Abstract: A tube fitting capable of quick assembly and disassembly includes a joint, a plug-in tube, and a buckle; the joint includes a tube body and a connector provided at an end of the tube body; an accommodating cavity is provided in the connector, an inserting opening is provided on a side wall of the connector, and a tube inserting hole is provided in a top wall of the connector; the accommodating cavity is in communication with the inner cavity of the tube body, the inserting opening, and the tube inserting hole; the buckle is passed through the inserting opening, inserted into the connector and clamped therewith, partially accommodated in the accommodating cavity, and partially exposed outside the connector.

Abstract: A tube fitting capable of quick assembly and disassembly includes a joint, a plug-in tube, and a buckle; the joint includes a tube body and a connector provided at an end of the tube body; an accommodating cavity is provided in the connector, an inserting opening is provided on a side wall of the connector, and a tube inserting hole is provided in a top wall of the connector; the accommodating cavity is in communication with the inner cavity of the tube body, the inserting opening, and the tube inserting hole; the buckle is passed through the inserting opening, inserted into the connector and clamped therewith, partially accommodated in the accommodating cavity, and partially exposed outside the connector.

Abstract: Provided are a method for automatic parallelization of a mixture of experts model, an apparatus for automatic parallelization of a mixture of experts model, a device, a medium, and a program. The method includes acquiring a computational graph of the mixture of experts model; determining a process mesh of an expert weight tensor of an expert model, where the processes are supported to execute by computing devices in the distributed system; splitting a global data tensor into sub-data tensors required by corresponding expert models, and configuring a process mesh of a sub-data tensor to be the same as a process mesh of a corresponding expert model; performing, by the expert model, processing based on an input sub-data tensor and the expert weight tensor to output a sub-result tensor; and determining a result tensor of the mixture of experts model based on at least one sub-result tensor.

Abstract: A method for controlling a video memory for model training, an electronic device and a storage medium are provided, relating to the field of artificial intelligence technology, and in particular to the fields of neural network, large model, training optimization and other technologies. The method includes: reconstructing a video memory space for one or more backward calculations during model training according to grouping information of parameter gradient information required for the one or more backward calculations; performing the one or more backward calculations to obtain one or more backward calculation results; storing the one or more backward calculation results into the video memory space reconstructed for the one or more backward calculations; and releasing the video memory space reconstructed for the one or more backward calculations.

Abstract: A drain plug linkage device comprising; a pull rod assembly including a pull rod and pull rod connector, a link rod having adjustment teeth and square spaces between them along its height, and a lever member having adjustment holes along its length. The link rod and lever member are connected through an adjustment mechanism which contains a push button lever adjustment mechanism by which a specific adjustment hole can be selected and locked into place, and a push button link rod adjustment mechanism by which a specific by space between the teeth may be selected and locked into place, whereby a user can easily select both the effective length of the lever member and the effective height of the link rod following installation of the drain plug linkage device and thereby optimize efficient operation of the linkage device.

Abstract: Provided is a tensor processing method, an electronic device, and a storage medium, relating to the fields of deep learning and artificial intelligence. The method includes: determining relevant information of a conversion function corresponding to each of one or more target input tensors of a first operator in a target computation graph based on computation logic of the first operator and source split states of at least part of source input tensors of the first operator; splitting each source input tensor of the first operator based on the relevant information of the conversion function corresponding to each target input tensor to obtain each target input tensor; and sending each target input tensor to a plurality of computing devices. The plurality of computing devices are configured to perform distributed parallel communication based on each target input tensor and the first operator, to obtain an output tensor of the first operator.

Abstract: A method for detecting a fault, an electronic device and a storage medium are provided, relating to the field of computer technology, and in particular to the fields of deep learning, large model training, fault detection and other technologies. The method includes: determining a plurality of computing devices, where the plurality of computing devices are used to perform model training based on a pipeline parallelism strategy; determining a parameter and a scheduling strategy used by the pipeline parallelism strategy; determining idle time of each computing device among the plurality of computing devices in a model training process based on the parameter and the scheduling strategy; and performing fault detection on each computing device during the idle time of each computing device in the model training process.

Abstract: A method for distributed operation based on a neural network model and a related apparatus are provided, relating to the field of computer technology and in particular to the fields of artificial intelligence, deep learning, machine learning, distributed training and other technologies. The method includes: parsing code of the neural network model to construct an operator topology graph corresponding to the neural network model; generating a distributed operation strategy of the neural network model based on the operator topology graph and a preset resource constraint; and modifying the code of the neural network model based on the distributed operation strategy to obtain target code; where the target code is used to operate the neural network model based on the distributed operation strategy on a computing device corresponding to the resource constraint.

Abstract: A method for parallel processing of model is suggested, which relates to the field of artificial intelligence technologies such as deep learning, natural language processing, image processing, and large language models.

Abstract: A cluster-based training method includes: in response to a hardware fault in the training node, selecting a target standby node from the plurality of standby nodes, and obtaining a target training snapshot of the model training task in the training node, in which the target training snapshot includes training state data of the model training task; and initializing the target standby node based on a container image of a model training program in the training node and the training state data to replace the training node with the target standby node to continue executing the model training task.

Abstract: A method of executing a task for a large language model, a device, and a storage medium are provided, which relate to a field of artificial intelligence technology, and in particular to fields of deep learning, large language model, natural language processing and computer vision technologies. The method includes: determining, by using a determination unit, a target attention task from a plurality of attention tasks to be processed, based on a sparse representation corresponding to a feature to be processed, where the target attention task is a task corresponding to a non-fully masked region of the feature, the sparse representation represents a mask position of the feature, and the mask position represents mask endpoint positions in at least two non-intersecting intervals in a mask matrix corresponding to the feature; and executing the target attention task by using a computing unit, so as to obtain an attention feature.

Abstract: A drain plug linkage device comprising; a pull rod assembly including a pull rod and pull rod connector, a link rod having adjustment teeth and square spaces between them along its height, and a lever member having adjustment holes along its length. The link rod and lever member are connected through an adjustment mechanism which contains a push button lever adjustment mechanism by which a specific adjustment hole can be selected and locked into place, and a push button link rod adjustment mechanism by which a specific by space between the teeth may be selected and locked into place, whereby a user can easily select both the effective length of the lever member and the effective height of the link rod following installation of the drain plug linkage device and thereby optimize efficient operation of the linkage device.

Abstract: The present disclosure provides an operator registration method and apparatus for a deep learning framework, a device and a storage medium, relates to the field of computer technologies, and specifically to the field of artificial intelligence such as deep learning. The operator registration method for a deep learning framework includes: receiving registration information provided by a user for registering operators with the deep learning framework, the registration information including: a custom calculation function, the custom calculation function being written in a manner irrelevant to the deep learning framework; building operator meta-information in the deep learning framework based on the registration information; and constructing a to-be-registered operator within the deep learning framework based on the operator meta-information, and registering the to-be-registered operator in a global operator table within the deep learning framework.

Abstract: The present disclosure provides an operator registration method and apparatus for a deep learning framework, a device and a storage medium, relates to the field of computer technologies, and specifically to the field of artificial intelligence such as deep learning. The operator registration method for a deep learning framework includes: receiving registration information provided by a user for registering operators with the deep learning framework, the registration information including: a custom calculation function, the custom calculation function being written in a manner irrelevant to the deep learning framework; building operator meta-information in the deep learning framework based on the registration information; and constructing a to-be-registered operator within the deep learning framework based on the operator meta-information, and registering the to-be-registered operator in a global operator table within the deep learning framework.