如上例所示,您可以通过检查torch.cuda.is_available()来决定是否启用fp16。如果代码在CPU上运行,则不应启用fp16。 4. 测试修改后的代码以确保问题已解决 在修改代码后,重新运行您的程序以确保问题已解决。注意检查是否还有类似的错误或性能问题。 5. 查找并参考官方文档或社区支持 如果您在解决此问题时遇到困难,...
No description provided. Remove use_cuda_fp16 arg. GPTQ kernels are fp16 by default. c420804 Qubitium closed this Jun 17, 2024 Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment Reviewers No reviews Assignees No one assigned Labels...
disable_regex_jump_forward=False, disable_cuda_graph=False, disable_disk_cache=False, enable_mixed_chunk=False, enable_torch_compile=False, enable_p2p_check=False, enable_mla=False, attention_reduce_in_fp32=False, efficient_weight_load=False, nccl_init_addr=None, nnodes=1, node_rank=None)...
cuda_fp16.hpp cusparse_v2.h nppi_geometry_transforms.h sm_60_atomic_functions.hpp cuda_gl_interop.h device_atomic_functions.h nppi_linear_transforms.h sm_61_intrinsics.h cuda_occupancy.h device_atomic_functions.hpp nppi_morphological_operations.h sm_61_intrinsics.hpp ...
数据较多或者模型较大时，为提高机器学习模型训练效率，一般采用多GPU的分布式训练。
I converted my own weights into IR model(FP32), and use GPU to inference, I get a lot of false positives. and I also see some confidence values bigger than 1.0 see the demostration here. http://sanyafruits.com/temp/tiny-yolo-v3-demo.html By using CUDA, the output looks perfect. ...
For more information, see Unified Memory for CUDA Beginners. Prerequisites Before you perform operations, make sure that your GPU-accelerated instance meets the following requirements: The instance belongs to one of the following instance families: gn7i, gn6i, gn6v, gn6e, gn5i, gn5, eb...
System environment: Python 3.6 or later, GNU Compiler Collection (GCC) 5.4 or later, NVIDIA Tesla T4, CUDA 10.2, and cuDNN 8.0.5.39 Framework: PyTorch 1.8.1 or later, and Detectron2 0.4.1 or later Inference optimization tool: PAI-Blade V3.16.0 or later ...
Linear8bitLt(64, 10, has_fp16_weights=False) ) def forward(self, x): x = self.flatten(x) x = self.model(x) return F.log_softmax(x, dim=1) device = torch.device("cuda") # Load model = Net8Bit() model.load_state_dict(torch.load("mnist_model.pt")) ...
} } IBuilderConfig* config = builder->createBuilderConfig(); bool useFp16 = builder->platformHasFastFp16(); if(useFp16) { config->setFlag(BuilderFlag::kFP16); std::cout<<"set fp16"<<std::endl; } config->setMaxWorkspaceSize(1 << 20000); ICudaEngine* engine = builder->buildEngin...