show ()Īnother example can be found in the tutorial: set_title ( "n occurences" ) for a in ax : a. subplots ( 1, 3, figsize = ( 8, 4 )) gr_dur. read_csv ( full_name ) # but a graph is usually better. join ( ".", ".", "inference_profiling.csv" ))) df = pandas. Import os import pandas import matplotlib.pyplot as plt full_name = os. args_provider args_exec_plan_index 0 Session 21048. run ( None, ] Index (, dtype = 'object' ) cat pid. SerializeToString (), so ) # execution for i in range ( 0, 111 ): sess. enable_profiling = True sess = InferenceSession ( onx. float32 )) # creation of a session that enables the profiling so = SessionOptions () so. X, y = make_classification ( 100000 ) km = KMeans ( max_iter = 10 ) km. Import json import numpy from pandas import DataFrame from onnxruntime import InferenceSession, RunOptions, SessionOptions from sklearn.datasets import make_classification from sklearn.cluster import KMeans from skl2onnx import to_onnx from _ssion import OnnxWholeSession # creation of an ONNX graph. The list of available providers is a subset which depends on the machine The list of all providers depends on the compilation CUDAExecutionProvider does the same for GPU and CPUExecutionProvider provides implementationsįor all operator on CPU. An exemple can be seen in sectionĪ provider is usually a list of implementation of ONNX operatorįor a specific environment. Mostly focusing on text processing (tokenizers) or simple Onnxruntime-extensions is one of these extensions Intra_op_num_threads: Sets the number of threads used toĪttribute register_custom_ops_library to register anĪssembly implementing the runtime for custom nodes. Parallelize the execution of the graph (across nodes). Inter_op_num_threads: Sets the number of threads used to Within every node but does not run multiple node at the same time. Set this option to false if you don’t want it.Įnable_mem_pattern: Enable the memory pattern optimization.Įnable_mem_reuse: Enable the memory reuse optimization.īy default, onnxruntime parallelizes the execution May be changed to trade efficiency against memory usage.Įnable_cpu_mem_arena: Enables the memory arena on CPU.Īrena may pre-allocate memory for future usage. Onnxruntime focuses on efficiency first and memory peaks.įollowing what should be the priority, following members The logging during execution can be modified with the sameĪttributes but in class RunOptions. The verbosity level when the model is loaded. Parameters log_severity_level and log_verbosity_level may change From onnxruntime import InferenceSession, SessionOptions so = SessionOptions () # so.
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