Remove duplicate data under /tmp folder, and other small changes. (#2484)

examples/nas/classic_nas/mnist.py

@@ -109,8 +109,7 @@ def main(args):
 
     kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
 
-    #data_dir = os.path.join(args['data_dir'], nni.get_trial_id())
-    data_dir = os.path.join(args['data_dir'], 'data')
+    data_dir = args['data_dir']
 
     train_loader = torch.utils.data.DataLoader(
         datasets.MNIST(data_dir, train=True, download=True,

examples/trials/mnist-pbt-tuner-pytorch/mnist.py

@@ -88,7 +88,7 @@ def main(args):
 
     kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
 
-    data_dir = os.path.join(args['data_dir'], nni.get_trial_id())
+    data_dir = args['data_dir']
 
     train_loader = torch.utils.data.DataLoader(
         datasets.MNIST(data_dir, train=True, download=True,
@@ -144,7 +144,7 @@ def get_params():
     # Training settings
     parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
     parser.add_argument("--data_dir", type=str,
-                        default='/tmp/pytorch/mnist/input_data', help="data directory")
+                        default='./data', help="data directory")
     parser.add_argument('--batch_size', type=int, default=64, metavar='N',
                         help='input batch size for training (default: 64)')
     parser.add_argument('--lr', type=float, default=0.01, metavar='LR',

examples/trials/mnist-pytorch/mnist.py

@@ -88,7 +88,7 @@ def main(args):
 
     kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
 
-    data_dir = os.path.join(args['data_dir'], nni.get_trial_id())
+    data_dir = args['data_dir']
 
     train_loader = torch.utils.data.DataLoader(
         datasets.MNIST(data_dir, train=True, download=True,
@@ -129,7 +129,7 @@ def get_params():
     # Training settings
     parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
     parser.add_argument("--data_dir", type=str,
-                        default='/tmp/pytorch/mnist/input_data', help="data directory")
+                        default='./data', help="data directory")
     parser.add_argument('--batch_size', type=int, default=64, metavar='N',
                         help='input batch size for training (default: 64)')
     parser.add_argument("--batch_num", type=int, default=None)

test/config/integration_tests.yml

@@ -51,6 +51,8 @@ testCases:
 
 - name: mnist-pytorch
   configFile: test/config/examples/mnist-pytorch.yml
+  # download data first, to prevent concurrent issue.
+  launchCommand: python3 ../examples/trials/mnist-pytorch/mnist.py --epochs 1 --batch_num 0 --data_dir ../examples/trials/mnist-pytorch/data && nnictl create --config $configFile --debug
 
 - name: mnist-annotation
   configFile: test/config/examples/mnist-annotation.yml
@@ -84,7 +86,7 @@ testCases:
   configFile: test/config/examples/classic-nas-pytorch.yml
   # remove search space file
   stopCommand: nnictl stop
-  onExitCommand: python3 -c 'import os; os.remove("config/examples/nni-nas-search-space.json")'
+  onExitCommand: python3 -c "import os; os.remove('config/examples/nni-nas-search-space.json')"
   trainingService: local
 
 #########################################################################

test/nni_test/nnitest/run_tests.py

 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-import sys
-import os
 import argparse
-import subprocess
-import time
 import datetime
-import shlex
-import traceback
 import json
+import os
+import subprocess
+import sys
+import time
+
 import ruamel.yaml as yaml
 
-from utils import get_experiment_status, get_yml_content, dump_yml_content, get_experiment_id, \
-    parse_max_duration_time, get_trial_stats, deep_update, print_trial_job_log, get_failed_trial_jobs, \
-    get_experiment_dir, print_experiment_log
-from utils import GREEN, RED, CLEAR, STATUS_URL, TRIAL_JOBS_URL, EXPERIMENT_URL, REST_ENDPOINT, wait_for_port_available
 import validators
+from utils import (CLEAR, EXPERIMENT_URL, GREEN, RED, REST_ENDPOINT,
+                   STATUS_URL, TRIAL_JOBS_URL, deep_update, dump_yml_content,
+                   get_experiment_dir, get_experiment_id,
+                   get_experiment_status, get_failed_trial_jobs,
+                   get_trial_stats, get_yml_content, parse_max_duration_time,
+                   print_experiment_log, print_trial_job_log,
+                   wait_for_port_available)
 
 it_variables = {}
 
+
 def update_training_service_config(config, training_service):
     it_ts_config = get_yml_content(os.path.join('config', 'training_service.yml'))
 
@@ -39,6 +42,7 @@ def update_training_service_config(config, training_service):
     deep_update(config, it_ts_config['all'])
     deep_update(config, it_ts_config[training_service])
 
+
 def prepare_config_file(test_case_config, it_config, args):
     config_path = args.nni_source_dir + test_case_config['configFile']
     test_yml_config = get_yml_content(config_path)
@@ -63,6 +67,7 @@ def prepare_config_file(test_case_config, it_config, args):
 
     return new_config_file
 
+
 def run_test_case(test_case_config, it_config, args):
     new_config_file = prepare_config_file(test_case_config, it_config, args)
     # set configFile variable
@@ -75,15 +80,16 @@ def run_test_case(test_case_config, it_config, args):
         stop_command = get_command(test_case_config, 'stopCommand')
         print('Stop command:', stop_command, flush=True)
         if stop_command:
-            subprocess.run(shlex.split(stop_command))
+            subprocess.run(stop_command, shell=True)
         exit_command = get_command(test_case_config, 'onExitCommand')
         print('Exit command:', exit_command, flush=True)
         if exit_command:
-            subprocess.run(shlex.split(exit_command), check=True)
+            subprocess.run(exit_command, shell=True, check=True)
         # remove tmp config file
         if os.path.exists(new_config_file):
             os.remove(new_config_file)
 
+
 def invoke_validator(test_case_config, nni_source_dir, training_service):
     validator_config = test_case_config.get('validator')
     if validator_config is None or validator_config.get('class') is None:
@@ -100,10 +106,12 @@ def invoke_validator(test_case_config, nni_source_dir, training_service):
         print_trial_job_log(training_service, TRIAL_JOBS_URL)
         raise
 
+
 def get_max_values(config_file):
     experiment_config = get_yml_content(config_file)
     return parse_max_duration_time(experiment_config['maxExecDuration']), experiment_config['maxTrialNum']
 
+
 def get_command(test_case_config, commandKey):
     command = test_case_config.get(commandKey)
     if commandKey == 'launchCommand':
@@ -121,11 +129,12 @@ def get_command(test_case_config, commandKey):
 
     return command
 
+
 def launch_test(config_file, training_service, test_case_config):
     launch_command = get_command(test_case_config, 'launchCommand')
     print('launch command: ', launch_command, flush=True)
 
-    proc = subprocess.run(shlex.split(launch_command))
+    proc = subprocess.run(launch_command, shell=True)
 
     assert proc.returncode == 0, 'launch command failed with code %d' % proc.returncode
 
@@ -176,6 +185,7 @@ def launch_test(config_file, training_service, test_case_config):
         print_trial_job_log(training_service, TRIAL_JOBS_URL)
         raise AssertionError('Failed to finish in maxExecDuration')
 
+
 def case_excluded(name, excludes):
     if name is None:
         return False
@@ -186,6 +196,7 @@ def case_excluded(name, excludes):
                 return True
     return False
 
+
 def case_included(name, cases):
     assert cases is not None
     for case in cases.split(','):
@@ -193,9 +204,11 @@ def case_included(name, cases):
             return True
     return False
 
+
 def match_platform(test_case_config):
     return sys.platform in test_case_config['platform'].split(' ')
 
+
 def match_training_service(test_case_config, cur_training_service):
     case_ts = test_case_config['trainingService']
     assert case_ts is not None
@@ -205,6 +218,7 @@ def match_training_service(test_case_config, cur_training_service):
         return True
     return False
 
+
 def run(args):
     it_config = get_yml_content(args.config)
 
@@ -227,7 +241,8 @@ def run(args):
             continue
 
         if not match_training_service(test_case_config, args.ts):
-            print('skipped {}, training service {} not match [{}]'.format(name, args.ts, test_case_config['trainingService']))
+            print('skipped {}, training service {} not match [{}]'.format(
+                name, args.ts, test_case_config['trainingService']))
             continue
 
         wait_for_port_available(8080, 30)
@@ -244,7 +259,8 @@ if __name__ == '__main__':
     parser.add_argument("--nni_source_dir", type=str, default='../')
     parser.add_argument("--cases", type=str, default=None)
     parser.add_argument("--exclude", type=str, default=None)
-    parser.add_argument("--ts", type=str, choices=['local', 'remote', 'pai', 'kubeflow', 'frameworkcontroller'], default='local')
+    parser.add_argument("--ts", type=str, choices=['local', 'remote', 'pai',
+                                                   'kubeflow', 'frameworkcontroller'], default='local')
     args = parser.parse_args()
 
     run(args)

test/pipelines/pipelines-it-local-tf2.yml

@@ -11,7 +11,7 @@ jobs:
   - script: |
       set -e
       python3 -m pip install scikit-learn==0.20.0 --user
-      python3 -m pip install torch==1.3.1 torchvision==0.4.1 -f https://download.pytorch.org/whl/torch_stable.html --user
+      python3 -m pip install torch==1.3.1 torchvision==0.4.2 -f https://download.pytorch.org/whl/torch_stable.html --user
       python3 -m pip install tensorflow-gpu==2.2.0 tensorflow-estimator==2.2.0 --force --user
       python3 -m pip install keras==2.4.2 --user
       sudo apt-get install swig -y

test/pipelines/pipelines-it-local.yml

@@ -11,7 +11,7 @@ jobs:
   - script: |
       set -e
       python3 -m pip install scikit-learn==0.20.0 --user
-      python3 -m pip install torchvision==0.4.1 --user
+      python3 -m pip install torchvision==0.4.2 --user
       python3 -m pip install torch==1.3.1 --user
       python3 -m pip install keras==2.1.6 --user
       python3 -m pip install tensorflow-gpu==1.15.2 tensorflow-estimator==1.15.1 --force --user