Merge pull request #109 from microsoft/master

pull

azure-pipelines.yml

@@ -28,6 +28,7 @@ jobs:
       set -e
       sudo apt-get install -y pandoc
       python3 -m pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
+      python3 -m pip install tensorboardX==1.9
       python3 -m pip install tensorflow==2.2.0 --user
       python3 -m pip install keras==2.4.2 --user
       python3 -m pip install gym onnx peewee thop --user
@@ -49,9 +50,9 @@ jobs:
       sphinx-build -M html . _build -W
     displayName: 'Sphinx Documentation Build check'
 
-- job: 'ubuntu_1604_python35_legacy_torch_tf'
+- job: 'ubuntu_1804_python36_legacy_torch_tf'
   pool:
-    vmImage: 'Ubuntu 16.04'
+    vmImage: 'Ubuntu 18.04'
 
   steps:
   - script: |
@@ -68,6 +69,7 @@ jobs:
   - script: |
       set -e
       python3 -m pip install torch==1.3.1+cpu torchvision==0.4.2+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
+      python3 -m pip install tensorboardX==1.9
       python3 -m pip install tensorflow==1.15.2 --user
       python3 -m pip install keras==2.1.6 --user
       python3 -m pip install gym onnx peewee --user
@@ -117,6 +119,7 @@ jobs:
       set -e
       # pytorch Mac binary does not support CUDA, default is cpu version
       python3 -m pip install torchvision==0.6.0 torch==1.5.0 --user
+      python3 -m pip install tensorboardX==1.9
       python3 -m pip install tensorflow==1.15.2 --user
       brew install swig@3
       rm -f /usr/local/bin/swig
@@ -141,9 +144,10 @@ jobs:
       powershell.exe -file install.ps1
     displayName: 'Install nni toolkit via source code'
   - script: |
-      python -m pip install scikit-learn==0.20.0 --user
+      python -m pip install scikit-learn==0.23.2 --user
       python -m pip install keras==2.1.6 --user
       python -m pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
+      python -m pip install tensorboardX==1.9
       python -m pip install tensorflow==1.15.2 --user
     displayName: 'Install dependencies'
   - script: |

deployment/docker/Dockerfile

@@ -1,12 +1,13 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT license.
 
-FROM nvidia/cuda:9.0-cudnn7-runtime-ubuntu16.04
+FROM nvidia/cuda:9.2-cudnn7-runtime-ubuntu18.04
 
 LABEL maintainer='Microsoft NNI Team<nni@microsoft.com>'
 
-RUN DEBIAN_FRONTEND=noninteractive && \
-    apt-get -y update && \
+ENV DEBIAN_FRONTEND=noninteractive 
+
+RUN apt-get -y update && \
     apt-get -y install sudo \
     apt-utils \
     git \
@@ -21,7 +22,7 @@ RUN DEBIAN_FRONTEND=noninteractive && \
     openssh-client \
     openssh-server \
     lsof \
-    python3.5 \
+    python3.6 \
     python3-dev \
     python3-pip \
     python3-tk \
@@ -37,7 +38,7 @@ RUN cp /usr/bin/python3 /usr/bin/python
 #
 # update pip
 #
-RUN python3 -m pip install --upgrade pip setuptools==39.1.0
+RUN python3 -m pip install --upgrade pip==20.0.2 setuptools==39.1.0
 
 # numpy 1.14.3  scipy 1.1.0
 RUN python3 -m pip --no-cache-dir install \
@@ -46,7 +47,7 @@ RUN python3 -m pip --no-cache-dir install \
 #
 # Tensorflow 1.15
 #
-RUN python3 -m pip --no-cache-dir install tensorflow-gpu==1.15
+RUN python3 -m pip --no-cache-dir install tensorflow-gpu==1.15.0
 
 #
 # Keras 2.1.6
@@ -60,9 +61,9 @@ RUN python3 -m pip --no-cache-dir install torch==1.4.0
 RUN python3 -m pip install torchvision==0.5.0
 
 #
-# sklearn 0.20.0
+# sklearn 0.23.2
 #
-RUN python3 -m pip --no-cache-dir install scikit-learn==0.20.0
+RUN python3 -m pip --no-cache-dir install scikit-learn==0.23.2
 
 #
 # pandas==0.23.4 lightgbm==2.2.2

deployment/docker/README.md

@@ -11,7 +11,7 @@ scipy 1.1.0
 tensorflow-gpu 1.15.0
 keras 2.1.6
 torch 1.4.0
-scikit-learn 0.20.0
+scikit-learn 0.23.2
 pandas 0.23.4
 lightgbm 2.2.2
 nni

deployment/docker/README_zh_CN.md

@@ -47,4 +47,4 @@
 
 使用下列命令从 docker Hub 中拉取 NNI docker 映像。
 
-    docker pull msranni/nni:latest
+    docker pull msranni/nni:latest

deployment/pypi/setup.py

@@ -63,7 +63,7 @@
         'scipy',
         'coverage',
         'colorama',
-        'scikit-learn>=0.20,<0.22',
+        'scikit-learn>=0.23.2',
         'pkginfo',
         'websockets'
     ],

docs/en_US/Compressor/Pruner.md

@@ -20,6 +20,7 @@ We provide several pruning algorithms that support fine-grained weight pruning a
 * [NetAdapt Pruner](#netadapt-pruner)
 * [SimulatedAnnealing Pruner](#simulatedannealing-pruner)
 * [AutoCompress Pruner](#autocompress-pruner)
+* [AutoML for Model Compression Pruner](#automl-for-model-compression-pruner)
 * [Sensitivity Pruner](#sensitivity-pruner)
 
 **Others**
@@ -38,7 +39,7 @@ Tensorflow code
 ```python
 from nni.compression.tensorflow import LevelPruner
 config_list = [{ 'sparsity': 0.8, 'op_types': ['default'] }]
-pruner = LevelPruner(model_graph, config_list)
+pruner = LevelPruner(model, config_list)
 pruner.compress()
 ```
 
@@ -117,17 +118,6 @@ FPGMPruner prune filters with the smallest geometric median.
 
 ### Usage
 
-Tensorflow code
-```python
-from nni.compression.tensorflow import FPGMPruner
-config_list = [{
-    'sparsity': 0.5,
-    'op_types': ['Conv2D']
-}]
-pruner = FPGMPruner(model, config_list)
-pruner.compress()
-```
-
 PyTorch code
 ```python
 from nni.compression.torch import FPGMPruner
@@ -146,11 +136,6 @@ pruner.compress()
 ..  autoclass:: nni.compression.torch.FPGMPruner
 ```
 
-##### Tensorflow
-```eval_rst
-..  autoclass:: nni.compression.tensorflow.FPGMPruner
-```
-
 ## L1Filter Pruner
 
 This is an one-shot pruner, In ['PRUNING FILTERS FOR EFFICIENT CONVNETS'](https://arxiv.org/abs/1608.08710), authors Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet and Hans Peter Graf.
@@ -383,12 +368,6 @@ You can view [example](https://github.com/microsoft/nni/blob/master/examples/mod
 ..  autoclass:: nni.compression.torch.AGPPruner
 ```
 
-##### Tensorflow
-
-```eval_rst
-..  autoclass:: nni.compression.tensorflow.AGPPruner
-```
-
 ***
 
 ## NetAdapt Pruner
@@ -498,6 +477,39 @@ You can view [example](https://github.com/microsoft/nni/blob/master/examples/mod
 ..  autoclass:: nni.compression.torch.AutoCompressPruner
 ```
 
+## AutoML for Model Compression Pruner
+
+AutoML for Model Compression Pruner (AMCPruner) leverages reinforcement learning to provide the model compression policy.
+This learning-based compression policy outperforms conventional rule-based compression policy by having higher compression ratio,
+better preserving the accuracy and freeing human labor.
+
+![](../../img/amc_pruner.jpg)
+
+For more details, please refer to [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://arxiv.org/pdf/1802.03494.pdf).
+
+
+#### Usage
+
+PyTorch code
+
+```python
+from nni.compression.torch import AMCPruner
+config_list = [{
+        'op_types': ['Conv2d', 'Linear']
+    }]
+pruner = AMCPruner(model, config_list, evaluator, val_loader, flops_ratio=0.5)
+pruner.compress()
+```
+
+You can view [example](https://github.com/microsoft/nni/blob/master/examples/model_compress/amc/) for more information.
+
+#### User configuration for AutoCompress Pruner
+
+##### PyTorch
+
+```eval_rst
+..  autoclass:: nni.compression.torch.AMCPruner
+```
 
 ## ADMM Pruner
 Alternating Direction Method of Multipliers (ADMM) is a mathematical optimization technique,
@@ -620,4 +632,4 @@ pruner.compress(eval_args=[model], finetune_args=[model])
 
 ```eval_rst
 ..  autoclass:: nni.compression.torch.SensitivityPruner
-```
+```

docs/en_US/Tutorial/Nnictl.md

@@ -262,7 +262,7 @@ Debug mode will disable version check function in Trialkeeper.
   |Name, shorthand|Required|Default|Description|
   |------|------|------ |------|
   |id|  False| |ID of the experiment you want to set|
-  |--value, -v|  True| |the experiment duration will be NUMBER seconds. SUFFIX may be 's' for seconds (the default), 'm' for minutes, 'h' for hours or 'd' for days.|
+  |--value, -v|  True| | Strings like '1m' for one minute or '2h' for two hours. SUFFIX may be 's' for seconds, 'm' for minutes, 'h' for hours or 'd' for days.|
 
   * Example
 
@@ -305,19 +305,23 @@ Debug mode will disable version check function in Trialkeeper.
 
   * Description
 
-    You can use this command to show trial's information.
+    You can use this command to show trial's information. Note that if `head` or `tail` is set, only complete trials will be listed.
 
   * Usage
 
     ```bash
     nnictl trial ls
+    nnictl trial ls --head 10
+    nnictl trial ls --tail 10
     ```
 
   * Options
 
   |Name, shorthand|Required|Default|Description|
   |------|------|------ |------|
   |id|  False| |ID of the experiment you want to set|
+  |--head|False||the number of items to be listed with the highest default metric|
+  |--tail|False||the number of items to be listed with the lowest default metric|
 
 * __nnictl trial kill__
 
@@ -444,9 +448,6 @@ Debug mode will disable version check function in Trialkeeper.
   |--all|  False| |delete all of experiments|
 
 
-
-<a name="export"></a>
-
 * __nnictl experiment export__
   * Description
 
@@ -465,13 +466,14 @@ Debug mode will disable version check function in Trialkeeper.
   |id|  False| |ID of the experiment    |
   |--filename, -f|  True| |File path of the output file     |
   |--type|  True| |Type of output file, only support "csv" and "json"|
+  |--intermediate, -i|False||Are intermediate results included|
 
   * Examples
 
   > export all trial data in an experiment as json format
 
   ```bash
-  nnictl experiment export [experiment_id] --filename [file_path] --type json
+  nnictl experiment export [experiment_id] --filename [file_path] --type json --intermediate
   ```
 
 * __nnictl experiment import__
@@ -531,6 +533,62 @@ Debug mode will disable version check function in Trialkeeper.
     nnictl experiment import [experiment_id] -f experiment_data.json
     ```
 
+* __nnictl experiment save__
+  * Description
+
+    Save nni experiment metadata and code data.
+
+  * Usage
+
+    ```bash
+    nnictl experiment save [OPTIONS]
+    ```
+
+  * Options
+
+  |Name, shorthand|Required|Default|Description|
+  |------|------|------ |------|
+  |id|  True| |The id of the experiment you want to save|
+  |--path, -p|  False| |the folder path to store nni experiment data, default current working directory|
+  |--saveCodeDir, -s| False| |save codeDir data of the experiment, default False|
+
+  * Examples
+
+  > save an expeirment
+
+  ```bash
+  nnictl experiment save [experiment_id] --saveCodeDir
+  ```
+
+* __nnictl experiment load__
+  * Description
+
+    Load an nni experiment.
+
+  * Usage
+
+    ```bash
+    nnictl experiment load [OPTIONS]
+    ```
+
+  * Options
+
+  |Name, shorthand|Required|Default|Description|
+  |------|------|------ |------|
+  |--path, -p|  True| |the file path of nni package|
+  |--codeDir, -c| True| |the path of codeDir for loaded experiment, this path will also put the code in the loaded experiment package|
+  |--logDir, -l| False| |the path of logDir for loaded experiment|
+
+  * Examples
+
+  > load an expeirment
+
+  ```bash
+  nnictl experiment load --path [path] --codeDir [codeDir]
+  ```
+
+
+
 <a name="platform"></a>
 ### Manage platform information
 
@@ -850,4 +908,3 @@ Debug mode will disable version check function in Trialkeeper.
     ```bash
     nnictl --version
     ```
-    

docs/en_US/conf.py

@@ -17,6 +17,7 @@
 import os
 import sys
 sys.path.insert(0, os.path.abspath('../../src/sdk/pynni'))
+sys.path.insert(1, os.path.abspath('../../src/sdk/pycli'))
 
 
 # -- Project information ---------------------------------------------------