Overview
Function Description
TF Adapter provides a series of session configurations for function debugging, performance improvement, and precision improvement. Developers can use these session configurations when performing model training or online inference on the Ascend AI Processor.
You can view related configuration definitions in the python/site-packages/npu_bridge/estimator/npu/npu_estimator.py file in the TensorFlow Adapter installation directory. The parameters that are not listed in this section are reserved or applicable to other Ascend AI Processor versions.
Examples
The usage of session configurations is as follows:
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import tensorflow as tf from npu_bridge.npu_init import * ... config = tf.ConfigProto() custom_op = config.graph_options.rewrite_options.custom_optimizers.add() custom_op.name = "NpuOptimizer" custom_op.parameter_map["use_off_line"].b = True config.graph_options.rewrite_options.remapping = RewriterConfig.OFF config.graph_options.rewrite_options.memory_optimization = RewriterConfig.OFF ... with tf.Session(config=config) as sess: sess.run(cost) |
After scripts are automatically ported by using the porting tool, if you need to enable related functions through session configurations, see (Optional) Follow-up Procedure.