It works fine when you set create_graph=False in the inner updates but then it wouldn't compute the higher order derivatives. I don't get the error when using torch.device("cpu") and torch.device("cuda"). Here is the code to reproduce the error: device = torch.device("mps") model...
device=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")model.to(device) 这两行代码放在读取数据之前。 代码语言:javascript 复制 mytensor=my_tensor.to(device) 这行代码的意思是将所有最开始读取数据时的tensor变量copy一份到device所指定的GPU上去,之后的运算都在GPU上进行。 这句话需要写的次...
device=torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")model.to(device) 这两行代码放在读取数据之前。 代码语言:javascript 复制 mytensor=my_tensor.to(device) 这行代码的意思是将所有最开始读取数据时的tensor变量copy一份到device所指定的GPU上去,之后的运算都在GPU上进行。 这句话需要写的次...
1、目前主流方法:.to(device)方法 (推荐) import torch import time #1.通常用法 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") data = data.to(device) model = model.to(device) ''' 1.先创建device对象 2.to函数指定数据或者模型放到哪里 ''' #2.将构建的tensor或者模...
False CUDA used to build PyTorch: 11.8 ROCM used to build PyTorch: N/A OS: Ubuntu 20.04.6 LTS (x86_64) GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0 Clang version: Could not collect CMake version: version 3.27.7 Libc version: glibc-2.31 Python version: 3.11.8 (main, Feb ...
a = torch.randn(3,1).to(device) # 将模型加入device model = seq2seq() model = model.to(device) # 很常用:在loader中读取的数据加入device for batch_x, batch_y in loader: batch_x, batch_y = batch_x.to(device), batch_y.to(device) ...
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Copied to Clipboard Error: Could not Copy #!/bin/bash # # The run script for Megatron-LM on OCI HPC Cluster of BM.GPU4.8 # Launched by sbatch submit_direct.slm # VOCAB_FILE=../data/pubmed6B/bert-large-uncased-vocab.txt DATASET=../data/pubmed6B/pubmed6B_bert_text_sentence ...
python -u run_generation.py --benchmark -m meta-llama/Llama-2-7b-hf --num-beams 4 --num-iter 10 --batch-size 1 --input-tokens 1024 --max-new-tokens 128 --device xpu --ipex --dtype float16 --token-latency The argument to pay attention to is the device where we specifyxpuin...
I’d reflash your SD card with a fresh install of JetPack and check deviceQuery from the beginning to make sure that your GPU is working: Jetson Nano 4GB - https://developer.nvidia.com/embedded/l4t/r32_release_v7.1/jp_4.6.1_b110_sd_card/jeston_nano/jetson-nano-jp461-sd-card-image....