Merge pull request #27 from microsoft/master

pull code

docs/en_US/GbdtExample.md → docs/en_US/TrialExample/GbdtExample.md

@@ -98,7 +98,7 @@ If you like to tune `num_leaves`, `learning_rate`, `bagging_fraction` and `baggi
 }
 ```
 
-More support variable type you could reference [here](SearchSpaceSpec.md).
+More support variable type you could reference [here](../Tutorial/SearchSpaceSpec.md).
 
 ### 3.3 Add SDK of nni into your code.
 

docs/en_US/SklearnExamples.md → docs/en_US/TrialExample/SklearnExamples.md

@@ -6,7 +6,7 @@ NNI supports many kinds of tuning algorithms to search the best models and/or hy
 
 ## 1. How to run the example
 
-To start using NNI, you should install the NNI package, and use the command line tool `nnictl` to start an experiment. For more information about installation and preparing for the environment,  please refer [here](QuickStart.md).
+To start using NNI, you should install the NNI package, and use the command line tool `nnictl` to start an experiment. For more information about installation and preparing for the environment,  please refer [here](../Tutorial/QuickStart.md).
 
 After you installed NNI, you could enter the corresponding folder and start the experiment using following commands:
 

docs/en_US/SquadEvolutionExamples.md → docs/en_US/TrialExample/SquadEvolutionExamples.md

@@ -12,7 +12,7 @@ Since attention and RNN have been proven effective in Reading Comprehension, we
 6. ADD-SKIP (Identity between random layers).
 7. REMOVE-SKIP (Removes random skip).
 
-![](../../examples/trials/ga_squad/ga_squad.png)
+![](../../../examples/trials/ga_squad/ga_squad.png)
 
 ### New version
 Also we have another version which time cost is less and performance is better. We will release soon.

docs/en_US/Trials.md → docs/en_US/TrialExample/Trials.md

@@ -20,7 +20,7 @@ An example is shown below:
 }
 ```
 
-Refer to [SearchSpaceSpec.md](./SearchSpaceSpec.md) to learn more about search space. Tuner will generate configurations from this search space, that is, choosing a value for each hyperparameter from the range.
+Refer to [SearchSpaceSpec.md](../Tutorial/SearchSpaceSpec.md) to learn more about search space. Tuner will generate configurations from this search space, that is, choosing a value for each hyperparameter from the range.
 
 ### Step 2 - Update model codes
 
@@ -44,14 +44,14 @@ RECEIVED_PARAMS = nni.get_next_parameter()
 nni.report_intermediate_result(metrics)
 ```
 
-`metrics` could be any python object. If users use NNI built-in tuner/assessor, `metrics` can only have two formats: 1) a number e.g., float, int, 2) a dict object that has a key named `default` whose value is a number. This `metrics` is reported to [assessor](BuiltinAssessor.md). Usually, `metrics` could be periodically evaluated loss or accuracy.
+`metrics` could be any python object. If users use NNI built-in tuner/assessor, `metrics` can only have two formats: 1) a number e.g., float, int, 2) a dict object that has a key named `default` whose value is a number. This `metrics` is reported to [assessor](../Assessor/BuiltinAssessor.md). Usually, `metrics` could be periodically evaluated loss or accuracy.
 
 - Report performance of the configuration
 
 ```python
 nni.report_final_result(metrics)
 ```
-`metrics` also could be any python object. If users use NNI built-in tuner/assessor, `metrics` follows the same format rule as that in `report_intermediate_result`, the number indicates the model's performance, for example, the model's accuracy, loss etc. This `metrics` is reported to [tuner](BuiltinTuner.md).
+`metrics` also could be any python object. If users use NNI built-in tuner/assessor, `metrics` follows the same format rule as that in `report_intermediate_result`, the number indicates the model's performance, for example, the model's accuracy, loss etc. This `metrics` is reported to [tuner](../Tuner/BuiltinTuner.md).
 
 ### Step 3 - Enable NNI API
 
@@ -62,7 +62,7 @@ useAnnotation: false
 searchSpacePath: /path/to/your/search_space.json
 ```
 
-You can refer to [here](ExperimentConfig.md) for more information about how to set up experiment configurations.
+You can refer to [here](../Tutorial/ExperimentConfig.md) for more information about how to set up experiment configurations.
 
 *Please refer to [here](https://nni.readthedocs.io/en/latest/sdk_reference.html) for more APIs (e.g., `nni.get_sequence_id()`) provided by NNI.
 
@@ -117,7 +117,7 @@ with tf.Session() as sess:
 - `@nni.variable` will take effect on its following line, which is an assignment statement whose leftvalue must be specified by the keyword `name` in `@nni.variable`.
 - `@nni.report_intermediate_result`/`@nni.report_final_result` will send the data to assessor/tuner at that line.
 
-For more information about annotation syntax and its usage, please refer to [Annotation](AnnotationSpec.md).
+For more information about annotation syntax and its usage, please refer to [Annotation](../Tutorial/AnnotationSpec.md).
 
 
 ### Step 2 - Enable NNI Annotation
@@ -153,7 +153,7 @@ echo $? `date +%s%3N` >/home/user_name/nni/experiments/$experiment_id$/trials/$t
 
 When running trials on other platform like remote machine or PAI, the environment variable `NNI_OUTPUT_DIR` only refers to the output directory of the trial, while trial code and `run.sh` might not be there. However, the `trial.log` will be transmitted back to local machine in trial's directory, which defaults to `~/nni/experiments/$experiment_id$/trials/$trial_id$/`
 
-For more information, please refer to [HowToDebug](HowToDebug.md)
+For more information, please refer to [HowToDebug](../Tutorial/HowToDebug.md)
 
 <a name="more-examples"></a>
 ## More Trial Examples

docs/en_US/BatchTuner.md → docs/en_US/Tuner/BatchTuner.md

@@ -3,6 +3,6 @@ Batch Tuner on NNI
 
 ## Batch Tuner
 
-Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in [search space spec](SearchSpaceSpec.md).
+Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in [search space spec](../Tutorial/SearchSpaceSpec.md).
 
 Suggested scenario: If the configurations you want to try have been decided, you can list them in SearchSpace file (using choice) and run them using batch tuner.

docs/en_US/BohbAdvisor.md → docs/en_US/Tuner/BohbAdvisor.md

@@ -12,7 +12,7 @@ Below we divide introduction of the BOHB process into two parts:
 
 We follow Hyperband’s way of choosing the budgets and continue to use SuccessiveHalving, for more details, you can refer to the [Hyperband in NNI](HyperbandAdvisor.md) and [reference paper of Hyperband](https://arxiv.org/abs/1603.06560). This procedure is summarized by the pseudocode below.
 
-![](../img/bohb_1.png)
+![](../../img/bohb_1.png)
 
 ### BO (Bayesian Optimization)
 
@@ -20,11 +20,11 @@ The BO part of BOHB closely resembles TPE, with one major difference: we opted f
 
 Tree Parzen Estimator(TPE): uses a KDE(kernel density estimator) to model the densities.
 
-![](../img/bohb_2.png)
+![](../../img/bohb_2.png)
 
 To fit useful KDEs, we require a minimum number of data points Nmin; this is set to d + 1 for our experiments, where d is the number of hyperparameters. To build a model as early as possible, we do not wait until Nb = |Db|, the number of observations for budget b, is large enough to satisfy q · Nb ≥ Nmin. Instead, after initializing with Nmin + 2 random configurations, we choose the
 
-![](../img/bohb_3.png)
+![](../../img/bohb_3.png)
 
 best and worst configurations, respectively, to model the two densities.
 
@@ -32,14 +32,14 @@ Note that we alse sample a constant fraction named **random fraction** of the co
 
 ## 2. Workflow
 
-![](../img/bohb_6.jpg)
+![](../../img/bohb_6.jpg)
 
 This image shows the workflow of BOHB. Here we set max_budget = 9, min_budget = 1, eta = 3, others as default. In this case, s_max = 2, so we will continuesly run the {s=2, s=1, s=0, s=2, s=1, s=0, ...} cycle. In each stage of SuccessiveHalving (the orange box), we will pick the top 1/eta configurations and run them again with more budget, repeated SuccessiveHalving stage until the end of this iteration. At the same time, we collect the configurations, budgets and final metrics of each trial, and use this to build a multidimensional KDEmodel with the key "budget".
  Multidimensional KDE is used to guide the selection of configurations for the next iteration.
 
 The way of sampling procedure(use Multidimensional KDE to guide the selection) is summarized by the pseudocode below.
 
-![](../img/bohb_4.png)
+![](../../img/bohb_4.png)
 
 ## 3. Usage
 
@@ -96,6 +96,6 @@ code implementation: [examples/trials/mnist-advisor](https://github.com/Microsof
 
 We chose BOHB to build CNN on the MNIST dataset. The following is our experimental final results:
 
-![](../img/bohb_5.png)
+![](../../img/bohb_5.png)
 
 More experimental result can be found in the [reference paper](https://arxiv.org/abs/1807.01774), we can see that BOHB makes good use of previous results, and has a balance trade-off in exploration and exploitation.
\ No newline at end of file

docs/en_US/BuiltinTuner.md → docs/en_US/Tuner/BuiltinTuner.md

@@ -2,7 +2,7 @@
 
 NNI provides state-of-the-art tuning algorithm as our built-in tuners and makes them easy to use. Below is the brief summary of NNI currently built-in tuners:
 
-Note: Click the **Tuner's name** to get the Tuner's installation requirements, suggested scenario and using example. The link for a detailed description of the algorithm is at the end of the suggested scenario of each tuner. Here is an [article](./CommunitySharings/HpoComparision.md) about the comparison of different Tuners on several problems.
+Note: Click the **Tuner's name** to get the Tuner's installation requirements, suggested scenario and using example. The link for a detailed description of the algorithm is at the end of the suggested scenario of each tuner. Here is an [article](../CommunitySharings/HpoComparision.md) about the comparison of different Tuners on several problems.
 
 Currently we support the following algorithms:
 
@@ -211,7 +211,7 @@ The search space file including the high-level key `combine_params`. The type of
 
 **Suggested scenario**
 
-Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](./SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
+Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](../Tutorial/SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
 
 It is suggested when search space is small, it is feasible to exhaustively sweeping the whole search space. [Detailed Description](./GridsearchTuner.md)
 

docs/en_US/GridsearchTuner.md → docs/en_US/Tuner/GridsearchTuner.md

@@ -3,4 +3,4 @@ Grid Search on NNI
 
 ## Grid Search
 
-Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
+Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](../Tutorial/SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
\ No newline at end of file

docs/en_US/SmacTuner.md → docs/en_US/Tuner/SmacTuner.md

@@ -5,4 +5,4 @@ SMAC Tuner on NNI
 
 [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO, in order to handle categorical parameters. The SMAC supported by nni is a wrapper on [the SMAC3 github repo](https://github.com/automl/SMAC3).
 
-Note that SMAC on nni only supports a subset of the types in [search space spec](SearchSpaceSpec.md), including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`.
+Note that SMAC on nni only supports a subset of the types in [search space spec](../Tutorial/SearchSpaceSpec.md), including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`.
\ No newline at end of file

docs/en_US/AnnotationSpec.md → docs/en_US/Tutorial/AnnotationSpec.md

@@ -84,10 +84,10 @@ h_pooling = max_pool(hidden_layer, pool_size)
 
    `'''@nni.report_intermediate_result(metrics)'''`
 
-`@nni.report_intermediate_result` is used to report intermediate result, whose usage is the same as `nni.report_intermediate_result` in [Trials.md](Trials.md)
+`@nni.report_intermediate_result` is used to report intermediate result, whose usage is the same as `nni.report_intermediate_result` in [Trials.md](../TrialExample/Trials.md)
 
 ### 4. Annotate final result
 
    `'''@nni.report_final_result(metrics)'''`
 
-`@nni.report_final_result` is used to report the final result of the current trial, whose usage is the same as `nni.report_final_result` in [Trials.md](Trials.md)
+`@nni.report_final_result` is used to report the final result of the current trial, whose usage is the same as `nni.report_final_result` in [Trials.md](../TrialExample/Trials.md)

docs/en_US/Contributing.md → docs/en_US/Tutorial/Contributing.md

@@ -6,7 +6,7 @@ Firstly, if you are unsure or afraid of anything, just ask or submit the issue o
 
 However, for those individuals who want a bit more guidance on the best way to contribute to the project, read on. This document will cover all the points we're looking for in your contributions, raising your chances of quickly merging or addressing your contributions.
 
-Looking for a quickstart, get acquainted with our [Get Started](./QuickStart.md) guide.
+Looking for a quickstart, get acquainted with our [Get Started](QuickStart.md) guide.
 
 There are a few simple guidelines that you need to follow before providing your hacks.