Port trial examples' config file to v2

examples/trials/auto-gbdt/config.yml

@@ -1,21 +1,10 @@
-authorName: default
-experimentName: example_auto-gbdt
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
 trialConcurrency: 1
-maxExecDuration: 10h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
-  #SMAC (SMAC should be installed through nnictl)
-  builtinTunerName: TPE
+  name: TPE
   classArgs:
-    #choice: maximize, minimize
     optimize_mode: minimize
-trial:
-  command: python3 main.py
-  codeDir: .
-  gpuNum: 0
+trainingService:  # For other platforms, check mnist-pytorch example
+  platform: local

examples/trials/auto-gbdt/config_metis.yml

@@ -1,21 +1,22 @@
-authorName: default
-experimentName: example_auto-gbdt-metis
+# The search space of Metis tuner is slightly different from TPE and others.
+# See Metis tuner' doc for details: https://nni.readthedocs.io/en/stable/Tuner/MetisTuner.html
+searchSpace:
+  num_leaves:
+    _type: choice
+    _value: [31, 28, 24, 20]
+  learning_rate:
+    _type: choice
+    _value: [0.01, 0.05, 0.1, 0.2]
+  bagging_freq:
+    _type: choice
+    _value: [1, 2, 4, 8, 10]
+
+trialCommand: python3 main.py
 trialConcurrency: 1
-maxExecDuration: 10h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space_metis.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
-  #SMAC (SMAC should be installed through nnictl)
-  builtinTunerName: MetisTuner
+  name: MetisTuner
   classArgs:
-    #choice: maximize, minimize
     optimize_mode: minimize
-trial:
-  command: python3 main.py
-  codeDir: .
-  gpuNum: 0
+trainingService:
+  platform: local

examples/trials/auto-gbdt/search_space.json

@@ -1,6 +1,18 @@
 {
-    "num_leaves":{"_type":"randint","_value":[20, 31]},
-    "learning_rate":{"_type":"choice","_value":[0.01, 0.05, 0.1, 0.2]},
-    "bagging_fraction":{"_type":"uniform","_value":[0.7, 1.0]},
-    "bagging_freq":{"_type":"choice","_value":[1, 2, 4, 8, 10]}
-}
+    "num_leaves": {
+        "_type": "randint",
+        "_value": [20, 31]
+    },
+    "learning_rate": {
+        "_type": "choice",
+        "_value": [0.01, 0.05, 0.1, 0.2]
+    },
+    "bagging_fraction": {
+        "_type": "uniform",
+        "_value": [0.7, 1.0]
+    },
+    "bagging_freq": {
+        "_type": "choice",
+        "_value": [1, 2, 4, 8, 10]
+    }
+}

examples/trials/benchmarking/hyperband/config_hyperband.yml

@@ -1,27 +1,20 @@
-authorName: default
-experimentName: example_mnist_hyperband
-trialConcurrency: 2
-maxExecDuration: 100h
-maxTrialNum: 10000
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
 advisor:
-  #choice: Hyperband, BOHB
-  builtinAdvisorName: Hyperband
+  name: Hyperband
   classArgs:
+    optimize_mode: maximize
+
     #R: the maximum trial budget (could be the number of mini-batches or epochs) can be
     #   allocated to a trial. Each trial should use trial budget to control how long it runs.
     R: 60
+
     #eta: proportion of discarded trials
     eta: 3
-    #choice: maximize, minimize
-    optimize_mode: maximize
+
     #choice: serial, parallelism
     exec_mode: serial
-trial:
-  command: python3 main.py
-  codeDir: .
-  gpuNum: 0
+
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
+trialConcurrency: 10
+trainingService:
+  platform: local

examples/trials/cifar10_pytorch/config.yml

@@ -1,23 +1,14 @@
-authorName: default
-experimentName: example_pytorch_cifar10
+searchSpaceFile: search_space.json
+trialCommand: python3 main.py
+trialGpuNumber: 1
 trialConcurrency: 4
-maxExecDuration: 100h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-searchSpacePath: search_space.json
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
-  #SMAC (SMAC should be installed through nnictl)
-  builtinTunerName: TPE
+  name: TPE
   classArgs:
-    #choice: maximize, minimize
     optimize_mode: maximize
-trial:
-  command: python3 main.py
-  codeDir: .
-  gpuNum: 1
-localConfig:
-  maxTrialNumPerGpu:  2
+trainingService:  # For other platforms, check mnist-pytorch example
+  platform: local
+  maxTrialNumberPerGpu: 2
+  useActiveGpu: false  # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+                       # Check the doc for details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegpu

examples/trials/cifar10_pytorch/search_space.json

@@ -1,5 +1,14 @@
 {
-    "lr":{"_type":"choice", "_value":[0.1, 0.01, 0.001, 0.0001]},
-    "optimizer":{"_type":"choice", "_value":["SGD", "Adadelta", "Adagrad", "Adam", "Adamax"]},
-    "model":{"_type":"choice", "_value":["vgg", "resnet18", "googlenet", "densenet121", "mobilenet", "dpn92", "senet18"]}
+    "lr": {
+        "_type": "choice",
+        "_value": [0.1, 0.01, 0.001, 0.0001]
+    },
+    "optimizer": {
+        "_type": "choice",
+        "_value": ["SGD", "Adadelta", "Adagrad", "Adam", "Adamax"]
+    },
+    "model": {
+        "_type": "choice",
+        "_value": ["vgg", "resnet18", "googlenet", "densenet121", "mobilenet", "dpn92", "senet18"]
+    }
 }

examples/trials/efficientnet/config.yml

@@ -0,0 +1,15 @@
+searchSpaceFile: search_net.json
+trialCodeDirectory: EfficientNet-PyTorch
+trialCommand: python3 main.py /data/imagenet -j 12 -a efficientnet --batch-size 48 --lr 0.048 --wd 1e-5 --epochs 5 --request-from-nni
+trialGpuNumber: 1
+trialConcurrency: 4
+maxTrialNumber: 100
+tuner:
+  className: tuner.FixedProductTuner
+  codeDirectory: .
+  classArgs:
+    product: 2
+trainingService:  # For other platforms, check mnist-pytorch example
+  platform: local
+  useActiveGpu: false  # NOTE: Use "true" if you are using an OS with graphical interface (e.g. Windows 10, Ubuntu desktop)
+                       # Check the doc for details: https://nni.readthedocs.io/en/latest/reference/experiment_config.html#useactivegputrial:

examples/trials/efficientnet/tuner.py

@@ -1,4 +1,4 @@
-from nni.gridsearch_tuner.gridsearch_tuner import GridSearchTuner
+from nni.algorithms.hpo.gridsearch_tuner import GridSearchTuner
 
 
 class FixedProductTuner(GridSearchTuner):

examples/trials/ga_squad/config.yml

@@ -1,19 +1,13 @@
-authorName: default
-experimentName: example_ga_squad
+trialCommand: python3 trial.py
+trialGpuNumber: 0
 trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: local
-#choice: true, false
-useAnnotation: false
+maxTrialNumber: 10
+maxExperimentDuration: 1h
+searchSpace: {}  # hard-coded in tuner
 tuner:
-  codeDir: ../../tuners/ga_customer_tuner
-  classFileName: customer_tuner.py
-  className: CustomerTuner
+  className: customer_tuner.CustomerTuner
+  codeDirectory: ../../tuners/ga_customer_tuner
   classArgs:
     optimize_mode: maximize
-trial:
-  command: python3 trial.py
-  codeDir: .
-  gpuNum: 0
+trainingService:  # For other platforms, check mnist-pytorch example
+  platform: local