pull

README.md

@@ -16,7 +16,7 @@
 
 **NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering</a>, <a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>, <a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a> and <a href="docs/en_US/Compressor/Overview.md">Model Compression</a>.
 
-The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a>, <a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a>, <a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a>, <a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a>, <a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a>, <a href="docs/en_US/TrainingService/DLTSMode.md">DLWorkspace (aka. DLTS)</a> and other cloud options.
+The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a>, <a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a>, <a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a>, <a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a>, <a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a>, <a href="docs/en_US/TrainingService/DLTSMode.md">DLWorkspace (aka. DLTS)</a>, <a href="docs/en_US/TrainingService/AMLMode.md">AML (Azure Machine Learning)</a> and other cloud options.
 
 ## **Who should consider using NNI**
 
@@ -170,6 +170,7 @@ Within the following table, we summarized the current NNI capabilities, we are g
       <ul>
         <li><a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a></li>
         <li><a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a></li>
+        <li><a href="docs/en_US/TrainingService/AMLMode.md">AML(Azure Machine Learning)</a></li>
         <li><b>Kubernetes based services</b></li>
             <ul><li><a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a></li>
             <li><a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a></li>

docs/en_US/CommunitySharings/community_sharings.rst

@@ -9,8 +9,8 @@ In addtion to the official tutorilas and examples, we encourage community contri
 
     NNI in Recommenders <RecommendersSvd>
     Automatically tuning SPTAG with NNI <SptagAutoTune>
-    Neural Architecture Search Comparison <NasComparision>
-    Hyper-parameter Tuning Algorithm Comparsion <HpoComparision>
+    Neural Architecture Search Comparison <NasComparison>
+    Hyper-parameter Tuning Algorithm Comparison <HpoComparison>
     Parallelizing Optimization for TPE <ParallelizingTpeSearch>
     Automatically tune systems with NNI <TuningSystems>
     NNI review article from Zhihu: - By Garvin Li <NNI_AutoFeatureEng>

docs/en_US/Release.md

@@ -139,7 +139,7 @@
 * [BNN Quantizer](https://github.com/microsoft/nni/blob/v1.3/docs/en_US/Compressor/Quantizer.md#bnn-quantizer)
 #### Training Service
 * NFS Support for PAI
-    
+
     Instead of using HDFS as default storage, since OpenPAI v0.11, OpenPAI can have NFS or AzureBlob or other storage as default storage. In this release, NNI extended the support for this recent change made by OpenPAI, and could integrate with OpenPAI v0.11 or later version with various default storage.
 
 * Kubeflow update adoption
@@ -273,11 +273,11 @@
 ### Major Features
 * General NAS programming interface
     * Add `enas-mode`  and `oneshot-mode` for NAS interface: [PR #1201](https://github.com/microsoft/nni/pull/1201#issue-291094510)
-* [Gaussian Process Tuner with Matern kernel](Tuner/GPTuner.md) 
+* [Gaussian Process Tuner with Matern kernel](Tuner/GPTuner.md)
 
 * (deprecated) Multiphase experiment supports
     * Added new training service support for multiphase experiment: PAI mode supports multiphase experiment since v0.9.
-    * Added multiphase capability for the following builtin tuners: 
+    * Added multiphase capability for the following builtin tuners:
         * TPE, Random Search, Anneal, Naïve Evolution, SMAC, Network Morphism, Metis Tuner.
 
 * Web Portal
@@ -326,8 +326,8 @@
 * Fix bug of table entries
 * Nested search space refinement
 * Refine 'randint' type and support lower bound
-* [Comparison of different hyper-parameter tuning algorithm](CommunitySharings/HpoComparision.md)
-* [Comparison of NAS algorithm](CommunitySharings/NasComparision.md)
+* [Comparison of different hyper-parameter tuning algorithm](CommunitySharings/HpoComparison.md)
+* [Comparison of NAS algorithm](CommunitySharings/NasComparison.md)
 * [NNI practice on Recommenders](CommunitySharings/RecommendersSvd.md)
 
 ## Release 0.7 - 4/29/2018

docs/en_US/TrainingService/FrameworkControllerMode.md

@@ -57,7 +57,6 @@ assessor:
   builtinAssessorName: Medianstop
   classArgs:
     optimize_mode: maximize
-  gpuNum: 0
 trial:
   codeDir: ~/nni/examples/trials/mnist-tfv1
   taskRoles:

docs/en_US/TrainingService/KubeflowMode.md

@@ -107,7 +107,6 @@ assessor:
   builtinAssessorName: Medianstop
   classArgs:
     optimize_mode: maximize
-  gpuNum: 0
 trial:
   codeDir: .
   worker:

docs/en_US/TrainingService/Overview.md

@@ -4,7 +4,7 @@
 
 NNI training service is designed to allow users to focus on AutoML itself, agnostic to the underlying computing infrastructure where the trials are actually run. When migrating from one cluster to another (e.g., local machine to Kubeflow), users only need to tweak several configurations, and the experiment can be easily scaled.
 
-Users can use training service provided by NNI, to run trial jobs on [local machine](./LocalMode.md), [remote machines](./RemoteMachineMode.md), and on clusters like [PAI](./PaiMode.md), [Kubeflow](./KubeflowMode.md) and [FrameworkController](./FrameworkControllerMode.md). These are called *built-in training services*.
+Users can use training service provided by NNI, to run trial jobs on [local machine](./LocalMode.md), [remote machines](./RemoteMachineMode.md), and on clusters like [PAI](./PaiMode.md), [Kubeflow](./KubeflowMode.md), [FrameworkController](./FrameworkControllerMode.md), [DLTS](./DLTSMode.md) and [AML](./AMLMode.md). These are called *built-in training services*.
 
 If the computing resource customers try to use is not listed above, NNI provides interface that allows users to build their own training service easily. Please refer to "[how to implement training service](./HowToImplementTrainingService)" for details.
 
@@ -20,12 +20,13 @@ In case users intend to use large files in their experiment (like large-scaled d
 
 |TrainingService|Brief Introduction|
 |---|---|
-|[__Local__](./LocalMode.html)|NNI supports running an experiment on local machine, called local mode. Local mode means that NNI will run the trial jobs and nniManager process in same machine, and support gpu schedule function for trial jobs.|
-|[__Remote__](./RemoteMachineMode.html)|NNI supports running an experiment on multiple machines through SSH channel, called remote mode. NNI assumes that you have access to those machines, and already setup the environment for running deep learning training code. NNI will submit the trial jobs in remote machine, and schedule suitable machine with enough gpu resource if specified.|
-|[__PAI__](./PaiMode.html)|NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai) (aka PAI), called PAI mode. Before starting to use NNI PAI mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In PAI mode, your trial program will run in PAI's container created by Docker.|
-|[__Kubeflow__](./KubeflowMode.html)|NNI supports running experiment on [Kubeflow](https://github.com/kubeflow/kubeflow), called kubeflow mode. Before starting to use NNI kubeflow mode, you should have a Kubernetes cluster, either on-premises or [Azure Kubernetes Service(AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/), a Ubuntu machine on which [kubeconfig](https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/) is setup to connect to your Kubernetes cluster. If you are not familiar with Kubernetes, [here](https://kubernetes.io/docs/tutorials/kubernetes-basics/) is a good start. In kubeflow mode, your trial program will run as Kubeflow job in Kubernetes cluster.|
-|[__FrameworkController__](./FrameworkControllerMode.html)|NNI supports running experiment using [FrameworkController](https://github.com/Microsoft/frameworkcontroller), called frameworkcontroller mode. FrameworkController is built to orchestrate all kinds of applications on Kubernetes, you don't need to install Kubeflow for specific deep learning framework like tf-operator or pytorch-operator. Now you can use FrameworkController as the training service to run NNI experiment.|
-|[__DLTS__](./DLTSMode.html)|NNI supports running experiment using [DLTS](https://github.com/microsoft/DLWorkspace.git), which is an open source toolkit, developed by Microsoft, that allows AI scientists to spin up an AI cluster in turn-key fashion.|
+|[__Local__](./LocalMode.md)|NNI supports running an experiment on local machine, called local mode. Local mode means that NNI will run the trial jobs and nniManager process in same machine, and support gpu schedule function for trial jobs.|
+|[__Remote__](./RemoteMachineMode.md)|NNI supports running an experiment on multiple machines through SSH channel, called remote mode. NNI assumes that you have access to those machines, and already setup the environment for running deep learning training code. NNI will submit the trial jobs in remote machine, and schedule suitable machine with enough gpu resource if specified.|
+|[__PAI__](./PaiMode.md)|NNI supports running an experiment on [OpenPAI](https://github.com/Microsoft/pai) (aka PAI), called PAI mode. Before starting to use NNI PAI mode, you should have an account to access an [OpenPAI](https://github.com/Microsoft/pai) cluster. See [here](https://github.com/Microsoft/pai#how-to-deploy) if you don't have any OpenPAI account and want to deploy an OpenPAI cluster. In PAI mode, your trial program will run in PAI's container created by Docker.|
+|[__Kubeflow__](./KubeflowMode.md)|NNI supports running experiment on [Kubeflow](https://github.com/kubeflow/kubeflow), called kubeflow mode. Before starting to use NNI kubeflow mode, you should have a Kubernetes cluster, either on-premises or [Azure Kubernetes Service(AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/), a Ubuntu machine on which [kubeconfig](https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/) is setup to connect to your Kubernetes cluster. If you are not familiar with Kubernetes, [here](https://kubernetes.io/docs/tutorials/kubernetes-basics/) is a good start. In kubeflow mode, your trial program will run as Kubeflow job in Kubernetes cluster.|
+|[__FrameworkController__](./FrameworkControllerMode.md)|NNI supports running experiment using [FrameworkController](https://github.com/Microsoft/frameworkcontroller), called frameworkcontroller mode. FrameworkController is built to orchestrate all kinds of applications on Kubernetes, you don't need to install Kubeflow for specific deep learning framework like tf-operator or pytorch-operator. Now you can use FrameworkController as the training service to run NNI experiment.|
+|[__DLTS__](./DLTSMode.md)|NNI supports running experiment using [DLTS](https://github.com/microsoft/DLWorkspace.git), which is an open source toolkit, developed by Microsoft, that allows AI scientists to spin up an AI cluster in turn-key fashion.|
+|[__AML__](./AMLMode.md)|NNI supports running an experiment on [AML](https://azure.microsoft.com/en-us/services/machine-learning/) , called aml mode.
 
 ## What does Training Service do?
 

docs/en_US/TrainingService/PaiMode.md

@@ -28,7 +28,6 @@ For example, use the following command:
 ```bash
 sudo mount -t nfs4 gcr-openpai-infra02:/pai/data /local/mnt
 ```
-
 Then the `/data` folder in container will be mounted to `/local/mnt` folder in your local machine.  
 You could use the following configuration in your NNI's config file:
 
@@ -87,7 +86,7 @@ paiConfig:
   reuse: true
 ```
 
-Note: You should set `trainingServicePlatform: pai` in NNI config YAML file if you want to start experiment in pai mode.
+Note: You should set `trainingServicePlatform: pai` in NNI config YAML file if you want to start experiment in pai mode. The host field in configuration file is PAI's job submission page uri, like `10.10.5.1`, the default http protocol in NNI is `http`, if your PAI's cluster enabled https, please use the uri in `https://10.10.5.1` format.
 
 ### Trial configurations
 

docs/en_US/Tuner/BuiltinTuner.md

@@ -2,7 +2,7 @@
 
 NNI provides state-of-the-art tuning algorithms as part of our built-in tuners and makes them easy to use. Below is the brief summary of NNI's current built-in tuners:
 
-Note: Click the **Tuner's name** to get the Tuner's installation requirements, suggested scenario, and an example configuration. A link for a detailed description of each algorithm is located at the end of the suggested scenario for each tuner. Here is an [article](../CommunitySharings/HpoComparision.md) comparing different Tuners on several problems.
+Note: Click the **Tuner's name** to get the Tuner's installation requirements, suggested scenario, and an example configuration. A link for a detailed description of each algorithm is located at the end of the suggested scenario for each tuner. Here is an [article](../CommunitySharings/HpoComparison.md) comparing different Tuners on several problems.
 
 Currently, we support the following algorithms:
 
@@ -218,7 +218,7 @@ The search space file should include the high-level key `combine_params`. The ty
 
 **Suggested scenario**
 
-Note that the only acceptable types within the search space are `choice`, `quniform`, and `randint`. 
+Note that the only acceptable types within the search space are `choice`, `quniform`, and `randint`.
 
 This is suggested when the search space is small. It's suggested when it is feasible to exhaustively sweep the whole search space. [Detailed Description](./GridsearchTuner.md)
 
@@ -388,7 +388,7 @@ As a strategy in a Sequential Model-based Global Optimization (SMBO) algorithm,
 **classArgs Requirements:**
 
 * **optimize_mode** (*'maximize' or 'minimize', optional, default = 'maximize'*) - If 'maximize', the tuner will try to maximize metrics. If 'minimize', the tuner will try to minimize metrics.
-* **utility** (*'ei', 'ucb' or 'poi', optional, default = 'ei'*) - The utility function (acquisition function). 'ei', 'ucb', and 'poi' correspond to 'Expected Improvement', 'Upper Confidence Bound', and 'Probability of Improvement', respectively. 
+* **utility** (*'ei', 'ucb' or 'poi', optional, default = 'ei'*) - The utility function (acquisition function). 'ei', 'ucb', and 'poi' correspond to 'Expected Improvement', 'Upper Confidence Bound', and 'Probability of Improvement', respectively.
 * **kappa** (*float, optional, default = 5*) - Used by the 'ucb' utility function. The bigger `kappa` is, the more exploratory the tuner will be.
 * **xi** (*float, optional, default = 0*) - Used by the 'ei' and 'poi' utility functions. The bigger `xi` is, the more exploratory the tuner will be.
 * **nu** (*float, optional, default = 2.5*) - Used to specify the Matern kernel. The smaller nu, the less smooth the approximated function is.

docs/en_US/Tutorial/InstallCustomizedAlgos.md

@@ -77,7 +77,7 @@ Once you have the meta info in `setup.py`, you can build your pip installation s
 
 NNI will look for the classifier starts with `NNI Package` to retrieve the package meta information while the package being installed with `nnictl package install <source>` command.
 
-Reference [customized tuner example](https://github.com/microsoft/nni/blob/master/examples/tuners/customized_tuner/README.md) for a full example.
+Reference [customized tuner example](../Tuner/InstallCustomizedTuner.md) for a full example.
 
 ### 4. Install customized algorithms package into NNI