The Training C API that holds onnxruntime training function pointers. More...

#include <onnxruntime_training_c_api.h>

Accessing The Training Session State
OrtStatus *	LoadCheckpoint (const char checkpoint_path, OrtCheckpointState *checkpoint_state)
	Load a checkpoint state from a file on disk into checkpoint_state.

OrtStatus *	SaveCheckpoint (OrtCheckpointState checkpoint_state, const char checkpoint_path, const bool include_optimizer_state)
	Save the given state to a checkpoint file on disk.

OrtStatus *	GetParametersSize (OrtTrainingSession sess, size_t out, bool trainable_only)
	Retrieves the size of all the parameters.

OrtStatus *	CopyParametersToBuffer (OrtTrainingSession sess, OrtValue parameters_buffer, bool trainable_only)
	Copy all parameters to a contiguous buffer held by the argument parameters_buffer.

OrtStatus *	CopyBufferToParameters (OrtTrainingSession sess, OrtValue parameters_buffer, bool trainable_only)
	Copy parameter values from the given contiguous buffer held by parameters_buffer to the training state.

OrtStatus *	AddProperty (OrtCheckpointState checkpoint_state, const char property_name, enum OrtPropertyType property_type, void *property_value)
	Adds the given property to the checkpoint state.

OrtStatus *	GetProperty (const OrtCheckpointState checkpoint_state, const char property_name, OrtAllocator allocator, enum OrtPropertyType property_type, void **property_value)
	Gets the property value associated with the given name from the checkpoint state.

OrtStatus *	LoadCheckpointFromBuffer (const void checkpoint_buffer, const size_t num_bytes, OrtCheckpointState *checkpoint_state)
	Load a checkpoint state from a buffer into checkpoint_state.

Implementing The Training Loop
OrtStatus *	CreateTrainingSession (const OrtEnv env, const OrtSessionOptions options, OrtCheckpointState checkpoint_state, const char train_model_path, const char eval_model_path, const char optimizer_model_path, OrtTrainingSession **out)
	Create a training session that can be used to begin or resume training.

OrtStatus *	CreateTrainingSessionFromBuffer (const OrtEnv env, const OrtSessionOptions options, OrtCheckpointState checkpoint_state, const void train_model_data, size_t train_data_length, const void eval_model_data, size_t eval_data_length, const void optim_model_data, size_t optim_data_length, OrtTrainingSession **out)
	Create a training session that can be used to begin or resume training. This api provides a way to load all the training artifacts from buffers instead of files.

OrtStatus *	LazyResetGrad (OrtTrainingSession *session)
	Reset the gradients of all trainable parameters to zero lazily.

OrtStatus *	TrainStep (OrtTrainingSession sess, const OrtRunOptions run_options, size_t inputs_len, const OrtValue const inputs, size_t outputs_len, OrtValue **outputs)
	Computes the outputs of the training model and the gradients of the trainable parameters for the given inputs.

OrtStatus *	EvalStep (const OrtTrainingSession sess, const OrtRunOptions run_options, size_t inputs_len, const OrtValue const inputs, size_t outputs_len, OrtValue **outputs)
	Computes the outputs for the eval model for the given inputs.

OrtStatus *	SetLearningRate (OrtTrainingSession *sess, float learning_rate)
	Sets the learning rate for this training session.

OrtStatus *	GetLearningRate (OrtTrainingSession sess, float learning_rate)
	Gets the current learning rate for this training session.

OrtStatus *	OptimizerStep (OrtTrainingSession sess, const OrtRunOptions run_options)
	Performs the weight updates for the trainable parameters using the optimizer model.

OrtStatus *	RegisterLinearLRScheduler (OrtTrainingSession *sess, const int64_t warmup_step_count, const int64_t total_step_count, const float initial_lr)
	Registers a linear learning rate scheduler for the training session.

OrtStatus *	SchedulerStep (OrtTrainingSession *sess)
	Update the learning rate based on the registered learing rate scheduler.

Model IO Information
OrtStatus *	TrainingSessionGetTrainingModelOutputCount (const OrtTrainingSession sess, size_t out)
	Retrieves the number of user outputs in the training model.

OrtStatus *	TrainingSessionGetEvalModelOutputCount (const OrtTrainingSession sess, size_t out)
	Retrieves the number of user outputs in the eval model.

OrtStatus *	TrainingSessionGetTrainingModelOutputName (const OrtTrainingSession sess, size_t index, OrtAllocator allocator, char **output)
	Retrieves the names of user outputs in the training model.

OrtStatus *	TrainingSessionGetEvalModelOutputName (const OrtTrainingSession sess, size_t index, OrtAllocator allocator, char **output)
	Retrieves the names of user outputs in the eval model.

OrtStatus *	TrainingSessionGetTrainingModelInputCount (const OrtTrainingSession sess, size_t out)
	Retrieves the number of user inputs in the training model.

OrtStatus *	TrainingSessionGetEvalModelInputCount (const OrtTrainingSession sess, size_t out)
	Retrieves the number of user inputs in the eval model.

OrtStatus *	TrainingSessionGetTrainingModelInputName (const OrtTrainingSession sess, size_t index, OrtAllocator allocator, char **output)
	Retrieves the name of the user input at given index in the training model.

OrtStatus *	TrainingSessionGetEvalModelInputName (const OrtTrainingSession sess, size_t index, OrtAllocator allocator, char **output)
	Retrieves the name of the user input at given index in the eval model.

Release Training Resources
void	ReleaseTrainingSession (OrtTrainingSession *input)
	Frees up the memory used up by the training session.

void	ReleaseCheckpointState (OrtCheckpointState *input)
	Frees up the memory used up by the checkpoint state.

Prepare For Inferencing
OrtStatus *	ExportModelForInferencing (OrtTrainingSession sess, const char inference_model_path, size_t graph_outputs_len, const char const graph_output_names)
	Export a model that can be used for inferencing.

Training Utilities
OrtStatus *	SetSeed (const int64_t seed)
	Sets the seed used for random number generation in Onnxruntime.

Detailed Description

The Training C API that holds onnxruntime training function pointers.

All the Training C API functions are defined inside this structure as pointers to functions. Call OrtApi::GetTrainingApi to get a pointer to this struct.

Member Function Documentation

◆ AddProperty()

OrtStatus * OrtTrainingApi::AddProperty	(	OrtCheckpointState *	checkpoint_state,
		const char *	property_name,
		enum OrtPropertyType	property_type,
		void *	property_value
	)

Adds the given property to the checkpoint state.

Runtime properties such as epoch, training step, best score, and others can be added to the checkpoint state by the user if they desire by calling this function with the appropriate property name and value. The given property name must be unique to be able to successfully add the property.

Parameters

[in]	checkpoint_state	The checkpoint state which should hold the property.
[in]	property_name	Unique name of the property being added.
[in]	property_type	Type of the property associated with the given name.
[in]	property_value	Property value associated with the given name.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ CopyBufferToParameters()

OrtStatus * OrtTrainingApi::CopyBufferToParameters	(	OrtTrainingSession *	sess,
		OrtValue *	parameters_buffer,
		bool	trainable_only
	)

Copy parameter values from the given contiguous buffer held by parameters_buffer to the training state.

The parameters_buffer argument has to be of the size given by OrtTrainingApi::GetParametersSize api call, with matching setting for trainable_only argument. All the target parameters must be of the same datatype. This is a complementary function to OrtTrainingApi::CopyBufferToParameters and can be used to load updated buffer values onto the training state. Parameter ordering is preserved. User is responsible for allocating and freeing the resources used by the parameters_buffer.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	trainable_only	Whether to skip non-trainable parameters
[out]	parameters_buffer	The pre-allocated OrtValue buffer to copy from.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ CopyParametersToBuffer()

OrtStatus * OrtTrainingApi::CopyParametersToBuffer	(	OrtTrainingSession *	sess,
		OrtValue *	parameters_buffer,
		bool	trainable_only
	)

Copy all parameters to a contiguous buffer held by the argument parameters_buffer.

The parameters_buffer has to be of the size given by GetParametersSize api call, with matching setting for the argument trainable_only. All the target parameters must be of the same datatype. The OrtValue must be pre-allocated onto the desired device. This is a complementary function to OrtTrainingApi::CopyBufferToParameters. Parameter ordering is preserved. User is responsible for allocating and freeing the resources used by the parameters_buffer.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	trainable_only	Whether to skip non-trainable parameters
[out]	parameters_buffer	The pre-allocated OrtValue buffer to copy onto.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ CreateTrainingSession()

OrtStatus * OrtTrainingApi::CreateTrainingSession	(	const OrtEnv *	env,
		const OrtSessionOptions *	options,
		OrtCheckpointState *	checkpoint_state,
		const char *	train_model_path,
		const char *	eval_model_path,
		const char *	optimizer_model_path,
		OrtTrainingSession **	out
	)

Create a training session that can be used to begin or resume training.

This function creates a training session based on the env and session options provided that can begin or resume training from a given checkpoint state for the given onnx models. The checkpoint state represents the parameters of the training session which will be moved to the device specified by the user through the session options (if necessary). The training session requires four training artifacts

The training onnx model
The evaluation onnx model (optional)
The optimizer onnx model
The checkpoint file

These artifacts can be generated using the onnxruntime-training python utility.

Parameters

[in]	env	Environment to be used for the training session.
[in]	options	Session options that the user can customize for this training session.
[in]	checkpoint_state	Training states that the training session uses as a starting point for training.
[in]	train_model_path	Model to be used to perform training.
[in]	eval_model_path	Model to be used to perform evaluation.
[in]	optimizer_model_path	Model to be used to perform gradient descent.
[out]	out	Created training session.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ CreateTrainingSessionFromBuffer()

OrtStatus * OrtTrainingApi::CreateTrainingSessionFromBuffer	(	const OrtEnv *	env,
		const OrtSessionOptions *	options,
		OrtCheckpointState *	checkpoint_state,
		const void *	train_model_data,
		size_t	train_data_length,
		const void *	eval_model_data,
		size_t	eval_data_length,
		const void *	optim_model_data,
		size_t	optim_data_length,
		OrtTrainingSession **	out
	)

Create a training session that can be used to begin or resume training. This api provides a way to load all the training artifacts from buffers instead of files.

Parameters

[in]	env	Environment to be used for the training session.
[in]	options	Session options that the user can customize for this training session.
[in]	checkpoint_state	Training states that the training session uses as a starting point for training.
[in]	train_model_data	Buffer containing the model data to be used to perform training
[in]	train_data_length	Length of the buffer containing train_model_data
[in]	eval_model_data	Buffer containing the model data to be used to perform evaluation
[in]	eval_data_length	Length of the buffer containing eval_model_data
[in]	optim_model_data	Buffer containing the model data to be used to perform weight update
[in]	optim_data_length	Length of the buffer containing optim_model_data
[out]	out	Created training session.

◆ EvalStep()

OrtStatus * OrtTrainingApi::EvalStep	(	const OrtTrainingSession *	sess,
		const OrtRunOptions *	run_options,
		size_t	inputs_len,
		const OrtValue const	inputs,
		size_t	outputs_len,
		OrtValue **	outputs
	)

Computes the outputs for the eval model for the given inputs.

This function performs an eval step that computes the outputs of the eval model for the given inputs. The eval step is performed based on the eval model that was provided to the training session.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	run_options	Run options for this eval step.
[in]	inputs_len	Number of user inputs to the eval model.
[in]	inputs	The user inputs to the eval model.
[in]	outputs_len	Number of user outputs expected from this eval step.
[out]	outputs	User outputs computed by eval step.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ ExportModelForInferencing()

OrtStatus * OrtTrainingApi::ExportModelForInferencing	(	OrtTrainingSession *	sess,
		const char *	inference_model_path,
		size_t	graph_outputs_len,
		const char const	graph_output_names
	)

Export a model that can be used for inferencing.

If the training session was provided with an eval model, the training session can generate an inference model if it knows the inference graph outputs. The input inference graph outputs are used to prune the eval model so that the inference model's outputs align with the provided outputs. The exported model is saved at the path provided and can be used for inferencing with InferenceSession.

Note: Note that the function re-loads the eval model from the path provided to OrtTrainingApi::CreateTrainingSession and expects that this path still be valid.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	inference_model_path	Path where the inference model should be serialized to.
[in]	graph_outputs_len	Size of the graph output names array.
[in]	graph_output_names	Names of the outputs that are needed in the inference model.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ GetLearningRate()

OrtStatus * OrtTrainingApi::GetLearningRate	(	OrtTrainingSession *	sess,
		float *	learning_rate
	)

Gets the current learning rate for this training session.

This function allows users to get the learning rate for the training session. The current learning rate is maintained by the training session, and users can query it for the purpose of implementing their own learning rate schedulers.

Parameters

[in]	sess	The `this` pointer to the training session.
[out]	learning_rate	Learning rate currently in use by the training session.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ GetParametersSize()

OrtStatus * OrtTrainingApi::GetParametersSize	(	OrtTrainingSession *	sess,
		size_t *	out,
		bool	trainable_only
	)

Retrieves the size of all the parameters.

Calculates the total number of primitive (datatype of the parameters) elements of all the parameters in the training state. When trainable_only argument is true, the size is calculated for trainable params only.

Parameters

[in]	sess	The `this` pointer to the training session.
[out]	out	Size of all parameter elements.
[in]	trainable_only	Whether to skip non-trainable parameters

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ GetProperty()

OrtStatus * OrtTrainingApi::GetProperty	(	const OrtCheckpointState *	checkpoint_state,
		const char *	property_name,
		OrtAllocator *	allocator,
		enum OrtPropertyType *	property_type,
		void **	property_value
	)

Gets the property value associated with the given name from the checkpoint state.

Gets the property value from an existing entry in the checkpoint state. The property must exist in the checkpoint state to be able to retrieve it successfully.

Parameters

[in]	checkpoint_state	The checkpoint state that is currently holding the property.
[in]	property_name	Unique name of the property being retrieved.
[in]	allocator	Allocator used to allocate the memory for the property_value.
[out]	property_type	Type of the property associated with the given name.
[out]	property_value	Property value associated with the given name.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ LazyResetGrad()

OrtStatus * OrtTrainingApi::LazyResetGrad ( OrtTrainingSession * session )

Reset the gradients of all trainable parameters to zero lazily.

This function sets the internal state of the training session such that the gradients of the trainable parameters in the OrtCheckpointState will be scheduled to be reset just before the new gradients are computed on the next invocation of the next OrtTrainingApi::TrainStep.

Parameters

[in] session The this pointer to the training session.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ LoadCheckpoint()

OrtStatus * OrtTrainingApi::LoadCheckpoint	(	const char *	checkpoint_path,
		OrtCheckpointState **	checkpoint_state
	)

Load a checkpoint state from a file on disk into checkpoint_state.

This function will parse a checkpoint file, pull relevant data and load the training state into the checkpoint_state. This checkpoint state can then be used to create the training session by invoking OrtTrainingApi::CreateTrainingSession. By doing so, the training session will resume training from the given checkpoint state.

Note: Note that the training session created with a checkpoint state uses this state to store the entire training state (including model parameters, its gradients, the optimizer states and the properties). As a result, it is required that the checkpoint state outlive the lifetime of the training session.

Parameters

[in]	checkpoint_path	Path to the checkpoint file
[out]	checkpoint_state	Checkpoint state that contains the states of the training session.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ LoadCheckpointFromBuffer()

OrtStatus * OrtTrainingApi::LoadCheckpointFromBuffer	(	const void *	checkpoint_buffer,
		const size_t	num_bytes,
		OrtCheckpointState **	checkpoint_state
	)

Load a checkpoint state from a buffer into checkpoint_state.

This function will parse a checkpoint bytes buffer, pull relevant data and load the training state into the checkpoint_state. This checkpoint state can then be used to create the training session by invoking OrtTrainingApi::CreateTrainingSession. By doing so, the training session will resume training from the given checkpoint state.

Note: Note that the training session created with a checkpoint state uses this state to store the entire training state (including model parameters, its gradients, the optimizer states and the properties). As a result, it is required that the checkpoint state outlive the lifetime of the training session.

Parameters

[in]	checkpoint_buffer	Path to the checkpoint bytes buffer.
[in]	num_bytes	Number of bytes in the checkpoint buffer.
[out]	checkpoint_state	Checkpoint state that contains the states of the training session.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ OptimizerStep()

OrtStatus * OrtTrainingApi::OptimizerStep	(	OrtTrainingSession *	sess,
		const OrtRunOptions *	run_options
	)

Performs the weight updates for the trainable parameters using the optimizer model.

This function performs the weight update step that updates the trainable parameters such that they take a step in the direction of their gradients (gradient descent). The optimizer step is performed based on the optimizer model that was provided to the training session. The updated parameters are stored inside the training state so that they can be used by the next OrtTrainingApi::TrainStep function call.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	run_options	Run options for this optimizer step.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ RegisterLinearLRScheduler()

OrtStatus * OrtTrainingApi::RegisterLinearLRScheduler	(	OrtTrainingSession *	sess,
		const int64_t	warmup_step_count,
		const int64_t	total_step_count,
		const float	initial_lr
	)

Registers a linear learning rate scheduler for the training session.

Register a linear learning rate scheduler that decays the learning rate by linearly updated multiplicative factor from the initial learning rate set on the training session to 0. The decay is performed after the initial warm up phase where the learning rate is linearly incremented from 0 to the initial learning rate provided.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	warmup_step_count	Warmup steps for LR warmup.
[in]	total_step_count	Total step count.
[in]	initial_lr	The initial learning rate to be used by the training session.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ ReleaseCheckpointState()

void OrtTrainingApi::ReleaseCheckpointState ( OrtCheckpointState * input )

Frees up the memory used up by the checkpoint state.

This function frees up any memory that was allocated in the checkpoint state. The checkpoint state can no longer be used after this call.

Note: Note that the checkpoint state must be released only after the training session has been released.

◆ ReleaseTrainingSession()

void OrtTrainingApi::ReleaseTrainingSession ( OrtTrainingSession * input )

Frees up the memory used up by the training session.

This function frees up any memory that was allocated in the training session. The training session can no longer be used after this call.

◆ SaveCheckpoint()

OrtStatus * OrtTrainingApi::SaveCheckpoint	(	OrtCheckpointState *	checkpoint_state,
		const char *	checkpoint_path,
		const bool	include_optimizer_state
	)

Save the given state to a checkpoint file on disk.

This function serializes the provided checkpoint state to a file on disk. This checkpoint can later be loaded by invoking OrtTrainingApi::LoadCheckpoint to resume training from this snapshot of the state.

Parameters

[in]	checkpoint_state	The checkpoint state to save.
[in]	checkpoint_path	Path to the checkpoint file.
[in]	include_optimizer_state	Flag to indicate whether to save the optimizer state or not.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ SchedulerStep()

OrtStatus * OrtTrainingApi::SchedulerStep ( OrtTrainingSession * sess )

Update the learning rate based on the registered learing rate scheduler.

Takes a scheduler step that updates the learning rate that is being used by the training session. This function should typically be called before invoking the optimizer step for each round, or as determined necessary to update the learning rate being used by the training session.

Note: Please note that a valid predefined learning rate scheduler must be first registered to invoke this function.

Parameters

[in] sess The this pointer to the training session.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ SetLearningRate()

OrtStatus * OrtTrainingApi::SetLearningRate	(	OrtTrainingSession *	sess,
		float	learning_rate
	)

Sets the learning rate for this training session.

This function allows users to set the learning rate for the training session. The current learning rate is maintained by the training session and can be overwritten by invoking this function with the desired learning rate. This function should not be used when a valid learning rate scheduler is registered. It should be used either to set the learning rate derived from a custom learning rate scheduler or to set a constant learning rate to be used throughout the training session.

Note: Please note that this function does not set the initial learning rate that may be needed by the predefined learning rate schedulers. To set the initial learning rate for learning rate schedulers, please look at the function OrtTrainingApi::RegisterLinearLRScheduler.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	learning_rate	Desired learning rate to be set.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ SetSeed()

OrtStatus * OrtTrainingApi::SetSeed ( const int64_t seed )

Sets the seed used for random number generation in Onnxruntime.

Use this function to generate reproducible results. It should be noted that completely reproducible results are not guaranteed.

Parameters

[in] seed The seed to be set.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainingSessionGetEvalModelInputCount()

OrtStatus * OrtTrainingApi::TrainingSessionGetEvalModelInputCount	(	const OrtTrainingSession *	sess,
		size_t *	out
	)

Retrieves the number of user inputs in the eval model.

This function returns the number of inputs of the eval model so that the user can accordingly allocate the OrtValue(s) provided to the OrtTrainingApi::EvalStep function.

Parameters

[in]	sess	The `this` pointer to the training session.
[out]	out	Number of user inputs in the eval model.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainingSessionGetEvalModelInputName()

OrtStatus * OrtTrainingApi::TrainingSessionGetEvalModelInputName	(	const OrtTrainingSession *	sess,
		size_t	index,
		OrtAllocator *	allocator,
		char **	output
	)

Retrieves the name of the user input at given index in the eval model.

This function returns the names of inputs of the eval model that can be associated with the OrtValue(s) provided to the OrtTrainingApi::EvalStep function.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	index	The index of the eval model input name requested.
[in]	allocator	The allocator to use to allocate the memory for the requested name.
[out]	output	Name of the user input for the eval model at the given index.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainingSessionGetEvalModelOutputCount()

OrtStatus * OrtTrainingApi::TrainingSessionGetEvalModelOutputCount	(	const OrtTrainingSession *	sess,
		size_t *	out
	)

Retrieves the number of user outputs in the eval model.

This function returns the number of outputs of the eval model so that the user can allocate space for the number of outputs when OrtTrainingApi::EvalStep is invoked.

Parameters

[in]	sess	The `this` pointer to the training session.
[out]	out	Number of user outputs in the eval model.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainingSessionGetEvalModelOutputName()

OrtStatus * OrtTrainingApi::TrainingSessionGetEvalModelOutputName	(	const OrtTrainingSession *	sess,
		size_t	index,
		OrtAllocator *	allocator,
		char **	output
	)

Retrieves the names of user outputs in the eval model.

This function returns the names of outputs of the eval model that can be associated with the OrtValue(s) returned by the OrtTrainingApi::EvalStep function.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	index	Index of the output name requested.
[in]	allocator	Allocator to use to allocate the memory for the name.
[out]	output	Name of the eval model output at the given index.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainingSessionGetTrainingModelInputCount()

OrtStatus * OrtTrainingApi::TrainingSessionGetTrainingModelInputCount	(	const OrtTrainingSession *	sess,
		size_t *	out
	)

Retrieves the number of user inputs in the training model.

This function returns the number of inputs of the training model so that the user can accordingly allocate the OrtValue(s) provided to the OrtTrainingApi::TrainStep function.

Parameters

[in]	sess	The `this` pointer to the training session.
[out]	out	Number of user inputs in the training model.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainingSessionGetTrainingModelInputName()

OrtStatus * OrtTrainingApi::TrainingSessionGetTrainingModelInputName	(	const OrtTrainingSession *	sess,
		size_t	index,
		OrtAllocator *	allocator,
		char **	output
	)

Retrieves the name of the user input at given index in the training model.

This function returns the names of inputs of the training model that can be associated with the OrtValue(s) provided to the OrtTrainingApi::TrainStep function.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	index	The index of the training model input name requested.
[in]	allocator	The allocator to use to allocate the memory for the requested name.
[out]	output	Name of the user input for the training model at the given index.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainingSessionGetTrainingModelOutputCount()

OrtStatus * OrtTrainingApi::TrainingSessionGetTrainingModelOutputCount	(	const OrtTrainingSession *	sess,
		size_t *	out
	)

Retrieves the number of user outputs in the training model.

This function returns the number of outputs of the training model so that the user can allocate space for the number of outputs when OrtTrainingApi::TrainStep is invoked.

Parameters

[in]	sess	The `this` pointer to the training session.
[out]	out	Number of user outputs in the training model.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainingSessionGetTrainingModelOutputName()

OrtStatus * OrtTrainingApi::TrainingSessionGetTrainingModelOutputName	(	const OrtTrainingSession *	sess,
		size_t	index,
		OrtAllocator *	allocator,
		char **	output
	)

Retrieves the names of user outputs in the training model.

This function returns the names of outputs of the training model that can be associated with the OrtValue(s) returned by the OrtTrainingApi::TrainStep function.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	index	Index of the output name requested.
[in]	allocator	Allocator to use to allocate the memory for the name.
[out]	output	Name of the training model output at the given index.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

◆ TrainStep()

OrtStatus * OrtTrainingApi::TrainStep	(	OrtTrainingSession *	sess,
		const OrtRunOptions *	run_options,
		size_t	inputs_len,
		const OrtValue const	inputs,
		size_t	outputs_len,
		OrtValue **	outputs
	)

Computes the outputs of the training model and the gradients of the trainable parameters for the given inputs.

This function performs a training step that computes the outputs of the training model and the gradients of the trainable parameters for the given inputs. The train step is performed based on the training model that was provided to the training session. The OrtTrainingApi::TrainStep is equivalent of running forward propagation and backward propagation in a single step. The gradients computed are stored inside the training session state so they can be later consumed by the OrtTrainingApi::OptimizerStep function. The gradients can be lazily reset by invoking the OrtTrainingApi::LazyResetGrad function.

Parameters

[in]	sess	The `this` pointer to the training session.
[in]	run_options	Run options for this training step.
[in]	inputs_len	Number of user inputs to the training model.
[in]	inputs	The user inputs to the training model.
[in]	outputs_len	Number of user outputs expected from this training step.
[out]	outputs	User outputs computed by train step.

Returns: If no error, nullptr will be returned. If there is an error, a pointer to an OrtStatus that contains error details will be returned. Use OrtApi::ReleaseStatus to free this pointer.

Accessing The Training Session State

Implementing The Training Loop

Model IO Information

Release Training Resources

Prepare For Inferencing

Training Utilities

Detailed Description

Member Function Documentation

◆ AddProperty()

◆ CopyBufferToParameters()

◆ CopyParametersToBuffer()

◆ CreateTrainingSession()

◆ CreateTrainingSessionFromBuffer()

◆ EvalStep()

◆ ExportModelForInferencing()

◆ GetLearningRate()

◆ GetParametersSize()

◆ GetProperty()

◆ LazyResetGrad()

◆ LoadCheckpoint()

◆ LoadCheckpointFromBuffer()

◆ OptimizerStep()

◆ RegisterLinearLRScheduler()

◆ ReleaseCheckpointState()

◆ ReleaseTrainingSession()

◆ SaveCheckpoint()

◆ SchedulerStep()

◆ SetLearningRate()

◆ SetSeed()

◆ TrainingSessionGetEvalModelInputCount()

◆ TrainingSessionGetEvalModelInputName()

◆ TrainingSessionGetEvalModelOutputCount()

◆ TrainingSessionGetEvalModelOutputName()

◆ TrainingSessionGetTrainingModelInputCount()

◆ TrainingSessionGetTrainingModelInputName()

◆ TrainingSessionGetTrainingModelOutputCount()

◆ TrainingSessionGetTrainingModelOutputName()

◆ TrainStep()