add config test for mnist torch exp (#1523)

* add mnist pytorch example

* refine comments

* change links in readme for mnist example

* add config test for pytorch mnist example

* delete duplicated result

examples/trials/mnist-pytorch/mnist.py

@@ -108,17 +108,16 @@ def main(args):
         train(args, model, device, train_loader, optimizer, epoch)
         test_acc = test(args, model, device, test_loader)
 
-        # report intermediate result
-        nni.report_intermediate_result(test_acc)
-        logger.debug('test accuracy %g', test_acc)
-        logger.debug('Pipe send intermediate result done.')
-
-    test_acc = test(args, model, device, test_loader)
-    # report final result
-    nni.report_final_result(test_acc)
-    logger.debug('Final result is %g', test_acc)
-    logger.debug('Send final result done.')
-
+        if epoch < args['epochs']:
+            # report intermediate result
+            nni.report_intermediate_result(test_acc)
+            logger.debug('test accuracy %g', test_acc)
+            logger.debug('Pipe send intermediate result done.')
+        else:
+            # report final result
+            nni.report_final_result(test_acc)
+            logger.debug('Final result is %g', test_acc)
+            logger.debug('Send final result done.')
 
 
 def get_params():

test/config_test/examples/mnist-pytorch.test.yml

+authorName: nni
+experimentName: default_test
+maxExecDuration: 15m
+maxTrialNum: 4
+trialConcurrency: 2
+searchSpacePath: ./mnist_pytorch_search_space.json
+
+tuner:
+  builtinTunerName: Random
+assessor:
+  builtinAssessorName: Medianstop
+  classArgs:
+    optimize_mode: maximize
+trial:
+  codeDir: ../../../examples/trials/mnist-pytorch
+  command: python3 mnist.py  --epochs 2
+  gpuNum: 0
+
+useAnnotation: false
+multiPhase: false
+multiThread: false
+
+trainingServicePlatform: local

test/config_test/examples/mnist_pytorch_search_space.json

+{
+    "batch_size": {"_type":"choice", "_value": [16, 32, 64, 128]},
+    "hidden_size":{"_type":"choice","_value":[128, 256, 512, 1024]},
+    "lr":{"_type":"choice","_value":[0.0001, 0.001, 0.01, 0.1]},
+    "momentum":{"_type":"uniform","_value":[0, 1]}
+}