V1.0

Makefile

@@ -54,7 +54,7 @@ NNI_NODE_FOLDER = $(NNI_DEPENDENCY_FOLDER)/nni-node-$(OS_SPEC)-x64
 NNI_NODE ?= $(BIN_FOLDER)/node
 NNI_YARN_TARBALL ?= $(NNI_DEPENDENCY_FOLDER)/nni-yarn.tar.gz
 NNI_YARN_FOLDER ?= $(NNI_DEPENDENCY_FOLDER)/nni-yarn
-NNI_YARN := PATH=$(BIN_FOLDER):$${PATH} $(NNI_YARN_FOLDER)/bin/yarn
+NNI_YARN ?= PATH=$(BIN_FOLDER):$${PATH} $(NNI_YARN_FOLDER)/bin/yarn
 
 ## Version number
 NNI_VERSION_VALUE = $(shell git describe --tags)

docs/en_US/TrainingService/SupportTrainingService.md

@@ -0,0 +1,36 @@
+# TrainingService
+
+NNI TrainingService provides the training platform for running NNI trial jobs. NNI supports [local](./LocalMode.md), [remote](./RemoteMachineMode.md), [pai](./PaiMode.md), [kubeflow](./KubeflowMode.md) and [frameworkcontroller](./FrameworkControllerMode.md) built-in training services.   
+NNI not only provides few built-in training service options, but also provides a method for customers to build their own training service easily.
+
+## Built-in TrainingService
+|TrainingService|Brief Introduction|
+|---|---|
+|[__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 enouth 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.|
+
+## TrainingService Implementation
+
+TrainingService is designed to be easily implemented, we define an abstract class TrainingService as the parent class of all kinds of TrainingService, users just need to inherit the parent class and complete their own child class if they want to implement customized TrainingService.  
+The abstract function in TrainingService is shown below:
+```
+abstract class TrainingService {
+    public abstract listTrialJobs(): Promise<TrialJobDetail[]>;
+    public abstract getTrialJob(trialJobId: string): Promise<TrialJobDetail>;
+    public abstract addTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void;
+    public abstract removeTrialJobMetricListener(listener: (metric: TrialJobMetric) => void): void;
+    public abstract submitTrialJob(form: JobApplicationForm): Promise<TrialJobDetail>;
+    public abstract updateTrialJob(trialJobId: string, form: JobApplicationForm): Promise<TrialJobDetail>;
+    public abstract get isMultiPhaseJobSupported(): boolean;
+    public abstract cancelTrialJob(trialJobId: string, isEarlyStopped?: boolean): Promise<void>;
+    public abstract setClusterMetadata(key: string, value: string): Promise<void>;
+    public abstract getClusterMetadata(key: string): Promise<string>;
+    public abstract cleanUp(): Promise<void>;
+    public abstract run(): Promise<void>;
+}
+```
+The parent class of TrainingService has a few abstract functions, users need to inherit the parent class and implement all of these abstract functions.  
+For more information about how to write your own TrainingService, please [refer](https://github.com/SparkSnail/nni/blob/dev-trainingServiceDoc/docs/en_US/TrainingService/HowToImplementTrainingService.md).

docs/en_US/Tutorial/WebUI.md

@@ -50,13 +50,11 @@ Click the tab "Intermediate Result" to see the lines graph.
 
 ![](../../img/webui-img/trials_intermeidate.png)
 
-We set a filter function for the intermediate result graph because that the trials may have many intermediate results in the training progress. You need to provide data if you want to use the filter button to see the trend of some trial.
+The trial may have many intermediate results in the training progress. In order to see the trend of some trials more clearly, we set a filtering function for the intermediate result graph.
 
-What data should be written in the first input? Maybe you find an intermediate count those trials became better or worse. In other word, it's an important and concerned intermediate count. Just input it into the first input.
+You may find that these trials will get better or worse at one of intermediate results. In other words, this is an important and relevant intermediate result. To take a closer look at the point here, you need to enter its corresponding abscissa value at #Intermediate.
 
-After selecting the intermeidate count, you should input your focus metric's range on this intermediate count. Yes, it's the min and max value. Like this picture, I choose the intermeidate count is 9 and the metric's range is 60-80.
-
-As a result, I filter these trials that the metric's range is 20-60 on the 13 intermediate count.
+And then input the range of metrics on this intermedia result. Like below picture, it chooses No. 4 intermediate result and set the range of metrics to 0.8-1.
 
 ![](../../img/webui-img/filter-intermediate.png)
 ## View trials status

src/webui/config/env.js

@@ -0,0 +1,96 @@
+'use strict';
+
+const fs = require('fs');
+const path = require('path');
+const paths = require('./paths');
+
+// Make sure that including paths.js after env.js will read .env variables.
+delete require.cache[require.resolve('./paths')];
+
+const NODE_ENV = process.env.NODE_ENV;
+if (!NODE_ENV) {
+  throw new Error(
+    'The NODE_ENV environment variable is required but was not specified.'
+  );
+}
+
+// https://github.com/bkeepers/dotenv#what-other-env-files-can-i-use
+var dotenvFiles = [
+  `${paths.dotenv}.${NODE_ENV}.local`,
+  `${paths.dotenv}.${NODE_ENV}`,
+  // Don't include `.env.local` for `test` environment
+  // since normally you expect tests to produce the same
+  // results for everyone
+  NODE_ENV !== 'test' && `${paths.dotenv}.local`,
+  paths.dotenv,
+].filter(Boolean);
+
+// Load environment variables from .env* files. Suppress warnings using silent
+// if this file is missing. dotenv will never modify any environment variables
+// that have already been set.  Variable expansion is supported in .env files.
+// https://github.com/motdotla/dotenv
+// https://github.com/motdotla/dotenv-expand
+dotenvFiles.forEach(dotenvFile => {
+  if (fs.existsSync(dotenvFile)) {
+    require('dotenv-expand')(
+      require('dotenv').config({
+        path: dotenvFile,
+      })
+    );
+  }
+});
+
+// We support resolving modules according to `NODE_PATH`.
+// This lets you use absolute paths in imports inside large monorepos:
+// https://github.com/facebookincubator/create-react-app/issues/253.
+// It works similar to `NODE_PATH` in Node itself:
+// https://nodejs.org/api/modules.html#modules_loading_from_the_global_folders
+// Note that unlike in Node, only *relative* paths from `NODE_PATH` are honored.
+// Otherwise, we risk importing Node.js core modules into an app instead of Webpack shims.
+// https://github.com/facebookincubator/create-react-app/issues/1023#issuecomment-265344421
+// We also resolve them to make sure all tools using them work consistently.
+const appDirectory = fs.realpathSync(process.cwd());
+process.env.NODE_PATH = (process.env.NODE_PATH || '')
+  .split(path.delimiter)
+  .filter(folder => folder && !path.isAbsolute(folder))
+  .map(folder => path.resolve(appDirectory, folder))
+  .join(path.delimiter);
+
+// Grab NODE_ENV and REACT_APP_* environment variables and prepare them to be
+// injected into the application via DefinePlugin in Webpack configuration.
+const REACT_APP = /^REACT_APP_/i;
+
+function getClientEnvironment(publicUrl) {
+  const raw = Object.keys(process.env)
+    .filter(key => REACT_APP.test(key))
+    .reduce(
+      (env, key) => {
+        env[key] = process.env[key];
+        return env;
+      },
+      {
+        // Useful for determining whether we’re running in production mode.
+        // Most importantly, it switches React into the correct mode.
+        NODE_ENV: process.env.NODE_ENV || 'development',
+        // Useful for resolving the correct path to static assets in `public`.
+        // For example, <img src={process.env.PUBLIC_URL + '/img/logo.png'} />.
+        // This should only be used as an escape hatch. Normally you would put
+        // images into the `src` and `import` them in code to get their paths.
+        PUBLIC_URL: publicUrl,
+      }
+    );
+  // Stringify all values so we can feed into Webpack DefinePlugin
+  const stringified = {
+    'process.env': Object.keys(raw).reduce(
+      (env, key) => {
+        env[key] = JSON.stringify(raw[key]);
+        return env;
+      },
+      {}
+    ),
+  };
+
+  return { raw, stringified };
+}
+
+module.exports = getClientEnvironment;

src/webui/config/jest/cssTransform.js

@@ -0,0 +1,14 @@
+'use strict';
+
+// This is a custom Jest transformer turning style imports into empty objects.
+// http://facebook.github.io/jest/docs/en/webpack.html
+
+module.exports = {
+  process() {
+    return 'module.exports = {};';
+  },
+  getCacheKey() {
+    // The output is always the same.
+    return 'cssTransform';
+  },
+};

src/webui/config/jest/fileTransform.js

@@ -0,0 +1,12 @@
+'use strict';
+
+const path = require('path');
+
+// This is a custom Jest transformer turning file imports into filenames.
+// http://facebook.github.io/jest/docs/en/webpack.html
+
+module.exports = {
+  process(src, filename) {
+    return `module.exports = ${JSON.stringify(path.basename(filename))};`;
+  },
+};

src/webui/config/jest/typescriptTransform.js

@@ -0,0 +1,7 @@
+// Copyright 2004-present Facebook. All Rights Reserved.
+
+'use strict';
+
+const tsJestPreprocessor = require('ts-jest/preprocessor');
+
+module.exports = tsJestPreprocessor;