Create Training Session From Model

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Description

Initialize a Training Session from a DeepLearning Toolkit Model. Type : polymorphic.

Documentation illustration (illustration unavailable in the archive).

Input parameters

Execution Device : enum, selects the hardware device on which the model will run.
Model in : object, model architecture.
Parameters : cluster,

max_norm : float, maximum global gradient norm (enables clipping if > 0).
norm_type : enum, type of norm used to compute grad_norm (commonly 1 = L1, 2 = L2).
display_norm : boolean, adds grad_norm as a model output if set to 1.
keep_outputs : boolean, keeps the original model outputs in addition to the new ones (loss values and, if enabled, gradients or gradient norms).
Sessions Parameters : cluster

intra_op_num_threads : integer, number of threads used within each operator to parallelize computations. If the value is 0, ONNX Runtime automatically uses the number of physical CPU cores.
inter_op_num_threads : integer, number of threads used between operators, to execute multiple graph nodes in parallel. If set to 0, this parameter is ignored when execution_mode is ORT_SEQUENTIAL. In ORT_PARALLEL mode, 0 means ORT automatically selects a suitable number of threads (usually equal to the number of cores).
execution_mode : enum, controls whether the graph executes nodes one after another or allows parallel execution when possible. ORT_SEQUENTIAL runs nodes in order, ORT_PARALLEL runs them concurrently.
deterministic_compute : boolean, forces deterministic execution, meaning results will always be identical for the same inputs.
graph_optimization_level : enum, defines how much ONNX Runtime optimizes the computation graph before running the model.
optimized_model_file_path : path, file path to save the optimized model after graph analysis.

checkpoint generation : enum, defines what is saved in the checkpoint: "Weight" (weights only) or "Weight + Momentum" (weights and optimizer momentums). Momentums must already be set in the "Model in".
Documentation illustration (illustration unavailable in the archive).

Output parameters

Training out : object, training session.

Example

All these exemples are snippets PNG, you can drop these Snippet onto the block diagram and get the depicted code added to your VI (Do not forget to install Deep Learning library to run it).