Merge pull request #2837 from microsoft/v1.8

Merge v1.8 back to master

README.md

@@ -25,7 +25,7 @@ The tool manages automated machine learning (AutoML) experiments, **dispatches a
 * Researchers and data scientists who want to easily **implement and experiment new AutoML algorithms**, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
 * ML Platform owners who want to **support AutoML in their platform**.
 
-### **[NNI v1.7 has been released!](https://github.com/microsoft/nni/releases) &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
+### **[NNI v1.8 has been released!](https://github.com/microsoft/nni/releases) &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
 
 ## **NNI capabilities in a glance**
 
@@ -246,7 +246,7 @@ The following example is built on TensorFlow 1.x. Make sure **TensorFlow 1.x is
 * Download the examples via clone the source code.
 
   ```bash
-  git clone -b v1.7 https://github.com/Microsoft/nni.git
+  git clone -b v1.8 https://github.com/Microsoft/nni.git
   ```
 
 * Run the MNIST example.

deployment/pypi/install.ps1

@@ -44,6 +44,7 @@ $env:PATH = $NNI_NODE_FOLDER+';'+$env:PATH
 cd $CWD\..\..\src\nni_manager
 yarn
 yarn build
+Copy-Item config -Destination .\dist\ -Recurse -Force
 cd $CWD\..\..\src\webui
 yarn
 yarn build

docs/en_US/CommunitySharings/ModelCompressionComparison.md

@@ -60,7 +60,8 @@ From the experiment result, we get the following conclusions:
 
 * The experiment results are all collected with the default configuration of the pruners in nni, which means that when we call a pruner class in nni, we don't change any default class arguments.
 
-* Both FLOPs and the number of parameters are counted with [Model FLOPs/Parameters Counter](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/CompressionUtils.md#model-flopsparameters-counter) after [model speed up](https://github.com/microsoft/nni/blob/master/docs/en_US/Compressor/ModelSpeedup.md). This avoids potential issues of counting them of masked models.
+* Both FLOPs and the number of parameters are counted with [Model FLOPs/Parameters Counter](https://github.com/microsoft/nni/tree/master/docs/en_US/Compressor/CompressionUtils.md#model-flopsparameters-counter) after [model speed up](https://github.com/microsoft/nni/tree/master/docs/en_US/Compressor/ModelSpeedup.md).
+This avoids potential issues of counting them of masked models.
 
 * The experiment code can be found [here]( https://github.com/microsoft/nni/tree/master/examples/model_compress/auto_pruners_torch.py).
 
@@ -75,8 +76,8 @@ From the experiment result, we get the following conclusions:
     }
     ```
 
-* The experiment results are saved [here](https://github.com/microsoft/nni/tree/master/examples/model_compress/experiment_data). 
-You can refer to [analyze](https://github.com/microsoft/nni/tree/master/examples/model_compress/experiment_data/analyze.py) to plot new performance comparison figures.
+* The experiment results are saved [here](https://github.com/microsoft/nni/tree/master/examples/model_compress/comparison_of_pruners). 
+You can refer to [analyze](https://github.com/microsoft/nni/tree/master/examples/model_compress/comparison_of_pruners/analyze.py) to plot new performance comparison figures.
 
 ## Contribution
 

docs/en_US/Compressor/Overview.md

@@ -42,7 +42,7 @@ Pruning algorithms compress the original network by removing redundant weights o
 | [SimulatedAnnealing Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#simulatedannealing-pruner) | Automatic pruning with a guided heuristic search method, Simulated Annealing algorithm [Reference Paper](https://arxiv.org/abs/1907.03141) |
 | [AutoCompress Pruner](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#autocompress-pruner) | Automatic pruning by iteratively call SimulatedAnnealing Pruner and ADMM Pruner [Reference Paper](https://arxiv.org/abs/1907.03141) |
 
-You can refer to this [benchmark](https://github.com/microsoft/nni/tree/master/docs/en_US/Benchmark.md) for the performance of these pruners on some benchmark problems.
+You can refer to this [benchmark](https://github.com/microsoft/nni/tree/master/docs/en_US/CommunitySharings/ModelCompressionComparison.md) for the performance of these pruners on some benchmark problems.
 
 ### Quantization Algorithms
 

docs/en_US/NAS/Benchmarks.md

-# NAS Benchmarks (experimental)
+# NAS Benchmarks
 
 ```eval_rst
 ..  toctree::
@@ -8,12 +8,13 @@
 ```
 
 ## Introduction
+
 To imporve the reproducibility of NAS algorithms as well as reducing computing resource requirements, researchers proposed a series of NAS benchmarks such as [NAS-Bench-101](https://arxiv.org/abs/1902.09635), [NAS-Bench-201](https://arxiv.org/abs/2001.00326), [NDS](https://arxiv.org/abs/1905.13214), etc. NNI provides a query interface for users to acquire these benchmarks. Within just a few lines of code, researcher are able to evaluate their NAS algorithms easily and fairly by utilizing these benchmarks.
 
 ## Prerequisites
 
-* Please prepare a folder to household all the benchmark databases. By default, it can be found at `${HOME}/.nni/nasbenchmark`. You can place it anywhere you like, and specify it in `NASBENCHMARK_DIR` before importing NNI.
-* Please install `peewee` via `pip install peewee`, which NNI uses to connect to database.
+* Please prepare a folder to household all the benchmark databases. By default, it can be found at `${HOME}/.nni/nasbenchmark`. You can place it anywhere you like, and specify it in `NASBENCHMARK_DIR` via `export NASBENCHMARK_DIR=/path/to/your/nasbenchmark` before importing NNI.
+* Please install `peewee` via `pip3 install peewee`, which NNI uses to connect to database.
 
 ## Data Preparation
 
@@ -24,7 +25,7 @@ To avoid storage and legality issues, we do not provide any prepared databases.
 git clone -b ${NNI_VERSION} https://github.com/microsoft/nni
 cd nni/examples/nas/benchmarks
 ```
-Replace `${NNI_VERSION}` with a released version name or branch name, e.g., `v1.7`.
+Replace `${NNI_VERSION}` with a released version name or branch name, e.g., `v1.8`.
 
 2. Install dependencies via `pip3 install -r xxx.requirements.txt`. `xxx` can be `nasbench101`, `nasbench201` or `nds`.
 3. Generate the database via `./xxx.sh`. The directory that stores the benchmark file can be configured with `NASBENCHMARK_DIR` environment variable, which defaults to `~/.nni/nasbenchmark`. Note that the NAS-Bench-201 dataset will be downloaded from a google drive.

docs/en_US/Release.md

 # ChangeLog
 
+# Release 1.8 - 8/27/2020
+
+## Major updates
+
+### Training service
+
+* Access trial log directly on WebUI (local mode only) (#2718)
+* Add OpenPAI trial job detail link (#2703)
+* Support GPU scheduler in reusable environment (#2627) (#2769)
+* Add timeout for `web_channel` in `trial_runner` (#2710)
+* Show environment error message in AzureML mode (#2724)
+* Add more log information when copying data in OpenPAI mode (#2702)
+
+### WebUI, nnictl and nnicli
+
+* Improve hyper-parameter parallel coordinates plot (#2691) (#2759)
+* Add pagination for trial job list (#2738) (#2773)
+* Enable panel close when clicking overlay region (#2734)
+* Remove support for Multiphase on WebUI (#2760)
+* Support save and restore experiments (#2750)
+* Add intermediate results in export result (#2706)
+* Add [command](https://github.com/microsoft/nni/blob/v1.8/docs/en_US/Tutorial/Nnictl.md#nnictl-trial) to list trial results with highest/lowest metrics (#2747)
+* Improve the user experience of [nnicli](https://github.com/microsoft/nni/blob/v1.8/docs/en_US/nnicli_ref.md) with [examples](https://github.com/microsoft/nni/blob/v1.8/examples/notebooks/retrieve_nni_info_with_python.ipynb) (#2713)
+
+### Neural architecture search
+
+* [Search space zoo: ENAS and DARTS](https://github.com/microsoft/nni/blob/v1.8/docs/en_US/NAS/SearchSpaceZoo.md) (#2589)
+* API to query intermediate results in NAS benchmark (#2728)
+
+### Model compression
+
+* Support the List/Tuple Construct/Unpack operation for TorchModuleGraph (#2609)
+* Model speedup improvement: Add support of DenseNet and InceptionV3 (#2719)
+* Support the multiple successive tuple unpack operations (#2768)
+* [Doc of comparing the performance of supported pruners](https://github.com/microsoft/nni/blob/v1.8/docs/en_US/CommunitySharings/ModelCompressionComparison.md) (#2742)
+* New pruners: [Sensitivity pruner](https://github.com/microsoft/nni/blob/v1.8/docs/en_US/Compressor/Pruner.md#sensitivity-pruner) (#2684) and [AMC pruner](https://github.com/microsoft/nni/blob/v1.8/docs/en_US/Compressor/Pruner.md) (#2573) (#2786)
+* TensorFlow v2 support in model compression (#2755)
+
+### Backward incompatible changes
+
+* Update the default experiment folder from `$HOME/nni/experiments` to `$HOME/nni-experiments`. If you want to view the experiments created by previous NNI releases, you can move the experiments folders from  `$HOME/nni/experiments` to `$HOME/nni-experiments` manually. (#2686) (#2753)
+* Dropped support for Python 3.5 and scikit-learn 0.20 (#2778) (#2777) (2783) (#2787) (#2788) (#2790)
+
+### Others
+
+* Upgrade TensorFlow version in Docker image (#2732) (#2735) (#2720)
+
+## Examples
+
+* Remove gpuNum in assessor examples (#2641)
+
+## Documentation
+
+* Improve customized tuner documentation (#2628)
+* Fix several typos and grammar mistakes in documentation (#2637 #2638, thanks @tomzx)
+* Improve AzureML training service documentation (#2631)
+* Improve CI of Chinese translation (#2654)
+* Improve OpenPAI training service documenation (#2685)
+* Improve documentation of community sharing (#2640)
+* Add tutorial of Colab support (#2700)
+* Improve documentation structure for model compression (#2676)
+
+## Bug fixes
+
+* Fix mkdir error in training service (#2673)
+* Fix bug when using chmod in remote training service (#2689)
+* Fix dependency issue by making `_graph_utils` imported inline (#2675)
+* Fix mask issue in `SimulatedAnnealingPruner` (#2736)
+* Fix intermediate graph zooming issue (#2738)
+* Fix issue when dict is unordered when querying NAS benchmark (#2728)
+* Fix import issue for gradient selector dataloader iterator (#2690)
+* Fix support of adding tens of machines in remote training service (#2725)
+* Fix several styling issues in WebUI (#2762 #2737)
+* Fix support of unusual types in metrics including NaN and Infinity (#2782)
+* Fix nnictl experiment delete (#2791)
+
 # Release 1.7 - 7/8/2020
 
 ## Major Features

docs/en_US/TrainingService/AMLMode.md

@@ -89,4 +89,4 @@ cd nni/examples/trials/mnist-tfv1
 
 nnictl create --config config_aml.yml
 ```
-Replace `${NNI_VERSION}` with a released version name or branch name, e.g., `v1.7`.
+Replace `${NNI_VERSION}` with a released version name or branch name, e.g., `v1.8`.

docs/en_US/Tutorial/InstallationLinux.md

@@ -19,7 +19,7 @@ Installation on Linux and macOS follow the same instructions, given below.
   Prerequisites: `python 64-bit >=3.6`, `git`, `wget`
 
   ```bash
-  git clone -b v1.7 https://github.com/Microsoft/nni.git
+  git clone -b v1.8 https://github.com/Microsoft/nni.git
   cd nni
   ./install.sh
   ```
@@ -35,7 +35,7 @@ The following example is built on TensorFlow 1.x. Make sure **TensorFlow 1.x is
 * Download the examples via cloning the source code.
 
   ```bash
-  git clone -b v1.7 https://github.com/Microsoft/nni.git
+  git clone -b v1.8 https://github.com/Microsoft/nni.git
   ```
 
 * Run the MNIST example.

docs/en_US/Tutorial/InstallationWin.md

@@ -29,7 +29,7 @@ If you want to contribute to NNI, refer to [setup development environment](Setup
 * From source code
 
     ```bat
-    git clone -b v1.7 https://github.com/Microsoft/nni.git
+    git clone -b v1.8 https://github.com/Microsoft/nni.git
     cd nni
     powershell -ExecutionPolicy Bypass -file install.ps1
     ```
@@ -41,7 +41,7 @@ The following example is built on TensorFlow 1.x. Make sure **TensorFlow 1.x is
 * Clone examples within source code.
 
     ```bat
-    git clone -b v1.7 https://github.com/Microsoft/nni.git
+    git clone -b v1.8 https://github.com/Microsoft/nni.git
     ```
 
 * Run the MNIST example.

docs/en_US/conf.py

@@ -29,7 +29,7 @@ author = 'Microsoft'
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = 'v1.7'
+release = 'v1.8'
 
 # -- General configuration ---------------------------------------------------
 

examples/model_compress/model_prune_tf.py

@@ -28,7 +28,12 @@ def get_dataset(dataset_name='mnist'):
 
 def create_model(model_name='naive'):
     assert model_name == 'naive'
-    return tf.keras.Sequential([
+    return NaiveModel()
+
+class NaiveModel(tf.keras.Model):
+    def __init__(self):
+        super().__init__()
+        self.seq_layers = [
             tf.keras.layers.Conv2D(filters=20, kernel_size=5),
             tf.keras.layers.BatchNormalization(),
             tf.keras.layers.ReLU(),
@@ -42,7 +47,12 @@ def create_model(model_name='naive'):
             tf.keras.layers.ReLU(),
             tf.keras.layers.Dense(units=10),
             tf.keras.layers.Softmax()
-    ])
+        ]
+
+    def call(self, x):
+        for layer in self.seq_layers:
+            x = layer(x)
+        return x
 
 
 def create_pruner(model, pruner_name):
@@ -57,18 +67,38 @@ def main(args):
     train_set, test_set = get_dataset(dataset_name)
     model = create_model(model_name)
 
-    optimizer = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.9, decay=1e-4)
-    model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-
     print('start training')
-    model.fit(train_set[0], train_set[1], batch_size=args.batch_size, epochs=args.pretrain_epochs, validation_data=test_set)
+    optimizer = tf.keras.optimizers.SGD(learning_rate=0.1, momentum=0.9, decay=1e-4)
+    model.compile(
+        optimizer=optimizer,
+        loss='sparse_categorical_crossentropy',
+        metrics=['accuracy']
+    )
+    model.fit(
+        train_set[0],
+        train_set[1],
+        batch_size=args.batch_size,
+        epochs=args.pretrain_epochs,
+        validation_data=test_set
+    )
 
     print('start model pruning')
     optimizer_finetune = tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.9, decay=1e-4)
     pruner = create_pruner(model, args.pruner_name)
     model = pruner.compress()
-    model.compile(optimizer=optimizer_finetune, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
-    model.fit(train_set[0], train_set[1], batch_size=args.batch_size, epochs=args.prune_epochs, validation_data=test_set)
+    model.compile(
+        optimizer=optimizer_finetune,
+        loss='sparse_categorical_crossentropy',
+        metrics=['accuracy'],
+        run_eagerly=True  # NOTE: Important, model compression does not work in graph mode!
+    )
+    model.fit(
+        train_set[0],
+        train_set[1],
+        batch_size=args.batch_size,
+        epochs=args.prune_epochs,
+        validation_data=test_set
+    )
 
 
 if __name__ == '__main__':

examples/model_compress/models/mobilenet.py

@@ -53,7 +53,7 @@ class MobileNet(nn.Module):
     def forward(self, x):
         x = self.conv1(x)
         x = self.features(x)
-        x = x.mean(3).mean(2)  # global average pooling
+        x = x.mean([2, 3]) # global average pooling
 
         x = self.classifier(x)
         return x

examples/model_compress/models/mobilenet_v2.py

@@ -108,7 +108,10 @@ class MobileNetV2(nn.Module):
 
     def forward(self, x):
         x = self.features(x)
-        x = x.mean(3).mean(2)
+        # it's same with .mean(3).mean(2), but
+        # speedup only suport the mean option
+        # whose output only have two dimensions
+        x = x.mean([2, 3])
         x = self.classifier(x)
         return x
 

examples/nas/benchmarks/nasbench101.sh

@@ -15,5 +15,5 @@ fi
 echo "Generating database..."
 rm -f ${NASBENCHMARK_DIR}/nasbench101.db ${NASBENCHMARK_DIR}/nasbench101.db-journal
 mkdir -p ${NASBENCHMARK_DIR}
-python -m nni.nas.benchmarks.nasbench101.db_gen nasbench_full.tfrecord
+python3 -m nni.nas.benchmarks.nasbench101.db_gen nasbench_full.tfrecord
 rm -f nasbench_full.tfrecord

examples/nas/benchmarks/nasbench201.sh

@@ -15,5 +15,5 @@ fi
 echo "Generating database..."
 rm -f ${NASBENCHMARK_DIR}/nasbench201.db ${NASBENCHMARK_DIR}/nasbench201.db-journal
 mkdir -p ${NASBENCHMARK_DIR}
-python -m nni.nas.benchmarks.nasbench201.db_gen a.pth
+python3 -m nni.nas.benchmarks.nasbench201.db_gen a.pth
 rm -f a.pth

examples/nas/benchmarks/nds.sh

@@ -16,5 +16,5 @@ unzip data.zip
 echo "Generating database..."
 rm -f ${NASBENCHMARK_DIR}/nds.db ${NASBENCHMARK_DIR}/nds.db-journal
 mkdir -p ${NASBENCHMARK_DIR}
-python -m nni.nas.benchmarks.nds.db_gen nds_data
+python3 -m nni.nas.benchmarks.nds.db_gen nds_data
 rm -rf data.zip nds_data

examples/nas/search_space_zoo/darts_example.py

@@ -14,7 +14,7 @@ from nni.nas.pytorch.darts import DartsTrainer
 from utils import accuracy
 
 from nni.nas.pytorch.search_space_zoo import DartsCell
-from darts_search_space import DartsStackedCells
+from darts_stack_cells import DartsStackedCells
 
 logger = logging.getLogger('nni')
 

examples/nas/search_space_zoo/darts_stack_cells.py

@@ -2,7 +2,7 @@
 # Licensed under the MIT license.
 
 import torch.nn as nn
-import ops
+from nni.nas.pytorch.search_space_zoo.darts_ops import DropPath
 
 
 class DartsStackedCells(nn.Module):
@@ -79,5 +79,5 @@ class DartsStackedCells(nn.Module):
 
     def drop_path_prob(self, p):
         for module in self.modules():
-            if isinstance(module, ops.DropPath):
+            if isinstance(module, DropPath):
                 module.p = p

examples/nas/search_space_zoo/enas_macro_example.py

@@ -58,7 +58,6 @@ if __name__ == "__main__":
     parser = ArgumentParser("enas")
     parser.add_argument("--batch-size", default=128, type=int)
     parser.add_argument("--log-frequency", default=10, type=int)
-    # parser.add_argument("--search-for", choices=["macro", "micro"], default="macro")
     parser.add_argument("--epochs", default=None, type=int, help="Number of epochs (default: macro 310, micro 150)")
     parser.add_argument("--visualization", default=False, action="store_true")
     args = parser.parse_args()
@@ -71,7 +70,6 @@ if __name__ == "__main__":
     criterion = nn.CrossEntropyLoss()
     optimizer = torch.optim.SGD(model.parameters(), 0.05, momentum=0.9, weight_decay=1.0E-4)
     lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=0.001)
-
     trainer = enas.EnasTrainer(model,
                                loss=criterion,
                                metrics=accuracy,

examples/nas/search_space_zoo/enas_micro_example.py

@@ -62,7 +62,7 @@ class MicroNetwork(nn.Module):
             reduction = False
             if layer_id in pool_layers:
                 c_cur, reduction = c_p * 2, True
-            self.layers.append(ENASMicroLayer(self.layers, num_nodes, c_pp, c_p, c_cur, reduction))
+            self.layers.append(ENASMicroLayer(num_nodes, c_pp, c_p, c_cur, reduction))
             if reduction:
                 c_pp = c_p = c_cur
             c_pp, c_p = c_p, c_cur
@@ -98,7 +98,6 @@ if __name__ == "__main__":
     parser = ArgumentParser("enas")
     parser.add_argument("--batch-size", default=128, type=int)
     parser.add_argument("--log-frequency", default=10, type=int)
-    # parser.add_argument("--search-for", choices=["macro", "micro"], default="macro")
     parser.add_argument("--epochs", default=None, type=int, help="Number of epochs (default: macro 310, micro 150)")
     parser.add_argument("--visualization", default=False, action="store_true")
     args = parser.parse_args()