batch_size = 3 batch = torch.zeros(batch_size, 3, 256, 256, dtype=torch.uint8) import os data_dir = '../../data/p1ch4/image-cats/' #设定相对路径 filenames = [name for name in os.listdir(data_dir) #读取文件名 if os.pat
# the size of input image(using the resize the original image,default is w=224,h=224) # -t, --thread (value:4) # count of processing threads #example: ./ncnn2table --param=squeezenet-fp32.param --bin=squeezenet-fp32.bin --images=images/ --output=squeezenet.table --mean=104.0,117...
$ sudo apt-get install -y nvidia-docker2 $ sudo systemctl restart docker 下载镜像 终端中输入以下指令 pull 最新的镜像。 $ docker pullmltooling/ml-workspace-gpu 由于镜像较大,根据网速不同等待成功后输入如下指令可以看到 image 已经下载到本地。 $ docker images REPOSITORY TAG IMAGE ID CREATED SIZE ...
# maintain the image input as an 8-bit uint8 tensortransform = T.Compose([T.Resize(224),T.PILToTensor()])train_set = FakeCIFAR(transform=transform)train_loader = torch.utils.data.DataLoader(train_set, batch_size=1024, shuffle=True, num_workers=8...
ip-address(vm)$ docker run--shm-size 128G-v~/imagenet:/tmp/imagenet-eXRT_TPU_CONFIG="tpu_worker;0;$TPU_IP_ADDRESS:8470"gcr.io/tpu-pytorch/xla:r0.5python3 pytorch/xla/test/test_train_imagenet.py--model=resnet50--num_epochs=90--num_workers=64--log_steps=200--datadir=/tmp/image...
graph 的计算图结构以及权重均保存在 "model" 文件夹下，以供部署使用：flow.save(inference_graph, "model")然后只需要运行docker run --rm --runtime=nvidia --network=host -v$(pwd)/model:/models/resnet101/1 \ oneflowinc/oneflow-serving:nightly由此可以启动一个部署着 ResNet101 模型的 Docker ...
docker updated Dockerfile based on NGC PyG 24.09 image (#9794) 5个月前 docs [Docs] Grammatical fixes in compile.rst (#10208) 16天前 examples Explicitly chain exceptions (#10242) 3天前 graphgym Explicitly chain exceptions (#10242) 3天前 ...
启动ROCm 基础 Docker 容器: docker run -it --network=host --device=/dev/kfd --device=/dev/dri --group-add=video --ipc=host --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --shm-size 8G -v ~/profiler_tutorial:/profiler_tutorial rocm/dev-ubuntu-20.04:5.6 ...
# Dockerfile # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch FROM nvcr.io/nvidia/pytorch:20.03-py3 代码 单GPU代码 ## main.py文件 import torch # 构造模型 model = nn.Linear(10, 10).to(local_rank) # 前向传播 outputs = model(torch.randn(20, 10...
pic = Image.open(datapath+'\\'+pic) pic = transforms.ToTensor()(pic) return pic,label #实例化对象 data = MyDataset(txtpath) #将数据集导入DataLoader,进行shuffle以及选取batch_size data_loader = DataLoader(data,batch_size=2,shuffle=True,num_workers=0) ...