WARNING: GPU found but will not be used. Please set `experiment.config.trial_gpu_number` to the number of GPUs you want to use for each trial.

[yyh7773]

''Hello,NAS'':GPU can't be used but i set all: and if i use CPU,there is a error :策略执行失败

Environment:

• NNI version:3.0
• Training service (local|remote|pai|aml|etc):；local
• Client OS:Windows 10
• Server OS (for remote mode only):No
• Python version:3.10
• PyTorch/TensorFlow version:1.12.1+cu113
• Is conda/virtualenv/venv used?:Pycharm Edit
• Is running in Docker?:No

Configuration:

• Experiment config (remember to remove secrets!):{ "experimentType": "nas", "searchSpace": {}, "trialCommand": "D:\learnsaoftware\anaconda3\python.exe -m nni.nas.execution.training_service trial", "trialCodeDirectory": "C:\Users\Administrator\Desktop\yyh", "trialConcurrency": 100, "trialGpuNumber": 1, "maxTrialNumber": 100, "useAnnotation": false, "debug": false, "logLevel": "info", "experimentWorkingDirectory": "C:\Users\Administrator\nni-experiments", "trainingService": { "platform": "local", "trialCommand": "D:\learnsaoftware\anaconda3\python.exe -m nni.nas.execution.training_service trial", "trialCodeDirectory": "C:\Users\Administrator\Desktop\yyh", "trialGpuNumber": 1, "debug": false, "useActiveGpu": true, "maxTrialNumberPerGpu": 1, "reuseMode": false }, "executionEngine": { "name": "ts" }, "modelFormat": { "name": "simplified" } }

• Search space:class MyModelSpace(ModelSpace): def init(self): super().init() self.conv1 = nn.Conv2d(1, 32, 3, 1)

  LayerChoice is used to select a layer between Conv2d and DwConv.

  self.conv2 = LayerChoice([
      nn.Conv2d(32, 64, 3, 1),
      DepthwiseSeparableConv(32, 64)
  ], label='conv2')
  # nni.choice is used to select a dropout rate.
  # The result can be used as parameters of `MutableXXX`.
  self.dropout1 = MutableDropout(nni.choice('dropout', [0.25, 0.5, 0.75]))  # choose dropout rate from 0.25, 0.5 and 0.75
  self.dropout2 = nn.Dropout(0.5)
  feature = nni.choice('feature', [64, 128, 256])
  self.fc1 = MutableLinear(9216, feature)
  self.fc2 = MutableLinear(feature, 10)

  def forward(self, x): x = F.relu(self.conv1(x)) x = F.max_pool2d(self.conv2(x), 2) x = torch.flatten(self.dropout1(x), 1) x = self.fc2(self.dropout2(F.relu(self.fc1(x)))) output = F.log_softmax(x, dim=1) return output

model_space = MyModelSpace()

上述是我的设置情况，请问应该如何解决？

[yyh7773]

MyModelSpace( (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1)) (conv2): LayerChoice( label='conv2' (0): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1)) (1): DepthwiseSeparableConv( (depthwise): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), groups=32) (pointwise): Conv2d(32, 64, kernel_size=(1, 1), stride=(1, 1)) ) ) (dropout1): MutableDropout(p=Categorical([0.25, 0.5, 0.75], label='dropout')) (dropout2): Dropout(p=0.5, inplace=False) (fc1): MutableLinear(in_features=9216, out_features=Categorical([64, 128, 256], label='feature')) (fc2): MutableLinear(in_features=Categorical([64, 128, 256], label='feature'), out_features=10) ) 我的搜索空间与Demo中一样

[XuanmaiXue]

I got same problem, have you solved it ?