Merge branch 'master' of github.com:Microsoft/nni into dev-retiarii

nni/tools/trial_tool/trial_keeper.py

@@ -28,6 +28,7 @@ logger = logging.getLogger('trial_keeper')
 regular = re.compile('v?(?P<version>[0-9](\.[0-9]){0,1}).*')
 
 _hdfs_client = None
+_trial_process = None
 
 
 def get_hdfs_client(args):
@@ -62,6 +63,7 @@ def get_hdfs_client(args):
 
 def main_loop(args):
     '''main loop logic for trial keeper'''
+    global _trial_process
 
     if not os.path.exists(LOG_DIR):
         os.makedirs(LOG_DIR)
@@ -90,13 +92,13 @@ def main_loop(args):
 
     # Notice: We don't appoint env, which means subprocess wil inherit current environment and that is expected behavior
     log_pipe_stdout = trial_syslogger_stdout.get_pipelog_reader()
-    process = Popen(args.trial_command, shell=True, stdout=log_pipe_stdout, stderr=log_pipe_stdout)
-    nni_log(LogType.Info, 'Trial keeper spawns a subprocess (pid {0}) to run command: {1}'.format(process.pid,
+    _trial_process = Popen(args.trial_command, shell=True, stdout=log_pipe_stdout, stderr=log_pipe_stdout, preexec_fn=os.setsid)
+    nni_log(LogType.Info, 'Trial keeper spawns a subprocess (pid {0}) to run command: {1}'.format(_trial_process.pid,
                                                                                                   shlex.split(
                                                                                                       args.trial_command)))
 
     while True:
-        retCode = process.poll()
+        retCode = _trial_process.poll()
         # child worker process exits and all stdout data is read
         if retCode is not None and log_pipe_stdout.set_process_exit() and log_pipe_stdout.is_read_completed == True:
             # In Windows, the retCode -1 is 4294967295. It's larger than c_long, and raise OverflowError.
@@ -213,6 +215,20 @@ def fetch_parameter_file(args):
     fetch_file_thread.start()
 
 
+def _set_adaptdl_signal_handler():
+    import signal
+    global _trial_process
+    def _handler(signum, frame):
+        nni_log(LogType.Info, "RECEIVED SIGNAL {}".format(signum))
+        nni_log(LogType.Debug, "TRIAL PROCESS ID {}".format(_trial_process.pid))
+        if _trial_process and (signum == signal.SIGTERM or signum == signal.SIGINT):
+            os.killpg(os.getpgid(_trial_process.pid), signal.SIGINT)
+            os.waitpid(_trial_process.pid, 0)
+        exit(1)
+    signal.signal(signal.SIGTERM, _handler)
+    signal.signal(signal.SIGINT, _handler)
+
+
 if __name__ == '__main__':
     '''NNI Trial Keeper main function'''
     PARSER = argparse.ArgumentParser()
@@ -237,6 +253,8 @@ if __name__ == '__main__':
     try:
         if NNI_PLATFORM == 'paiYarn' and is_multi_phase():
             fetch_parameter_file(args)
+        if NNI_PLATFORM == 'adl':
+            _set_adaptdl_signal_handler()
         main_loop(args)
     except SystemExit as se:
         nni_log(LogType.Info, 'NNI trial keeper exit with code {}'.format(se.code))

nni/trial.py

@@ -97,6 +97,21 @@ def get_sequence_id():
 
 _intermediate_seq = 0
 
+
+def overwrite_intermediate_seq(value):
+    """
+    Overwrite intermediate sequence value.
+
+    Parameters
+    ----------
+    value:
+        int
+    """
+    assert isinstance(value, int)
+    global _intermediate_seq
+    _intermediate_seq = value
+
+
 def report_intermediate_result(metric):
     """
     Reports intermediate result to NNI.

pipelines/fast-test.yml

@@ -34,7 +34,7 @@ jobs:
       set -e
       sudo apt-get install -y pandoc
       python3 -m pip install -U --upgrade pygments
-      python3 -m pip install -U torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      python3 -m pip install -U torch==1.7.0+cpu torchvision==0.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
       python3 -m pip install -U tensorflow==2.3.1
       python3 -m pip install -U keras==2.4.2
       python3 -m pip install -U gym onnx peewee thop
@@ -96,7 +96,7 @@ jobs:
 
   - script: |
       set -e
-      python3 -m pip install -U torch==1.3.1+cpu torchvision==0.4.2+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      python3 -m pip install -U torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html
       python3 -m pip install -U tensorflow==1.15.2
       python3 -m pip install -U keras==2.1.6
       python3 -m pip install -U gym onnx peewee
@@ -131,12 +131,16 @@ jobs:
   # This platform runs TypeScript unit test first.
 
   steps:
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: 3.8
+    displayName: Configure Python
+
   - script: |
       set -e
-      export PYTHON38_BIN_DIR=/usr/local/Cellar/python@3.8/`ls /usr/local/Cellar/python@3.8`/bin
-      echo "##vso[task.setvariable variable=PATH]${PYTHON38_BIN_DIR}:${HOME}/Library/Python/3.8/bin:${PATH}"
-      python3 -m pip install -U --upgrade pip setuptools
-      python3 -m pip install -U pytest coverage
+      echo "##vso[task.setvariable variable=PATH]${PATH}:${HOME}/.local/bin"
+      python -m pip install -U --upgrade pip setuptools wheel
+      python -m pip install -U pytest coverage
     displayName: 'Install Python tools'
 
   - script: |
@@ -145,10 +149,9 @@ jobs:
 
   - script: |
       set -e
-      cd ts/nni_manager
-      yarn test
-      cd ../nasui
-      CI=true yarn test
+      export CI=true
+      (cd ts/nni_manager && yarn test)
+      (cd ts/nasui && yarn test)
     displayName: 'TypeScript unit test'
 
   - script: |
@@ -188,7 +191,7 @@ jobs:
     displayName: 'Install Python tools'
 
   - script: |
-      python setup.py develop
+      python setup.py develop --no-user
     displayName: 'Install NNI'
 
   - script: |
@@ -201,16 +204,18 @@ jobs:
       cd test
       python -m pytest ut
     displayName: 'Python unit test'
-    continueOnError: true
 
   - script: |
       cd ts/nni_manager
       yarn test
     displayName: 'TypeScript unit test'
-    continueOnError: true
 
   - script: |
       cd test
       python nni_test/nnitest/run_tests.py --config config/pr_tests.yml
     displayName: 'Simple integration test'
-    continueOnError: true
+
+
+trigger:
+  branches:
+    exclude: [ l10n_master ]

setup.py

@@ -72,7 +72,9 @@ dependencies = [
     'colorama',
     'scikit-learn>=0.23.2',
     'pkginfo',
-    'websockets'
+    'websockets',
+    'filelock',
+    'prettytable'
 ]
 
 

test/async_sharing_test/simple_tuner.py

@@ -35,8 +35,7 @@ class SimpleTuner(Tuner):
                 'checksum': None,
                 'path': '',
             }
-            _logger.info('generate parameter for father trial %s' %
-                         parameter_id)
+            _logger.info('generate parameter for father trial %s', parameter_id)
             self.thread_lock.release()
             return {
                 'prev_id': 0,

test/config/naive_test/naive_assessor.py

@@ -18,7 +18,7 @@ class NaiveAssessor(Assessor):
         _logger.info('init')
 
     def assess_trial(self, trial_job_id, trial_history):
-        _logger.info('assess trial %s %s' % (trial_job_id, trial_history))
+        _logger.info('assess trial %s %s', trial_job_id, trial_history)
 
         id_ = trial_history[0]
         if id_ in self._killed:

test/config/naive_test/naive_tuner.py

@@ -21,17 +21,17 @@ class NaiveTuner(Tuner):
 
     def generate_parameters(self, parameter_id, **kwargs):
         self.cur += 1
-        _logger.info('generate parameters: %s' % self.cur)
+        _logger.info('generate parameters: %s', self.cur)
         return { 'x': self.cur }
 
     def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
         reward = extract_scalar_reward(value)
-        _logger.info('receive trial result: %s, %s, %s' % (parameter_id, parameters, reward))
+        _logger.info('receive trial result: %s, %s, %s', parameter_id, parameters, reward)
         _result.write('%d %d\n' % (parameters['x'], reward))
         _result.flush()
 
     def update_search_space(self, search_space):
-        _logger.info('update_search_space: %s' % search_space)
+        _logger.info('update_search_space: %s', search_space)
         with open(os.path.join(_pwd, 'tuner_search_space.json'), 'w') as file_:
             json.dump(search_space, file_)
 

test/config/tuners/hyperband.yml

@@ -11,6 +11,7 @@ advisor:
     optimize_mode: maximize
     R: 60
     eta: 3
+    exec_mode: parallelism
 trial:
   codeDir: ../../../examples/trials/mnist-advisor
   command: python3 mnist.py

test/nni_test/nnitest/utils.py

@@ -124,7 +124,7 @@ def print_file_content(filepath):
 def print_trial_job_log(training_service, trial_jobs_url):
     trial_jobs = get_trial_jobs(trial_jobs_url)
     for trial_job in trial_jobs:
-        trial_log_dir = os.path.join(get_experiment_dir(EXPERIMENT_URL), 'trials', trial_job['id'])
+        trial_log_dir = os.path.join(get_experiment_dir(EXPERIMENT_URL), 'trials', trial_job['trialJobId'])
         log_files = ['stderr', 'trial.log'] if training_service == 'local' else ['stdout_log_collection.log']
         for log_file in log_files:
             print_file_content(os.path.join(trial_log_dir, log_file))

test/ut/sdk/test_compression_utils.py

@@ -12,6 +12,7 @@ import numpy as np
 from nni.algorithms.compression.pytorch.pruning import L1FilterPruner
 from nni.compression.pytorch.utils.shape_dependency import ChannelDependency
 from nni.compression.pytorch.utils.mask_conflict import fix_mask_conflict
+from nni.compression.pytorch.utils.counter import count_flops_params
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 prefix = 'analysis_test'
@@ -60,7 +61,6 @@ channel_dependency_ground_truth = {
 unittest.TestLoader.sortTestMethodsUsing = None
 
 
-@unittest.skipIf(torch.__version__ >= '1.6.0', 'not supported')
 class AnalysisUtilsTest(TestCase):
     @unittest.skipIf(torch.__version__ < "1.3.0", "not supported")
     def test_channel_dependency(self):
@@ -138,5 +138,49 @@ class AnalysisUtilsTest(TestCase):
                         assert b_index1 == b_index2
 
 
+    def test_flops_params(self):
+        class Model1(nn.Module):
+            def __init__(self):
+                super(Model1, self).__init__()
+                self.conv = nn.Conv2d(3, 5, 1, 1)
+                self.bn = nn.BatchNorm2d(5)
+                self.relu = nn.LeakyReLU()
+                self.linear = nn.Linear(20, 10)
+                self.upsample = nn.UpsamplingBilinear2d(size=2)
+                self.pool = nn.AdaptiveAvgPool2d((2, 2))
+
+            def forward(self, x):
+                x = self.conv(x)
+                x = self.bn(x)
+                x = self.relu(x)
+                x = self.upsample(x)
+                x = self.pool(x)
+                x = x.view(x.size(0), -1)
+                x = self.linear(x)
+                return x
+
+        class Model2(nn.Module):
+            def __init__(self):
+                super(Model2, self).__init__()
+                self.conv = nn.Conv2d(3, 5, 1, 1)
+                self.conv2 = nn.Conv2d(5, 5, 1, 1)
+
+            def forward(self, x):
+                x = self.conv(x)
+                for _ in range(5):
+                    x = self.conv2(x)
+                return x
+        
+        flops, params, results = count_flops_params(Model1(), (1, 3, 2, 2), mode='full', verbose=False)
+        assert (flops, params)  == (610, 240)
+
+        flops, params, results = count_flops_params(Model2(), (1, 3, 2, 2), verbose=False)
+        assert (flops, params)  == (560, 50)
+
+        from torchvision.models import resnet50
+        flops, params, results = count_flops_params(resnet50(), (1, 3, 224, 224), verbose=False)
+        assert (flops, params) == (4089184256, 25503912)
+
+
 if __name__ == '__main__':
     main()

test/ut/sdk/test_dependecy_aware.py

@@ -47,7 +47,6 @@ def generate_random_sparsity_v2(model):
     return cfg_list
 
 
-@unittest.skipIf(torch.__version__ >= '1.6.0', 'not supported')
 class DependencyawareTest(TestCase):
     @unittest.skipIf(torch.__version__ < "1.3.0", "not supported")
     def test_dependency_aware_pruning(self):

test/ut/sdk/test_model_speedup.py

@@ -177,7 +177,6 @@ def channel_prune(model):
     pruner.compress()
     pruner.export_model(model_path=MODEL_FILE, mask_path=MASK_FILE)
 
-@unittest.skipIf(torch.__version__ >= '1.6.0', 'not supported')
 class SpeedupTestCase(TestCase):
     def test_speedup_vgg16(self):
         prune_model_l1(vgg16())
@@ -218,11 +217,9 @@ class SpeedupTestCase(TestCase):
         assert model.backbone2.conv2.out_channels == int(orig_model.backbone2.conv2.out_channels * SPARSITY)
         assert model.backbone2.fc1.in_features == int(orig_model.backbone2.fc1.in_features * SPARSITY)
 
-    # FIXME:
-    # This test case failed on macOS:
-    # https://msrasrg.visualstudio.com/NNIOpenSource/_build/results?buildId=15658
+    # FIXME: This test case might fail randomly, no idea why
+    # Example: https://msrasrg.visualstudio.com/NNIOpenSource/_build/results?buildId=16282
 
-    @unittest.skipIf(sys.platform == 'darwin', 'Failed for unknown reason')
     def test_speedup_integration(self):
         for model_name in ['resnet18', 'squeezenet1_1', 'mobilenet_v2', 'densenet121', 'densenet169', 'inception_v3', 'resnet50']:
             kwargs = {
@@ -251,7 +248,7 @@ class SpeedupTestCase(TestCase):
             zero_bn_bias(net)
             zero_bn_bias(speedup_model)
 
-            data = torch.ones(BATCH_SIZE, 3, 224, 224).to(device)
+            data = torch.ones(BATCH_SIZE, 3, 128, 128).to(device)
             ms = ModelSpeedup(speedup_model, data, MASK_FILE)
             ms.speedup_model()
 
@@ -281,7 +278,7 @@ class SpeedupTestCase(TestCase):
         net.load_state_dict(state_dict)
         net.eval()
 
-        data = torch.randn(BATCH_SIZE, 3, 224, 224).to(device)
+        data = torch.randn(BATCH_SIZE, 3, 128, 128).to(device)
         ms = ModelSpeedup(net, data, MASK_FILE)
         ms.speedup_model()
         ms.bound_model(data)

test/ut/sdk/test_pruners.py

@@ -151,12 +151,37 @@ prune_config = {
             lambda model: validate_sparsity(model.conv1, 0.5, model.bias)
         ]
     },
-    'autocompress': {
+    'autocompress_l1': {
         'pruner_class': AutoCompressPruner,
         'config_list': [{
             'sparsity': 0.5,
             'op_types': ['Conv2d'],
         }],
+        'base_algo': 'l1',
+        'trainer': lambda model, optimizer, criterion, epoch, callback : model,
+        'evaluator': lambda model: 0.9,
+        'dummy_input': torch.randn([64, 1, 28, 28]),
+        'validators': []
+    },
+    'autocompress_l2': {
+        'pruner_class': AutoCompressPruner,
+        'config_list': [{
+            'sparsity': 0.5,
+            'op_types': ['Conv2d'],
+        }],
+        'base_algo': 'l2',
+        'trainer': lambda model, optimizer, criterion, epoch, callback : model,
+        'evaluator': lambda model: 0.9,
+        'dummy_input': torch.randn([64, 1, 28, 28]),
+        'validators': []
+    },
+    'autocompress_fpgm': {
+        'pruner_class': AutoCompressPruner,
+        'config_list': [{
+            'sparsity': 0.5,
+            'op_types': ['Conv2d'],
+        }],
+        'base_algo': 'fpgm',
         'trainer': lambda model, optimizer, criterion, epoch, callback : model,
         'evaluator': lambda model: 0.9,
         'dummy_input': torch.randn([64, 1, 28, 28]),
@@ -181,7 +206,7 @@ class Model(nn.Module):
     def forward(self, x):
         return self.fc(self.pool(self.bn1(self.conv1(x))).view(x.size(0), -1))
 
-def pruners_test(pruner_names=['level', 'agp', 'slim', 'fpgm', 'l1', 'l2', 'taylorfo', 'mean_activation', 'apoz', 'netadapt', 'simulatedannealing', 'admm', 'autocompress'], bias=True):
+def pruners_test(pruner_names=['level', 'agp', 'slim', 'fpgm', 'l1', 'l2', 'taylorfo', 'mean_activation', 'apoz', 'netadapt', 'simulatedannealing', 'admm', 'autocompress_l1', 'autocompress_l2', 'autocompress_fpgm',], bias=True):
     for pruner_name in pruner_names:
         print('testing {}...'.format(pruner_name))
         device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -203,8 +228,8 @@ def pruners_test(pruner_names=['level', 'agp', 'slim', 'fpgm', 'l1', 'l2', 'tayl
             pruner = prune_config[pruner_name]['pruner_class'](model, config_list, evaluator=prune_config[pruner_name]['evaluator'])
         elif pruner_name == 'admm':
             pruner = prune_config[pruner_name]['pruner_class'](model, config_list, trainer=prune_config[pruner_name]['trainer'])
-        elif pruner_name == 'autocompress':
-            pruner = prune_config[pruner_name]['pruner_class'](model, config_list, trainer=prune_config[pruner_name]['trainer'], evaluator=prune_config[pruner_name]['evaluator'], dummy_input=x)
+        elif pruner_name.startswith('autocompress'):
+            pruner = prune_config[pruner_name]['pruner_class'](model, config_list, trainer=prune_config[pruner_name]['trainer'], evaluator=prune_config[pruner_name]['evaluator'], dummy_input=x, base_algo=prune_config[pruner_name]['base_algo'])
         else:
             pruner = prune_config[pruner_name]['pruner_class'](model, config_list, optimizer)
         pruner.compress()
@@ -264,7 +289,6 @@ class SimpleDataset:
     def __len__(self):
         return 1000
 
-@unittest.skipIf(torch.__version__ >= '1.6.0', 'not supported')
 class PrunerTestCase(TestCase):
     def test_pruners(self):
         pruners_test(bias=True)
@@ -273,7 +297,7 @@ class PrunerTestCase(TestCase):
         pruners_test(bias=False)
 
     def test_agp_pruner(self):
-        for pruning_algorithm in ['l1', 'l2', 'taylorfo', 'apoz']:
+        for pruning_algorithm in ['l1', 'l2', 'fpgm', 'taylorfo', 'apoz']:
             _test_agp(pruning_algorithm)
 
         for pruning_algorithm in ['level']:

test/ut/tools/annotation/testcase/annotated/mnist.py

@@ -38,8 +38,7 @@ class MnistNetwork(object):
                 input_dim = int(math.sqrt(self.x_dim))
             except:
                 logger.debug(
-                    'input dim cannot be sqrt and reshape. input dim: ' +
-                    str(self.x_dim))
+                    'input dim cannot be sqrt and reshape. input dim: ', str(self.x_dim))
                 raise
             x_image = tf.reshape(self.x, [-1, input_dim, input_dim, 1])
         with tf.name_scope('conv1'):
@@ -132,7 +131,7 @@ def main():
     mnist_network.build_network()
     logger.debug('Mnist build network done.')
     graph_location = tempfile.mkdtemp()
-    logger.debug('Saving graph to: %s' % graph_location)
+    logger.debug('Saving graph to: %s', graph_location)
     train_writer = tf.summary.FileWriter(graph_location)
     train_writer.add_graph(tf.get_default_graph())
     test_acc = 0.0

test/ut/tools/annotation/testcase/usercode/mnist.py

@@ -53,7 +53,7 @@ class MnistNetwork(object):
                 input_dim = int(math.sqrt(self.x_dim))
             except:
                 #print('input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim))
-                logger.debug('input dim cannot be sqrt and reshape. input dim: ' + str(self.x_dim))
+                logger.debug('input dim cannot be sqrt and reshape. input dim: ', str(self.x_dim))
                 raise
             x_image = tf.reshape(self.x, [-1, input_dim, input_dim, 1])
 
@@ -147,7 +147,7 @@ def main():
 
     # Write log
     graph_location = tempfile.mkdtemp()
-    logger.debug('Saving graph to: %s' % graph_location)
+    logger.debug('Saving graph to: %s', graph_location)
     # print('Saving graph to: %s' % graph_location)
     train_writer = tf.summary.FileWriter(graph_location)
     train_writer.add_graph(tf.get_default_graph())

test/ut/tools/nnictl/mock/experiment.py

@@ -11,9 +11,9 @@ from nni.tools.nnictl.nnictl_utils import get_yml_content
 
 def create_mock_experiment():
     nnictl_experiment_config = Experiments()
-    nnictl_experiment_config.add_experiment('xOpEwA5w', '8080', '1970/01/1 01:01:01', 'aGew0x',
+    nnictl_experiment_config.add_experiment('xOpEwA5w', '8080', 123456,
                                             'local', 'example_sklearn-classification')
-    nni_config = Config('aGew0x')
+    nni_config = Config('xOpEwA5w')
     # mock process
     cmds = ['sleep', '3600000']
     process = Popen(cmds, stdout=PIPE, stderr=STDOUT)

test/ut/tools/nnictl/test_common_utils.py

@@ -3,7 +3,8 @@
 
 from pathlib import Path
 from subprocess import Popen, PIPE, STDOUT
-from unittest import TestCase, main
+import sys
+from unittest import TestCase, main, skipIf
 
 from mock.restful_server import init_response
 
@@ -28,8 +29,12 @@ class CommonUtilsTestCase(TestCase):
         content = get_json_content(str(json_path))
         self.assertEqual(content, {'field':'test'})
 
+    @skipIf(sys.platform == 'win32', 'FIXME: Fails randomly on Windows, cannot reproduce locally')
     def test_detect_process(self):
-        cmds = ['sleep', '360000']
+        if sys.platform == 'win32':
+            cmds = ['timeout', '360000']
+        else:
+            cmds = ['sleep', '360000']
         process = Popen(cmds, stdout=PIPE, stderr=STDOUT)
         self.assertTrue(detect_process(process.pid))
         kill_command(process.pid)

test/ut/tools/nnictl/test_config_utils.py

@@ -19,7 +19,7 @@ class CommonUtilsTestCase(TestCase):
     
     def test_update_experiment(self):
         experiment = Experiments(HOME_PATH)
-        experiment.add_experiment('xOpEwA5w', 8081, 'N/A', 'aGew0x', 'local', 'test', endTime='N/A', status='INITIALIZED')
+        experiment.add_experiment('xOpEwA5w', 8081, 'N/A', 'local', 'test', endTime='N/A', status='INITIALIZED')
         self.assertTrue('xOpEwA5w' in experiment.get_all_experiments())
         experiment.remove_experiment('xOpEwA5w')
         self.assertFalse('xOpEwA5w' in experiment.get_all_experiments())

test/ut/tools/nnictl/test_nnictl_utils.py

@@ -46,7 +46,7 @@ class CommonUtilsTestCase(TestCase):
     @responses.activate
     def test_get_config_file_name(self):
         args = generate_args()
-        self.assertEqual('aGew0x', get_config_filename(args))
+        self.assertEqual('xOpEwA5w', get_config_filename(args))
     
     @responses.activate
     def test_get_experiment_port(self):

ts/nni_manager/common/datastore.ts

@@ -43,9 +43,10 @@ interface MetricDataRecord {
 }
 
 interface TrialJobInfo {
-    id: string;
+    trialJobId: string;
     sequenceId?: number;
     status: TrialJobStatus;
+    message?: string;
     startTime?: number;
     endTime?: number;
     hyperParameters?: string[];
@@ -63,7 +64,7 @@ interface HyperParameterFormat {
 interface ExportedDataFormat {
     parameter: Record<string, any>;
     value: Record<string, any>;
-    id: string;
+    trialJobId: string;
 }
 
 abstract class DataStore {