:model prune

Prerequisites

OSS is activated, and Machine Learning Studio is authorized to access OSS. For more information, see Activate OSS and Grant PAI the permissions to access OSS.

Limits

• The model quantize component is available only in Machine Learning Designer of Machine Learning Platform for AI.
• You can use the model quantize component based only on the computing resources of Deep Learning Containers (DLC) of Machine Learning Platform for AI.
• If an object detection component is configured as an upstream component of the model quantize component, make sure that the model-related configurations and the dataset configurations for the two components are the same.

Configure the component in Machine Learning Designer

• Input ports

  Input port (from left to right)	Data type	Recommended upstream component	Required
  Input models	OSS	object detection	No
  Training data	OSS	Read File Data	Yes
  Evaluation data	OSS	Read File Data	Yes
  yolov5 class list files	OSS	Read File Data	Yes

• Component parameters

  Tab	Parameter	Description
  Fields Setting	model type	The type of the model to train. Valid values: YOLOX YOLOX_EDGE Default value: YOLOX_EDGE.
  	oss path to save checkpoint	The Object Storage Service (OSS) path of the model and logs generated during training.
  	oss path to training tfrecord	The OSS path of the file that contains the training data. If a Read File Data component is configured as an upstream node, you do not need to set this parameter.
  	oss annotation path for training set	The OSS path of the file that contains the labeled training data.
  	oss path to evaluation file	The OSS path of the file that contains the evaluation data. If a Read File Data component is configured as an upstream node, you do not need to set this parameter.
  	oss annotation path for validataion set	The OSS path of the file that contains the labeled evaluation data.
  	oss path of class list file	The OSS path of the category file. If a Read File Data component is configured as an upstream node, you do not need to set this parameter.
  	YOLOX data format	The format of the datasets. Valid values: COCO DetSourcePAI Default value: COCO.
  	oss path to pretrained model	The OSS path of a pre-trained model. You must import the pre-trained weight file. Optional.
  Parameters Setting	YOLOX edge model type	This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The type of the YOLOX preset model. Default value: yolox-customized.
  	YOLOX model type	This parameter is required only if you set the model type parameter to YOLOX on the Fields Setting tab. Valid values: yolox-s yolox-m yolox-l yolox-x Default value: yolox-s.
  	pruning class	The type of the pruning algorithm. Default value: AGP.
  	pruning algorithm	The pruning algorithm. Default value: taylorfo.
  	yolox_edge_model_depth	This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The depth of the YOLOX model. The value is a floating-point number. Default value: 1.0. Valid values: 0.01 to 1.0.
  	yolox_edge_model_width	This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The width of the YOLOX model. The value is a floating-point number. Default value: 1.0. Valid values: 0.01 to 1.0.
  	YOLOX edge model activation	This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The type of the activation function for the YOLOX model. Valid values: relu lrelu silu hsilu Default value: relu.
  	test confidence:[0.01,1.0]	This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The confidence level of the evaluation. The value is a floating-point number. Default value: 0.01. Valid values: 0.01 to 1.0.
  	nms threshold:[0.01,1.0]	This parameter is required only if you set the model type parameter to YOLOX_EDGE on the Fields Setting tab. The Non-Maximum Suppression (NMS) threshold. The value is a floating-point number. Default value: 0.65. Valid values: 0.01 to 1.0.
  	num classes	The number of categories.
  	image scale	The resolution of resized images for YOLOX. The value is in the format of Height Width. Default value: 640 640.
  	initial learning rate	The initial learning rate for model training. The value is a floating-point number. Default value: 0.01.
  	train batch size	The size of the data that is used to train the model in each batch. Default value: 16.
  	eval batch size	The size of the data that is used to evaluate the model in each batch. Default value: 1.
  	num epochs	The total number of training epochs. Default value: 20.
  	warmup epochs	By default, five epochs are set for the warm-up strategy.
  	last no augmented lr epochs	The number of epochs to complete after the learning rate is stable if the warm-up strategy is used. Default value: 5.
  	save checkpoint epoch	The interval at which a checkpoint is saved. The interval is measured in epochs. Default value: 1. The default value indicates that a checkpoint is saved every time an epoch is complete.
  Tuning	io thread num for training.	The number of concurrent threads that are used to read the training data. Default value: 4.
  	single worker or distributed on MaxCompute or DLC	The compute engine that is used to run the component. The system automatically sets this parameter based on the type of the training model that you select.
  	number of worker	The number of workers used for distributed training. Default value: 1.
  	gpu machine type	The instance type of the GPU-accelerated instance to be used. Default value: 8vCPU+32GB Mem+1xv100-ecs.gn6v-c8g1.2xlarge.

Example

The following figure shows a sample pipeline in which a model prune component is used. In this example, the components are configured by performing the following steps:

1. Perform data labeling by using iTAG that is provided by PAI. For more information, see iTAG.
2. Use the Read File Data-1, Read File Data-2, and Read File Data-3 components to read the training dataset, evaluation dataset, and category file. You can configure the dataset to be read for a Read File Data component by setting the OSS Data Path component parameter to the OSS path of the training dataset, evaluation dataset, or category file.
3. Use the object detection-1 component to generate an object detection model.
4. Use the Read File Data-4, Read File Data-5, and Read File Data-6 components to read the training dataset, evaluation dataset, and category file. You can configure the dataset to be read for a Read File Data component by setting the OSS Data Path component parameter to the OSS path of the training dataset, evaluation dataset, or category file.
5. Connect the object detection-1, Read File Data-4, Read File Data-5, and Read File Data-6 components to the model prune component and set the component parameters. For more information, see Configure the component in Machine Learning Designer in this topic.