Merge pull request #150 from Microsoft/master

merge master

docs/en_US/ExperimentConfig.md

@@ -4,9 +4,10 @@ A config file is needed when create an experiment, the path of the config file i
 The config file is written in YAML format, and need to be written correctly.
 This document describes the rule to write config file, and will provide some examples and templates. 
 
-* [Template](#Template) (the templates of an config file)
-* [Configuration spec](#Configuration) (the configuration specification of every attribute in config file)
-* [Examples](#Examples) (the examples of config file)
+- [Experiment config reference](#experiment-config-reference)
+  - [Template](#template)
+  - [Configuration spec](#configuration-spec)
+  - [Examples](#examples)
 
 <a name="Template"></a>
 ## Template
@@ -205,6 +206,7 @@ machineList:
 
 * __logCollection__
   * Description
+
     __logCollection__ set the way to collect log in remote, pai, kubeflow, frameworkcontroller platform. There are two ways to collect log, one way is from `http`, trial keeper will post log content back from http request in this way, but this way may slow down the speed to process logs in trialKeeper. The other way is `none`, trial keeper will not post log content back, and only post job metrics. If your log content is too big, you could consider setting this param be `none`.
 
 * __tuner__
@@ -215,6 +217,7 @@ machineList:
     * __builtinTunerName__
 
       __builtinTunerName__ specifies the name of system tuner, NNI sdk provides four kinds of tuner, including {__TPE__, __Random__, __Anneal__, __Evolution__, __BatchTuner__, __GridSearch__}
+
     * __classArgs__
 
       __classArgs__ specifies the arguments of tuner algorithm. If the __builtinTunerName__ is in {__TPE__, __Random__, __Anneal__, __Evolution__}, user should set __optimize_mode__.
@@ -573,7 +576,7 @@ machineList:
 
 * __remote mode__
 
-  If run trial jobs in remote machine, users could specify the remote mahcine information as fllowing format:
+  If run trial jobs in remote machine, users could specify the remote machine information as following format:
 
   ```yaml
   authorName: test

docs/en_US/KubeflowMode.md

@@ -69,7 +69,7 @@ kubeflowConfig:
 
 
 ## Run an experiment
-Use `examples/trials/mnist` as an example. This is a tensorflow job, and use tf-operator of kubeflow. The NNI config yml file's content is like: 
+Use `examples/trials/mnist` as an example. This is a tensorflow job, and use tf-operator of kubeflow. The NNI config YAML file's content is like: 
 ```
 authorName: default
 experimentName: example_mnist
@@ -119,9 +119,9 @@ kubeflowConfig:
     path: {your_nfs_server_export_path}
 ```
 
-Note: You should explicitly set `trainingServicePlatform: kubeflow` in NNI config yml file if you want to start experiment in kubeflow mode. 
+Note: You should explicitly set `trainingServicePlatform: kubeflow` in NNI config YAML file if you want to start experiment in kubeflow mode. 
 
-If you want to run Pytorch jobs, you could set your config files as follow:
+If you want to run PyTorch jobs, you could set your config files as follow:
 ```
 authorName: default
 experimentName: example_mnist_distributed_pytorch

docs/en_US/NNICTLDOC.md

@@ -207,8 +207,8 @@ nnictl support commands:
 
   |Name, shorthand|Required|Default|Description|
   |------|------|------ |------|
-  |id|  False| |ID of the experiment you want to set|
-  |--trialid, -t|  True| |ID of the trial you want to kill.|
+  |id|  False| |ID of the trial to be killed|
+  |--experiment, -E|  True| |Experiment id of the trial|
 
 <a name="top"></a>
 * __nnictl top__
@@ -354,7 +354,8 @@ nnictl support commands:
 
   |Name, shorthand|Required|Default|Description|
   |------|------|------ |------|
-  |id| False| |the id of trial|
+  |id|  False| |ID of the trial to be found the log path|
+  |--experiment, -E|  False| |Experiment ID of the trial, required when id is not empty.|
 
 <a name="webui"></a>
 ### Manage webui

docs/en_US/PAIMode.md

@@ -56,9 +56,9 @@ Compared with LocalMode and [RemoteMachineMode](RemoteMachineMode.md), trial con
 * outputDir 
     * Optional key. It specifies the HDFS output directory for trial. Once the trial is completed (either succeed or fail), trial's stdout, stderr will be copied to this directory by NNI sdk automatically. The format should be something like hdfs://{your HDFS host}:9000/{your output directory}
 * virturlCluster
-    * Optional key. Set the virtualCluster of PAI. If omitted, the job will run on default virtual cluster.
+    * Optional key. Set the virtualCluster of OpenPAI. If omitted, the job will run on default virtual cluster.
 * shmMB
-    * Optional key. Set the shmMB configuration of PAI, it set the shared memory for one task in the task role.
+    * Optional key. Set the shmMB configuration of OpenPAI, it set the shared memory for one task in the task role.
 
 Once complete to fill NNI experiment config file and save (for example, save as exp_pai.yml), then run the following command
 ```

docs/en_US/RELEASE.md

@@ -14,8 +14,8 @@
 * Fix search space parsing error when using SMAC tuner.
 * Fix cifar10 example broken pipe issue.
 * Add unit test cases for nnimanager and local training service.
-* Add integration test azure pipelines for remote machine, PAI and kubeflow training services.
-* Support Pylon in PAI webhdfs client.
+* Add integration test azure pipelines for remote machine, OpenPAI and kubeflow training services.
+* Support Pylon in OpenPAI webhdfs client.
 
 
 ## Release 0.5.1 - 1/31/2018
@@ -28,7 +28,7 @@
 
 ### Bug Fixes and Other Changes
 * Fix the bug of installation in python virtualenv, and refactor the installation logic
-* Fix the bug of HDFS access failure on PAI mode after PAI is upgraded. 
+* Fix the bug of HDFS access failure on OpenPAI mode after OpenPAI is upgraded. 
 * Fix the bug that sometimes in-place flushed stdout makes experiment crash
 
 

docs/en_US/cifar10_examples.md

@@ -16,9 +16,9 @@ In this example, we have selected the following common deep learning optimizer:
 
 #### Preparations
 
-This example requires pytorch. Pytorch install package should be chosen based on python version and cuda version.
+This example requires PyTorch. PyTorch install package should be chosen based on python version and cuda version.
 
-Here is an example of the environment python==3.5 and cuda == 8.0, then using the following commands to install [pytorch][2]:
+Here is an example of the environment python==3.5 and cuda == 8.0, then using the following commands to install [PyTorch][2]:
 
 ```bash
 python3 -m pip install http://download.pytorch.org/whl/cu80/torch-0.4.1-cp35-cp35m-linux_x86_64.whl
@@ -81,4 +81,4 @@ nnictl create --config nni/examples/trials/cifar10_pytorch/config.yml
 [6]: https://github.com/Microsoft/nni/blob/master/examples/trials/cifar10_pytorch/config.yml
 [7]: https://github.com/Microsoft/nni/blob/master/examples/trials/cifar10_pytorch/config_pai.yml
 [8]: https://github.com/Microsoft/nni/blob/master/examples/trials/cifar10_pytorch/search_space.json
-[9]: https://github.com/Microsoft/nni/blob/master/examples/trials/cifar10_pytorch/main.py
+[9]: https://github.com/Microsoft/nni/blob/master/examples/trials/cifar10_pytorch/main.py
\ No newline at end of file

docs/en_US/hyperbandAdvisor.md

@@ -10,7 +10,7 @@ Frist, this is an example of how to write an automl algorithm based on MsgDispat
 Second, this implementation fully leverages Hyperband's internal parallelism. More specifically, the next bucket is not started strictly after the current bucket, instead, it starts when there is available resource.
 
 ## 3. Usage
-To use Hyperband, you should add the following spec in your experiment's yml config file:
+To use Hyperband, you should add the following spec in your experiment's YAML config file:
 
 ```
 advisor:

examples/trials/auto-gbdt/config.yml

@@ -9,7 +9,7 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:

examples/trials/auto-gbdt/config_metis.yml

@@ -9,7 +9,7 @@ searchSpacePath: search_space_metis.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: MetisTuner
   classArgs:

examples/trials/auto-gbdt/config_pai.yml

@@ -9,7 +9,7 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:

examples/trials/cifar10_pytorch/config.yml

@@ -9,7 +9,7 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:

examples/trials/cifar10_pytorch/config_pai.yml

@@ -9,7 +9,7 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:

examples/trials/mnist-annotation/config.yml

@@ -8,7 +8,7 @@ trainingServicePlatform: local
 #choice: true, false
 useAnnotation: true
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:

examples/trials/mnist-annotation/config_gpu.yml

@@ -8,7 +8,7 @@ trainingServicePlatform: local
 #choice: true, false
 useAnnotation: true
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:

examples/trials/mnist-annotation/config_kubeflow.yml

@@ -8,7 +8,7 @@ trainingServicePlatform: kubeflow
 #choice: true, false
 useAnnotation: true
 tuner:
-  #choice: TPE, Random, Anneal, Evolution
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   builtinTunerName: TPE
   classArgs:
     #choice: maximize, minimize

examples/trials/mnist-annotation/config_pai.yml

@@ -8,7 +8,7 @@ trainingServicePlatform: pai
 #choice: true, false
 useAnnotation: true
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:

examples/trials/mnist-annotation/config_remote.yml

@@ -8,7 +8,7 @@ trainingServicePlatform: remote
 #choice: true, false
 useAnnotation: true
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs:

examples/trials/mnist-batch-tune-keras/config.yml

@@ -9,7 +9,7 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: BatchTuner
 trial:

examples/trials/mnist-batch-tune-keras/config_pai.yml

@@ -9,7 +9,7 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: BatchTuner
 trial:

examples/trials/mnist-keras/config.yml

@@ -9,7 +9,7 @@ searchSpacePath: search_space.json
 #choice: true, false
 useAnnotation: false
 tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner
   #SMAC (SMAC should be installed through nnictl)
   builtinTunerName: TPE
   classArgs: