModel(modelPathToConvert, argument, "Placeholder", "add");
-
-        assertEquals("Operation 'add' produces equal results", tensorFlowResult, tf2OnnxResult);
-    }
-
-    private Tensor evaluateTensorFlowModel(String path, Tensor argument, String input, String output) {
-        ImportedModel model = new TensorFlowImporter().importModel("test", path);
-        String outputExpr = model.signatures().values().iterator().next().outputs().values().iterator().next();
-        return evaluateExpression(model.expressions().get(outputExpr), contextFrom(model), argument, input);
-    }
-
-    private Tensor evaluateExpression(RankingExpression expression, Context context, Tensor argument, String input) {
-        context.put(input, new TensorValue(argument));
-        return expression.evaluate(context).asTensor();
-    }
-
-    private Context contextFrom(ImportedModel result) {
-        MapContext context = new MapContext();
-        result.largeConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor))));
-        result.smallConstants().forEach((name, tensor) -> context.put("constant(" + name + ")", new TensorValue(Tensor.from(tensor))));
-        return context;
-    }
-
-    private Tensor placeholderArgument() {
-        Tensor.Builder b = Tensor.Builder.of(new TensorType.Builder().indexed("d0", 1).indexed("d1", 784).build());
-        for (int d0 = 0; d0 < 1; d0++)
-            for (int d1 = 0; d1 < 784; d1++)
-                b.cell(d1 * 1.0 / 784, d0, d1);
-        return b.build();
-    }
-
-
-}
diff --git a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverterTestCase.java b/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverterTestCase.java
deleted file mode 100644
index c9fffe143b4..00000000000
--- a/model-integration/src/test/java/ai/vespa/rankingexpression/importer/tensorflow/VariableConverterTestCase.java
+++ /dev/null
@@ -1,21 +0,0 @@
-// Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-package ai.vespa.rankingexpression.importer.tensorflow;
-
-import org.junit.Test;
-
-import java.nio.charset.StandardCharsets;
-
-import static org.junit.Assert.assertEquals;
-
-public class VariableConverterTestCase {
-
-    @Test
-    public void testConversion() {
-        byte[] converted = VariableConverter.importVariable("src/test/models/tensorflow/mnist_softmax/saved",
-                                                            "Variable_1",
-                                                            "tensor(d0[10],d1[1])");
-        assertEquals("{\"cells\":[{\"address\":{\"d0\":\"0\",\"d1\":\"0\"},\"value\":-0.3546536862850189},{\"address\":{\"d0\":\"1\",\"d1\":\"0\"},\"value\":0.3759574592113495},{\"address\":{\"d0\":\"2\",\"d1\":\"0\"},\"value\":0.06054411828517914},{\"address\":{\"d0\":\"3\",\"d1\":\"0\"},\"value\":-0.251544713973999},{\"address\":{\"d0\":\"4\",\"d1\":\"0\"},\"value\":0.017951013520359993},{\"address\":{\"d0\":\"5\",\"d1\":\"0\"},\"value\":1.2899067401885986},{\"address\":{\"d0\":\"6\",\"d1\":\"0\"},\"value\":-0.10389615595340729},{\"address\":{\"d0\":\"7\",\"d1\":\"0\"},\"value\":0.6367976665496826},{\"address\":{\"d0\":\"8\",\"d1\":\"0\"},\"value\":-1.4136744737625122},{\"address\":{\"d0\":\"9\",\"d1\":\"0\"},\"value\":-0.2573896050453186}]}",
-                     new String(converted, StandardCharsets.UTF_8));
-    }
-
-}
diff --git a/model-integration/src/test/models/tensorflow/9662/saved_model.pbtxt b/model-integration/src/test/models/tensorflow/9662/saved_model.pbtxt
deleted file mode 100644
index 83c601edfc0..00000000000
--- a/model-integration/src/test/models/tensorflow/9662/saved_model.pbtxt
+++ /dev/null
@@ -1,1318 +0,0 @@
-saved_model_schema_version: 1
-meta_graphs {
-  meta_info_def {
-    stripped_op_list {
-      op {
-        name: "Add"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "y"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "z"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_BFLOAT16
-              type: DT_HALF
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_UINT8
-              type: DT_INT8
-              type: DT_INT16
-              type: DT_INT32
-              type: DT_INT64
-              type: DT_COMPLEX64
-              type: DT_COMPLEX128
-              type: DT_STRING
-            }
-          }
-        }
-      }
-      op {
-        name: "BiasAdd"
-        input_arg {
-          name: "value"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "bias"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "output"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_INT32
-              type: DT_UINT8
-              type: DT_INT16
-              type: DT_INT8
-              type: DT_COMPLEX64
-              type: DT_INT64
-              type: DT_QINT8
-              type: DT_QUINT8
-              type: DT_QINT32
-              type: DT_BFLOAT16
-              type: DT_UINT16
-              type: DT_COMPLEX128
-              type: DT_HALF
-              type: DT_UINT32
-              type: DT_UINT64
-            }
-          }
-        }
-        attr {
-          name: "data_format"
-          type: "string"
-          default_value {
-            s: "NHWC"
-          }
-          allowed_values {
-            list {
-              s: "NHWC"
-              s: "NCHW"
-            }
-          }
-        }
-      }
-      op {
-        name: "Const"
-        output_arg {
-          name: "output"
-          type_attr: "dtype"
-        }
-        attr {
-          name: "value"
-          type: "tensor"
-        }
-        attr {
-          name: "dtype"
-          type: "type"
-        }
-      }
-      op {
-        name: "ExpandDims"
-        input_arg {
-          name: "input"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "dim"
-          type_attr: "Tdim"
-        }
-        output_arg {
-          name: "output"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-        }
-        attr {
-          name: "Tdim"
-          type: "type"
-          default_value {
-            type: DT_INT32
-          }
-          allowed_values {
-            list {
-              type: DT_INT32
-              type: DT_INT64
-            }
-          }
-        }
-      }
-      op {
-        name: "MatMul"
-        input_arg {
-          name: "a"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "b"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "product"
-          type_attr: "T"
-        }
-        attr {
-          name: "transpose_a"
-          type: "bool"
-          default_value {
-            b: false
-          }
-        }
-        attr {
-          name: "transpose_b"
-          type: "bool"
-          default_value {
-            b: false
-          }
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_BFLOAT16
-              type: DT_HALF
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_INT32
-              type: DT_INT64
-              type: DT_COMPLEX64
-              type: DT_COMPLEX128
-            }
-          }
-        }
-      }
-      op {
-        name: "Maximum"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "y"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "z"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_BFLOAT16
-              type: DT_HALF
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_INT32
-              type: DT_INT64
-            }
-          }
-        }
-        is_commutative: true
-      }
-      op {
-        name: "Mul"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "y"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "z"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_BFLOAT16
-              type: DT_HALF
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_UINT8
-              type: DT_INT8
-              type: DT_UINT16
-              type: DT_INT16
-              type: DT_INT32
-              type: DT_INT64
-              type: DT_COMPLEX64
-              type: DT_COMPLEX128
-            }
-          }
-        }
-        is_commutative: true
-      }
-      op {
-        name: "Placeholder"
-        output_arg {
-          name: "output"
-          type_attr: "dtype"
-        }
-        attr {
-          name: "dtype"
-          type: "type"
-        }
-        attr {
-          name: "shape"
-          type: "shape"
-          default_value {
-            shape {
-              unknown_rank: true
-            }
-          }
-        }
-      }
-      op {
-        name: "PlaceholderWithDefault"
-        input_arg {
-          name: "input"
-          type_attr: "dtype"
-        }
-        output_arg {
-          name: "output"
-          type_attr: "dtype"
-        }
-        attr {
-          name: "dtype"
-          type: "type"
-        }
-        attr {
-          name: "shape"
-          type: "shape"
-        }
-      }
-      op {
-        name: "Rsqrt"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "y"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_BFLOAT16
-              type: DT_HALF
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_COMPLEX64
-              type: DT_COMPLEX128
-            }
-          }
-        }
-      }
-      op {
-        name: "Sigmoid"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "y"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_BFLOAT16
-              type: DT_HALF
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_COMPLEX64
-              type: DT_COMPLEX128
-            }
-          }
-        }
-      }
-      op {
-        name: "Square"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "y"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_BFLOAT16
-              type: DT_HALF
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_INT32
-              type: DT_INT64
-              type: DT_COMPLEX64
-              type: DT_COMPLEX128
-            }
-          }
-        }
-      }
-      op {
-        name: "Sub"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "y"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "z"
-          type_attr: "T"
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_BFLOAT16
-              type: DT_HALF
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_UINT8
-              type: DT_INT8
-              type: DT_UINT16
-              type: DT_INT16
-              type: DT_INT32
-              type: DT_INT64
-              type: DT_COMPLEX64
-              type: DT_COMPLEX128
-            }
-          }
-        }
-      }
-      op {
-        name: "Sum"
-        input_arg {
-          name: "input"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "reduction_indices"
-          type_attr: "Tidx"
-        }
-        output_arg {
-          name: "output"
-          type_attr: "T"
-        }
-        attr {
-          name: "keep_dims"
-          type: "bool"
-          default_value {
-            b: false
-          }
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
-            list {
-              type: DT_FLOAT
-              type: DT_DOUBLE
-              type: DT_INT32
-              type: DT_UINT8
-              type: DT_INT16
-              type: DT_INT8
-              type: DT_COMPLEX64
-              type: DT_INT64
-              type: DT_QINT8
-              type: DT_QUINT8
-              type: DT_QINT32
-              type: DT_BFLOAT16
-              type: DT_UINT16
-              type: DT_COMPLEX128
-              type: DT_HALF
-              type: DT_UINT32
-              type: DT_UINT64
-            }
-          }
-        }
-        attr {
-          name: "Tidx"
-          type: "type"
-          default_value {
-            type: DT_INT32
-          }
-          allowed_values {
-            list {
-              type: DT_INT32
-              type: DT_INT64
-            }
-          }
-        }
-      }
-    }
-    tags: "serve"
-    tensorflow_version: "1.13.1"
-    tensorflow_git_version: "b\'v1.13.1-0-g6612da8951\'"
-  }
-  graph_def {
-    node {
-      name: "keras_learning_phase/input"
-      op: "Const"
-      attr {
-        key: "_output_shapes"
-        value {
-          list {
-            shape {
-            }
-          }
-        }
-      }
-      attr {
-        key: "dtype"
-        value {
-          type: DT_BOOL
-        }
-      }
-      attr {
-        key: "value"
-        value {
-          tensor {
-            dtype: DT_BOOL
-            tensor_shape {
-            }
-            bool_val: false
-          }
-        }
-      }
-    }
-    node {
-      name: "keras_learning_phase"
-      op: "PlaceholderWithDefault"
-      input: "keras_learning_phase/input"
-      attr {
-        key: "_output_shapes"
-        value {
-          list {
-            shape {
-            }
-          }
-        }
-      }
-      attr {
-        key: "dtype"
-        value {
-          type: DT_BOOL
-        }
-      }
-      attr {
-        key: "shape"
-        value {
-          shape {
-          }
-        }
-      }
-    }
-    node {
-      name: "Dot/l2_normalize/Maximum/y"
-      op: "Const"
-      attr {
-        key: "_output_shapes"
-        value {
-          list {
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-    node {
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-      op: "BiasAdd"
-      input: "dense_out/MatMul"
-      input: "batch_normalization_v1/moving_mean"
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-    node {
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-      op: "Sigmoid"
-      input: "dense_out/BiasAdd"
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-    versions {
-    }
-  }
-  signature_def {
-    key: "serving_default"
-    value {
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-        key: "input_embedding_user_guid"
-        value {
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-          tensor_shape {
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-      outputs {
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-        value {
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-          tensor_shape {
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-}
diff --git a/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py b/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py
deleted file mode 100644
index bc6ea13ebc1..00000000000
--- a/model-integration/src/test/models/tensorflow/batch_norm/batch_normalization_mnist.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import tensorflow as tf
-
-from functools import partial
-from tensorflow.examples.tutorials.mnist import input_data
-from datetime import datetime
-
-now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
-root_logdir = "tf_logs"
-logdir = "{}/run-{}/".format(root_logdir, now)
-
-mnist = input_data.read_data_sets("/tmp/data/")
-X_train = mnist.train.images
-X_test = mnist.test.images
-y_train = mnist.train.labels.astype("int")
-y_test = mnist.test.labels.astype("int")
-
-n_inputs = 28*28  # MNIST
-n_hidden1 = 300
-n_hidden2 = 100
-n_hidden3 = 40
-n_outputs = 10
-
-learning_rate = 0.01
-n_epochs = 20
-batch_size = 200
-batch_norm_momentum = 0.9
-
-X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
-y = tf.placeholder(tf.int64, shape=(None), name="y")
-training = tf.placeholder_with_default(False, shape=(), name='training')
-
-def leaky_relu(z, name=None):
-    return tf.maximum(0.01 * z, z, name=name)
-
-with tf.name_scope("dnn"):
-    he_init = tf.contrib.layers.variance_scaling_initializer()
-
-    batch_norm_layer = partial(tf.layers.batch_normalization, training=training, momentum=batch_norm_momentum)
-    dense_layer = partial(tf.layers.dense, kernel_initializer=he_init)
-
-    hidden1 = dense_layer(X, n_hidden1, name="hidden1", activation=leaky_relu)
-    bn1 = tf.nn.elu(batch_norm_layer(hidden1))
-    hidden2 = dense_layer(bn1, n_hidden2, name="hidden2", activation=tf.nn.elu)
-    bn2 = tf.nn.elu(batch_norm_layer(hidden2))
-    logits_before_bn = dense_layer(bn2, n_outputs, name="outputs", activation=tf.nn.selu)
-    logits = batch_norm_layer(logits_before_bn)
-
-with tf.name_scope("loss"):
-    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
-    loss = tf.reduce_mean(xentropy, name="loss")
-
-with tf.name_scope("train"):
-    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
-    training_op = optimizer.minimize(loss)
-
-with tf.name_scope("eval"):
-    correct = tf.nn.in_top_k(logits, y, 1)
-    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
-
-init = tf.global_variables_initializer()
-accuracy_summary = tf.summary.scalar('Accuracy', accuracy)
-file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
-extra_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
-
-with tf.Session() as sess:
-    init.run()
-    for epoch in range(n_epochs):
-        for iteration in range(mnist.train.num_examples // batch_size):
-            X_batch, y_batch = mnist.train.next_batch(batch_size)
-            sess.run([training_op, extra_update_ops],
-                     feed_dict={training: True, X: X_batch, y: y_batch})
-
-        accuracy_val = accuracy.eval(feed_dict={X: mnist.test.images,
-                                                y: mnist.test.labels})
-        print(epoch, "Test accuracy:", accuracy_val)
-
-        # Save summary for tensorboard
-        summary_str = accuracy_summary.eval(feed_dict={X: mnist.validation.images,
-                                                       y: mnist.validation.labels})
-        file_writer.add_summary(summary_str, epoch)
-
-    export_path = "saved"
-    print('Exporting trained model to ', export_path)
-    builder = tf.saved_model.builder.SavedModelBuilder(export_path)
-    signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':X}, outputs = {'y':logits})
-    builder.add_meta_graph_and_variables(sess,
-                                         [tf.saved_model.tag_constants.SERVING],
-                                         signature_def_map={'serving_default':signature})
-    builder.save(as_text=True)
-
-file_writer.close()
-
-
diff --git a/model-integration/src/test/models/tensorflow/batch_norm/saved/saved_model.pbtxt b/model-integration/src/test/models/tensorflow/batch_norm/saved/saved_model.pbtxt
deleted file mode 100644
index f3ce68a1cbd..00000000000
--- a/model-integration/src/test/models/tensorflow/batch_norm/saved/saved_model.pbtxt
+++ /dev/null
@@ -1,32648 +0,0 @@
-saved_model_schema_version: 1
-meta_graphs {
-  meta_info_def {
-    stripped_op_list {
-      op {
-        name: "Add"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "y"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "z"
-          type_attr: "T"
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-        attr {
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-          type: "type"
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-              type: DT_COMPLEX64
-              type: DT_COMPLEX128
-              type: DT_STRING
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-          }
-        }
-      }
-      op {
-        name: "AddN"
-        input_arg {
-          name: "inputs"
-          type_attr: "T"
-          number_attr: "N"
-        }
-        output_arg {
-          name: "sum"
-          type_attr: "T"
-        }
-        attr {
-          name: "N"
-          type: "int"
-          has_minimum: true
-          minimum: 1
-        }
-        attr {
-          name: "T"
-          type: "type"
-          allowed_values {
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-        value: "\n\024b_hidden_2/Adagrad:0\022\031b_hidden_2/Adagrad/Assign\032\031b_hidden_2/Adagrad/read:02&b_hidden_2/Adagrad/Initializer/Const:0"
-        value: "\n\021W_final/Adagrad:0\022\026W_final/Adagrad/Assign\032\026W_final/Adagrad/read:02#W_final/Adagrad/Initializer/Const:0"
-        value: "\n\021b_final/Adagrad:0\022\026b_final/Adagrad/Assign\032\026b_final/Adagrad/read:02#b_final/Adagrad/Initializer/Const:0"
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-  signature_def {
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-        }
-      }
-      method_name: "tensorflow/serving/predict"
-    }
-  }
-}
diff --git a/model-integration/src/test/models/tensorflow/blog/saved/variables/variables.data-00000-of-00001 b/model-integration/src/test/models/tensorflow/blog/saved/variables/variables.data-00000-of-00001
deleted file mode 100644
index 1efd102aef9..00000000000
--- a/model-integration/src/test/models/tensorflow/blog/saved/variables/variables.data-00000-of-00001
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/blog/saved/variables/variables.index b/model-integration/src/test/models/tensorflow/blog/saved/variables/variables.index
deleted file mode 100644
index 56c60dbe529..00000000000
--- a/model-integration/src/test/models/tensorflow/blog/saved/variables/variables.index
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/dropout/dropout.py b/model-integration/src/test/models/tensorflow/dropout/dropout.py
deleted file mode 100644
index 42c15cd2812..00000000000
--- a/model-integration/src/test/models/tensorflow/dropout/dropout.py
+++ /dev/null
@@ -1,44 +0,0 @@
-# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-# Common imports
-import numpy as np
-import tensorflow as tf
-import datetime
-
-now = datetime.datetime.utcnow().strftime("%Y%m%d%H%M%S")
-root_logdir = "tf_logs"
-logdir = "{}/run-{}/".format(root_logdir, now)
-
-n_inputs = 784
-n_outputs = 10
-dropout_rate = 0.5  # == 1 - keep_prob
-
-X = tf.placeholder(tf.float32, shape=(None, n_inputs), name="X")
-y = tf.placeholder(tf.int64, shape=(None), name="y")
-training = tf.placeholder_with_default(False, shape=(), name='training')
-
-def leaky_relu_with_small_constant(z, name=None):
-    return tf.maximum(tf.constant(0.01, shape=[1]) * z, z, name=name)
-
-X_drop = tf.layers.dropout(X, dropout_rate, training=training, name="xdrop")
-output = tf.layers.dense(X_drop, n_outputs, activation=leaky_relu_with_small_constant, name="outputs")
-
-init = tf.global_variables_initializer()
-file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
-
-with tf.Session() as sess:
-    init.run()
-    sess.run(output, feed_dict={training: False, X: np.random.random((1,784))})
-
-    export_path = "saved"
-    print('Exporting trained model to ', export_path)
-    builder = tf.saved_model.builder.SavedModelBuilder(export_path)
-    signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':X}, outputs = {'y':output})
-    builder.add_meta_graph_and_variables(sess,
-                                         [tf.saved_model.tag_constants.SERVING],
-                                         signature_def_map={'serving_default':signature})
-    builder.save(as_text=True)
-
-file_writer.close()
-
-
diff --git a/model-integration/src/test/models/tensorflow/dropout/saved/saved_model.pbtxt b/model-integration/src/test/models/tensorflow/dropout/saved/saved_model.pbtxt
deleted file mode 100644
index ad431f0460d..00000000000
--- a/model-integration/src/test/models/tensorflow/dropout/saved/saved_model.pbtxt
+++ /dev/null
@@ -1,2875 +0,0 @@
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diff --git a/model-integration/src/test/models/tensorflow/external/Model_C/factorization_machine_v1/export/standard/variables/variables.data-00000-of-00001 b/model-integration/src/test/models/tensorflow/external/Model_C/factorization_machine_v1/export/standard/variables/variables.data-00000-of-00001
deleted file mode 100644
index 8339979323b..00000000000
--- a/model-integration/src/test/models/tensorflow/external/Model_C/factorization_machine_v1/export/standard/variables/variables.data-00000-of-00001
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/external/Model_C/factorization_machine_v1/export/standard/variables/variables.index b/model-integration/src/test/models/tensorflow/external/Model_C/factorization_machine_v1/export/standard/variables/variables.index
deleted file mode 100644
index cf999e88e22..00000000000
--- a/model-integration/src/test/models/tensorflow/external/Model_C/factorization_machine_v1/export/standard/variables/variables.index
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/external/modelv1/saved_model.pb b/model-integration/src/test/models/tensorflow/external/modelv1/saved_model.pb
deleted file mode 100644
index 223ad4baf59..00000000000
--- a/model-integration/src/test/models/tensorflow/external/modelv1/saved_model.pb
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/external/modelv1/variables/variables.data-00000-of-00001 b/model-integration/src/test/models/tensorflow/external/modelv1/variables/variables.data-00000-of-00001
deleted file mode 100644
index 76e3a429612..00000000000
--- a/model-integration/src/test/models/tensorflow/external/modelv1/variables/variables.data-00000-of-00001
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/external/modelv1/variables/variables.index b/model-integration/src/test/models/tensorflow/external/modelv1/variables/variables.index
deleted file mode 100644
index 285449d22c5..00000000000
--- a/model-integration/src/test/models/tensorflow/external/modelv1/variables/variables.index
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/external/modelv2/saved_model.pb b/model-integration/src/test/models/tensorflow/external/modelv2/saved_model.pb
deleted file mode 100644
index e2af4d90552..00000000000
--- a/model-integration/src/test/models/tensorflow/external/modelv2/saved_model.pb
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/external/modelv2/variables/variables.data-00000-of-00001 b/model-integration/src/test/models/tensorflow/external/modelv2/variables/variables.data-00000-of-00001
deleted file mode 100644
index 4bc7ff8a5d5..00000000000
--- a/model-integration/src/test/models/tensorflow/external/modelv2/variables/variables.data-00000-of-00001
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/external/modelv2/variables/variables.index b/model-integration/src/test/models/tensorflow/external/modelv2/variables/variables.index
deleted file mode 100644
index 398e369c5e3..00000000000
--- a/model-integration/src/test/models/tensorflow/external/modelv2/variables/variables.index
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/external/train.py b/model-integration/src/test/models/tensorflow/external/train.py
deleted file mode 100644
index 32b14c481c3..00000000000
--- a/model-integration/src/test/models/tensorflow/external/train.py
+++ /dev/null
@@ -1,53 +0,0 @@
-# Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-import numpy as np
-import tensorflow as tf
-import tensorflow.keras.backend as K
-
-from tensorflow.keras.layers import Input, Dense, concatenate
-from tensorflow.keras.models import Model
-
-input_user = Input(shape=(3,))
-input_ad = Input(shape=(3,))
-
-merged = concatenate([input_user, input_ad])
-output_1 = Dense(64, activation='relu')(merged)
-output_2 = Dense(64, activation='relu')(output_1)
-predictions = Dense(1)(output_2)
-
-model = Model(inputs=[input_user, input_ad], outputs=predictions)
-model.compile(optimizer='adam',
-              loss='binary_crossentropy',
-              metrics=['accuracy'])
-model.summary()
-
-SAMPLES = 1000
-user_data = np.random.rand(SAMPLES,3)
-ad_data = np.random.rand(SAMPLES,3)
-labels = np.random.rand(SAMPLES,1)
-print(user_data[:10])
-print(ad_data[:10])
-print(labels[:10])
-
-model.fit([user_data, ad_data], labels, epochs=10, )  # starts training
-
-user_data_sample1 = np.random.rand(1, 3)
-ad_data_sample1 = np.random.rand(1, 3)
-
-print("predicting for:")
-print(user_data_sample1)
-print(ad_data_sample1)
-predictions = model.predict([user_data_sample1, ad_data_sample1])
-print(predictions)
-
-signature = tf.saved_model.signature_def_utils.predict_signature_def(
-    inputs={'input1': model.inputs[0],'input2': model.inputs[1] }, outputs={'pctr': model.outputs[0]})
-
-builder = tf.saved_model.builder.SavedModelBuilder('modelv1')
-builder.add_meta_graph_and_variables(
-    sess=K.get_session(),
-    tags=[tf.saved_model.tag_constants.SERVING],
-    signature_def_map={
-        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
-            signature
-    })
-builder.save()
diff --git a/model-integration/src/test/models/tensorflow/external/train_embed.py b/model-integration/src/test/models/tensorflow/external/train_embed.py
deleted file mode 100644
index 2d1ab18a0eb..00000000000
--- a/model-integration/src/test/models/tensorflow/external/train_embed.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-import numpy as np
-import tensorflow as tf
-import tensorflow.keras.backend as K
-
-from tensorflow.keras.layers import Input, Dense, concatenate, Embedding, Reshape
-from tensorflow.keras.models import Model
-
-input_user = Input(shape=(3,))
-input_ad = Input(shape=(3,))
-gender_samples = Input(shape=(1,), dtype='int32')
-
-gender_values = ['m', 'f', 'a']
-
-gender_embeddings = Embedding(len(gender_values), 1)(gender_samples)
-reshape_gender = Reshape(target_shape=[1])(gender_embeddings)
-
-model2 = Model(inputs=[gender_samples], outputs=reshape_gender)
-model2.summary()
-
-merged = concatenate([input_user, input_ad, reshape_gender])
-output_1 = Dense(64, activation='relu')(merged)
-output_2 = Dense(64, activation='relu')(output_1)
-predictions = Dense(1)(output_2)
-
-model = Model(inputs=[input_user, input_ad, gender_samples], outputs=predictions)
-model.compile(optimizer='adam',
-              loss='binary_crossentropy',
-              metrics=['accuracy'])
-model.summary()
-
-SAMPLES = 1000
-user_data = np.random.rand(SAMPLES,3)
-ad_data = np.random.rand(SAMPLES,3)
-gender_data = np.random.randint(len(gender_values), size=SAMPLES)
-labels = np.random.rand(SAMPLES,1)
-print(user_data[:10])
-print(ad_data[:10])
-print(gender_data[:10])
-print(labels[:10])
-
-model.fit([user_data, ad_data, gender_data], labels, epochs=10, )  # starts training
-
-user_data_sample1 = np.random.rand(1, 3)
-ad_data_sample1 = np.random.rand(1, 3)
-gender_data_sample1 = np.random.randint(len(gender_values), size=1)
-
-print("predicting for:")
-print(user_data_sample1)
-print(ad_data_sample1)
-print(gender_data_sample1)
-predictions = model.predict([user_data_sample1, ad_data_sample1, gender_data_sample1])
-print(predictions)
-
-signature = tf.saved_model.signature_def_utils.predict_signature_def(
-    inputs={'input1': model.inputs[0],'input2': model.inputs[1], 'input3': model.inputs[2] }, outputs={'pctrx': model.outputs[0]})
-
-builder = tf.saved_model.builder.SavedModelBuilder('modelv2')
-builder.add_meta_graph_and_variables(
-    sess=K.get_session(),
-    tags=[tf.saved_model.tag_constants.SERVING],
-    signature_def_map={
-        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
-            signature
-    })
-builder.save()
diff --git a/model-integration/src/test/models/tensorflow/mnist/saved/saved_model.pbtxt b/model-integration/src/test/models/tensorflow/mnist/saved/saved_model.pbtxt
deleted file mode 100644
index eb926836576..00000000000
--- a/model-integration/src/test/models/tensorflow/mnist/saved/saved_model.pbtxt
+++ /dev/null
@@ -1,7982 +0,0 @@
-saved_model_schema_version: 1
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-  meta_info_def {
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-        name: "Add"
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-          name: "x"
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-          number_attr: "N"
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-        output_arg {
-          name: "sum"
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-        attr {
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-          type: "int"
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-        is_aggregate: true
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-      op {
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-        input_arg {
-          name: "var"
-          type_attr: "T"
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-    node {
-      name: "save/restore_shard"
-      op: "NoOp"
-      input: "^save/Assign"
-      input: "^save/Assign_1"
-      input: "^save/Assign_2"
-      input: "^save/Assign_3"
-      input: "^save/Assign_4"
-      input: "^save/Assign_5"
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-      name: "save/restore_all"
-      op: "NoOp"
-      input: "^save/restore_shard"
-    }
-    versions {
-      producer: 24
-    }
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-  saver_def {
-    filename_tensor_name: "save/Const:0"
-    save_tensor_name: "save/Identity:0"
-    restore_op_name: "save/restore_all"
-    max_to_keep: 5
-    sharded: true
-    keep_checkpoint_every_n_hours: 10000.0
-    version: V2
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-}
diff --git a/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001 b/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001
deleted file mode 100644
index a7ca01888c7..00000000000
--- a/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.data-00000-of-00001
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.index b/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.index
deleted file mode 100644
index 7989c109a3a..00000000000
--- a/model-integration/src/test/models/tensorflow/mnist/saved/variables/variables.index
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py b/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py
deleted file mode 100644
index 86a17e81f8f..00000000000
--- a/model-integration/src/test/models/tensorflow/mnist/simple_mnist.py
+++ /dev/null
@@ -1,98 +0,0 @@
-# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-# Common imports
-import numpy as np
-import tensorflow as tf
-
-from tensorflow.examples.tutorials.mnist import input_data
-from datetime import datetime
-
-now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
-root_logdir = "tf_logs"
-logdir = "{}/run-{}/".format(root_logdir, now)
-
-mnist = input_data.read_data_sets("/tmp/data/")
-X_train = mnist.train.images
-X_test = mnist.test.images
-y_train = mnist.train.labels.astype("int")
-y_test = mnist.test.labels.astype("int")
-
-n_inputs = 28*28  # MNIST
-n_hidden1 = 300
-n_hidden2 = 100
-n_hidden3 = 40
-n_outputs = 10
-
-learning_rate = 0.01
-n_epochs = 20
-batch_size = 50
-
-input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input")
-y = tf.placeholder(tf.int64, shape=(None), name="y")
-
-
-def neuron_layer(X, n_neurons, name, activation=None):
-    with tf.name_scope(name):
-        n_inputs = int(X.get_shape()[1])
-        stddev = 2 / np.sqrt(n_inputs)
-        init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev)
-        W = tf.Variable(init, name="weights")
-        b = tf.Variable(tf.zeros([n_neurons]), name="bias")
-        Z = tf.matmul(X, W) + b
-        if activation is not None:
-            return activation(Z)
-        else:
-            return Z
-
-
-def leaky_relu(z, name=None):
-    return tf.maximum(0.01 * z, z, name=name)
-
-
-with tf.name_scope("dnn"):
-    hidden1 = neuron_layer(input, n_hidden1, name="hidden1", activation=leaky_relu)
-    hidden2 = neuron_layer(hidden1, n_hidden2, name="hidden2", activation=tf.nn.selu)
-    logits = neuron_layer(hidden2, n_outputs, name="outputs") #, activation=tf.nn.sigmoid)
-
-with tf.name_scope("loss"):
-    xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
-    loss = tf.reduce_mean(xentropy, name="loss")
-
-with tf.name_scope("train"):
-    optimizer = tf.train.GradientDescentOptimizer(learning_rate)
-    training_op = optimizer.minimize(loss)
-
-with tf.name_scope("eval"):
-    correct = tf.nn.in_top_k(logits, y, 1)
-    accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
-
-init = tf.global_variables_initializer()
-accuracy_summary = tf.summary.scalar('Accuracy', accuracy)
-file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
-
-with tf.Session() as sess:
-    init.run()
-    for epoch in range(n_epochs):
-        for iteration in range(mnist.train.num_examples // batch_size):
-            X_batch, y_batch = mnist.train.next_batch(batch_size)
-            sess.run(training_op, feed_dict={input: X_batch, y: y_batch})
-        acc_train = accuracy.eval(feed_dict={input: X_batch, y: y_batch})
-        acc_val = accuracy.eval(feed_dict={input: mnist.validation.images,
-                                           y: mnist.validation.labels})
-        print(epoch, "Train accuracy:", acc_train, "Val accuracy:", acc_val)
-
-        # Save summary for tensorboard
-        summary_str = accuracy_summary.eval(feed_dict={input: mnist.validation.images,
-                                                       y: mnist.validation.labels})
-        file_writer.add_summary(summary_str, epoch)
-
-    export_path = "saved"
-    print('Exporting trained model to ', export_path)
-    builder = tf.saved_model.builder.SavedModelBuilder(export_path)
-    signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':input}, outputs = {'y':logits})
-    builder.add_meta_graph_and_variables(sess,
-                                         [tf.saved_model.tag_constants.SERVING],
-                                         signature_def_map={'serving_default':signature})
-    builder.save(as_text=True)
-
-file_writer.close()
diff --git a/model-integration/src/test/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py b/model-integration/src/test/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py
deleted file mode 100644
index 07a9fa4a213..00000000000
--- a/model-integration/src/test/models/tensorflow/mnist_softmax/mnist_sftmax_with_saving.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# Copyright 2018 Yahoo Holdings. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""A very simple MNIST classifier.
-
-See extensive documentation at
-https://www.tensorflow.org/get_started/mnist/beginners
-"""
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import argparse
-import sys
-
-from tensorflow.examples.tutorials.mnist import input_data
-
-import tensorflow as tf
-
-FLAGS = None
-
-
-def main(_):
-  # Import data
-  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
-
-  # Create the model
-  x = tf.placeholder(tf.float32, [None, 784])
-  W = tf.Variable(tf.zeros([784, 10]))
-  b = tf.Variable(tf.zeros([10]))
-  y = tf.matmul(x, W) + b
-
-  # Define loss and optimizer
-  y_ = tf.placeholder(tf.float32, [None, 10])
-
-  # The raw formulation of cross-entropy,
-  #
-  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
-  #                                 reduction_indices=[1]))
-  #
-  # can be numerically unstable.
-  #
-  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
-  # outputs of 'y', and then average across the batch.
-  cross_entropy = tf.reduce_mean(
-      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
-  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
-
-  sess = tf.InteractiveSession()
-  tf.global_variables_initializer().run()
-  # Train
-  for _ in range(1000):
-    batch_xs, batch_ys = mnist.train.next_batch(100)
-    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
-
-  # Test trained model
-  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
-  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-  print(sess.run(accuracy, feed_dict={x: mnist.test.images,
-                                      y_: mnist.test.labels}))
-
-  # Save the model
-  export_path = "saved"
-  print('Exporting trained model to ', export_path)
-  builder = tf.saved_model.builder.SavedModelBuilder(export_path)
-  signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':x}, outputs = {'y':y})
-  builder.add_meta_graph_and_variables(sess,
-                                       [tf.saved_model.tag_constants.SERVING],
-                                       signature_def_map={'serving_default':signature})
-  builder.save(as_text=True)
-
-if __name__ == '__main__':
-  parser = argparse.ArgumentParser()
-  parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
-                      help='Directory for storing input data')
-  FLAGS, unparsed = parser.parse_known_args()
-  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
diff --git a/model-integration/src/test/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt b/model-integration/src/test/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt
deleted file mode 100644
index 8100dfd594d..00000000000
--- a/model-integration/src/test/models/tensorflow/mnist_softmax/saved/saved_model.pbtxt
+++ /dev/null
@@ -1,5039 +0,0 @@
-saved_model_schema_version: 1
-meta_graphs {
-  meta_info_def {
-    stripped_op_list {
-      op {
-        name: "Add"
-        input_arg {
-          name: "x"
-          type_attr: "T"
-        }
-        input_arg {
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-          type_attr: "T"
-        }
-        output_arg {
-          name: "z"
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-          }
-        }
-      }
-      op {
-        name: "ApplyGradientDescent"
-        input_arg {
-          name: "var"
-          type_attr: "T"
-          is_ref: true
-        }
-        input_arg {
-          name: "alpha"
-          type_attr: "T"
-        }
-        input_arg {
-          name: "delta"
-          type_attr: "T"
-        }
-        output_arg {
-          name: "out"
-          type_attr: "T"
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-        attr {
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-          type: "bool"
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-      }
-      op {
-        name: "ArgMax"
-        input_arg {
-          name: "input"
-          type_attr: "T"
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-        input_arg {
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-          type_attr: "Tidx"
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diff --git a/model-integration/src/test/models/tensorflow/mnist_softmax/tf_2_onnx/variables/variables.data-00000-of-00001 b/model-integration/src/test/models/tensorflow/mnist_softmax/tf_2_onnx/variables/variables.data-00000-of-00001
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diff --git a/model-integration/src/test/models/tensorflow/regression/test1/saved_model.pbtxt b/model-integration/src/test/models/tensorflow/regression/test1/saved_model.pbtxt
deleted file mode 100644
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-    restore_op_name: "save/restore_all"
-    max_to_keep: 5
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diff --git a/model-integration/src/test/models/tensorflow/softmax/saved/variables/variables.data-00000-of-00001 b/model-integration/src/test/models/tensorflow/softmax/saved/variables/variables.data-00000-of-00001
deleted file mode 100644
index a9edaf376d0..00000000000
--- a/model-integration/src/test/models/tensorflow/softmax/saved/variables/variables.data-00000-of-00001
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/softmax/saved/variables/variables.index b/model-integration/src/test/models/tensorflow/softmax/saved/variables/variables.index
deleted file mode 100644
index 0ae49491ce6..00000000000
--- a/model-integration/src/test/models/tensorflow/softmax/saved/variables/variables.index
+++ /dev/null
diff --git a/model-integration/src/test/models/tensorflow/softmax/softmax.py b/model-integration/src/test/models/tensorflow/softmax/softmax.py
deleted file mode 100644
index aab9956f914..00000000000
--- a/model-integration/src/test/models/tensorflow/softmax/softmax.py
+++ /dev/null
@@ -1,29 +0,0 @@
-# Copyright 2019 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
-
-import numpy as np
-import tensorflow as tf
-
-# Creates simple random neural network that has softmax on output. No training.
-
-n_inputs = 5
-n_outputs = 3
-
-input = tf.placeholder(tf.float32, shape=(None, n_inputs), name="input")
-W = tf.Variable(tf.random.uniform([n_inputs, n_outputs]), name="weights")
-b = tf.Variable(tf.random.uniform([n_outputs]), name="bias")
-Z = tf.matmul(input, W) + b
-hidden_layer = tf.nn.relu(Z)
-output_layer = tf.nn.softmax(hidden_layer, name="output")
-
-init = tf.global_variables_initializer()
-
-with tf.Session() as sess:
-    init.run()
-    export_path = "saved"
-    builder = tf.saved_model.builder.SavedModelBuilder(export_path)
-    signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':input}, outputs = {'y':output_layer})
-    builder.add_meta_graph_and_variables(sess,
-                                         [tf.saved_model.tag_constants.SERVING],
-                                         signature_def_map={'serving_default':signature})
-    builder.save(as_text=True)
-