RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! 解决方案 通过next(linear.parameters()).device确定 model 已经在 cuda:0 上了,同时输入model.forward()的张量也位于 cuda:0。输入的张量没什么好推敲的,于是考虑到模型具有多层结构,遂输出...
使用PyTorch 接下来,我们来看一下如何使用 PyTorch 来列出 GPU 设备。以下是示例代码: importtorchdeflist_gpu_devices():gpu_devices=torch.cuda.device_count()ifgpu_devices>0:fordeviceinrange(gpu_devices):print(f"Device name:{torch.cuda.get_device_name(device)}")else:print("No GPU devices found....
可以通过以下代码验证 CUDA 和 cuDNN 是否被正确识别:python import tensorflow as tf print(tf.config.list_physical_devices('GPU'))或者对于 PyTorch:python import torch print(torch.cuda.is_available())如果输出显示 GPU 可用,说明 CUDA 和 cuDNN 已经正确安装并与 Python 环境兼容。
## 将创建好的tensor分配到指定的设备中 os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1' device = 'cuda' if torch.cuda.is_available() else 'cpu' a_tensor_device = a_tensora_tensor.to(device) # 此处是将a_tensor分配到cuda 0 和1 1. 2. 3. 4. 5. 6. 7. 8. 9. list 转 torch.Te...
为什么“`conda list cudnn`”在“`conda install pytorch` cudatoolkit=10.2 -c pytorch”安装后没...
为什么会这样?估计是因为使用micromamba安装环境时,默认是安装了python3.10,而python3.10默认自带的cuda版本就是v12。 那么重新创建一个虚拟环境,指定python版本为3.8,是否就可以了呢? 哎,配置环境真是麻烦死了! #人工智能AI#AI技术LLM #LLM(大型语言模型) GPT#PythonPyTorch #TensorFlow 学习 深度学习(Deep Learning...
While updating to CUDA 12.6 eager test, PR: #148602 Failing workflow: https://github.com/pytorch/pytorch/actions/runs/13690790469/job/38285054097#step:22:4164 We see following test failure: _ TestForeachCUDA.test_pointwise_op_with_tensor_of_scalarlist_overload__foreach_addcmul_is_fastpath_Tr...
From the DISABLED prefix in this issue title, it looks like you are attempting to disable a test in PyTorch CI. The information I have parsed is below: Test name: test_binary_op_list_slow_path__foreach_div_cuda_uint8 (__main__.TestForeachCUDA) Platforms for which to skip the test...
我这边是cuda在前面精度才一致,如果链接tensorrt目录下面的libnvinfer.so.7这个库精度就会不一致。看了git工程,大家写的cmakelist大部分都是把cuda写在前面。 试想想,如果我一开始把tensorrt写前面,然后精度不一致,又不报错,然后这个精度问题根本查找不出来的吧?太难了。
Companion docker images consist of all combinations of python versions, machine learning frameworks (Keras, PyTorch and Tensorflow) and CPU/CUDA versions. DVC - Data Science Version Control is an open-source version control system for machine learning projects with pipelines support. It makes ML ...