Merge pull request #208 from microsoft/master

merge master

README.md

@@ -18,7 +18,7 @@ NNI (Neural Network Intelligence) is a toolkit to help users run automated machi
 The tool dispatches and runs trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments like local machine, remote servers and cloud.
 
 
-### **NNI [v1.0](https://github.com/Microsoft/nni/blob/master/docs/en_US/Release_v1.0.md) has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
+### **NNI v1.1 has been released! &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
 
 <p align="center">
   <a href="#nni-has-been-released"><img src="docs/img/overview.svg" /></a>
@@ -211,7 +211,7 @@ Linux and MacOS
 * Run the following commands in an environment that has `python >= 3.5`, `git` and `wget`.
 
 ```bash
-    git clone -b v1.0 https://github.com/Microsoft/nni.git
+    git clone -b v1.1 https://github.com/Microsoft/nni.git
     cd nni
     source install.sh
 ```
@@ -221,7 +221,7 @@ Windows
 * Run the following commands in an environment that has `python >=3.5`, `git` and `PowerShell`
 
 ```bash
-  git clone -b v1.0 https://github.com/Microsoft/nni.git
+  git clone -b v1.1 https://github.com/Microsoft/nni.git
   cd nni
   powershell -ExecutionPolicy Bypass -file install.ps1
 ```
@@ -232,12 +232,12 @@ For NNI on Windows, please refer to [NNI on Windows](docs/en_US/Tutorial/NniOnWi
 
 **Verify install**
 
-The following example is an experiment built on TensorFlow. Make sure you have **TensorFlow installed** before running it.
+The following example is an experiment built on TensorFlow. Make sure you have **TensorFlow 1.x installed** before running it. Note that **currently Tensorflow 2.0 is NOT supported**.
 
 * Download the examples via clone the source code.
 
 ```bash
-    git clone -b v1.0 https://github.com/Microsoft/nni.git
+    git clone -b v1.1 https://github.com/Microsoft/nni.git
 ```
 
 Linux and MacOS

deployment/pypi/setup.py

@@ -73,7 +73,7 @@ setuptools.setup(
         'requests',
         'astor',
         'PythonWebHDFS',
-        'hyperopt',
+        'hyperopt==0.1.2',
         'json_tricks',
         'numpy',
         'scipy',

docs/en_US/Compressor/AutoCompression.md

 # Automatic Model Compression on NNI
 
-TBD.
+It's convenient to implement auto model compression with NNI compression and NNI tuners
\ No newline at end of file
+
+## First, model compression with NNI
+
+You can easily compress a model with NNI compression. Take pruning for example, you can prune a pretrained model with LevelPruner like this
+
+```python
+from nni.compression.torch import LevelPruner
+config_list = [{ 'sparsity': 0.8, 'op_types': 'default' }]
+pruner = LevelPruner(config_list)
+pruner(model)
+```
+
+```{ 'sparsity': 0.8, 'op_types': 'default' }```means that **all layers with weight will be compressed with the same 0.8 sparsity**. When ```pruner(model)``` called, the model is compressed with masks and after that you can normally fine tune this model and **pruned weights won't be updated** which have been masked.
+
+## Then, make this automatic
+
+The previous example manually choosed LevelPruner and pruned all layers with the same sparsity, this is obviously sub-optimal because different layers may have different redundancy. Layer sparsity should be carefully tuned to achieve least model performance degradation and this can be done with NNI tuners.
+
+The first thing we need to do is to design a search space, here we use a nested search space which contains  choosing pruning algorithm and optimizing layer sparsity.
+
+```json
+{
+  "prune_method": {
+    "_type": "choice",
+    "_value": [
+      {
+        "_name": "agp",
+        "conv0_sparsity": {
+          "_type": "uniform",
+          "_value": [
+            0.1,
+            0.9
+          ]
+        },
+        "conv1_sparsity": {
+          "_type": "uniform",
+          "_value": [
+            0.1,
+            0.9
+          ]
+        },
+      },
+      {
+        "_name": "level",
+        "conv0_sparsity": {
+          "_type": "uniform",
+          "_value": [
+            0.1,
+            0.9
+          ]
+        },
+        "conv1_sparsity": {
+          "_type": "uniform",
+          "_value": [
+            0.01,
+            0.9
+          ]
+        },
+      }
+    ]
+  }
+}
+```
+
+Then we need to modify our codes for few lines
+
+```python
+import nni
+from nni.compression.torch import *
+params = nni.get_parameters()
+conv0_sparsity = params['prune_method']['conv0_sparsity']
+conv1_sparsity = params['prune_method']['conv1_sparsity']
+# these raw sparsity should be scaled if you need total sparsity constrained
+config_list_level = [{ 'sparsity': conv0_sparsity, 'op_name': 'conv0' },
+                     { 'sparsity': conv1_sparsity, 'op_name': 'conv1' }]
+config_list_agp = [{'initial_sparsity': 0, 'final_sparsity': conv0_sparsity,
+                    'start_epoch': 0, 'end_epoch': 3,
+                    'frequency': 1,'op_name': 'conv0' },
+                   {'initial_sparsity': 0, 'final_sparsity': conv1_sparsity,
+                    'start_epoch': 0, 'end_epoch': 3,
+                    'frequency': 1,'op_name': 'conv1' },]
+PRUNERS = {'level':LevelPruner(config_list_level)，'agp':AGP_Pruner(config_list_agp)}
+pruner = PRUNERS(params['prune_method']['_name'])
+pruner(model)
+... # fine tuning
+acc = evaluate(model) # evaluation
+nni.report_final_results(acc)
+```
+
+Last, define our task and automatically tuning pruning methods with layers sparsity
+
+```yaml
+authorName: default
+experimentName: Auto_Compression
+trialConcurrency: 2
+maxExecDuration: 100h
+maxTrialNum: 500
+#choice: local, remote, pai
+trainingServicePlatform: local
+#choice: true, false
+useAnnotation: False
+searchSpacePath: search_space.json
+tuner:
+  #choice: TPE, Random, Anneal...
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: bash run_prune.sh
+  codeDir: .
+  gpuNum: 1
+
+```
+

docs/en_US/Compressor/Overview.md

 # Compressor
+
+We are glad to announce the alpha release for model compression toolkit on top of NNI, it's still in the experiment phase which might evolve based on usage feedback. We'd like to invite you to use, feedback and even contribute.
+
 NNI provides an easy-to-use toolkit to help user design and use compression algorithms. It supports Tensorflow and PyTorch with unified interface. For users to compress their models, they only need to add several lines in their code. There are some popular model compression algorithms built-in in NNI. Users could further use NNI's auto tuning power to find the best compressed model, which is detailed in [Auto Model Compression](./AutoCompression.md). On the other hand, users could easily customize their new compression algorithms using NNI's interface, refer to the tutorial [here](#customize-new-compression-algorithms).
 
 ## Supported algorithms
-We have provided two naive compression algorithms and four popular ones for users, including three pruning algorithms and three quantization algorithms:
+We have provided two naive compression algorithms and three popular ones for users, including two pruning algorithms and three quantization algorithms:
 
 |Name|Brief Introduction of Algorithm|
 |---|---|
 | [Level Pruner](./Pruner.md#level-pruner) | Pruning the specified ratio on each weight based on absolute values of weights |
 | [AGP Pruner](./Pruner.md#agp-pruner) | Automated gradual pruning (To prune, or not to prune: exploring the efficacy of pruning for model compression) [Reference Paper](https://arxiv.org/abs/1710.01878)|
-| [Sensitivity Pruner](./Pruner.md#sensitivity-pruner) | Learning both Weights and Connections for Efficient Neural Networks. [Reference Paper](https://arxiv.org/abs/1506.02626)|
 | [Naive Quantizer](./Quantizer.md#naive-quantizer) |  Quantize weights to default 8 bits |
 | [QAT Quantizer](./Quantizer.md#qat-quantizer) | Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. [Reference Paper](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jacob_Quantization_and_Training_CVPR_2018_paper.pdf)|
 | [DoReFa Quantizer](./Quantizer.md#dorefa-quantizer) | DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients. [Reference Paper](https://arxiv.org/abs/1606.06160)|

docs/en_US/Compressor/Pruner.md

@@ -48,7 +48,7 @@ from nni.compression.tensorflow import AGP_Pruner
 config_list = [{
     'initial_sparsity': 0,
     'final_sparsity': 0.8,
-    'start_epoch': 1,
+    'start_epoch': 0,
     'end_epoch': 10,
     'frequency': 1,
     'op_types': 'default'
@@ -62,7 +62,7 @@ from nni.compression.torch import AGP_Pruner
 config_list = [{
     'initial_sparsity': 0,
     'final_sparsity': 0.8,
-    'start_epoch': 1,
+    'start_epoch': 0,
     'end_epoch': 10,
     'frequency': 1,
     'op_types': 'default'
@@ -86,47 +86,9 @@ You can view example for more information
 #### User configuration for AGP Pruner
 * **initial_sparsity:** This is to specify the sparsity when compressor starts to compress
 * **final_sparsity:** This is to specify the sparsity when compressor finishes to compress
-* **start_epoch:** This is to specify the epoch number when compressor starts to compress
+* **start_epoch:** This is to specify the epoch number when compressor starts to compress, default start from epoch 0
 * **end_epoch:** This is to specify the epoch number when compressor finishes to compress
-* **frequency:** This is to specify every *frequency* number epochs compressor compress once
+* **frequency:** This is to specify every *frequency* number epochs compressor compress once, default frequency=1
 
 ***
 
-## Sensitivity Pruner
-In [Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626), author Song Han and provide an algorithm to find the sensitivity of each layer and set the pruning threshold to each layer.
-
->We used the sensitivity results to find each layer’s threshold: for example, the smallest threshold was applied to the most sensitive layer, which is the first convolutional layer... The pruning threshold is chosen as a quality parameter multiplied by the standard deviation of a layer’s weights
-
-### Usage
-You can prune weight step by step and reach one target sparsity by Sensitivity Pruner with the code below.
-
-Tensorflow code
-```python
-from nni.compression.tensorflow import SensitivityPruner
-config_list = [{ 'sparsity':0.8, 'op_types': 'default' }]
-pruner = SensitivityPruner(config_list)
-pruner(tf.get_default_graph())
-```
-PyTorch code
-```python
-from nni.compression.torch import SensitivityPruner
-config_list = [{ 'sparsity':0.8, 'op_types': 'default' }]
-pruner = SensitivityPruner(config_list)
-pruner(model)
-```
-Like AGP Pruner, you should update mask information every epoch by adding code below
-
-Tensorflow code 
-```python
-pruner.update_epoch(epoch, sess)
-```
-PyTorch code
-```python
-pruner.update_epoch(epoch)
-```
-You can view example for more information
-
-#### User configuration for Sensitivity Pruner
-* **sparsity:** This is to specify the sparsity operations to be compressed to
-
-***

docs/en_US/TrialExample/RocksdbExamples.md

+# Tuning RocksDB on NNI
+
+## Overview
+
+[RocksDB](https://github.com/facebook/rocksdb) is a popular high performance embedded key-value database used in production systems at various web-scale enterprises including Facebook, Yahoo!, and LinkedIn.. It is a fork of [LevelDB](https://github.com/google/leveldb) by Facebook optimized to exploit many central processing unit (CPU) cores, and make efficient use of fast storage, such as solid-state drives (SSD), for input/output (I/O) bound workloads.
+
+The performance of RocksDB is highly contingent on its tuning. However, because of the complexity of its underlying technology and a large number of configurable parameters, a good configuration is sometimes hard to obtain. NNI can help to address this issue. NNI supports many kinds of tuning algorithms to search the best configuration of RocksDB, and support many kinds of environments like local machine, remote servers and cloud. 
+
+This example illustrates how to use NNI to search the best configuration of RocksDB for a `fillrandom` benchmark supported by a benchmark tool `db_bench`, which is an official benchmark tool provided by RocksDB itself. Therefore, before running this example, please make sure NNI is installed and [`db_bench`](https://github.com/facebook/rocksdb/wiki/Benchmarking-tools) is in your `PATH`. Please refer to [here](../Tutorial/QuickStart.md) for detailed information about installation and preparing of NNI environment, and [here](https://github.com/facebook/rocksdb/blob/master/INSTALL.md) for compiling RocksDB as well as `db_bench`.
+
+We also provide a simple script [`db_bench_installation.sh`](../../../examples/trials/systems/rocksdb-fillrandom/db_bench_installation.sh) helping to compile and install `db_bench` as well as its dependencies on Ubuntu. Installing RocksDB on other systems can follow the same procedure.
+
+*code directory: [`example/trials/systems/rocksdb-fillrandom`](../../../examples/trials/systems/rocksdb-fillrandom)*
+
+## Experiment setup
+
+There are mainly three steps to setup an experiment of tuning systems on NNI. Define search space with a `json` file, write a benchmark code, and start NNI experiment by passing a config file to NNI manager.
+
+### Search Space
+
+For simplicity, this example tunes three parameters, `write_buffer_size`, `min_write_buffer_num` and `level0_file_num_compaction_trigger`, for writing 16M keys with 20 Bytes of key size and 100 Bytes of value size randomly, based on writing operations per second (OPS). `write_buffer_size` sets the size of a single memtable. Once memtable exceeds this size, it is marked immutable and a new one is created. `min_write_buffer_num` is the minimum number of memtables to be merged before flushing to storage. Once the number of files in level 0 reaches `level0_file_num_compaction_trigger`, level 0 to level 1 compaction is triggered.
+
+In this example, the search space is specified by a `search_space.json` file as shown below. Detailed explanation of search space could be found [here](../Tutorial/SearchSpaceSpec.md).
+
+```json
+{
+    "write_buffer_size": {
+        "_type": "quniform",
+        "_value": [2097152, 16777216, 1048576]
+    },
+    "min_write_buffer_number_to_merge": {
+        "_type": "quniform",
+        "_value": [2, 16, 1]
+    },
+    "level0_file_num_compaction_trigger": {
+        "_type": "quniform",
+        "_value": [2, 16, 1]
+    }
+}
+```
+
+*code directory: [`example/trials/systems/rocksdb-fillrandom/search_space.json`](../../../examples/trials/systems/rocksdb-fillrandom/search_space.json)*
+
+### Benchmark code
+
+Benchmark code should receive a configuration from NNI manager, and report the corresponding benchmark result back. Following NNI APIs are designed for this purpose. In this example, writing operations per second (OPS) is used as a performance metric. Please refer to [here](Trials.md) for detailed information.
+
+* Use `nni.get_next_parameter()` to get next system configuration.
+* Use `nni.report_final_result(metric)` to report the benchmark result.
+
+*code directory: [`example/trials/systems/rocksdb-fillrandom/main.py`](../../../examples/trials/systems/rocksdb-fillrandom/main.py)*
+
+### Config file
+
+One could start a NNI experiment with a config file. A config file for NNI is a `yaml` file usually including experiment settings (`trialConcurrency`, `maxExecDuration`, `maxTrialNum`, `trial gpuNum`, etc.), platform settings (`trainingServicePlatform`, etc.), path settings (`searchSpacePath`, `trial codeDir`, etc.) and tuner settings (`tuner`, `tuner optimize_mode`, etc.). Please refer to [here](../Tutorial/QuickStart.md) for more information.
+
+Here is an example of tuning RocksDB with SMAC algorithm:
+
+*code directory: [`example/trials/systems/rocksdb-fillrandom/config_smac.yml`](../../../examples/trials/systems/rocksdb-fillrandom/config_smac.yml)*
+
+Here is an example of tuning RocksDB with TPE algorithm:
+
+*code directory: [`example/trials/systems/rocksdb-fillrandom/config_tpe.yml`](../../../examples/trials/systems/rocksdb-fillrandom/config_tpe.yml)*
+
+Other tuners can be easily adopted in the same way. Please refer to [here](../Tuner/BuiltinTuner.md) for more information.
+
+Finally, we could enter the example folder and start the experiment using following commands:
+
+```bash
+# tuning RocksDB with SMAC tuner
+nnictl create --config ./config_smac.yml
+# tuning RocksDB with TPE tuner
+nnictl create --config ./config_tpe.yml
+```
+
+## Experiment results
+
+We ran these two examples on the same machine with following details:
+
+* 16 * Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz
+* 465 GB of rotational hard drive with ext4 file system
+* 128 GB of RAM
+* Kernel version: 4.15.0-58-generic
+* NNI version: v1.0-37-g1bd24577
+* RocksDB version: 6.4
+* RocksDB DEBUG_LEVEL: 0
+
+The detailed experiment results are shown in the below figure. Horizontal axis is sequential order of trials. Vertical axis is the metric, write OPS in this example. Blue dots represent trials for tuning RocksDB with SMAC tuner, and orange dots stand for trials for tuning RocksDB with TPE tuner. 
+
+![image](../../../examples/trials/systems/rocksdb-fillrandom/plot.png)
+
+Following table lists the best trials and corresponding parameters and metric obtained by the two tuners. Unsurprisingly, both of them found the same optimal configuration for `fillrandom` benchmark.
+
+| Tuner | Best trial | Best OPS | write_buffer_size | min_write_buffer_number_to_merge | level0_file_num_compaction_trigger |
+| :---: | :--------: | :------: | :---------------: | :------------------------------: | :--------------------------------: |
+| SMAC  | 255        | 779289   | 2097152           | 7.0                              | 7.0                                |
+| TPE   | 169        | 761456   | 2097152           | 7.0                              | 7.0                                |

docs/en_US/TrialExample/Trials.md

@@ -163,3 +163,4 @@ For more information, please refer to [HowToDebug](../Tutorial/HowToDebug.md)
 * [How to tune Scikit-learn on NNI](SklearnExamples.md)
 * [Automatic Model Architecture Search for Reading Comprehension.](SquadEvolutionExamples.md)
 * [Tuning GBDT on NNI](GbdtExample.md)
+* [Tuning RocksDB on NNI](RocksdbExamples.md)

docs/en_US/conf.py

@@ -28,7 +28,7 @@ author = 'Microsoft'
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = 'v1.0'
+release = 'v1.1'
 
 # -- General configuration ---------------------------------------------------
 

docs/zh_CN/TrialExample/RocksdbExamples.md

+# 使用 NNI 调优 RocksDB
+
+## 概述
+
+[RocksDB](https://github.com/facebook/rocksdb) 是一种很受欢迎的高性能嵌入式键值数据库，被许多公司，如 Facebook， Yahoo! 和 LinkedIn 等，广泛应用于各种网络规模的产品中。它是 Facebook 在 [LevelDB](https://github.com/google/leveldb) 的基础上，通过充分利用多核心中央处理器和快速存储器（如固态硬盘）的特点，针对IO密集型应用优化而成的。
+
+RocksDB 的性能高度依赖于运行参数的调优。然而，由于其底层技术极为复杂，需要调整的参数过多，有时很难找到合适的运行参数。NNI 可以帮助数据库运维工程师解决这个问题。NNI 支持多种自动调参算法，并且支持运行于本地、远程和云端的各种负载。
+
+本例展示了如何使用 NNI 搜索 RocksDB 在 `fillrandom` 基准测试中的最佳运行参数，`fillrandom` 基准测试是 RocksDB 官方提供的基准测试工具 `db_bench` 所支持的一种基准测试，因此在运行本例之前请确保您已经安装了 NNI，并且 `db_bench` 在您的 `PATH` 路径中。关于如何安装和准备 NNI 环境，请参考[这里](../Tuner/BuiltinTuner.md)，关于如何编译 RocksDB 以及 `db_bench`，请参考[这里](https://github.com/facebook/rocksdb/blob/master/INSTALL.md)。
+
+我们还提供了一个简单的脚本 [`db_bench_installation.sh`](../../../examples/trials/systems/rocksdb-fillrandom/db_bench_installation.sh)，用来在 Ubuntu 系统上编译和安装 `db_bench` 和相关依赖。在其他系统中的安装也可以参考该脚本实现。
+
+*代码目录: [`example/trials/systems/rocksdb-fillrandom`](../../../examples/trials/systems/rocksdb-fillrandom)*
+
+## 实验配置
+
+使用 NNI 进行调优系统主要有三个步骤，分别是，使用一个 `json` 文件定义搜索空间；准备一个基准测试程序；和一个用来启动 NNI 实验的配置文件。
+
+### 搜索空间
+
+简便起见，本例基于 Rocks_DB 每秒的写入操作数（Operations Per Second, OPS），在随机写入 16M 个键长为 20 字节值长为 100 字节的键值对的情况下，对三个系统运行参数，`write_buffer_size`，`min_write_buffer_num` 和 `level0_file_num_compaction_trigger`，进行了调优。`write_buffer_size` 控制了单个 memtable 的大小。在写入过程中，当 memtable 的大小超过了 `write_buffer_size` 指定的数值，该 memtable 将会被标记为不可变，并创建一个新的 memtable。`min_write_buffer_num` 是在写入（flush）磁盘之前需要合并（merge）的 memtable 的最小数量。一旦 level 0 中的文件数量超过了 `level0_file_num_compaction_trigger` 所指定的数，level 0 向 level 1 的压缩（compaction）将会被触发。
+
+搜索空间由如下所示的文件 `search_space.json` 指定。更多关于搜索空间的解释请参考[这里](../Tutorial/SearchSpaceSpec.md)。
+
+```json
+{
+    "write_buffer_size": {
+        "_type": "quniform",
+        "_value": [2097152, 16777216, 1048576]
+    },
+    "min_write_buffer_number_to_merge": {
+        "_type": "quniform",
+        "_value": [2, 16, 1]
+    },
+    "level0_file_num_compaction_trigger": {
+        "_type": "quniform",
+        "_value": [2, 16, 1]
+    }
+}
+```
+
+*代码目录: [`example/trials/systems/rocksdb-fillrandom/search_space.json`](../../../examples/trials/systems/rocksdb-fillrandom/search_space.json)*
+
+### 基准测试
+
+基准测试程序需要从 NNI manager 接收一个运行参数，并在运行基准测试以后向 NNI manager 汇报基准测试结果。NNI 提供了下面两个 APIs 来完成这些任务。更多关于 NNI trials 的信息请参考[这里](Trials.md)。
+
+* 使用 `nni.get_next_parameter()` 从 NNI manager 得到需要测试的系统运行参数。
+* 使用 `nni.report_final_result(metric)` 向 NNI manager 汇报基准测试的结果。
+
+*代码目录: [`example/trials/systems/rocksdb-fillrandom/main.py`](../../../examples/trials/systems/rocksdb-fillrandom/main.py)*
+
+### 配置文件
+
+NNI 实验可以通过配置文件来启动。通常而言，NNI 配置文件是一个 `yaml` 文件，通常包含实验设置（`trialConcurrency`，`maxExecDuration`，`maxTrialNum`，`trial gpuNum` 等），运行平台设置（`trainingServicePlatform` 等），路径设置（`searchSpacePath`，`trial codeDir` 等）和 调参器设置（`tuner`，`tuner optimize_mode` 等）。更多关于 NNI 配置文件的信息请参考[这里](../Tutorial/QuickStart.md)。
+
+下面是使用 SMAC 算法调优 RocksDB 配置文件的例子：
+
+*代码目录: [`example/trials/systems/rocksdb-fillrandom/config_smac.yml`](../../../examples/trials/systems/rocksdb-fillrandom/config_smac.yml)*
+
+下面是使用 TPE 算法调优 RocksDB 配置文件的例子：
+
+*代码目录: [`example/trials/systems/rocksdb-fillrandom/config_tpe.yml`](../../../examples/trials/systems/rocksdb-fillrandom/config_tpe.yml)*
+
+其他的调参器可以使用同样的方式应用，更多关于调参器的信息请参考[这里](../Tuner/BuiltinTuner.md)。
+
+最后，我们可以进入本例的文件夹内，用下面的命令启动实验：
+
+```bash
+# tuning RocksDB with SMAC tuner
+nnictl create --config ./config_smac.yml
+# tuning RocksDB with TPE tuner
+nnictl create --config ./config_tpe.yml
+```
+
+## 实验结果
+
+我们在同一台机器上运行了这两个实验，相关信息如下：
+
+* 16 * Intel(R) Xeon(R) CPU E5-2650 v2 @ 2.60GHz
+* 465 GB of rotational hard drive with ext4 file system
+* 128 GB of RAM
+* Kernel version: 4.15.0-58-generic
+* NNI version: v1.0-37-g1bd24577
+* RocksDB version: 6.4
+* RocksDB DEBUG_LEVEL: 0
+
+具体的实验结果如下图所示。横轴是基准测试的顺序，纵轴是基准测试得到的结果，在本例中是每秒钟写操作的次数。蓝色的圆点代表用 SMAC 调优 RocksDB 得到的基准测试结果，而橘黄色的圆点表示用 TPE 调优得到的基准测试结果。
+
+![image](../../../examples/trials/systems/rocksdb-fillrandom/plot.png)
+
+下面的表格列出了使用两种调参器得到的最好的基准测试结果及相对应的参数。毫不意外，使用这两种调参器在 `fillrandom` 基准测试中搜索得到了相同的最优参数。
+
+| Tuner | Best trial | Best OPS | write_buffer_size | min_write_buffer_number_to_merge | level0_file_num_compaction_trigger |
+| :---: | :--------: | :------: | :---------------: | :------------------------------: | :--------------------------------: |
+| SMAC  | 255        | 779289   | 2097152           | 7.0                              | 7.0                                |
+| TPE   | 169        | 761456   | 2097152           | 7.0                              | 7.0                                |

docs/zh_CN/TrialExample/Trials.md

@@ -169,3 +169,4 @@ echo $? `date +%s%3N` >/home/user_name/nni/experiments/$experiment_id$/trials/$t
 * [如何在 NNI 调优 SciKit-learn 的参数](SklearnExamples.md)
 * [在阅读理解上使用自动模型架构搜索。](SquadEvolutionExamples.md)
 * [如何在 NNI 上调优 GBDT](GbdtExample.md)
+* [如何在 NNI 上调优 RocksDB](RocksdbExamples.md)
\ No newline at end of file

docs/zh_CN/conf.py

@@ -28,7 +28,7 @@ author = 'Microsoft'
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = 'v1.0'
+release = 'v1.1'
 
 # -- General configuration ---------------------------------------------------
 

examples/model_compress/README.md

+# Run model compression examples
+
+You can run these examples easily like this, take torch pruning for example
+
+```bash
+python main_torch_pruner.py
+```
+
+This example uses AGP Pruner. Initiating a pruner needs a user provided configuration which can be provided in two ways:
+
+- By reading ```configure_example.yaml```, this can make code clean when your configuration is complicated
+- Directly config in your codes
+
+In our example, we simply config model compression in our codes like this
+
+```python
+configure_list = [{
+    'initial_sparsity': 0,
+    'final_sparsity': 0.8,
+    'start_epoch': 0,
+    'end_epoch': 10,
+    'frequency': 1,
+    'op_type': 'default'
+}]
+pruner = AGP_Pruner(configure_list)
+```
+
+When ```pruner(model)``` is called, your model is injected with masks as embedded operations. For example, a layer takes a weight as input, we will insert an operation between the weight and the layer, this operation takes the weight as input and outputs a new weight applied by the mask. Thus, the masks are applied at any time the computation goes through the operations. You can fine-tune your model **without** any modifications.
+
+```python
+for epoch in range(10):
+    # update_epoch is for pruner to be aware of epochs, so that it could adjust masks during training.
+    pruner.update_epoch(epoch)
+    print('# Epoch {} #'.format(epoch))
+    train(model, device, train_loader, optimizer)
+    test(model, device, test_loader)
+```
+
+When fine tuning finished,  pruned weights are all masked and you can get masks like this
+
+```
+masks = pruner.mask_list
+layer_name = xxx
+mask = masks[layer_name]
+```
+
+
+

examples/model_compress/configure_example.yaml

 AGPruner: 
   config:
     -
-        start_epoch: 1
+        start_epoch: 0
         end_epoch: 10
         frequency: 1
         initial_sparsity: 0.05

examples/model_compress/main_tf_pruner.py

@@ -4,23 +4,26 @@ from tensorflow.examples.tutorials.mnist import input_data
 
 
 def weight_variable(shape):
-    return tf.Variable(tf.truncated_normal(shape, stddev = 0.1))
+    return tf.Variable(tf.truncated_normal(shape, stddev=0.1))
+
 
 def bias_variable(shape):
-    return tf.Variable(tf.constant(0.1, shape = shape))
+    return tf.Variable(tf.constant(0.1, shape=shape))
+
 
 def conv2d(x_input, w_matrix):
-    return tf.nn.conv2d(x_input, w_matrix, strides = [ 1, 1, 1, 1 ], padding = 'SAME')
+    return tf.nn.conv2d(x_input, w_matrix, strides=[1, 1, 1, 1], padding='SAME')
+
 
 def max_pool(x_input, pool_size):
-    size = [ 1, pool_size, pool_size, 1 ]
-    return tf.nn.max_pool(x_input, ksize = size, strides = size, padding = 'SAME')
+    size = [1, pool_size, pool_size, 1]
+    return tf.nn.max_pool(x_input, ksize=size, strides=size, padding='SAME')
 
 
 class Mnist:
     def __init__(self):
-        images = tf.placeholder(tf.float32, [ None, 784 ], name = 'input_x')
-        labels = tf.placeholder(tf.float32, [ None, 10 ], name = 'input_y')
+        images = tf.placeholder(tf.float32, [None, 784], name='input_x')
+        labels = tf.placeholder(tf.float32, [None, 10], name='input_y')
         keep_prob = tf.placeholder(tf.float32, name='keep_prob')
 
         self.images = images
@@ -35,35 +38,35 @@ class Mnist:
         self.fcw1 = None
         self.cross = None
         with tf.name_scope('reshape'):
-            x_image = tf.reshape(images, [ -1, 28, 28, 1 ])
+            x_image = tf.reshape(images, [-1, 28, 28, 1])
         with tf.name_scope('conv1'):
-            w_conv1 = weight_variable([ 5, 5, 1, 32 ])
+            w_conv1 = weight_variable([5, 5, 1, 32])
             self.w1 = w_conv1
-            b_conv1 = bias_variable([ 32 ])
+            b_conv1 = bias_variable([32])
             self.b1 = b_conv1
             h_conv1 = tf.nn.relu(conv2d(x_image, w_conv1) + b_conv1)
         with tf.name_scope('pool1'):
             h_pool1 = max_pool(h_conv1, 2)
         with tf.name_scope('conv2'):
-            w_conv2 = weight_variable([ 5, 5, 32, 64 ])
-            b_conv2 = bias_variable([ 64 ])
+            w_conv2 = weight_variable([5, 5, 32, 64])
+            b_conv2 = bias_variable([64])
             h_conv2 = tf.nn.relu(conv2d(h_pool1, w_conv2) + b_conv2)
         with tf.name_scope('pool2'):
             h_pool2 = max_pool(h_conv2, 2)
         with tf.name_scope('fc1'):
-            w_fc1 = weight_variable([ 7 * 7 * 64, 1024 ])
+            w_fc1 = weight_variable([7 * 7 * 64, 1024])
             self.fcw1 = w_fc1
-            b_fc1 = bias_variable([ 1024 ])
-        h_pool2_flat = tf.reshape(h_pool2, [ -1, 7 * 7 * 64 ])
+            b_fc1 = bias_variable([1024])
+        h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
         h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, w_fc1) + b_fc1)
         with tf.name_scope('dropout'):
             h_fc1_drop = tf.nn.dropout(h_fc1, 0.5)
         with tf.name_scope('fc2'):
-            w_fc2 = weight_variable([ 1024, 10 ])
-            b_fc2 = bias_variable([ 10 ])
+            w_fc2 = weight_variable([1024, 10])
+            b_fc2 = bias_variable([10])
             y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2
         with tf.name_scope('loss'):
-            cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = labels, logits = y_conv))
+            cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=labels, logits=y_conv))
             self.cross = cross_entropy
         with tf.name_scope('adam_optimizer'):
             self.train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy)
@@ -75,17 +78,17 @@ class Mnist:
 def main():
     tf.set_random_seed(0)
 
-    data = input_data.read_data_sets('data', one_hot = True)
+    data = input_data.read_data_sets('data', one_hot=True)
 
     model = Mnist()
 
-    '''you can change this to SensitivityPruner to implement it
-    pruner = SensitivityPruner(configure_list)
+    '''you can change this to LevelPruner to implement it
+    pruner = LevelPruner(configure_list)
     '''
     configure_list = [{
         'initial_sparsity': 0,
         'final_sparsity': 0.8,
-                        'start_epoch': 1,
+        'start_epoch': 0,
         'end_epoch': 10,
         'frequency': 1,
         'op_type': 'default'
@@ -100,27 +103,26 @@ def main():
     # you can also use compress(model) or compress_default_graph() for tensorflow compressor
     # pruner.compress(tf.get_default_graph())
 
-    
     with tf.Session() as sess:
         sess.run(tf.global_variables_initializer())
         for batch_idx in range(2000):
+            if batch_idx % 10 == 0:
+                pruner.update_epoch(batch_idx / 10, sess)
             batch = data.train.next_batch(2000)
-            model.train_step.run(feed_dict = {
+            model.train_step.run(feed_dict={
                 model.images: batch[0],
                 model.labels: batch[1],
                 model.keep_prob: 0.5
             })
             if batch_idx % 10 == 0:
-                test_acc = model.accuracy.eval(feed_dict = {
+                test_acc = model.accuracy.eval(feed_dict={
                     model.images: data.test.images,
                     model.labels: data.test.labels,
                     model.keep_prob: 1.0
                 })
-                pruner.update_epoch(batch_idx / 10,sess)
                 print('test accuracy', test_acc)
 
-        
-        test_acc = model.accuracy.eval(feed_dict = {
+        test_acc = model.accuracy.eval(feed_dict={
             model.images: data.test.images,
             model.labels: data.test.labels,
             model.keep_prob: 1.0

examples/model_compress/main_torch_pruner.py

@@ -20,7 +20,7 @@ class Mnist(torch.nn.Module):
         x = x.view(-1, 4 * 4 * 50)
         x = F.relu(self.fc1(x))
         x = self.fc2(x)
-        return F.log_softmax(x, dim = 1)
+        return F.log_softmax(x, dim=1)
 
 
 def train(model, device, train_loader, optimizer):
@@ -35,6 +35,7 @@ def train(model, device, train_loader, optimizer):
         if batch_idx % 100 == 0:
             print('{:2.0f}%  Loss {}'.format(100 * batch_idx / len(train_loader), loss.item()))
 
+
 def test(model, device, test_loader):
     model.eval()
     test_loss = 0
@@ -43,35 +44,36 @@ def test(model, device, test_loader):
         for data, target in test_loader:
             data, target = data.to(device), target.to(device)
             output = model(data)
-            test_loss += F.nll_loss(output, target, reduction = 'sum').item()
-            pred = output.argmax(dim = 1, keepdim = True)
+            test_loss += F.nll_loss(output, target, reduction='sum').item()
+            pred = output.argmax(dim=1, keepdim=True)
             correct += pred.eq(target.view_as(pred)).sum().item()
     test_loss /= len(test_loader.dataset)
 
     print('Loss: {}  Accuracy: {}%)\n'.format(
         test_loss, 100 * correct / len(test_loader.dataset)))
 
+
 def main():
     torch.manual_seed(0)
     device = torch.device('cpu')
 
     trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
     train_loader = torch.utils.data.DataLoader(
-        datasets.MNIST('data', train = True, download = True, transform = trans),
-        batch_size = 64, shuffle = True)
+        datasets.MNIST('data', train=True, download=True, transform=trans),
+        batch_size=64, shuffle=True)
     test_loader = torch.utils.data.DataLoader(
-        datasets.MNIST('data', train = False, transform = trans),
-        batch_size = 1000, shuffle = True)
+        datasets.MNIST('data', train=False, transform=trans),
+        batch_size=1000, shuffle=True)
 
     model = Mnist()
 
-    '''you can change this to SensitivityPruner to implement it
-    pruner = SensitivityPruner(configure_list)
+    '''you can change this to LevelPruner to implement it
+    pruner = LevelPruner(configure_list)
     '''
     configure_list = [{
         'initial_sparsity': 0,
         'final_sparsity': 0.8,
-                        'start_epoch': 1,
+        'start_epoch': 0,
         'end_epoch': 10,
         'frequency': 1,
         'op_type': 'default'
@@ -82,14 +84,13 @@ def main():
     # you can also use compress(model) method
     # like that pruner.compress(model)
 
-    optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum = 0.5)
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
     for epoch in range(10):
+        pruner.update_epoch(epoch)
         print('# Epoch {} #'.format(epoch))
         train(model, device, train_loader, optimizer)
         test(model, device, test_loader)
 
-        pruner.update_epoch(epoch)
-
 
 if __name__ == '__main__':
     main()

examples/trials/nas_cifar10/README.md

- **Run Neural Network Architecture Search in NNI**	
- ===	
+# Run Neural Architecture Search in NNI
  
 Now we have an NAS example [NNI-NAS-Example](https://github.com/Crysple/NNI-NAS-Example) run in NNI using NAS interface from our contributors.	
 
 We have included its trial code in this folder, and provided example config files to show how to use PPO tuner to tune the trial code.
 
-> Download data
+To prepare for the dataset, please run `cd data && . download.sh`.
 
-- `cd data && . download.sh`
-- `tar xzf cifar-10-python.tar.gz && mv cifar-batches cifar10`
+Thanks our lovely contributors, and welcome more and more people to join us!
 
-Thanks our lovely contributors. 	
- 
-And welcome more and more people to join us!

examples/trials/nas_cifar10/data/download.sh

 wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
+tar xzf cifar-10-python.tar.gz && mv cifar-10-batches-py cifar10
\ No newline at end of file

examples/trials/sklearn/classification/main.py

@@ -41,7 +41,7 @@ def get_default_parameters():
     '''get default parameters'''
     params = {
         'C': 1.0,
-        'keral': 'linear',
+        'kernel': 'linear',
         'degree': 3,
         'gamma': 0.01,
         'coef0': 0.01
@@ -52,7 +52,7 @@ def get_model(PARAMS):
     '''Get model according to parameters'''
     model = SVC()
     model.C = PARAMS.get('C')
-    model.keral = PARAMS.get('keral')
+    model.kernel = PARAMS.get('kernel')
     model.degree = PARAMS.get('degree')
     model.gamma = PARAMS.get('gamma')
     model.coef0 = PARAMS.get('coef0')

examples/trials/sklearn/classification/search_space.json

 {
     "C": {"_type":"uniform","_value":[0.1, 1]},
-    "keral": {"_type":"choice","_value":["linear", "rbf", "poly", "sigmoid"]},
+    "kernel": {"_type":"choice","_value":["linear", "rbf", "poly", "sigmoid"]},
     "degree": {"_type":"choice","_value":[1, 2, 3, 4]},
     "gamma": {"_type":"uniform","_value":[0.01, 0.1]},
     "coef0 ": {"_type":"uniform","_value":[0.01, 0.1]}

examples/trials/systems/rocksdb-fillrandom/config_smac.yml

+authorName: default
+experimentName: auto_rocksdb_SMAC
+trialConcurrency: 1
+maxExecDuration: 12h
+maxTrialNum: 256
+#choice: local, remote, pai
+trainingServicePlatform: local
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
+  #SMAC (SMAC should be installed through nnictl)
+  builtinTunerName: SMAC
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: python3 main.py
+  codeDir: .
+  gpuNum: 0