pull code

Makefile

@@ -12,10 +12,12 @@ _END := $(shell echo -e '\033[0m')
 UNAME_S := $(shell uname -s)
 ifeq ($(UNAME_S), Linux)
     OS_SPEC := linux
+    NODE_URL := https://nodejs.org/dist/v10.22.1/node-v10.22.1-linux-x64.tar.xz
 else ifeq ($(UNAME_S), Darwin)
     OS_SPEC := darwin
+    NODE_URL := https://nodejs.org/dist/v10.22.1/node-v10.22.1-darwin-x64.tar.xz
 else
-	$(error platform $(UNAME_S) not supported)
+    $(error platform $(UNAME_S) not supported)
 endif
 
 ## Install directories
@@ -143,11 +145,11 @@ clean:
 
 $(NNI_NODE_TARBALL):
 	#$(_INFO) Downloading Node.js $(_END)
-	wget https://aka.ms/nni/nodejs-download/$(OS_SPEC) -O $(NNI_NODE_TARBALL)
+	wget $(NODE_URL) -O $(NNI_NODE_TARBALL)
 
 $(NNI_YARN_TARBALL):
 	#$(_INFO) Downloading Yarn $(_END)
-	wget https://aka.ms/yarn-download -O $(NNI_YARN_TARBALL)
+	wget https://github.com/yarnpkg/yarn/releases/download/v1.22.5/yarn-v1.22.5.tar.gz -O $(NNI_YARN_TARBALL)
 
 .PHONY: install-dependencies
 install-dependencies: $(NNI_NODE_TARBALL) $(NNI_YARN_TARBALL)

azure-pipelines.yml

@@ -27,6 +27,7 @@ jobs:
   - script: |
       set -e
       sudo apt-get install -y pandoc
+      python3 -m pip install pygments --user --upgrade
       python3 -m pip install torch==1.5.0+cpu torchvision==0.6.0+cpu -f https://download.pytorch.org/whl/torch_stable.html --user
       python3 -m pip install tensorflow==2.2.0 --user
       python3 -m pip install keras==2.4.2 --user
@@ -57,7 +58,7 @@ jobs:
   - script: |
       set -e
       python3 -m pip install --upgrade pip setuptools --user
-      python3 -m pip install pylint==2.3.1 astroid==2.2.5 --user
+      python3 -m pip install pylint==2.6.0 astroid==2.4.2 --user
       python3 -m pip install coverage --user
       python3 -m pip install thop --user
       echo "##vso[task.setvariable variable=PATH]${HOME}/.local/bin:${PATH}"

deployment/pypi/Makefile

@@ -4,9 +4,11 @@ UNAME_S := $(shell uname -s)
 ifeq ($(UNAME_S), Linux)
 	OS_SPEC := linux
 	WHEEL_SPEC := manylinux1_x86_64
+	NODE_URL := https://nodejs.org/dist/v10.22.1/node-v10.22.1-linux-x64.tar.xz
 else ifeq ($(UNAME_S), Darwin)
 	OS_SPEC := darwin
 	WHEEL_SPEC := macosx_10_9_x86_64
+	NODE_URL := https://nodejs.org/dist/v10.22.1/node-v10.22.1-darwin-x64.tar.xz
 else
 	$(error platform $(UNAME_S) not supported)
 endif
@@ -28,11 +30,11 @@ NNI_YARN := PATH=$(CWD)node-$(OS_SPEC)-x64/bin:$${PATH} $(NNI_YARN_FOLDER)/bin/y
 build:
 	# Building version $(NNI_VERSION_VALUE)
 	python3 -m pip install --user --upgrade setuptools wheel
-	wget -q https://aka.ms/nni/nodejs-download/$(OS_SPEC) -O $(CWD)node-$(OS_SPEC)-x64.tar.xz
+	wget -q $(NODE_URL) -O $(CWD)node-$(OS_SPEC)-x64.tar.xz
 	rm -rf $(CWD)node-$(OS_SPEC)-x64
 	mkdir $(CWD)node-$(OS_SPEC)-x64
 	tar xf $(CWD)node-$(OS_SPEC)-x64.tar.xz -C node-$(OS_SPEC)-x64 --strip-components 1
-	wget -q https://aka.ms/yarn-download -O $(NNI_YARN_TARBALL)
+	wget -q https://github.com/yarnpkg/yarn/releases/download/v1.22.5/yarn-v1.22.5.tar.gz -O $(NNI_YARN_TARBALL)
 	rm -rf $(NNI_YARN_FOLDER)
 	mkdir $(NNI_YARN_FOLDER)
 	tar -xf $(NNI_YARN_TARBALL) -C $(NNI_YARN_FOLDER) --strip-components 1

deployment/pypi/README.md

@@ -11,7 +11,7 @@ This is the PyPI build and upload tool for NNI project.
   Ubuntu 16.04 LTS
   make
   wget
-  Python >= 3.5
+  Python >= 3.6
   Pip
   Node.js
   Yarn
@@ -45,7 +45,7 @@ This is the PyPI build and upload tool for NNI project.
   ```
   Windows 10
   powershell
-  Python >= 3.5
+  Python >= 3.6
   Pip
   Yarn
   ```

deployment/pypi/install.ps1

@@ -7,10 +7,12 @@ $OS_SPEC = "windows"
 if($version_os -eq 64){
     $OS_VERSION = 'win64'
     $WHEEL_SPEC = 'win_amd64'
+    $NODE_URL = 'https://nodejs.org/download/release/v10.22.1/node-v10.22.1-win-x64.zip'
 }
 else{
     $OS_VERSION = 'win32'
     $WHEEL_SPEC = 'win32'
+    $NODE_URL = 'https://nodejs.org/download/release/v10.22.1/node-v10.22.1-win-x86.zip'
 }
 
 $TIME_STAMP = date -u "+%y%m%d%H%M"
@@ -28,11 +30,10 @@ $NNI_VERSION_TEMPLATE = "999.0.0-developing"
 
 python -m pip install --upgrade setuptools wheel
 
-$nodeUrl = "https://aka.ms/nni/nodejs-download/" + $OS_VERSION
 $NNI_NODE_ZIP = "$CWD\node-$OS_SPEC.zip"
 $NNI_NODE_FOLDER = "$CWD\node-$OS_SPEC"
 $unzipNodeDir = "node-v*"
-(New-Object Net.WebClient).DownloadFile($nodeUrl, $NNI_NODE_ZIP)
+(New-Object Net.WebClient).DownloadFile($NODE_URL, $NNI_NODE_ZIP)
 if(Test-Path $NNI_NODE_FOLDER){
     Remove-Item $NNI_NODE_FOLDER -Recurse -Force
 }

docs/en_US/CommunitySharings/AutoCompletion.md

@@ -0,0 +1,39 @@
+# Auto Completion for nnictl Commands
+
+NNI's command line tool __nnictl__ support auto-completion, i.e., you can complete a nnictl command by pressing the `tab` key.
+
+For example, if the current command is
+```
+nnictl cre
+```
+By pressing the `tab` key, it will be completed to
+```
+nnictl create
+```
+
+For now, auto-completion will not be enabled by default if you install NNI through `pip`, and it only works on Linux with bash shell. If you want to enable this feature on your computer, please refer to the following steps:
+
+### Step 1. Download `bash-completion`
+```
+cd ~
+wget https://raw.githubusercontent.com/microsoft/nni/{nni-version}/tools/bash-completion
+```
+Here, {nni-version} should by replaced by the version of NNI, e.g., `master`, `v1.9`. You can also check the latest `bash-completion` script [here](https://github.com/microsoft/nni/blob/master/tools/bash-completion).
+
+### Step 2. Install the script
+If you are running a root account and want to install this script for all the users
+```
+install -m644 ~/bash-completion /usr/share/bash-completion/completions/nnictl
+```
+If you just want to install this script for your self
+```
+mkdir -p ~/.bash_completion.d
+install -m644 ~/bash-completion ~/.bash_completion.d/nnictl
+echo '[[ -f ~/.bash_completion.d/nnictl ]] && source ~/.bash_completion.d/nnictl' >> ~/.bash_completion
+```
+
+### Step 3. Reopen your terminal
+Reopen your terminal and you should be able to use the auto-completion feature. Enjoy!
+
+### Step 4. Uninstall
+If you want to uninstall this feature, just revert the changes in the steps above.

docs/en_US/CommunitySharings/community_sharings.rst

@@ -13,3 +13,4 @@ Different from the tutorials and examples in the rest of the document which show
     Feature Engineering <feature_engineering>
     Performance measurement, comparison and analysis <perf_compare>
     Use NNI on Google Colab <NNI_colab_support>
+    Auto Completion for nnictl Commands <AutoCompletion>

docs/en_US/Compressor/QuickStart.md

@@ -123,7 +123,7 @@ It means following the algorithm's default setting for compressed operations wit
 
 #### Quantization specific keys
 
-**If you use quantization algorithms, you need to specify more keys. If you use pruning algorithms, you can safely skip these keys**
+Besides the keys explained above, if you use quantization algorithms you need to specify more keys in `config_list`, which are explained below.
 
 * __quant_types__ : list of string. 
 
@@ -148,6 +148,31 @@ when the value is int type, all quantization types share same bits length. eg.
 }
 ```
 
+The following example shows a more complete `config_list`, it uses `op_names` (or `op_types`) to specify the target layers along with the quantization bits for those layers.
+```
+configure_list = [{
+        'quant_types': ['weight'],        
+        'quant_bits': 8, 
+        'op_names': ['conv1']
+    }, {
+        'quant_types': ['weight'],
+        'quant_bits': 4,
+        'quant_start_step': 0,
+        'op_names': ['conv2']
+    }, {
+        'quant_types': ['weight'],
+        'quant_bits': 3,
+        'op_names': ['fc1']
+        },
+       {
+        'quant_types': ['weight'],
+        'quant_bits': 2,
+        'op_names': ['fc2']
+        }
+]
+```
+In this example, 'op_names' is the name of layer and four layers will be quantized to different quant_bits.
+
 ### APIs for Updating Fine Tuning Status
 
 Some compression algorithms use epochs to control the progress of compression (e.g. [AGP](https://nni.readthedocs.io/en/latest/Compressor/Pruner.html#agp-pruner)), and some algorithms need to do something after every minibatch. Therefore, we provide another two APIs for users to invoke: `pruner.update_epoch(epoch)` and `pruner.step()`.
@@ -168,4 +193,4 @@ pruner.export_model(model_path='model.pth')
 pruner.export_model(model_path='model.pth', mask_path='mask.pth', onnx_path='model.onnx', input_shape=[1, 1, 28, 28])
 ```
 
-If you want to really speed up the compressed model, please refer to [NNI model speedup](./ModelSpeedup.md) for details.
+If you want to really speed up the compressed model, please refer to [NNI model speedup](./ModelSpeedup.md) for details.

docs/en_US/Overview.md

@@ -10,7 +10,7 @@ NNI (Neural Network Intelligence) is a toolkit to help users design and tune mac
 The figure below shows high-level architecture of NNI.
 
 <p align="center">
-<img src="https://user-images.githubusercontent.com/23273522/51816536-ed055580-2301-11e9-8ad8-605a79ee1b9a.png" alt="drawing" width="700"/>
+<img src="https://user-images.githubusercontent.com/16907603/92089316-94147200-ee00-11ea-9944-bf3c4544257f.png" alt="drawing" width="700"/>
 </p>
 
 ## Key Concepts
@@ -86,4 +86,4 @@ The auto-feature-engineering algorithms usually have a bunch of hyperparameters
 * [Examples](TrialExample/MnistExamples.md)
 * [Neural Architecture Search on NNI](NAS/Overview.md)
 * [Automatic model compression on NNI](Compressor/Overview.md)
-* [Automatic feature engineering on NNI](FeatureEngineering/Overview.md)
+* [Automatic feature engineering on NNI](FeatureEngineering/Overview.md)

docs/en_US/Tuner/BuiltinTuner.md

@@ -1,6 +1,6 @@
-# Built-in Tuners for Hyperparameter Tuning
+# HyperParameter Tuning with NNI Built-in Tuners
 
-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:
+To fit a machine/deep learning model into different tasks/problems, hyperparameters always need to be tuned. Automating the process of hyperparaeter tuning always requires a good tuning algorithm. NNI has provided 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/HpoComparison.md) comparing different Tuners on several problems.
 

docs/en_US/Tutorial/ExperimentConfig.md

@@ -573,7 +573,7 @@ Used to specify designated GPU devices for NNI, if it is set, only the specified
 
 #### maxTrialNumPerGpu
 
-Optional. Integer. Default: 99999.
+Optional. Integer. Default: 1.
 
 Used to specify the max concurrency trial number on a GPU device.
 

docs/en_US/Tutorial/Nnictl.md

@@ -295,7 +295,7 @@ Debug mode will disable version check function in Trialkeeper.
     > update experiment's trial num
 
     ```bash
-    nnictl update trialnum --id [experiment_id] --value [trial_num]
+    nnictl update trialnum [experiment_id] --value [trial_num]
     ```
 
 <a name="trial"></a>
@@ -347,7 +347,7 @@ Debug mode will disable version check function in Trialkeeper.
     > kill trail job
 
     ```bash
-    nnictl trial [trial_id] --experiment [experiment_id]
+    nnictl trial kill [experiment_id] --trial_id [trial_id]
     ```
 
 <a name="top"></a>
@@ -704,6 +704,21 @@ Debug mode will disable version check function in Trialkeeper.
 ### Manage webui
 
 * __nnictl webui url__
+  * Description
+
+    Show an experiment's webui url
+
+  * Usage
+
+    ```bash
+    nnictl webui url [options]
+    ```
+
+  * Options
+
+  |Name, shorthand|Required|Default|Description|
+  |------|------|------ |------|
+  |id|  False| |Experiment ID|
 
 <a name="tensorboard"></a>
 ### Manage tensorboard

docs/en_US/Tutorial/QuickStart.md

@@ -27,6 +27,9 @@ python -m pip install --upgrade nni
 ```eval_rst
 .. Note:: For the system requirements of NNI, please refer to :doc:`Install NNI on Linux & Mac <InstallationLinux>` or :doc:`Windows <InstallationWin>`.
 ```
+### Enable NNI Command-line Auto-Completion (Optional)
+
+After the installation, you may want to enable the auto-completion feature for __nnictl__ commands. Please refer to this [tutorial](../CommunitySharings/AutoCompletion.md).
 
 ## "Hello World" example on MNIST
 

examples/model_compress/model_prune_tf.py

@@ -28,31 +28,21 @@ def get_dataset(dataset_name='mnist'):
 
 def create_model(model_name='naive'):
     assert model_name == 'naive'
-    return NaiveModel()
-
-class NaiveModel(tf.keras.Model):
-    def __init__(self):
-        super().__init__()
-        self.seq_layers = [
-            tf.keras.layers.Conv2D(filters=20, kernel_size=5),
-            tf.keras.layers.BatchNormalization(),
-            tf.keras.layers.ReLU(),
-            tf.keras.layers.MaxPool2D(pool_size=2),
-            tf.keras.layers.Conv2D(filters=20, kernel_size=5),
-            tf.keras.layers.BatchNormalization(),
-            tf.keras.layers.ReLU(),
-            tf.keras.layers.MaxPool2D(pool_size=2),
-            tf.keras.layers.Flatten(),
-            tf.keras.layers.Dense(units=500),
-            tf.keras.layers.ReLU(),
-            tf.keras.layers.Dense(units=10),
-            tf.keras.layers.Softmax()
-        ]
-
-    def call(self, x):
-        for layer in self.seq_layers:
-            x = layer(x)
-        return x
+    return tf.keras.Sequential([
+        tf.keras.layers.Conv2D(filters=20, kernel_size=5),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.ReLU(),
+        tf.keras.layers.MaxPool2D(pool_size=2),
+        tf.keras.layers.Conv2D(filters=20, kernel_size=5),
+        tf.keras.layers.BatchNormalization(),
+        tf.keras.layers.ReLU(),
+        tf.keras.layers.MaxPool2D(pool_size=2),
+        tf.keras.layers.Flatten(),
+        tf.keras.layers.Dense(units=500),
+        tf.keras.layers.ReLU(),
+        tf.keras.layers.Dense(units=10),
+        tf.keras.layers.Softmax()
+    ])
 
 
 def create_pruner(model, pruner_name):

install.ps1

@@ -7,13 +7,14 @@ $install_yarn = $true
 
 if ([Environment]::Is64BitOperatingSystem) {
     $OS_VERSION = 'win64'
+    $nodeUrl = "https://nodejs.org/download/release/v10.22.1/node-v10.22.1-win-x64.zip"
 }
 else {
     $OS_VERSION = 'win32'
+    $nodeUrl = "https://nodejs.org/download/release/v10.22.1/node-v10.22.1-win-x86.zip"
 }
 # nodejs
-$nodeUrl = "https://aka.ms/nni/nodejs-download/" + $OS_VERSION
-$yarnUrl = "https://yarnpkg.com/latest.tar.gz"
+$yarnUrl = "https://github.com/yarnpkg/yarn/releases/download/v1.22.5/yarn-v1.22.5.tar.gz"
 $unzipNodeDir = "node-v*"
 $unzipYarnDir = "yarn-v*"
 

src/nni_manager/package.json

@@ -26,7 +26,7 @@
     "stream-buffers": "^3.0.2",
     "tail-stream": "^0.3.4",
     "tar": "^6.0.2",
-    "tree-kill": "^1.2.0",
+    "tree-kill": "^1.2.2",
     "ts-deferred": "^1.0.4",
     "typescript-ioc": "^1.2.4",
     "typescript-string-operations": "^1.3.1",

src/nni_manager/training_service/reusable/aml/amlClient.ts

@@ -41,6 +41,7 @@ export class AMLClient {
         const deferred: Deferred<string> = new Deferred<string>();
         this.pythonShellClient = new PythonShell('amlUtil.py', {
             scriptPath: './config/aml',
+            pythonPath: process.platform === 'win32' ? 'python' : 'python3',
             pythonOptions: ['-u'], // get print results in real-time
             args: [
                 '--subscription_id', this.subscriptionId,

src/nni_manager/training_service/reusable/commandChannel.ts

@@ -65,8 +65,8 @@ export abstract class CommandChannel {
     protected abstract sendCommandInternal(environment: EnvironmentInformation, message: string): Promise<void>;
     protected abstract createRunnerConnection(environment: EnvironmentInformation): RunnerConnection;
 
-    public async sendCommand(environment: EnvironmentInformation, commantType: string, data: any): Promise<void> {
-        const command = encodeCommand(commantType, JSON.stringify(data));
+    public async sendCommand(environment: EnvironmentInformation, commandType: string, data: any): Promise<void> {
+        const command = encodeCommand(commandType, JSON.stringify(data));
         this.log.debug(`CommandChannel: env ${environment.id} sending command: ${command}`);
         await this.sendCommandInternal(environment, command.toString("utf8"));
     }