Merge v1.4.1 back to master

deployment/deployment-pipelines.yml

@@ -88,7 +88,7 @@ jobs:
         export IMG_NAME=$(dev_docker_img)
         export IMG_TAG=`git describe --tags --abbrev=0`.`date -u +%y%m%d%H%M`
         echo 'updating docker file for testpyi...'
-        sed -ie 's/RUN python3 -m pip --no-cache-dir install nni/RUN python3 -m pip install --user --no-cache-dir --index-url https:\/\/test.pypi.org\/simple --extra-index-url https:\/\/pypi.org\/simple nni/' Dockerfile
+        sed -ie 's/RUN python3 -m pip --no-cache-dir install nni/RUN python3 -m pip install --no-cache-dir --index-url https:\/\/test.pypi.org\/simple --extra-index-url https:\/\/pypi.org\/simple nni/' Dockerfile
       else
         docker login -u $(docker_hub_user) -p $(docker_hub_pwd)
         export IMG_NAME=msranni/nni

examples/trials/mnist-pytorch/mnist.py

@@ -15,6 +15,13 @@
 import torch.optim as optim
 from torchvision import datasets, transforms
 
+# Temporary patch this example until the MNIST dataset download issue get resolved
+# https://github.com/pytorch/vision/issues/1938
+import urllib
+
+opener = urllib.request.build_opener()
+opener.addheaders = [('User-agent', 'Mozilla/5.0')]
+urllib.request.install_opener(opener)
 
 logger = logging.getLogger('mnist_AutoML')
 

src/sdk/pynni/nni/curvefitting_assessor/curvefitting_assessor.py

@@ -4,6 +4,7 @@
 import logging
 import datetime
 from nni.assessor import Assessor, AssessResult
+from nni.utils import extract_scalar_history
 from .model_factory import CurveModel
 
 logger = logging.getLogger('curvefitting_Assessor')
@@ -91,10 +92,11 @@ def assess_trial(self, trial_job_id, trial_history):
         Exception
             unrecognize exception in curvefitting_assessor
         """
-        self.trial_history = trial_history
+        scalar_trial_history = extract_scalar_history(trial_history)
+        self.trial_history = scalar_trial_history
         if not self.set_best_performance:
             return AssessResult.Good
-        curr_step = len(trial_history)
+        curr_step = len(scalar_trial_history)
         if curr_step < self.start_step:
             return AssessResult.Good
 
@@ -106,7 +108,7 @@ def assess_trial(self, trial_job_id, trial_history):
             start_time = datetime.datetime.now()
             # Predict the final result
             curvemodel = CurveModel(self.target_pos)
-            predict_y = curvemodel.predict(trial_history)
+            predict_y = curvemodel.predict(scalar_trial_history)
             logger.info('Prediction done. Trial job id = %s. Predict value = %s', trial_job_id, predict_y)
             if predict_y is None:
                 logger.info('wait for more information to predict precisely')

src/sdk/pynni/nni/medianstop_assessor/medianstop_assessor.py

@@ -3,6 +3,7 @@
 
 import logging
 from nni.assessor import Assessor, AssessResult
+from nni.utils import extract_scalar_history
 
 logger = logging.getLogger('medianstop_Assessor')
 
@@ -91,20 +92,12 @@ def assess_trial(self, trial_job_id, trial_history):
         if curr_step < self._start_step:
             return AssessResult.Good
 
-        try:
-            num_trial_history = [float(ele) for ele in trial_history]
-        except (TypeError, ValueError) as error:
-            logger.warning('incorrect data type or value:')
-            logger.exception(error)
-        except Exception as error:
-            logger.warning('unrecognized exception in medianstop_assessor:')
-            logger.exception(error)
-
-        self._update_data(trial_job_id, num_trial_history)
+        scalar_trial_history = extract_scalar_history(trial_history)
+        self._update_data(trial_job_id, scalar_trial_history)
         if self._high_better:
-            best_history = max(trial_history)
+            best_history = max(scalar_trial_history)
         else:
-            best_history = min(trial_history)
+            best_history = min(scalar_trial_history)
 
         avg_array = []
         for id_ in self._completed_avg_history:

src/sdk/pynni/nni/msg_dispatcher.py

@@ -234,4 +234,5 @@ def _earlystop_notify_tuner(self, data):
         if multi_thread_enabled():
             self._handle_final_metric_data(data)
         else:
+            data['value'] = to_json(data['value'])
             self.enqueue_command(CommandType.ReportMetricData, data)

src/sdk/pynni/nni/utils.py

@@ -62,6 +62,13 @@ def extract_scalar_reward(value, scalar_key='default'):
     """
     Extract scalar reward from trial result.
 
+    Parameters
+    ----------
+    value : int, float, dict
+        the reported final metric data
+    scalar_key : str
+        the key name that indicates the numeric number
+
     Raises
     ------
     RuntimeError
@@ -78,6 +85,26 @@ def extract_scalar_reward(value, scalar_key='default'):
     return reward
 
 
+def extract_scalar_history(trial_history, scalar_key='default'):
+    """
+    Extract scalar value from a list of intermediate results.
+
+    Parameters
+    ----------
+    trial_history : list
+        accumulated intermediate results of a trial
+    scalar_key : str
+        the key name that indicates the numeric number
+
+    Raises
+    ------
+    RuntimeError
+        Incorrect final result: the final result should be float/int,
+        or a dict which has a key named "default" whose value is float/int.
+    """
+    return [extract_scalar_reward(ele, scalar_key) for ele in trial_history]
+
+
 def convert_dict2tuple(value):
     """
     convert dict type to tuple to solve unhashable problem.
@@ -90,7 +117,9 @@ def convert_dict2tuple(value):
 
 
 def init_dispatcher_logger():
-    """ Initialize dispatcher logging configuration"""
+    """
+    Initialize dispatcher logging configuration
+    """
     logger_file_path = 'dispatcher.log'
     if dispatcher_env_vars.NNI_LOG_DIRECTORY is not None:
         logger_file_path = os.path.join(dispatcher_env_vars.NNI_LOG_DIRECTORY, logger_file_path)

src/webui/src/components/trial-detail/TableList.tsx

@@ -241,13 +241,19 @@ class TableList extends React.Component<TableListProps, TableListState> {
             // final result in a succeed trial, it may be a dict.
             // get intermediate result dict keys array
             const { intermediateKey } = this.state;
-            let otherkeys: string[] = ['default'];
+            const otherkeys: string[] = [ ];
             if (res.data.length !== 0) {
-                otherkeys = Object.keys(parseMetrics(res.data[0].data));
+                // just add type=number keys
+                const intermediateMetrics = parseMetrics(res.data[0].data);
+                for(const key in intermediateMetrics){
+                    if(typeof intermediateMetrics[key] === 'number') {
+                        otherkeys.push(key);
+                    }
+                }
             }
             // intermediateArr just store default val
             Object.keys(res.data).map(item => {
-                if(res.data[item].type === 'PERIODICAL'){
+                if (res.data[item].type === 'PERIODICAL') {
                     const temp = parseMetrics(res.data[item].data);
                     if (typeof temp === 'object') {
                         intermediateArr.push(temp[intermediateKey]);
@@ -278,11 +284,13 @@ class TableList extends React.Component<TableListProps, TableListState> {
             // just watch default key-val
             if (isShowDefault === true) {
                 Object.keys(intermediateData).map(item => {
-                    const temp = parseMetrics(intermediateData[item].data);
-                    if (typeof temp === 'object') {
-                        intermediateArr.push(temp[value]);
-                    } else {
-                        intermediateArr.push(temp);
+                    if (intermediateData[item].type === 'PERIODICAL') {
+                        const temp = parseMetrics(intermediateData[item].data);
+                        if (typeof temp === 'object') {
+                            intermediateArr.push(temp[value]);
+                        } else {
+                            intermediateArr.push(temp);
+                        }
                     }
                 });
             } else {

test/generate_ts_config.py

@@ -36,11 +36,11 @@ def update_training_service_config(args):
             config[args.ts]['paiConfig']['token'] = args.pai_token
         if args.nni_docker_image is not None:
             config[args.ts]['trial']['image'] = args.nni_docker_image
-        if args.nniManagerNFSMountPath is not None:
+        if args.nni_manager_nfs_mount_path is not None:
             config[args.ts]['trial']['nniManagerNFSMountPath'] = args.nni_manager_nfs_mount_path
-        if args.containerNFSMountPath is not None:
+        if args.container_nfs_mount_path is not None:
             config[args.ts]['trial']['containerNFSMountPath'] = args.container_nfs_mount_path
-        if args.paiStoragePlugin is not None:
+        if args.pai_storage_plugin is not None:
             config[args.ts]['trial']['paiStoragePlugin'] = args.pai_storage_plugin
         if args.vc is not None:
             config[args.ts]['trial']['virtualCluster'] = args.vc