pull code

examples/trials/mnist-pytorch/mnist.py

@@ -5,6 +5,7 @@
 https://github.com/pytorch/examples/blob/master/mnist/main.py
 """
 
+import os
 import argparse
 import logging
 import nni
@@ -84,15 +85,18 @@ def main(args):
     device = torch.device("cuda" if use_cuda else "cpu")
 
     kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+
+    data_dir = os.path.join(args['data_dir'], nni.get_trial_id())
+
     train_loader = torch.utils.data.DataLoader(
-        datasets.MNIST(args['data_dir'], train=True, download=True,
+        datasets.MNIST(data_dir, train=True, download=True,
                        transform=transforms.Compose([
                            transforms.ToTensor(),
                            transforms.Normalize((0.1307,), (0.3081,))
                        ])),
         batch_size=args['batch_size'], shuffle=True, **kwargs)
     test_loader = torch.utils.data.DataLoader(
-        datasets.MNIST(args['data_dir'], train=False, transform=transforms.Compose([
+        datasets.MNIST(data_dir, train=False, transform=transforms.Compose([
             transforms.ToTensor(),
             transforms.Normalize((0.1307,), (0.3081,))
         ])),

src/sdk/pynni/nni/hyperband_advisor/hyperband_advisor.py

@@ -32,6 +32,7 @@
 from nni.msg_dispatcher_base import MsgDispatcherBase
 from nni.protocol import CommandType, send
 from nni.utils import NodeType, OptimizeMode, MetricType, extract_scalar_reward
+import nni.parameter_expressions as parameter_expressions
 
 _logger = logging.getLogger(__name__)
 

src/webui/src/index.css

@@ -19,8 +19,8 @@ time, mark, audio, video {
 	margin: 0;
 	padding: 0;
 	border: 0;
+	font: inherit;	
 	font-size: 100%;
-	font: inherit;
 }
 /* HTML5 display-role reset for older browsers */
 article, aside, details, figcaption, figure,
@@ -48,4 +48,4 @@ q:before, q:after {
 table {
 	border-collapse: collapse;
 	border-spacing: 0;
-}
+}

test/pipelines-it-kubeflow.yml

@@ -39,11 +39,6 @@ jobs:
     displayName: 'Install nni toolkit via source code'
 
   - script: |
-      python3 -m pip install scikit-learn==0.20.0 --user
-      python3 -m pip install torch==0.4.1 --user
-      python3 -m pip install torchvision==0.2.1 --user
-      python3 -m pip install keras==2.1.6 --user
-      python3 -m pip install tensorflow==1.12.0 --user
       sudo apt-get install swig -y
       PATH=$HOME/.local/bin:$PATH nnictl package install --name=SMAC
       PATH=$HOME/.local/bin:$PATH nnictl package install --name=BOHB

test/pipelines-it-local.yml

@@ -9,8 +9,8 @@ jobs:
     displayName: 'Install nni toolkit via source code'
   - script: |
       python3 -m pip install scikit-learn==0.20.0 --user
-      python3 -m pip install torch==0.4.1 --user
-      python3 -m pip install torchvision==0.2.1 --user
+      python3 -m pip install torch==1.2.0 --user
+      python3 -m pip install torchvision==0.4.0 --user
       python3 -m pip install keras==2.1.6 --user
       python3 -m pip install tensorflow-gpu==1.12.0 --user
       sudo apt-get install swig -y

test/pipelines-it-pai.yml

@@ -39,11 +39,6 @@ jobs:
     displayName: 'Install nni toolkit via source code'
 
   - script: |
-      python3 -m pip install scikit-learn==0.20.0 --user
-      python3 -m pip install torch==0.4.1 --user
-      python3 -m pip install torchvision==0.2.1 --user
-      python3 -m pip install keras==2.1.6 --user
-      python3 -m pip install tensorflow-gpu==1.12.0 --user
       sudo apt-get install swig -y
       PATH=$HOME/.local/bin:$PATH nnictl package install --name=SMAC
       PATH=$HOME/.local/bin:$PATH nnictl package install --name=BOHB