cuiyongchao commented Mar 19, 2024 CUDA_VISIBLE_DEVICES=0,1,2,3,4 python3 -m vllm.entrypoints.openai.api_server --served-model-name Qwen1.5-72B-Chat --model /data/models/Qwen1.5-72B-Chat --host 0.0.0.0 --port 8089 出现问题: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried ...
With loading the packaged drivers though, I had a hard time resolving the "chicken and egg" scenario presented: needed the library loaded to check if the device is present and ready to go, and needed to know if the device is present or not to figure out which library to load. In the...
line128,inselect_deviceraiseValueError(ValueError:InvalidCUDA'device=0'requested.Use'device=cpu'orpass validCUDAdevice(s)ifavailable,i.e.'device=0'or'device=0,1,2,3'forMulti-GPU.torch.cuda.is_available():Falsetorch.cuda.device_count():0os.environ['CUDA_...
importtorch# 步骤一:检查可用的GPU设备device_count=torch.cuda.device_count()ifdevice_count>0:print("可用的GPU设备数量:",device_count)else:print("未检测到可用的GPU设备")# 步骤二:设置使用的GPU设备device_index=0torch.cuda.set_device(device_index)# 步骤三:在代码中指定使用的GPU设备device=torch.d...
Select Target Platform Click on the green buttons that describe your target platform. Only supported platforms will be shown. By downloading and using the software, you agree to fully comply with the terms and conditions of theCUDA EULA.
CUDA programs are compiled in the whole program compilation mode by default, i.e., the device code cannot reference an entity from a separate file. In the whole program compilation mode, device link steps have no effect. For more information on the separate compilation and the whole program ...
s+=r"dml:"+str(torch_directml.device_name(0)) arg=torch_directml.device(0) elif not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7 ...
Initializes or sets device memory to a value. __host__ __device__ cudaError_t cudaMemset2DAsync ( void* devPtr, size_t pitch, int value, size_t width, size_t height, cudaStream_t stream = 0 ) Initializes or sets device memory to a value. __host__ cudaError_...
使用nvcc 编译的源文件可以包含主机代码(即在host上执行的代码)和设备代码(即在device上执行的代码。 nvcc 的基本工作流程包括将设备代码与主机代码分离,然后: 将设备代码编译成汇编形式(PTX代码)或二进制形式(cubin对象) 并通过CUDA运行时函数的调用来替换 <<<…>>> 语法对主机代码进行修改,以从PTX代码或cubin对...
dev_s = cuda.device_array((1,), dtype=s)reduce_numba(dev_a, res=dev_s)s = dev_s.copy_to_host()[0]np.isclose(s, s_cpu)# True 二维规约示例 并行约简技术是非常伟大的，如何将其扩展到更高的维度？虽然我们总是可以使用一个展开的数组(array2 .ravel())调用，但了解如何手动约简多维数组...