ModelDeviceConfig 此接口从DDK 5.0.1.0版本起废弃。 Struct Info struct ModelDeviceConfig 模型运行设备设置策略。 ……欲了解更多信息欢迎访问华为HarmonyOS开发者官网
Device Frequencies 这些计数器显示所有可用硬件组件的频率,如CPU、GPU、DDR、NPU等。 在HarmonyOS上,这些计数器从sys/devfreq/*/curfreq系统……欲了解更多信息欢迎访问华为HarmonyOS开发者官网
GPU间数据传输问题:当device_map="auto"时,模型的不同部分可能被分配到不同的GPU上。如果模型在执行过程中需要在不同GPU之间传输大量数据,可能会导致同步流失败。 硬件或驱动问题:可能是GPU硬件故障、驱动程序不兼容或未正确安装导致的问题。 模型或库版本问题:使用的transformers库、PyTorch或torch-npu版本可能存在bug...
Both companies report that the NPU or APU performance in their latest chipsets has improved by over 50%. In this context, why did Chinese OEMs not deploy larger AI models on devices? The answer lies in their pragmatic approach. Most general users do not require all-powerful on-device ...
获取device的信息的通用接口,获取各模块中的状态信息。 参数说明 表2-61sub_cmd对应的buf格式 表2-62基础和资源信息结构描述 表2-63部署场景 调用示例 … int ret = 0; int card_id = 0; int device_id = 0; int buf = 0; unsigned int size = sizeof(int); ...
unsigned int vfg_bitmap; struct dcmi_base_resource base; struct dcmi_computing_resource computing; struct dcmi_media_resource media; }; a. vdev_num indicates the number of virtual devices in the specified SoC device. b. vdev_id indicates the ID of the virtual device. c. vfg_num indicates...
NPUName: device.DeviceName, LargeModelFaultLevel: faultType, FaultLevel: faultType, FaultHandling: faultType, FaultCode: strings.ToUpper(common.Int64Tool.ToHexString(newCode)), FaultTimeAndLevelMap: tool.getFaultTimeAndLevelMap(device, true), ...
reserved-memory{#address-cells=<2>;#size-cells=<2>;ranges;foobar_reserved:foobar@70000000{compatible="shared-dma-pool";no-map;reg=<0x00x700000000x00x10000000>;};};foobar_driver:foobar_driver@0{memory-region=<&foobar_reserved>;};
This projectONEaims at providing a high-performance, on-device neural network (NN) inference framework that performs inference of a given NN model on processors, such as CPU, GPU, DSP or NPU. We develop a runtime that runs on a Linux kernel-based OS platform such as Ubuntu, Tizen, or ...
small_train_dataset = raw_datasets.map(tokenize_function, batched=True) # print(type(dataset_train['label'][0])) small_eval_dataset = raw_datasets.map(tokenize_function, batched=True) small_train_dataset = small_train_dataset.remove_columns(['text']) ...