Merge pull request #3023 from microsoft/v1.9

[do not squash!] merge v1.9 back to master

examples/model_compress/amc/README.md

+# AMCPruner Example
+This example shows us how to use AMCPruner example.
+
+## Step 1: train a model for pruning
+Run following command to train a mobilenetv2 model:
+```bash
+python3 amc_train.py --model_type mobilenetv2 --n_epoch 50
+```
+Once finished, saved checkpoint file can be found at:
+```
+logs/mobilenetv2_cifar10_train-run1/ckpt.best.pth
+```
+
+## Pruning with AMCPruner
+Run following command to prune the trained model:
+```bash
+python3 amc_search.py --model_type mobilenetv2 --ckpt logs/mobilenetv2_cifar10_train-run1/ckpt.best.pth
+```
+Once finished, pruned model and mask can be found at:
+```
+logs/mobilenetv2_cifar10_r0.5_search-run2
+```
+
+## Finetune pruned model
+Run `amc_train.py` again with `--ckpt` and `--mask` to speedup and finetune the pruned model:
+```bash
+python3 amc_train.py --model_type mobilenetv2 --ckpt logs/mobilenetv2_cifar10_r0.5_search-run2/best_model.pth --mask logs/mobilenetv2_cifar10_r0.5_search-run2/best_mask.pth --n_epoch 100
+```

examples/model_compress/amc/amc_search.py

@@ -20,7 +20,7 @@ def parse_args():
         help='model to prune')
     parser.add_argument('--dataset', default='cifar10', type=str, choices=['cifar10', 'imagenet'], help='dataset to use (cifar/imagenet)')
     parser.add_argument('--batch_size', default=50, type=int, help='number of data batch size')
-    parser.add_argument('--data_root', default='./cifar10', type=str, help='dataset path')
+    parser.add_argument('--data_root', default='./data', type=str, help='dataset path')
     parser.add_argument('--flops_ratio', default=0.5, type=float, help='target flops ratio to preserve of the model')
     parser.add_argument('--lbound', default=0.2, type=float, help='minimum sparsity')
     parser.add_argument('--rbound', default=1., type=float, help='maximum sparsity')

examples/model_compress/amc/amc_train.py

@@ -13,6 +13,7 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 from tensorboardX import SummaryWriter
+from torchvision.models import resnet
 
 from nni.compression.torch.pruning.amc.lib.net_measure import measure_model
 from nni.compression.torch.pruning.amc.lib.utils import get_output_folder
@@ -27,7 +28,9 @@ from mobilenet_v2 import MobileNetV2
 
 def parse_args():
     parser = argparse.ArgumentParser(description='AMC train / fine-tune script')
-    parser.add_argument('--model_type', default='mobilenet', type=str, help='name of the model to train')
+    parser.add_argument('--model_type', default='mobilenet', type=str,
+        choices=['mobilenet', 'mobilenetv2', 'resnet18', 'resnet34', 'resnet50'],
+        help='name of the model to train')
     parser.add_argument('--dataset', default='cifar10', type=str, help='name of the dataset to train')
     parser.add_argument('--lr', default=0.05, type=float, help='learning rate')
     parser.add_argument('--n_gpu', default=4, type=int, help='number of GPUs to use')
@@ -62,17 +65,21 @@ def get_model(args):
         net = MobileNet(n_class=n_class)
     elif args.model_type == 'mobilenetv2':
         net = MobileNetV2(n_class=n_class)
+    elif args.model_type.startswith('resnet'):
+        net = resnet.__dict__[args.model_type](pretrained=True)
+        in_features = net.fc.in_features
+        net.fc = nn.Linear(in_features, n_class)
     else:
         raise NotImplementedError
 
     if args.ckpt_path is not None:
         # the checkpoint can be state_dict exported by amc_search.py or saved by amc_train.py
         print('=> Loading checkpoint {} ..'.format(args.ckpt_path))
-        net.load_state_dict(torch.load(args.ckpt_path))
+        net.load_state_dict(torch.load(args.ckpt_path, torch.device('cpu')))
         if args.mask_path is not None:
             SZ = 224 if args.dataset == 'imagenet' else 32
             data = torch.randn(2, 3, SZ, SZ)
-            ms = ModelSpeedup(net, data, args.mask_path)
+            ms = ModelSpeedup(net, data, args.mask_path, torch.device('cpu'))
             ms.speedup_model()
 
     net.to(args.device)
@@ -179,11 +186,11 @@ def adjust_learning_rate(optimizer, epoch):
     return lr
 
 def save_checkpoint(state, is_best, checkpoint_dir='.'):
-    filename = os.path.join(checkpoint_dir, 'ckpt.pth.tar')
+    filename = os.path.join(checkpoint_dir, 'ckpt.pth')
     print('=> Saving checkpoint to {}'.format(filename))
     torch.save(state, filename)
     if is_best:
-        shutil.copyfile(filename, filename.replace('.pth.tar', '.best.pth.tar'))
+        shutil.copyfile(filename, filename.replace('.pth', '.best.pth'))
 
 if __name__ == '__main__':
     args = parse_args()

examples/nas/naive-tf/train.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
 import tensorflow as tf
 from tensorflow.keras import Model
 from tensorflow.keras.layers import (AveragePooling2D, BatchNormalization, Conv2D, Dense, MaxPool2D)
@@ -7,8 +10,6 @@ from tensorflow.keras.optimizers import SGD
 from nni.nas.tensorflow.mutables import LayerChoice, InputChoice
 from nni.nas.tensorflow.enas import EnasTrainer
 
-tf.get_logger().setLevel('ERROR')
-
 
 class Net(Model):
     def __init__(self):
@@ -53,35 +54,36 @@ class Net(Model):
         return x
 
 
-def accuracy(output, target):
-    bs = target.shape[0]
-    predicted = tf.cast(tf.argmax(output, 1), target.dtype)
-    target = tf.reshape(target, [-1])
-    return sum(tf.cast(predicted == target, tf.float32)) / bs
+def accuracy(truth, logits):
+    truth = tf.reshape(truth, -1)
+    predicted = tf.cast(tf.math.argmax(logits, axis=1), truth.dtype)
+    equal = tf.cast(predicted == truth, tf.int32)
+    return tf.math.reduce_sum(equal).numpy() / equal.shape[0]
+
+def accuracy_metrics(truth, logits):
+    acc = accuracy(truth, logits)
+    return {'accuracy': acc}
 
 
 if __name__ == '__main__':
     cifar10 = tf.keras.datasets.cifar10
-    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
-    x_train, x_test = x_train / 255.0, x_test / 255.0
-    split = int(len(x_train) * 0.9)
-    dataset_train = tf.data.Dataset.from_tensor_slices((x_train[:split], y_train[:split])).batch(64)
-    dataset_valid = tf.data.Dataset.from_tensor_slices((x_train[split:], y_train[split:])).batch(64)
-    dataset_test = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(64)
+    (x_train, y_train), (x_valid, y_valid) = cifar10.load_data()
+    x_train, x_valid = x_train / 255.0, x_valid / 255.0
+    train_set = (x_train, y_train)
+    valid_set = (x_valid, y_valid)
 
     net = Net()
+
     trainer = EnasTrainer(
         net,
-        loss=SparseCategoricalCrossentropy(reduction=Reduction.SUM),
-        metrics=accuracy,
+        loss=SparseCategoricalCrossentropy(from_logits=True, reduction=Reduction.NONE),
+        metrics=accuracy_metrics,
         reward_function=accuracy,
         optimizer=SGD(learning_rate=0.001, momentum=0.9),
         batch_size=64,
         num_epochs=2,
-        dataset_train=dataset_train,
-        dataset_valid=dataset_valid,
-        dataset_test=dataset_test
+        dataset_train=train_set,
+        dataset_valid=valid_set
     )
 
     trainer.train()
-    #trainer.export('checkpoint')

examples/nas/spos/supernet.py

@@ -45,6 +45,7 @@ if __name__ == "__main__":
     torch.backends.cudnn.deterministic = True
 
     model = ShuffleNetV2OneShot()
+    flops_func = model.get_candidate_flops
     if args.load_checkpoint:
         if not args.spos_preprocessing:
             logger.warning("You might want to use SPOS preprocessing if you are loading their checkpoints.")
@@ -52,7 +53,7 @@ if __name__ == "__main__":
     model.cuda()
     if torch.cuda.device_count() > 1:  # exclude last gpu, saving for data preprocessing on gpu
         model = nn.DataParallel(model, device_ids=list(range(0, torch.cuda.device_count() - 1)))
-    mutator = SPOSSupernetTrainingMutator(model, flops_func=model.module.get_candidate_flops,
+    mutator = SPOSSupernetTrainingMutator(model, flops_func=flops_func,
                                           flops_lb=290E6, flops_ub=360E6)
     criterion = CrossEntropyLabelSmooth(1000, args.label_smoothing)
     optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate,

examples/trials/mnist-pytorch/config_aml.yml

@@ -17,9 +17,9 @@ tuner:
 trial:
   command: python3 mnist.py
   codeDir: .
-  computeTarget: ${replace_to_your_computeTarget}
   image: msranni/nni
 amlConfig:
   subscriptionId: ${replace_to_your_subscriptionId}
   resourceGroup: ${replace_to_your_resourceGroup}
   workspaceName: ${replace_to_your_workspaceName}
+  computeTarget: ${replace_to_your_computeTarget}

examples/trials/mnist-tfv1/config_aml.yml

@@ -17,9 +17,9 @@ tuner:
 trial:
   command: python3 mnist.py
   codeDir: .
-  computeTarget: ${replace_to_your_computeTarget}
   image: msranni/nni
 amlConfig:
   subscriptionId: ${replace_to_your_subscriptionId}
   resourceGroup: ${replace_to_your_resourceGroup}
   workspaceName: ${replace_to_your_workspaceName}
+  computeTarget: ${replace_to_your_computeTarget}

src/nni_manager/training_service/remote_machine/extends/linuxCommands.ts

@@ -136,6 +136,10 @@ class LinuxCommands extends OsCommands {
             return `${preCommand} && ${command}`;
         }
     }
+
+    public fileExistCommand(filePath: string): string {
+        return `test -e ${filePath} && echo True || echo False`;
+    }
 }
 
 export { LinuxCommands };

src/nni_manager/training_service/remote_machine/extends/windowsCommands.ts

@@ -130,6 +130,10 @@ class WindowsCommands extends OsCommands {
             return `${preCommand} && set prePath=%path% && ${command}`;
         }
     }
+
+    public fileExistCommand(filePath: string): string {
+        return `powershell Test-Path ${filePath} -PathType Leaf`;
+    }
 }
 
 export { WindowsCommands };

src/nni_manager/training_service/remote_machine/osCommands.ts

@@ -29,6 +29,7 @@ abstract class OsCommands {
     public abstract extractFile(tarFileName: string, targetFolder: string): string;
     public abstract executeScript(script: string, isFile: boolean): string;
     public abstract addPreCommand(preCommand: string | undefined, command: string | undefined): string | undefined;
+    public abstract fileExistCommand(filePath: string): string | undefined;
 
     public joinPath(...paths: string[]): string {
         let dir: string = paths.filter((path: any) => path !== '').join(this.pathSpliter);

src/nni_manager/training_service/remote_machine/shellExecutor.ts

@@ -238,6 +238,12 @@ class ShellExecutor {
         return commandResult.exitCode == 0;
     }
 
+    public async fileExist(filePath: string): Promise<boolean> {
+        const commandText = this.osCommands && this.osCommands.fileExistCommand(filePath);
+        const commandResult = await this.execute(commandText);   
+        return commandResult.stdout !== undefined && commandResult.stdout.trim() === 'True';
+    }
+
     public async extractFile(tarFileName: string, targetFolder: string): Promise<boolean> {
         const commandText = this.osCommands && this.osCommands.extractFile(tarFileName, targetFolder);
         const commandResult = await this.execute(commandText);

src/nni_manager/training_service/reusable/environments/remoteEnvironmentService.ts

@@ -139,10 +139,15 @@ export class RemoteEnvironmentService extends EnvironmentService {
         const executor = await this.getExecutor(environment.id);
         const jobpidPath: string = `${environment.runnerWorkingFolder}/pid`;
         const runnerReturnCodeFilePath: string = `${environment.runnerWorkingFolder}/code`;
-            if (fs.existsSync(jobpidPath)) {
         /* eslint-disable require-atomic-updates */
         try {
+            // check if pid file exist
+            const pidExist = await executor.fileExist(jobpidPath);
+            if (!pidExist) {
+                return;
+            }
             const isAlive = await executor.isProcessAlive(jobpidPath);
+            environment.status = 'RUNNING';
             // if the process of jobpid is not alive any more
             if (!isAlive) {
                 const remoteEnvironment: RemoteMachineEnvironmentInformation = environment as RemoteMachineEnvironmentInformation;
@@ -167,11 +172,9 @@ export class RemoteEnvironmentService extends EnvironmentService {
                 }
             }
         } catch (error) {
-                    this.releaseEnvironmentResource(environment);
             this.log.error(`Update job status exception, error is ${error.message}`);
         }
     }
-    }
 
     public async refreshEnvironmentsStatus(environments: EnvironmentInformation[]): Promise<void> {
         const tasks: Promise<void>[] = [];
@@ -245,6 +248,7 @@ export class RemoteEnvironmentService extends EnvironmentService {
                 'envs', environment.id)
             environment.command = `cd ${environment.runnerWorkingFolder} && \
 ${environment.command} --job_pid_file ${environment.runnerWorkingFolder}/pid \
+1>${environment.runnerWorkingFolder}/trialrunner_stdout 2>${environment.runnerWorkingFolder}/trialrunner_stderr \
 && echo $? \`date +%s%3N\` >${environment.runnerWorkingFolder}/code`;
             return Promise.resolve(true);
         }
@@ -266,7 +270,6 @@ ${environment.command} --job_pid_file ${environment.runnerWorkingFolder}/pid \
         // Execute command in remote machine
         executor.executeScript(executor.joinPath(environment.runnerWorkingFolder,
             executor.getScriptName("run")), true, false);
-        environment.status = 'RUNNING';
         if (environment.rmMachineMeta === undefined) {
             throw new Error(`${environment.id} rmMachineMeta not initialized!`);
         }

src/nni_manager/training_service/reusable/trialDispatcher.ts

@@ -663,18 +663,21 @@ class TrialDispatcher implements TrainingService {
         await this.commandChannel.sendCommand(trial.environment, NEW_TRIAL_JOB, trial.settings);
     }
     
+    /**
+     * release the trial assigned environment resources
+     * @param trial 
+     */
     private releaseEnvironment(trial: TrialDetail): void {
-        if (undefined === trial.environment) {
-            throw new Error(`TrialDispatcher: environment is not assigned to trial ${trial.id}, and cannot be released!`);
-        }
+        if (trial.environment !== undefined) {
             if (trial.environment.runningTrialCount <= 0) {
                 throw new Error(`TrialDispatcher: environment ${trial.environment.id} has no counted running trial!`);
             }
+            trial.environment.runningTrialCount--;
+            trial.environment = undefined;
+        }
         if (true === this.enableGpuScheduler) {
             this.gpuScheduler.removeGpuReservation(trial);
         }
-        trial.environment.runningTrialCount--;
-        trial.environment = undefined;
     }
 
     private async handleMetricData(trialId: string, data: any): Promise<void> {

src/nni_manager/yarn.lock

@@ -1089,10 +1089,10 @@ cli-width@^2.0.0:
   version "2.2.0"
   resolved "https://registry.yarnpkg.com/cli-width/-/cli-width-2.2.0.tgz#ff19ede8a9a5e579324147b0c11f0fbcbabed639"
 
-cliui@^7.0.0:
-  version "7.0.1"
-  resolved "https://registry.yarnpkg.com/cliui/-/cliui-7.0.1.tgz#a4cb67aad45cd83d8d05128fc9f4d8fbb887e6b3"
-  integrity sha512-rcvHOWyGyid6I1WjT/3NatKj2kDt9OdSHSXpyLXaMWFbKpGACNW8pRhhdPUq9MWUOdwn8Rz9AVETjF4105rZZQ==
+cliui@^7.0.2:
+  version "7.0.3"
+  resolved "https://registry.yarnpkg.com/cliui/-/cliui-7.0.3.tgz#ef180f26c8d9bff3927ee52428bfec2090427981"
+  integrity sha512-Gj3QHTkVMPKqwP3f7B4KPkBZRMR9r4rfi5bXFpg1a+Svvj8l7q5CnkBkVQzfxT5DFSsGk2+PascOgL0JYkL2kw==
   dependencies:
     string-width "^4.2.0"
     strip-ansi "^6.0.0"
@@ -1329,7 +1329,7 @@ debug@^3.1.0:
   dependencies:
     ms "^2.1.1"
 
-debuglog@^1.0.1:
+debuglog@*, debuglog@^1.0.1:
   version "1.0.1"
   resolved "https://registry.yarnpkg.com/debuglog/-/debuglog-1.0.1.tgz#aa24ffb9ac3df9a2351837cfb2d279360cd78492"
 
@@ -1599,10 +1599,10 @@ es6-promisify@^5.0.0:
   dependencies:
     es6-promise "^4.0.3"
 
-escalade@^3.0.2:
-  version "3.1.0"
-  resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.0.tgz#e8e2d7c7a8b76f6ee64c2181d6b8151441602d4e"
-  integrity sha512-mAk+hPSO8fLDkhV7V0dXazH5pDc6MrjBTPyD3VeKzxnVFjH1MIxbCdqGZB9O8+EwWakZs3ZCbDS4IpRt79V1ig==
+escalade@^3.1.1:
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.1.1.tgz#d8cfdc7000965c5a0174b4a82eaa5c0552742e40"
+  integrity sha512-k0er2gUkLf8O0zKJiAhmkTnJlTvINGv7ygDNPbeIsX/TJjGJZHuh9B2UxbsaEkmlEo9MfhrSzmhIlhRlI2GXnw==
 
 escape-html@~1.0.3:
   version "1.0.3"
@@ -2375,7 +2375,7 @@ import-lazy@^2.1.0:
   version "2.1.0"
   resolved "https://registry.yarnpkg.com/import-lazy/-/import-lazy-2.1.0.tgz#05698e3d45c88e8d7e9d92cb0584e77f096f3e43"
 
-imurmurhash@^0.1.4:
+imurmurhash@*, imurmurhash@^0.1.4:
   version "0.1.4"
   resolved "https://registry.yarnpkg.com/imurmurhash/-/imurmurhash-0.1.4.tgz#9218b9b2b928a238b13dc4fb6b6d576f231453ea"
   integrity sha1-khi5srkoojixPcT7a21XbyMUU+o=
@@ -3057,6 +3057,11 @@ lockfile@^1.0.4:
   dependencies:
     signal-exit "^3.0.2"
 
+lodash._baseindexof@*:
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/lodash._baseindexof/-/lodash._baseindexof-3.1.0.tgz#fe52b53a1c6761e42618d654e4a25789ed61822c"
+  integrity sha1-/lK1OhxnYeQmGNZU5KJXie1hgiw=
+
 lodash._baseuniq@~4.6.0:
   version "4.6.0"
   resolved "https://registry.yarnpkg.com/lodash._baseuniq/-/lodash._baseuniq-4.6.0.tgz#0ebb44e456814af7905c6212fa2c9b2d51b841e8"
@@ -3064,10 +3069,32 @@ lodash._baseuniq@~4.6.0:
     lodash._createset "~4.0.0"
     lodash._root "~3.0.0"
 
+lodash._bindcallback@*:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/lodash._bindcallback/-/lodash._bindcallback-3.0.1.tgz#e531c27644cf8b57a99e17ed95b35c748789392e"
+  integrity sha1-5THCdkTPi1epnhftlbNcdIeJOS4=
+
+lodash._cacheindexof@*:
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/lodash._cacheindexof/-/lodash._cacheindexof-3.0.2.tgz#3dc69ac82498d2ee5e3ce56091bafd2adc7bde92"
+  integrity sha1-PcaayCSY0u5ePOVgkbr9Ktx73pI=
+
+lodash._createcache@*:
+  version "3.1.2"
+  resolved "https://registry.yarnpkg.com/lodash._createcache/-/lodash._createcache-3.1.2.tgz#56d6a064017625e79ebca6b8018e17440bdcf093"
+  integrity sha1-VtagZAF2JeeevKa4AY4XRAvc8JM=
+  dependencies:
+    lodash._getnative "^3.0.0"
+
 lodash._createset@~4.0.0:
   version "4.0.3"
   resolved "https://registry.yarnpkg.com/lodash._createset/-/lodash._createset-4.0.3.tgz#0f4659fbb09d75194fa9e2b88a6644d363c9fe26"
 
+lodash._getnative@*, lodash._getnative@^3.0.0:
+  version "3.9.1"
+  resolved "https://registry.yarnpkg.com/lodash._getnative/-/lodash._getnative-3.9.1.tgz#570bc7dede46d61cdcde687d65d3eecbaa3aaff5"
+  integrity sha1-VwvH3t5G1hzc3mh9ZdPuy6o6r/U=
+
 lodash._root@~3.0.0:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/lodash._root/-/lodash._root-3.0.1.tgz#fba1c4524c19ee9a5f8136b4609f017cf4ded692"
@@ -3116,6 +3143,11 @@ lodash.pick@^4.4.0:
   version "4.4.0"
   resolved "https://registry.yarnpkg.com/lodash.pick/-/lodash.pick-4.4.0.tgz#52f05610fff9ded422611441ed1fc123a03001b3"
 
+lodash.restparam@*:
+  version "3.6.1"
+  resolved "https://registry.yarnpkg.com/lodash.restparam/-/lodash.restparam-3.6.1.tgz#936a4e309ef330a7645ed4145986c85ae5b20805"
+  integrity sha1-k2pOMJ7zMKdkXtQUWYbIWuWyCAU=
+
 lodash.unescape@4.0.1:
   version "4.0.1"
   resolved "https://registry.yarnpkg.com/lodash.unescape/-/lodash.unescape-4.0.1.tgz#bf2249886ce514cda112fae9218cdc065211fc9c"
@@ -3679,10 +3711,11 @@ npm-run-path@^2.0.0:
     path-key "^2.0.0"
 
 npm-user-validate@~1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/npm-user-validate/-/npm-user-validate-1.0.0.tgz#8ceca0f5cea04d4e93519ef72d0557a75122e951"
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/npm-user-validate/-/npm-user-validate-1.0.1.tgz#31428fc5475fe8416023f178c0ab47935ad8c561"
+  integrity sha512-uQwcd/tY+h1jnEaze6cdX/LrhWhoBxfSknxentoqmIuStxUExxjWd3ULMLFPiFUrZKbOVMowH6Jq2FRWfmhcEw==
 
-npm@5.1.0, npm@^6.14.8:
+npm@5.1.0, npm@>=6.14.8:
   version "6.14.8"
   resolved "https://registry.yarnpkg.com/npm/-/npm-6.14.8.tgz#64ef754345639bc035982ec3f609353c8539033c"
   integrity sha512-HBZVBMYs5blsj94GTeQZel7s9odVuuSUHy1+AlZh7rPVux1os2ashvEGLy/STNK7vUjbrCg5Kq9/GXisJgdf6A==
@@ -3705,6 +3738,7 @@ npm@5.1.0, npm@^6.14.8:
     cmd-shim "^3.0.3"
     columnify "~1.5.4"
     config-chain "^1.1.12"
+    debuglog "*"
     detect-indent "~5.0.0"
     detect-newline "^2.1.0"
     dezalgo "~1.0.3"
@@ -3719,6 +3753,7 @@ npm@5.1.0, npm@^6.14.8:
     has-unicode "~2.0.1"
     hosted-git-info "^2.8.8"
     iferr "^1.0.2"
+    imurmurhash "*"
     infer-owner "^1.0.4"
     inflight "~1.0.6"
     inherits "^2.0.4"
@@ -3737,8 +3772,14 @@ npm@5.1.0, npm@^6.14.8:
     libnpx "^10.2.4"
     lock-verify "^2.1.0"
     lockfile "^1.0.4"
+    lodash._baseindexof "*"
     lodash._baseuniq "~4.6.0"
+    lodash._bindcallback "*"
+    lodash._cacheindexof "*"
+    lodash._createcache "*"
+    lodash._getnative "*"
     lodash.clonedeep "~4.5.0"
+    lodash.restparam "*"
     lodash.union "~4.6.0"
     lodash.uniq "~4.5.0"
     lodash.without "~4.4.0"
@@ -5664,10 +5705,10 @@ y18n@^4.0.0:
   resolved "https://registry.yarnpkg.com/y18n/-/y18n-4.0.0.tgz#95ef94f85ecc81d007c264e190a120f0a3c8566b"
   integrity sha512-r9S/ZyXu/Xu9q1tYlpsLIsa3EeLXXk0VwlxqTcFRfg9EhMW+17kbt9G0NrgCmhGb5vT2hyhJZLfDGx+7+5Uj/w==
 
-y18n@^5.0.1:
-  version "5.0.1"
-  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.1.tgz#1ad2a7eddfa8bce7caa2e1f6b5da96c39d99d571"
-  integrity sha512-/jJ831jEs4vGDbYPQp4yGKDYPSCCEQ45uZWJHE1AoYBzqdZi8+LDWas0z4HrmJXmKdpFsTiowSHXdxyFhpmdMg==
+y18n@^5.0.2:
+  version "5.0.4"
+  resolved "https://registry.yarnpkg.com/y18n/-/y18n-5.0.4.tgz#0ab2db89dd5873b5ec4682d8e703e833373ea897"
+  integrity sha512-deLOfD+RvFgrpAmSZgfGdWYE+OKyHcVHaRQ7NphG/63scpRvTHHeQMAxGGvaLVGJ+HYVcCXlzcTK0ZehFf+eHQ==
 
 yallist@^2.1.2:
   version "2.1.2"
@@ -5686,10 +5727,10 @@ yallist@^4.0.0:
   version "4.0.0"
   resolved "https://registry.yarnpkg.com/yallist/-/yallist-4.0.0.tgz#9bb92790d9c0effec63be73519e11a35019a3a72"
 
-yargs-parser@13.1.2, yargs-parser@^20.0.0, yargs-parser@^20.2.0:
-  version "20.2.0"
-  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.0.tgz#944791ca2be2e08ddadd3d87e9de4c6484338605"
-  integrity sha512-2agPoRFPoIcFzOIp6656gcvsg2ohtscpw2OINr/q46+Sq41xz2OYLqx5HRHabmFU1OARIPAYH5uteICE7mn/5A==
+yargs-parser@13.1.2, yargs-parser@>=20.2.0, yargs-parser@^20.2.2:
+  version "20.2.3"
+  resolved "https://registry.yarnpkg.com/yargs-parser/-/yargs-parser-20.2.3.tgz#92419ba867b858c868acf8bae9bf74af0dd0ce26"
+  integrity sha512-emOFRT9WVHw03QSvN5qor9QQT9+sw5vwxfYweivSMHTcAXPefwVae2FjO7JJjj8hCE4CzPOPeFM83VwT29HCww==
 
 yargs-unparser@1.6.1:
   version "1.6.1"
@@ -5702,18 +5743,18 @@ yargs-unparser@1.6.1:
     is-plain-obj "^1.1.0"
     yargs "^14.2.3"
 
-yargs@13.3.2, yargs@^11.0.0, yargs@^14.2.3, yargs@^15.0.2, yargs@^16.0.3, yargs@^8.0.2:
-  version "16.0.3"
-  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.0.3.tgz#7a919b9e43c90f80d4a142a89795e85399a7e54c"
-  integrity sha512-6+nLw8xa9uK1BOEOykaiYAJVh6/CjxWXK/q9b5FpRgNslt8s22F2xMBqVIKgCRjNgGvGPBy8Vog7WN7yh4amtA==
+yargs@13.3.2, yargs@>=16.0.3, yargs@^11.0.0, yargs@^14.2.3, yargs@^15.0.2, yargs@^8.0.2:
+  version "16.1.0"
+  resolved "https://registry.yarnpkg.com/yargs/-/yargs-16.1.0.tgz#fc333fe4791660eace5a894b39d42f851cd48f2a"
+  integrity sha512-upWFJOmDdHN0syLuESuvXDmrRcWd1QafJolHskzaw79uZa7/x53gxQKiR07W59GWY1tFhhU/Th9DrtSfpS782g==
   dependencies:
-    cliui "^7.0.0"
-    escalade "^3.0.2"
+    cliui "^7.0.2"
+    escalade "^3.1.1"
     get-caller-file "^2.0.5"
     require-directory "^2.1.1"
     string-width "^4.2.0"
-    y18n "^5.0.1"
-    yargs-parser "^20.0.0"
+    y18n "^5.0.2"
+    yargs-parser "^20.2.2"
 
 yn@^2.0.0:
   version "2.0.0"

src/sdk/pynni/nni/common.py

@@ -17,6 +17,11 @@ log_level_map = {
 
 _time_format = '%m/%d/%Y, %I:%M:%S %p'
 
+# FIXME
+# This hotfix the bug that querying installed tuners with `package_utils` will activate dispatcher logger.
+# This behavior depends on underlying implementation of `nnictl` and is likely to break in future.
+_logger_initialized = False
+
 class _LoggerFileWrapper(TextIOBase):
     def __init__(self, logger_file):
         self.file = logger_file
@@ -33,6 +38,11 @@ def init_logger(logger_file_path, log_level_name='info'):
     This will redirect anything from logging.getLogger() as well as stdout to specified file.
     logger_file_path: path of logger file (path-like object).
     """
+    global _logger_initialized
+    if _logger_initialized:
+        return
+    _logger_initialized = True
+
     log_level = log_level_map.get(log_level_name, logging.INFO)
     logger_file = open(logger_file_path, 'w')
     fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
@@ -55,6 +65,11 @@ def init_standalone_logger():
     Initialize root logger for standalone mode.
     This will set NNI's log level to INFO and print its log to stdout.
     """
+    global _logger_initialized
+    if _logger_initialized:
+        return
+    _logger_initialized = True
+
     fmt = '[%(asctime)s] %(levelname)s (%(name)s) %(message)s'
     formatter = logging.Formatter(fmt, _time_format)
     handler = logging.StreamHandler(sys.stdout)