diff --git a/src/core/infer.h b/src/core/infer.h
index 035c3115e0..6655510606 100644
--- a/src/core/infer.h
+++ b/src/core/infer.h
@@ -132,7 +132,6 @@ class HTTPInferRequestProvider : public InferRequestProvider {
   std::vector<std::vector<char>> contiguous_buffers_;
 };
 
-
 // Provide inference request outputs
 class InferResponseProvider {
  public:
@@ -236,7 +235,6 @@ class HTTPInferResponseProvider : public InferResponseProvider {
   size_t total_raw_byte_size_;
 };
 
-
 // Interface for servables that handle generic inference requests.
 class InferenceServable {
  public:
diff --git a/src/core/request_status.cc b/src/core/request_status.cc
index d9839094f8..8f1fd08125 100644
--- a/src/core/request_status.cc
+++ b/src/core/request_status.cc
@@ -26,7 +26,6 @@
 
 #include "src/core/request_status.h"
 
-
 namespace nvidia { namespace inferenceserver {
 
 namespace {
diff --git a/src/core/server.cc b/src/core/server.cc
index 3c1c560bc1..c5d57348a9 100644
--- a/src/core/server.cc
+++ b/src/core/server.cc
@@ -65,6 +65,7 @@
 #include "src/servables/tensorflow/graphdef_bundle.h"
 #include "src/servables/tensorflow/graphdef_bundle.pb.h"
 #include "src/servables/tensorflow/savedmodel_bundle.h"
+#include "src/servables/tensorflow/savedmodel_bundle.pb.h"
 #include "src/servables/tensorrt/plan_bundle.h"
 #include "src/servables/tensorrt/plan_bundle.pb.h"
 #include "tensorflow/core/lib/core/status.h"
@@ -82,7 +83,6 @@
 #include "tensorflow_serving/core/availability_preserving_policy.h"
 #include "tensorflow_serving/core/servable_handle.h"
 #include "tensorflow_serving/model_servers/server_core.h"
-#include "tensorflow_serving/servables/tensorflow/saved_model_bundle_source_adapter.pb.h"
 #include "tensorflow_serving/util/net_http/server/public/httpserver.h"
 #include "tensorflow_serving/util/net_http/server/public/response_code_enum.h"
 #include "tensorflow_serving/util/net_http/server/public/server_request_interface.h"
@@ -535,6 +535,7 @@ InferenceServer::InferenceServer()
   grpc_port_ = 8001;
   metrics_port_ = 8002;
   http_thread_cnt_ = 8;
+  strict_model_config_ = false;
   strict_readiness_ = true;
   model_load_unload_enabled_ = true;
   profiling_enabled_ = false;
@@ -567,6 +568,7 @@ InferenceServer::Init(int argc, char** argv)
   // On error, the init process will stop.
   // The difference is if the server will be terminated.
   bool exit_on_error = true;
+  bool strict_model_config = strict_model_config_;
   bool strict_readiness = strict_readiness_;
   bool allow_model_load_unload = model_load_unload_enabled_;
   bool allow_profiling = profiling_enabled_;
@@ -604,13 +606,20 @@ InferenceServer::Init(int argc, char** argv)
       "config instead of the default platform."),
     tensorflow::Flag(
       "exit-on-error", &exit_on_error,
-      "Exit the inference server if an error occurs during initialization."),
+      "Exit the inference server if an error occurs during "
+      "initialization."),
+    tensorflow::Flag(
+      "strict-model-config", &strict_model_config,
+      "If true model configuration files must be provided and all required "
+      "configuration settings must be specified. If false the model "
+      "configuration may be absent or only partially specified and the "
+      "server will attempt to derive the missing required configuration."),
     tensorflow::Flag(
       "strict-readiness", &strict_readiness,
       "If true /api/health/ready endpoint indicates ready if the server "
-      "is responsive and all models are available. If false /api/health/ready "
-      "endpoint indicates ready if server is responsive even if some/all "
-      "models are unavailable."),
+      "is responsive and all models are available. If false "
+      "/api/health/ready endpoint indicates ready if server is responsive even "
+      "if some/all models are unavailable."),
     tensorflow::Flag(
       "allow-model-load-unload", &allow_model_load_unload,
       "Allow models to be loaded and unloaded dynamically based on changes "
@@ -639,13 +648,13 @@ InferenceServer::Init(int argc, char** argv)
       "Number of threads handling HTTP requests."),
     tensorflow::Flag(
       "file-system-poll-secs", &file_system_poll_secs,
-      "Interval in seconds between each poll of the file "
-      "system for changes to the model store."),
+      "Interval in seconds between each poll of the file system for changes to "
+      "the model store."),
     tensorflow::Flag(
       "exit-timeout-secs", &exit_timeout_secs,
-      "Timeout (in seconds) when exiting to wait for in-flight inferences "
-      "to finish. After the timeout expires the server exits even if "
-      "inferences are still in flight."),
+      "Timeout (in seconds) when exiting to wait for in-flight inferences to "
+      "finish. After the timeout expires the server exits even if inferences "
+      "are still in flight."),
     tensorflow::Flag(
       "tf-allow-soft-placement", &tf_allow_soft_placement,
       "Instruct TensorFlow to use CPU implementation of an operation when a "
@@ -654,8 +663,8 @@ InferenceServer::Init(int argc, char** argv)
       "tf-gpu-memory-fraction", &tf_gpu_memory_fraction,
       "Reserve a portion of GPU memory for TensorFlow models. Default value "
       "0.0 indicates that TensorFlow should dynamically allocate memory as "
-      "needed. Value of 1.0 indicates that TensorFlow should allocate all "
-      "of GPU memory."),
+      "needed. Value of 1.0 indicates that TensorFlow should allocate all of "
+      "GPU memory."),
   };
 
   std::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
@@ -681,6 +690,7 @@ InferenceServer::Init(int argc, char** argv)
   metrics_port_ = allow_metrics ? metrics_port : -1;
   model_store_path_ = model_store_path;
   http_thread_cnt_ = http_thread_cnt;
+  strict_model_config_ = strict_model_config;
   strict_readiness_ = strict_readiness;
   model_load_unload_enabled_ = allow_model_load_unload;
   profiling_enabled_ = allow_profiling;
@@ -1315,6 +1325,8 @@ InferenceServer::BuildPlatformConfigMap(
   {
     GraphDefBundleSourceAdapterConfig graphdef_config;
 
+    graphdef_config.set_autofill(!strict_model_config_);
+
     // Tensorflow session config
     if (tf_gpu_memory_fraction == 0.0) {
       graphdef_config.mutable_session_config()
@@ -1333,35 +1345,36 @@ InferenceServer::BuildPlatformConfigMap(
 
   //// Tensorflow SavedModel
   {
-    tfs::SavedModelBundleSourceAdapterConfig saved_model_config;
+    SavedModelBundleSourceAdapterConfig saved_model_config;
+
+    saved_model_config.set_autofill(!strict_model_config_);
 
     if (tf_gpu_memory_fraction == 0.0) {
-      saved_model_config.mutable_legacy_config()
-        ->mutable_session_config()
+      saved_model_config.mutable_session_config()
         ->mutable_gpu_options()
         ->set_allow_growth(true);
     } else {
-      saved_model_config.mutable_legacy_config()
-        ->mutable_session_config()
+      saved_model_config.mutable_session_config()
         ->mutable_gpu_options()
         ->set_per_process_gpu_memory_fraction(tf_gpu_memory_fraction);
     }
 
-    saved_model_config.mutable_legacy_config()
-      ->mutable_session_config()
-      ->set_allow_soft_placement(tf_allow_soft_placement);
+    saved_model_config.mutable_session_config()->set_allow_soft_placement(
+      tf_allow_soft_placement);
     saved_model_source_adapter_config.PackFrom(saved_model_config);
   }
 
   //// Caffe NetDef
   {
     NetDefBundleSourceAdapterConfig netdef_config;
+    netdef_config.set_autofill(!strict_model_config_);
     netdef_source_adapter_config.PackFrom(netdef_config);
   }
 
   //// TensorRT
   {
     PlanBundleSourceAdapterConfig plan_config;
+    plan_config.set_autofill(!strict_model_config_);
     plan_source_adapter_config.PackFrom(plan_config);
   }
 
@@ -1404,7 +1417,12 @@ InferenceServer::BuildModelConfig(
   for (const auto& child : real_children) {
     const auto full_path = tensorflow::io::JoinPath(model_store_path_, child);
     ModelConfig* model_config = model_configs->add_config();
-    TF_RETURN_IF_ERROR(GetNormalizedModelConfig(full_path, model_config));
+
+    // If enabled, try to automatically generate missing parts of the
+    // model configuration from the model definition. In all cases
+    // normalize and validate the config.
+    TF_RETURN_IF_ERROR(
+      GetNormalizedModelConfig(full_path, !strict_model_config_, model_config));
     TF_RETURN_IF_ERROR(ValidateModelConfig(*model_config, std::string()));
 
     // Make sure the name of the model matches the name of the
diff --git a/src/core/server.h b/src/core/server.h
index 74d37722c2..e3c7f7304f 100644
--- a/src/core/server.h
+++ b/src/core/server.h
@@ -157,6 +157,7 @@ class InferenceServer {
 
   std::string model_store_path_;
   int http_thread_cnt_;
+  bool strict_model_config_;
   bool strict_readiness_;
   bool model_load_unload_enabled_;
   bool profiling_enabled_;
diff --git a/src/core/utils.cc b/src/core/utils.cc
index 506bae0851..8197313b00 100644
--- a/src/core/utils.cc
+++ b/src/core/utils.cc
@@ -34,6 +34,81 @@
 
 namespace nvidia { namespace inferenceserver {
 
+namespace {
+
+tensorflow::Status
+GetAutoFillPlatform(
+  const tensorflow::StringPiece& model_name,
+  const tensorflow::StringPiece& path, std::string* platform)
+{
+  const std::string model_path(path);
+
+  // Find version subdirectories...
+  std::vector<std::string> versions;
+  TF_RETURN_IF_ERROR(
+    tensorflow::Env::Default()->GetChildren(model_path, &versions));
+
+  // GetChildren() returns all descendants instead for cloud storage
+  // like GCS. In such case we should filter out all non-direct
+  // descendants.
+  std::set<std::string> real_versions;
+  for (size_t i = 0; i < versions.size(); ++i) {
+    const std::string& version = versions[i];
+    real_versions.insert(version.substr(0, version.find_first_of('/')));
+  }
+
+  if (real_versions.empty()) {
+    return tensorflow::errors::NotFound(
+      "no version sub-directories for model '", model_name, "'");
+  }
+
+  // If a default named file/directory exists in a version
+  // sub-directory then assume the corresponding platform.
+  for (const auto& version : real_versions) {
+    const auto vp = tensorflow::io::JoinPath(model_path, version);
+
+    // TensorRT
+    if (tensorflow::Env::Default()
+          ->FileExists(tensorflow::io::JoinPath(vp, kTensorRTPlanFilename))
+          .ok()) {
+      *platform = kTensorRTPlanPlatform;
+      return tensorflow::Status::OK();
+    }
+
+    // TensorFlow
+    if (tensorflow::Env::Default()
+          ->FileExists(
+            tensorflow::io::JoinPath(vp, kTensorFlowSavedModelFilename))
+          .ok()) {
+      *platform = kTensorFlowSavedModelPlatform;
+      return tensorflow::Status::OK();
+    }
+    if (tensorflow::Env::Default()
+          ->FileExists(
+            tensorflow::io::JoinPath(vp, kTensorFlowGraphDefFilename))
+          .ok()) {
+      *platform = kTensorFlowGraphDefPlatform;
+      return tensorflow::Status::OK();
+    }
+
+    // Caffe2
+    if (tensorflow::Env::Default()
+          ->FileExists(tensorflow::io::JoinPath(vp, kCaffe2NetDefFilename))
+          .ok()) {
+      *platform = kCaffe2NetDefPlatform;
+      return tensorflow::Status::OK();
+    }
+  }
+
+  return tensorflow::errors::NotFound(
+    "unable to derive platform for model '", model_name, "', the model ",
+    "definition file must be named '", kTensorRTPlanFilename, "', '",
+    kTensorFlowGraphDefFilename, "', '", kTensorFlowSavedModelFilename,
+    "', or '", kCaffe2NetDefFilename, "'");
+}
+
+}  // namespace
+
 tensorflow::Status
 GetModelVersionFromPath(const tensorflow::StringPiece& path, uint32_t* version)
 {
@@ -50,11 +125,29 @@ GetModelVersionFromPath(const tensorflow::StringPiece& path, uint32_t* version)
 
 tensorflow::Status
 GetNormalizedModelConfig(
-  const tensorflow::StringPiece& path, ModelConfig* config)
+  const tensorflow::StringPiece& path, const bool autofill, ModelConfig* config)
 {
+  // If 'autofill' then the configuration file can be empty.
   const auto config_path = tensorflow::io::JoinPath(path, kModelConfigPbTxt);
-  TF_RETURN_IF_ERROR(
-    ReadTextProto(tensorflow::Env::Default(), config_path, config));
+  if (autofill && !tensorflow::Env::Default()->FileExists(config_path).ok()) {
+    config->Clear();
+  } else {
+    TF_RETURN_IF_ERROR(
+      ReadTextProto(tensorflow::Env::Default(), config_path, config));
+  }
+
+  // Autofill the platform and name...
+  if (autofill) {
+    const std::string model_name(tensorflow::io::Basename(path));
+    if (config->name().empty()) {
+      config->set_name(model_name);
+    }
+
+    if (config->platform().empty()) {
+      TF_RETURN_IF_ERROR(
+        GetAutoFillPlatform(model_name, path, config->mutable_platform()));
+    }
+  }
 
   // If 'default_model_filename' is not specified set it appropriately
   // based upon 'platform'.
diff --git a/src/core/utils.h b/src/core/utils.h
index 7479202a94..803089279d 100644
--- a/src/core/utils.h
+++ b/src/core/utils.h
@@ -36,9 +36,11 @@ tensorflow::Status GetModelVersionFromPath(
 
 // Read a ModelConfig and normalize it as expected by model servables.
 // 'path' should be the full-path to the directory containing the
-// model configuration.
+// model configuration. If 'autofill' then attempt to determine any
+// missing required configuration from the model definition.
 tensorflow::Status GetNormalizedModelConfig(
-  const tensorflow::StringPiece& path, ModelConfig* config);
+  const tensorflow::StringPiece& path, const bool autofill,
+  ModelConfig* config);
 
 // Validate that a model is specified correctly (excluding inputs and
 // outputs which are validated via ValidateModelInput() and
diff --git a/src/servables/caffe2/netdef_bundle.proto b/src/servables/caffe2/netdef_bundle.proto
index d56f39ea4f..efc741a6c2 100644
--- a/src/servables/caffe2/netdef_bundle.proto
+++ b/src/servables/caffe2/netdef_bundle.proto
@@ -29,4 +29,9 @@ syntax = "proto3";
 package nvidia.inferenceserver;
 
 // Config proto for NetDefBundleSourceAdapter.
-message NetDefBundleSourceAdapterConfig {}
+message NetDefBundleSourceAdapterConfig
+{
+  // Autofill missing required model configuration settings based on
+  // model definition file.
+  bool autofill = 1;
+}
diff --git a/src/servables/caffe2/netdef_bundle_source_adapter.cc b/src/servables/caffe2/netdef_bundle_source_adapter.cc
index 5dbe5a4708..f91ac6a1a3 100644
--- a/src/servables/caffe2/netdef_bundle_source_adapter.cc
+++ b/src/servables/caffe2/netdef_bundle_source_adapter.cc
@@ -43,12 +43,15 @@ namespace {
 
 tensorflow::Status
 CreateNetDefBundle(
+  const NetDefBundleSourceAdapterConfig& adapter_config,
   const std::string& path, std::unique_ptr<NetDefBundle>* bundle)
 {
   const auto model_path = tensorflow::io::Dirname(path);
 
-  ModelConfig config;
-  TF_RETURN_IF_ERROR(GetNormalizedModelConfig(model_path, &config));
+  ModelConfig model_config;
+  model_config.set_platform(kCaffe2NetDefPlatform);
+  TF_RETURN_IF_ERROR(GetNormalizedModelConfig(
+    model_path, adapter_config.autofill(), &model_config));
 
   // Read all the netdef files in 'path'. GetChildren() returns all
   // descendants instead for cloud storage like GCS, so filter out all
@@ -74,7 +77,7 @@ CreateNetDefBundle(
   // Create the bundle for the model and all the execution contexts
   // requested for this model.
   bundle->reset(new NetDefBundle);
-  tensorflow::Status status = (*bundle)->Init(path, config);
+  tensorflow::Status status = (*bundle)->Init(path, model_config);
   if (status.ok()) {
     status = (*bundle)->CreateExecutionContexts(models);
   }
@@ -97,8 +100,11 @@ NetDefBundleSourceAdapter::Create(
   LOG_VERBOSE(1) << "Create NetDefBundleSourceAdaptor for config \""
                  << config.DebugString() << "\"";
 
+  Creator creator = std::bind(
+    &CreateNetDefBundle, config, std::placeholders::_1, std::placeholders::_2);
+
   adapter->reset(new NetDefBundleSourceAdapter(
-    config, CreateNetDefBundle, SimpleSourceAdapter::EstimateNoResources()));
+    config, creator, SimpleSourceAdapter::EstimateNoResources()));
   return tensorflow::Status::OK();
 }
 
@@ -114,5 +120,4 @@ namespace tensorflow { namespace serving {
 REGISTER_STORAGE_PATH_SOURCE_ADAPTER(
   nvidia::inferenceserver::NetDefBundleSourceAdapter,
   nvidia::inferenceserver::NetDefBundleSourceAdapterConfig);
-
 }}  // namespace tensorflow::serving
diff --git a/src/servables/tensorflow/BUILD b/src/servables/tensorflow/BUILD
index 98d9a635a6..ea8133a467 100644
--- a/src/servables/tensorflow/BUILD
+++ b/src/servables/tensorflow/BUILD
@@ -40,6 +40,16 @@ serving_proto_library(
     ],
 )
 
+serving_proto_library(
+    name = "savedmodel_bundle_proto",
+    srcs = ["savedmodel_bundle.proto"],
+    cc_api_version = 2,
+    deps = [
+        "@org_tensorflow//tensorflow/core:protos_all_cc",
+        "@protobuf_archive//:cc_wkt_protos",
+    ],
+)
+
 cc_library(
     name = "base_bundle",
     srcs = ["base_bundle.cc"],
@@ -103,12 +113,12 @@ cc_library(
     hdrs = ["savedmodel_bundle_source_adapter.h"],
     deps = [
         ":savedmodel_bundle",
+        ":savedmodel_bundle_proto",
         "//src/core:utils",
         "@tf_serving//tensorflow_serving/core:loader",
         "@tf_serving//tensorflow_serving/core:simple_loader",
         "@tf_serving//tensorflow_serving/core:source_adapter",
         "@tf_serving//tensorflow_serving/core:storage_path",
-        "@tf_serving//tensorflow_serving/servables/tensorflow:saved_model_bundle_source_adapter_proto",
         "@tf_serving//tensorflow_serving/util:optional",
         "@org_tensorflow//tensorflow/core:core_cpu",
         "@org_tensorflow//tensorflow/core:lib",
diff --git a/src/servables/tensorflow/graphdef_bundle.proto b/src/servables/tensorflow/graphdef_bundle.proto
index e9b25f245b..64cf9be17c 100644
--- a/src/servables/tensorflow/graphdef_bundle.proto
+++ b/src/servables/tensorflow/graphdef_bundle.proto
@@ -36,4 +36,8 @@ message GraphDefBundleSourceAdapterConfig
   // TensorFlow Session configuration options.
   // See details at tensorflow/core/protobuf/config.proto.
   tensorflow.ConfigProto session_config = 1;
+
+  // Autofill missing required model configuration settings based on
+  // model definition file.
+  bool autofill = 2;
 }
diff --git a/src/servables/tensorflow/graphdef_bundle_source_adapter.cc b/src/servables/tensorflow/graphdef_bundle_source_adapter.cc
index 7194ad1b28..8885826169 100644
--- a/src/servables/tensorflow/graphdef_bundle_source_adapter.cc
+++ b/src/servables/tensorflow/graphdef_bundle_source_adapter.cc
@@ -49,7 +49,8 @@ CreateGraphDefBundle(
   const auto model_path = tensorflow::io::Dirname(path);
 
   ModelConfig model_config;
-  TF_RETURN_IF_ERROR(GetNormalizedModelConfig(model_path, &model_config));
+  TF_RETURN_IF_ERROR(GetNormalizedModelConfig(
+    model_path, adapter_config.autofill(), &model_config));
 
   // Read all the graphdef files in 'path'. GetChildren() returns all
   // descendants instead for cloud storage like GCS, so filter out all
diff --git a/src/servables/tensorflow/savedmodel_bundle.proto b/src/servables/tensorflow/savedmodel_bundle.proto
new file mode 100644
index 0000000000..27b0f5f404
--- /dev/null
+++ b/src/servables/tensorflow/savedmodel_bundle.proto
@@ -0,0 +1,43 @@
+// Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+//
+// Redistribution and use in source and binary forms, with or without
+// modification, are permitted provided that the following conditions
+// are met:
+//  * Redistributions of source code must retain the above copyright
+//    notice, this list of conditions and the following disclaimer.
+//  * Redistributions in binary form must reproduce the above copyright
+//    notice, this list of conditions and the following disclaimer in the
+//    documentation and/or other materials provided with the distribution.
+//  * Neither the name of NVIDIA CORPORATION nor the names of its
+//    contributors may be used to endorse or promote products derived
+//    from this software without specific prior written permission.
+//
+// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+// PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+// OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+syntax = "proto3";
+
+import "tensorflow/core/protobuf/config.proto";
+
+package nvidia.inferenceserver;
+
+// Config proto for SavedModelBundleSourceAdapter.
+message SavedModelBundleSourceAdapterConfig
+{
+  // TensorFlow Session configuration options.
+  // See details at tensorflow/core/protobuf/config.proto.
+  tensorflow.ConfigProto session_config = 1;
+
+  // Autofill missing required model configuration settings based on
+  // model definition file.
+  bool autofill = 2;
+}
diff --git a/src/servables/tensorflow/savedmodel_bundle_source_adapter.cc b/src/servables/tensorflow/savedmodel_bundle_source_adapter.cc
index 35fde1b361..05a97eed01 100644
--- a/src/servables/tensorflow/savedmodel_bundle_source_adapter.cc
+++ b/src/servables/tensorflow/savedmodel_bundle_source_adapter.cc
@@ -43,13 +43,15 @@ namespace {
 
 tensorflow::Status
 CreateSavedModelBundle(
-  const tfs::SavedModelBundleSourceAdapterConfig& adapter_config,
+  const SavedModelBundleSourceAdapterConfig& adapter_config,
   const std::string& path, std::unique_ptr<SavedModelBundle>* bundle)
 {
   const auto model_path = tensorflow::io::Dirname(path);
 
   ModelConfig model_config;
-  TF_RETURN_IF_ERROR(GetNormalizedModelConfig(model_path, &model_config));
+  model_config.set_platform(kTensorFlowSavedModelPlatform);
+  TF_RETURN_IF_ERROR(GetNormalizedModelConfig(
+    model_path, adapter_config.autofill(), &model_config));
 
   // Read all the savedmodel directories in 'path'. GetChildren()
   // returns all descendants instead for cloud storage like GCS, so
@@ -74,7 +76,7 @@ CreateSavedModelBundle(
   tensorflow::Status status = (*bundle)->Init(path, model_config);
   if (status.ok()) {
     status = (*bundle)->CreateExecutionContexts(
-      adapter_config.legacy_config().session_config(), savedmodel_paths);
+      adapter_config.session_config(), savedmodel_paths);
   }
   if (!status.ok()) {
     bundle->reset();
@@ -85,10 +87,9 @@ CreateSavedModelBundle(
 
 }  // namespace
 
-
 tensorflow::Status
 SavedModelBundleSourceAdapter::Create(
-  const tfs::SavedModelBundleSourceAdapterConfig& config,
+  const SavedModelBundleSourceAdapterConfig& config,
   std::unique_ptr<
     tfs::SourceAdapter<tfs::StoragePath, std::unique_ptr<tfs::Loader>>>*
     adapter)
@@ -116,6 +117,5 @@ namespace tensorflow { namespace serving {
 
 REGISTER_STORAGE_PATH_SOURCE_ADAPTER(
   nvidia::inferenceserver::SavedModelBundleSourceAdapter,
-  SavedModelBundleSourceAdapterConfig);
-
+  nvidia::inferenceserver::SavedModelBundleSourceAdapterConfig);
 }}  // namespace tensorflow::serving
diff --git a/src/servables/tensorflow/savedmodel_bundle_source_adapter.h b/src/servables/tensorflow/savedmodel_bundle_source_adapter.h
index be301588ea..3bba952747 100644
--- a/src/servables/tensorflow/savedmodel_bundle_source_adapter.h
+++ b/src/servables/tensorflow/savedmodel_bundle_source_adapter.h
@@ -26,12 +26,12 @@
 #pragma once
 
 #include "src/servables/tensorflow/savedmodel_bundle.h"
+#include "src/servables/tensorflow/savedmodel_bundle.pb.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow_serving/core/loader.h"
 #include "tensorflow_serving/core/simple_loader.h"
 #include "tensorflow_serving/core/storage_path.h"
-#include "tensorflow_serving/servables/tensorflow/saved_model_bundle_source_adapter.pb.h"
 
 namespace tfs = tensorflow::serving;
 
@@ -44,7 +44,7 @@ class SavedModelBundleSourceAdapter final
         tfs::StoragePath, SavedModelBundle> {
  public:
   static tensorflow::Status Create(
-    const tfs::SavedModelBundleSourceAdapterConfig& config,
+    const SavedModelBundleSourceAdapterConfig& config,
     std::unique_ptr<
       SourceAdapter<tfs::StoragePath, std::unique_ptr<tfs::Loader>>>* adapter);
 
@@ -56,14 +56,14 @@ class SavedModelBundleSourceAdapter final
     tfs::SimpleLoaderSourceAdapter<tfs::StoragePath, SavedModelBundle>;
 
   SavedModelBundleSourceAdapter(
-    const tfs::SavedModelBundleSourceAdapterConfig& config,
+    const SavedModelBundleSourceAdapterConfig& config,
     typename SimpleSourceAdapter::Creator creator,
     typename SimpleSourceAdapter::ResourceEstimator resource_estimator)
       : SimpleSourceAdapter(creator, resource_estimator), config_(config)
   {
   }
 
-  const tfs::SavedModelBundleSourceAdapterConfig config_;
+  const SavedModelBundleSourceAdapterConfig config_;
 };
 
 }}  // namespace nvidia::inferenceserver
diff --git a/src/servables/tensorrt/plan_bundle.proto b/src/servables/tensorrt/plan_bundle.proto
index a32245bd33..59d1d37bbd 100644
--- a/src/servables/tensorrt/plan_bundle.proto
+++ b/src/servables/tensorrt/plan_bundle.proto
@@ -29,4 +29,9 @@ syntax = "proto3";
 package nvidia.inferenceserver;
 
 // Config proto for PlanBundleSourceAdapter.
-message PlanBundleSourceAdapterConfig {}
+message PlanBundleSourceAdapterConfig
+{
+  // Autofill missing required model configuration settings based on
+  // model definition file.
+  bool autofill = 1;
+}
diff --git a/src/servables/tensorrt/plan_bundle_source_adapter.cc b/src/servables/tensorrt/plan_bundle_source_adapter.cc
index fdc096197d..e8bb533957 100644
--- a/src/servables/tensorrt/plan_bundle_source_adapter.cc
+++ b/src/servables/tensorrt/plan_bundle_source_adapter.cc
@@ -42,12 +42,16 @@ namespace nvidia { namespace inferenceserver {
 namespace {
 
 tensorflow::Status
-CreatePlanBundle(const std::string& path, std::unique_ptr<PlanBundle>* bundle)
+CreatePlanBundle(
+  const PlanBundleSourceAdapterConfig& adapter_config, const std::string& path,
+  std::unique_ptr<PlanBundle>* bundle)
 {
   const auto model_path = tensorflow::io::Dirname(path);
 
-  ModelConfig config;
-  TF_RETURN_IF_ERROR(GetNormalizedModelConfig(model_path, &config));
+  ModelConfig model_config;
+  model_config.set_platform(kTensorRTPlanPlatform);
+  TF_RETURN_IF_ERROR(GetNormalizedModelConfig(
+    model_path, adapter_config.autofill(), &model_config));
 
   // Read all the plan files in 'path'. GetChildren() returns all
   // descendants instead for cloud storage like GCS, so filter out all
@@ -73,7 +77,7 @@ CreatePlanBundle(const std::string& path, std::unique_ptr<PlanBundle>* bundle)
   // Create the bundle for the model and all the execution contexts
   // requested for this model.
   bundle->reset(new PlanBundle);
-  tensorflow::Status status = (*bundle)->Init(path, config);
+  tensorflow::Status status = (*bundle)->Init(path, model_config);
   if (status.ok()) {
     status = (*bundle)->CreateExecutionContexts(models);
   }
@@ -86,7 +90,6 @@ CreatePlanBundle(const std::string& path, std::unique_ptr<PlanBundle>* bundle)
 
 }  // namespace
 
-
 tensorflow::Status
 PlanBundleSourceAdapter::Create(
   const PlanBundleSourceAdapterConfig& config,
@@ -96,8 +99,11 @@ PlanBundleSourceAdapter::Create(
   LOG_VERBOSE(1) << "Create PlanBundleSourceAdaptor for config \""
                  << config.DebugString() << "\"";
 
+  Creator creator = std::bind(
+    &CreatePlanBundle, config, std::placeholders::_1, std::placeholders::_2);
+
   adapter->reset(new PlanBundleSourceAdapter(
-    config, CreatePlanBundle, SimpleSourceAdapter::EstimateNoResources()));
+    config, creator, SimpleSourceAdapter::EstimateNoResources()));
   return tensorflow::Status::OK();
 }
 
@@ -113,5 +119,4 @@ namespace tensorflow { namespace serving {
 REGISTER_STORAGE_PATH_SOURCE_ADAPTER(
   nvidia::inferenceserver::PlanBundleSourceAdapter,
   nvidia::inferenceserver::PlanBundleSourceAdapterConfig);
-
 }}  // namespace tensorflow::serving
diff --git a/src/test/model_config_test_base.cc b/src/test/model_config_test_base.cc
index f883ed2572..74aeffcde0 100644
--- a/src/test/model_config_test_base.cc
+++ b/src/test/model_config_test_base.cc
@@ -43,7 +43,7 @@ ModelConfigTestBase::ValidateInit(
   result->clear();
 
   ModelConfig config;
-  tensorflow::Status status = GetNormalizedModelConfig(path, &config);
+  tensorflow::Status status = GetNormalizedModelConfig(path, autofill, &config);
   if (!status.ok()) {
     result->append(status.ToString());
     return false;