pdarts update

docs/en_US/AdvancedFeature/MultiPhase.md

@@ -79,7 +79,7 @@ With this information, the tuner could know which trial is requesting a configur
 
 ### Tuners support multi-phase experiments:
 
-[TPE](../Tuner/HyperoptTuner.md), [Random](../Tuner/HyperoptTuner.md), [Anneal](../Tuner/HyperoptTuner.md), [Evolution](../Tuner/EvolutionTuner.md), [SMAC](../Tuner/SmacTuner.md), [NetworkMorphism](../Tuner/NetworkmorphismTuner.md), [MetisTuner](../Tuner/MetisTuner.md), [BOHB](../Tuner/BohbAdvisor.md), [Hyperband](../Tuner/HyperbandAdvisor.md), [ENAS tuner](https://github.com/countif/enas_nni/blob/master/nni/examples/tuners/enas/nni_controller_ptb.py).
+[TPE](../Tuner/HyperoptTuner.md), [Random](../Tuner/HyperoptTuner.md), [Anneal](../Tuner/HyperoptTuner.md), [Evolution](../Tuner/EvolutionTuner.md), [SMAC](../Tuner/SmacTuner.md), [NetworkMorphism](../Tuner/NetworkmorphismTuner.md), [MetisTuner](../Tuner/MetisTuner.md), [BOHB](../Tuner/BohbAdvisor.md), [Hyperband](../Tuner/HyperbandAdvisor.md).
 
 ### Training services support multi-phase experiment:
 [Local Machine](../TrainingService/LocalMode.md), [Remote Servers](../TrainingService/RemoteMachineMode.md), [OpenPAI](../TrainingService/PaiMode.md)

docs/en_US/NAS/Overview.md

@@ -1,62 +1,71 @@
-# Neural Architecture Search (NAS) on NNI
-
-Automatic neural architecture search is taking an increasingly important role on finding better models. Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually designed and tuned models. Some of representative works are [NASNet][2], [ENAS][1], [DARTS][3], [Network Morphism][4], and [Evolution][5]. There are new innovations keeping emerging.
-
-However, it takes great efforts to implement NAS algorithms, and it is hard to reuse code base of existing algorithms in new one. To facilitate NAS innovations (e.g., design and implement new NAS models, compare different NAS models side-by-side), an easy-to-use and flexible programming interface is crucial.
-
-With this motivation, our ambition is to provide a unified architecture in NNI, to accelerate innovations on NAS, and apply state-of-art algorithms on real world problems faster.
-
-## Supported algorithms
-
-NNI supports below NAS algorithms now, and being adding more. User can reproduce an algorithm, or use it on owned dataset. we also encourage user to implement other algorithms with [NNI API](#use-nni-api), to benefit more people.
-
-Note, these algorithms run standalone without nnictl, and supports PyTorch only.
-
-### DARTS
-
-The main contribution of [DARTS: Differentiable Architecture Search][3] on algorithm is to introduce a novel algorithm for differentiable network architecture search on bilevel optimization.
-
-#### Usage
-
-```bash
-### In case NNI code is not cloned.
-git clone https://github.com/Microsoft/nni.git
-
-cd examples/nas/darts
-python search.py
-```
-
-### P-DARTS
-
-[Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760) bases on DARTS(#DARTS). It main contribution on algorithm is to introduce an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure.
-
-#### Usage
-
-```bash
-### In case NNI code is not cloned.
-git clone https://github.com/Microsoft/nni.git
-
-cd examples/nas/pdarts
-python main.py
-```
-
-## Use NNI API
-
-NOTE, we are trying to support various NAS algorithms with unified programming interface, and it's in very experimental stage. It means the current programing interface may be updated significantly.
-
-*previous [NAS annotation](../AdvancedFeature/GeneralNasInterfaces.md) interface will be deprecated soon.*
-
-### Programming interface
-
-The programming interface of designing and searching a model is often demanded in two scenarios.
-
-1. When designing a neural network, there may be multiple operation choices on a layer, sub-model, or connection, and it's undetermined which one or combination performs  best. So it needs an easy way to express the candidate layers or sub-models.
-2. When applying NAS on a neural network, it needs an unified way to express the search space of architectures, so that it doesn't need to update trial code for different searching algorithms.
-
-NNI proposed API is [here](https://github.com/microsoft/nni/tree/dev-nas-refactor/src/sdk/pynni/nni/nas/pytorch). And [here](https://github.com/microsoft/nni/tree/dev-nas-refactor/examples/nas/darts) is an example of NAS implementation, which bases on NNI proposed interface.
-
-[1]: https://arxiv.org/abs/1802.03268
-[2]: https://arxiv.org/abs/1707.07012
-[3]: https://arxiv.org/abs/1806.09055
-[4]: https://arxiv.org/abs/1806.10282
-[5]: https://arxiv.org/abs/1703.01041
+# Neural Architecture Search (NAS) on NNI
+
+Automatic neural architecture search is taking an increasingly important role on finding better models. Recent research works have proved the feasibility of automatic NAS, and also found some models that could beat manually designed and tuned models. Some of representative works are [NASNet][2], [ENAS][1], [DARTS][3], [Network Morphism][4], and [Evolution][5]. There are new innovations keeping emerging.
+
+However, it takes great efforts to implement NAS algorithms, and it is hard to reuse code base of existing algorithms in new one. To facilitate NAS innovations (e.g., design and implement new NAS models, compare different NAS models side-by-side), an easy-to-use and flexible programming interface is crucial.
+
+With this motivation, our ambition is to provide a unified architecture in NNI, to accelerate innovations on NAS, and apply state-of-art algorithms on real world problems faster.
+
+## Supported algorithms
+
+NNI supports below NAS algorithms now and being adding more. User can reproduce an algorithm or use it on owned dataset. we also encourage user to implement other algorithms with [NNI API](#use-nni-api), to benefit more people.
+
+Note, these algorithms run standalone without nnictl, and supports PyTorch only.
+
+### DARTS
+
+The main contribution of [DARTS: Differentiable Architecture Search][3] on algorithm is to introduce a novel algorithm for differentiable network architecture search on bilevel optimization.
+
+#### Usage
+
+```bash
+# In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
+git clone https://github.com/Microsoft/nni.git
+
+# search the best architecture
+cd examples/nas/darts
+python3 search.py
+
+# train the best architecture
+python3 retrain.py --arc-checkpoint ./checkpoints/epoch_49.json
+```
+
+### P-DARTS
+
+[Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation](https://arxiv.org/abs/1904.12760) bases on [DARTS](#DARTS). It's contribution on algorithm is to introduce an efficient algorithm which allows the depth of searched architectures to grow gradually during the training procedure.
+
+#### Usage
+
+```bash
+# In case NNI code is not cloned. If the code is cloned already, ignore this line and enter code folder.
+git clone https://github.com/Microsoft/nni.git
+
+# search the best architecture
+cd examples/nas/pdarts
+python3 search.py
+
+# train the best architecture, it's the same progress as darts.
+cd examples/nas/darts
+python3 retrain.py --arc-checkpoint ./checkpoints/epoch_2.json
+```
+
+## Use NNI API
+
+NOTE, we are trying to support various NAS algorithms with unified programming interface, and it's in very experimental stage. It means the current programing interface may be updated significantly.
+
+*previous [NAS annotation](../AdvancedFeature/GeneralNasInterfaces.md) interface will be deprecated soon.*
+
+### Programming interface
+
+The programming interface of designing and searching a model is often demanded in two scenarios.
+
+1. When designing a neural network, there may be multiple operation choices on a layer, sub-model, or connection, and it's undetermined which one or combination performs  best. So, it needs an easy way to express the candidate layers or sub-models.
+2. When applying NAS on a neural network, it needs an unified way to express the search space of architectures, so that it doesn't need to update trial code for different searching algorithms.
+
+NNI proposed API is [here](https://github.com/microsoft/nni/tree/master/src/sdk/pynni/nni/nas/pytorch). And [here](https://github.com/microsoft/nni/tree/master/examples/nas/darts) is an example of NAS implementation, which bases on NNI proposed interface.
+
+[1]: https://arxiv.org/abs/1802.03268
+[2]: https://arxiv.org/abs/1707.07012
+[3]: https://arxiv.org/abs/1806.09055
+[4]: https://arxiv.org/abs/1806.10282
+[5]: https://arxiv.org/abs/1703.01041

docs/en_US/Tutorial/SearchSpaceSpec.md

@@ -73,12 +73,6 @@ All types of sampling strategies and their parameter are listed here:
   * Which means the variable value is a value like `round(exp(normal(mu, sigma)) / q) * q`
   * Suitable for a discrete variable with respect to which the objective is smooth and gets smoother with the size of the variable, which is bounded from one side.
 
-* `{"_type": "mutable_layer", "_value": {mutable_layer_infomation}}`
-  * Type for [Neural Architecture Search Space][1]. Value is also a dictionary, which contains key-value pairs representing respectively name and search space of each mutable_layer.
-  * For now, users can only use this type of search space with annotation, which means that there is no need to define a json file for search space since it will be automatically generated according to the annotation in trial code.
-  * The following HPO tuners can be adapted to tune this search space: TPE, Random, Anneal, Evolution, Grid Search,
-  Hyperband and BOHB.
-  * For detailed usage, please refer to [General NAS Interfaces][1].
 
 ## Search Space Types Supported by Each Tuner
 
@@ -105,5 +99,3 @@ Known Limitations:
     * Only Random Search/TPE/Anneal/Evolution tuner supports nested search space
 
     * We do not support nested search space "Hyper Parameter" in visualization now, the enhancement is being considered in [#1110](https://github.com/microsoft/nni/issues/1110), any suggestions or discussions or contributions are warmly welcomed
-
-[1]: ../AdvancedFeature/GeneralNasInterfaces.md

docs/en_US/advanced.rst

@@ -3,5 +3,3 @@ Advanced Features
 
 ..  toctree::
     MultiPhase<./AdvancedFeature/MultiPhase>
-    AdvancedNas<./AdvancedFeature/AdvancedNas>
-    NAS Programming Interface<./AdvancedFeature/GeneralNasInterfaces>

examples/nas/darts/retrain.py

@@ -1,4 +1,5 @@
 import logging
+import time
 from argparse import ArgumentParser
 
 import torch
@@ -10,8 +11,17 @@
 from nni.nas.pytorch.fixed import apply_fixed_architecture
 from nni.nas.pytorch.utils import AverageMeter
 
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
+logger = logging.getLogger()
+
+fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
+logging.Formatter.converter = time.localtime
+formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
+
+std_out_info = logging.StreamHandler()
+std_out_info.setFormatter(formatter)
+logger.setLevel(logging.INFO)
+logger.addHandler(std_out_info)
+
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 

examples/nas/darts/search.py

@@ -1,14 +1,27 @@
+import logging
+import time
 from argparse import ArgumentParser
 
-import datasets
 import torch
 import torch.nn as nn
 
+import datasets
 from model import CNN
-from nni.nas.pytorch.callbacks import LearningRateScheduler, ArchitectureCheckpoint
+from nni.nas.pytorch.callbacks import (ArchitectureCheckpoint,
+                                       LearningRateScheduler)
 from nni.nas.pytorch.darts import DartsTrainer
 from utils import accuracy
 
+logger = logging.getLogger()
+
+fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
+logging.Formatter.converter = time.localtime
+formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
+
+std_out_info = logging.StreamHandler()
+std_out_info.setFormatter(formatter)
+logger.setLevel(logging.INFO)
+logger.addHandler(std_out_info)
 
 if __name__ == "__main__":
     parser = ArgumentParser("darts")

examples/nas/enas/search.py

@@ -1,3 +1,5 @@
+import logging
+import time
 from argparse import ArgumentParser
 
 import torch
@@ -10,6 +12,17 @@
 from nni.nas.pytorch.callbacks import LearningRateScheduler, ArchitectureCheckpoint
 from utils import accuracy, reward_accuracy
 
+logger = logging.getLogger()
+
+fmt = '[%(asctime)s] %(levelname)s (%(name)s/%(threadName)s) %(message)s'
+logging.Formatter.converter = time.localtime
+formatter = logging.Formatter(fmt, '%m/%d/%Y, %I:%M:%S %p')
+
+std_out_info = logging.StreamHandler()
+std_out_info.setFormatter(formatter)
+logger.setLevel(logging.INFO)
+logger.addHandler(std_out_info)
+
 if __name__ == "__main__":
     parser = ArgumentParser("enas")
     parser.add_argument("--batch-size", default=128, type=int)