.. # Copyright (c) 2018-2019, 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. .. _section-model-configuration: Model Configuration =================== Each model in a :ref:`section-model-repository` must include a model configuration that provides required and optional information about the model. Typically, this configuration is provided in a config.pbtxt file specified as :doc:`ModelConfig ` protobuf. In some cases, discussed in :ref:`section-generated-model-configuration`, the model configuration can be generated automatically by the inference server and so does not need to be provided explicitly. A minimal model configuration must specify :cpp:var:`name `, :cpp:var:`platform `, :cpp:var:`max_batch_size `, :cpp:var:`input `, and :cpp:var:`output `. As a running example consider a TensorRT model called *mymodel* that has two inputs, *input0* and *input1*, and one output, *output0*, all of which are 16 entry float32 tensors. The minimal configuration is:: name: "mymodel" platform: "tensorrt_plan" max_batch_size: 8 input [ { name: "input0" data_type: TYPE_FP32 dims: [ 16 ] }, { name: "input1" data_type: TYPE_FP32 dims: [ 16 ] } ] output [ { name: "output0" data_type: TYPE_FP32 dims: [ 16 ] } ] The name of the model must match the :cpp:var:`name ` of the model repository directory containing the model. The :cpp:var:`platform ` must be one of **tensorrt_plan**, **tensorflow_graphdef**, **tensorflow_savedmodel**, **caffe2_netdef**, or **custom**. The datatypes allowed for input and output tensors varies based on the type of the model. Section :ref:`section-datatypes` describes the allowed datatypes and how they map to the datatypes of each model type. Input shape specified by :cpp:var:`dims ` indicates the shape of input expected by the inference API. Output shape specified by :cpp:var:`dims ` indicates the shape of output returned by the inference API. Both input and output shape must have rank >= 1, that is, the empty shape **[ ]** is not allowed. The :ref:`reshape ` property must be used if the underlying framework model or custom backend requires an input or output with an empty shape. For models that support batched inputs the :cpp:var:`max_batch_size ` value must be >= 1. The TensorRT Inference Server assumes that the batching occurs along a first dimension that is not listed in the inputs or outputs. For the above example, the server expects to receive input tensors with shape **[ x, 16 ]** and produces an output tensor with shape **[ x, 16 ]**, where **x** is the batch size of the request. For models that do not support batched inputs the :cpp:var:`max_batch_size ` value must be zero. If the above example specified a :cpp:var:`max_batch_size ` of zero, the inference server would expect to receive input tensors with shape **[ 16 ]**, and would produce an output tensor with shape **[ 16 ]**. For models that support input and output tensors with variable-size dimensions, those dimensions can be listed as -1 in the input and output configuration. For example, if a model requires a 2-dimensional input tensor where the first dimension must be size 4 but the second dimension can be any size, the model configuration for that input would include **dims: [ 4, -1 ]**. The inference server would then accept inference requests where that input tensor's second dimension was any value >= 0. The model configuration can be more restrictive than what is allowed by the underlying model. For example, even though the model allows the second dimension to be any size, the model configuration could be specific as **dims: [ 4, 4 ]**. In this case, the inference server would only accept inference requests where the input tensor's shape was exactly **[ 4, 4 ]**. .. _section-generated-model-configuration: Generated Model Configuration ----------------------------- By default, the model configuration file containing the required settings must be provided with each model. However, if the server is started with the -\\-strict-model-config=false option, then in some cases the required portions of the model configuration file can be generated automatically by the inference server. The required portion of the model configuration are those settings shown in the example minimal configuration above. Specifically: * :ref:`TensorRT Plan ` models do not require a model configuration file because the inference server can derive all the required settings automatically. * Some :ref:`TensorFlow SavedModel ` models do not require a model configuration file. The models must specify all inputs and outputs as fixed-size tensors (with an optional initial batch dimension) for the model configuration to be generated automatically. The easiest way to determine if a particular SavedModel is supported is to try it with the server and check the console log and :ref:`Status API ` to determine if the model loaded successfully. When using -\\-strict-model-config=false you can see the model configuration that was generated for a model by using the :ref:`Status API `. The TensorRT Inference Server only generates the required portion of the model configuration file. You must still provide the optional portions of the model configuration if necessary, such as :cpp:var:`version_policy `, :cpp:var:`optimization `, :cpp:var:`scheduling and batching `, :cpp:var:`instance_group `, :cpp:var:`default_model_filename `, :cpp:var:`cc_model_filenames `, and :cpp:var:`tags `. .. _section-datatypes: Datatypes --------- The following table shows the tensor datatypes supported by the TensorRT Inference Server. The first column shows the name of the datatype as it appears in the model configuration file. The other columns show the corresponding datatype for the model frameworks supported by the server and for the Python numpy library. If a model framework does not have an entry for a given datatype, then the inference server does not support that datatype for that model. +--------------+--------------+--------------+--------------+--------------+ |Type |TensorRT |TensorFlow |Caffe2 |NumPy | +==============+==============+==============+==============+==============+ |TYPE_BOOL | |DT_BOOL |BOOL |bool | +--------------+--------------+--------------+--------------+--------------+ |TYPE_UINT8 | |DT_UINT8 |UINT8 |uint8 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_UINT16 | |DT_UINT16 |UINT16 |uint16 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_UINT32 | |DT_UINT32 | |uint32 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_UINT64 | |DT_UINT64 | |uint64 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_INT8 | kINT8 |DT_INT8 |INT8 |int8 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_INT16 | |DT_INT16 |INT16 |int16 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_INT32 | kINT32 |DT_INT32 |INT32 |int32 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_INT64 | |DT_INT64 |INT64 |int64 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_FP16 | kHALF |DT_HALF |FLOAT16 |float16 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_FP32 | kFLOAT |DT_FLOAT |FLOAT |float32 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_FP64 | |DT_DOUBLE |DOUBLE |float64 | +--------------+--------------+--------------+--------------+--------------+ |TYPE_STRING | |DT_STRING | |dtype(object) | +--------------+--------------+--------------+--------------+--------------+ For TensorRT each value is in the nvinfer1::DataType namespace. For example, nvinfer1::DataType::kFLOAT is the 32-bit floating-point datatype. For TensorFlow each value is in the tensorflow namespace. For example, tensorflow::DT_FLOAT is the 32-bit floating-point value. For Caffe2 each value is in the caffe2 namespace and is prepended with TensorProto\_DataType\_. For example, caffe2::TensorProto_DataType_FLOAT is the 32-bit floating-point datatype. For Numpy each value is in the numpy module. For example, numpy.float32 is the 32-bit floating-point datatype. .. _section-reshape: Reshape ------- The :cpp:var:`ModelTensorReshape ` property on a model configuration input or output is used to indicate that the input or output shape accepted by the inference API differs from the input or output shape expected or produced by the underlying framework model or custom backend. For an input, :cpp:var:`reshape ` can be used to reshape the input tensor to a different shape expected by the framework or backend. A common use-case is where a model that supports batching expects a batched input to have shape **[ batch-size ]**, which means that the batch dimension fully describes the shape. For the inference API the equivalent shape **[ batch-size, 1 ]** must be specified since each input in the batch must specify a non-empty shape. For this case the input should be specified as:: input [ { name: "in" dims: [ 1 ] reshape: { shape: [ ] } } ... For an output, :cpp:var:`reshape ` can be used to reshape the output tensor produced by the framework or backend to a different shape that is returned by the inference API. A common use-case is where a model that supports batching expects a batched output to have shape **[ batch-size ]**, which means that the batch dimension fully describes the shape. For the inference API the equivalent shape **[ batch-size, 1 ]** must be specified since each output in the batch must specify a non-empty shape. For this case the output should be specified as:: output [ { name: "in" dims: [ 1 ] reshape: { shape: [ ] } } ... .. _section-version-policy: Version Policy -------------- Each model can have one or more :ref:`versions available in the model repository `. The :cpp:var:`nvidia::inferenceserver::ModelVersionPolicy` schema allows the following policies. * :cpp:var:`All `: All versions of the model that are available in the model repository are available for inferencing. * :cpp:var:`Latest `: Only the latest ‘n’ versions of the model in the repository are available for inferencing. The latest versions of the model are the numerically greatest version numbers. * :cpp:var:`Specific `: Only the specifically listed versions of the model are available for inferencing. If no version policy is specified, then :cpp:var:`Latest ` (with num_version = 1) is used as the default, indicating that only the most recent version of the model is made available by the inference server. In all cases, the addition or removal of version subdirectories from the model repository can change which model version is used on subsequent inference requests. Continuing the above example, the following configuration specifies that all versions of the model will be available from the server:: name: "mymodel" platform: "tensorrt_plan" max_batch_size: 8 input [ { name: "input0" data_type: TYPE_FP32 dims: [ 16 ] }, { name: "input1" data_type: TYPE_FP32 dims: [ 16 ] } ] output [ { name: "output0" data_type: TYPE_FP32 dims: [ 16 ] } ] version_policy: { all { }} .. _section-instance-groups: Instance Groups --------------- The TensorRT Inference Server can provide multiple :ref:`execution instances ` of a model so that multiple simultaneous inference requests for that model can be handled simultaneously. The model configuration :cpp:var:`ModelInstanceGroup ` is used to specify the number of execution instances that should be made available and what compute resource should be used for those instances. By default, a single execution instance of the model is created for each GPU available in the system. The instance-group setting can be used to place multiple execution instances of a model on every GPU or on only certain GPUs. For example, the following configuration will place two execution instances of the model to be available on each system GPU:: instance_group [ { count: 2 kind: KIND_GPU } ] And the following configuration will place one execution instance on GPU 0 and two execution instances on GPUs 1 and 2:: instance_group [ { count: 1 kind: KIND_GPU gpus: [ 0 ] }, { count: 2 kind: KIND_GPU gpus: [ 1, 2 ] } ] The instance group setting is also used to enable exection of a model on the CPU. The following places two execution instances on the CPU:: instance_group [ { count: 2 kind: KIND_CPU } ] .. _section-scheduling-and-batching: Scheduling And Batching ----------------------- The TensorRT Inference Server supports batch inferencing by allowing individual inference requests to specify a batch of inputs. The inferencing for a batch of inputs is performed at the same time which is especially important for GPUs since it can greatly increase inferencing throughput. In many use-cases the individual inference requests are not batched, therefore, they do not benefit from the throughput benefits of batching. The inference server contains multiple scheduling and batching algorithms that support many different model types and use-cases. More information about model types and schedulers can be found in :ref:`section-models-and-schedulers`. .. _section-default-scheduler: Default Scheduler ^^^^^^^^^^^^^^^^^ The default scheduler is used for a model if none of the :cpp:var:`scheduling_choice ` configurations are specified. This scheduler distributes inference requests to all :ref:`instances ` configured for the model. .. _section-dynamic-batcher: Dynamic Batcher ^^^^^^^^^^^^^^^ Dynamic batching is a feature of the inference server that allows non-batched inference requests to be combined by the server, so that a batch is created dynamically, resulting in the same increased throughput seen for batched inference requests. The dynamic batcher should be used for :ref:`stateless ` models. The dynamically created batches are distributed to all :ref:`instances ` configured for the model. Dynamic batching is enabled and configured independently for each model using the :cpp:var:`ModelDynamicBatching ` settings in the model configuration. These settings control the preferred size(s) of the dynamically created batches as well as a maximum time that requests can be delayed in the scheduler to allow other requests to join the dynamic batch. The following configuration enables dynamic batching with preferred batch sizes of 4 and 8, and a maximum delay time of 100 microseconds:: dynamic_batching { preferred_batch_size: [ 4, 8 ] max_queue_delay_microseconds: 100 } The size of generated batches can be examined in aggregate using Count metrics, see :ref:`section-metrics`. Inference server verbose logging can be used to examine the size of individual batches. .. _section-sequence-batcher: Sequence Batcher ^^^^^^^^^^^^^^^^ Like the dynamic batcher, the sequence batcher combines non-batched inference requests, so that a batch is created dynamically. Unlike the dynamic batcher, the sequence batcher should be used for :ref:`stateful ` models where a sequence of inference requests must be routed to the same model instance. The dynamically created batches are distributed to all :ref:`instances ` configured for the model. Sequence batching is enabled and configured independently for each model using the :cpp:var:`ModelSequenceBatching ` settings in the model configuration. These settings control the sequence timeout as well as configuring how the inference server will send control signals to the model indicating sequence start and ready. See :ref:`section-models-and-schedulers` for more information and examples. The size of generated batches can be examined in aggregate using Count metrics, see :ref:`section-metrics`. Inference server verbose logging can be used to examine the size of individual batches. .. _section-optimization-policy: Optimization Policy ------------------- The model configuration :cpp:var:`ModelOptimizationPolicy ` is used to specify optimization and prioritization settings for a model. These settings control if/how a model is optimized by the backend framework and how it is scheduled and executed by the inference server. See the protobuf documentation for the currently available settings.