merge V1.6 back

Makefile

@@ -60,6 +60,7 @@ NNI_YARN ?= PATH=$(BIN_FOLDER):$${PATH} $(NNI_YARN_FOLDER)/bin/yarn
 
 ## Version number
 NNI_VERSION_VALUE = $(shell git describe --tags)
+NNI_VERSION_VALUE := $(NNI_VERSION_VALUE:v%=%)
 NNI_VERSION_TEMPLATE = 999.0.0-developing
 
 # Main targets

README.md

@@ -25,7 +25,7 @@ The tool manages automated machine learning (AutoML) experiments, **dispatches a
 * Researchers and data scientists who want to easily **implement and experiment new AutoML algorithms**, may it be: hyperparameter tuning algorithm, neural architect search algorithm or model compression algorithm.
 * ML Platform owners who want to **support AutoML in their platform**.
 
-### **[NNI v1.5 has been released!](https://github.com/microsoft/nni/releases) &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
+### **[NNI v1.6 has been released!](https://github.com/microsoft/nni/releases) &nbsp;<a href="#nni-released-reminder"><img width="48" src="docs/img/release_icon.png"></a>**
 
 ## **NNI capabilities in a glance**
 
@@ -239,7 +239,7 @@ The following example is built on TensorFlow 1.x. Make sure **TensorFlow 1.x is
 * Download the examples via clone the source code.
 
   ```bash
-  git clone -b v1.5 https://github.com/Microsoft/nni.git
+  git clone -b v1.6 https://github.com/Microsoft/nni.git
   ```
 
 * Run the MNIST example.

deployment/pypi/Makefile

@@ -13,6 +13,7 @@ endif
 
 TIME_STAMP = $(shell date -u "+%y%m%d%H%M")
 NNI_VERSION_VALUE = $(shell git describe --tags --abbrev=0)
+NNI_VERSION_VALUE := $(NNI_VERSION_VALUE:v%=%)
 
 # To include time stamp in version value, run:
 # make version_ts=true build
@@ -25,6 +26,7 @@ NNI_YARN_FOLDER ?= $(CWD)nni-yarn
 NNI_YARN := PATH=$(CWD)node-$(OS_SPEC)-x64/bin:$${PATH} $(NNI_YARN_FOLDER)/bin/yarn
 .PHONY: build
 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
 	rm -rf $(CWD)node-$(OS_SPEC)-x64

deployment/pypi/install.ps1

@@ -15,6 +15,7 @@ else{
 
 $TIME_STAMP = date -u "+%y%m%d%H%M"
 $NNI_VERSION_VALUE = git describe --tags --abbrev=0
+$NNI_VERSION_VALUE = $NNI_VERSION_VALUE.substring(1)
 
 # To include time stamp in version value, run:
 # make version_ts=true build

docs/en_US/Release.md

@@ -1,5 +1,46 @@
 # ChangeLog
 
+## Release 1.6 - 5/26/2020
+
+### Major Features
+
+#### New Features and improvement
+* Improve IPC limitation to 100W
+* improve code storage upload logic among trials in non-local platform
+* support `__version__` for SDK version
+* support windows dev intall
+
+#### Web UI
+* Show trial error message
+* finalize homepage layout
+* Refactor overview's best trials module
+* Remove multiphase from webui
+* add tooltip for trial concurrency in the overview page
+* Show top trials for hyper-parameter graph
+
+#### HPO Updates
+* Improve PBT on failure handling and support experiment resume for PBT
+
+#### NAS Updates
+* NAS support for TensorFlow 2.0 (preview) [TF2.0 NAS examples](https://github.com/microsoft/nni/tree/master/examples/nas/naive-tf)
+* Use OrderedDict for LayerChoice
+* Prettify the format of export
+* Replace layer choice with selected module after applied fixed architecture
+
+#### Model Compression Updates
+* Model compression PyTorch 1.4 support
+
+#### Training Service Updates
+* update pai yaml merge logic
+* support windows as remote machine in remote mode [Remote Mode](https://github.com/microsoft/nni/blob/master/docs/en_US/TrainingService/RemoteMachineMode.md#windows)
+
+### Bug Fix
+* fix dev install
+* SPOS example crash when the checkpoints do not have state_dict
+* Fix table sort issue when experiment had failed trial
+* Support multi python env (conda, pyenv etc)
+
+
 ## Release 1.5 - 4/13/2020
 
 ### New Features and Documentation

examples/nas/enas-tf/datasets.py

@@ -2,7 +2,6 @@
 # Licensed under the MIT license.
 
 import tensorflow as tf
-from tensorflow.data import Dataset
 
 def get_dataset():
     (x_train, y_train), (x_valid, y_valid) = tf.keras.datasets.cifar10.load_data()

src/nni_manager/core/nnimanager.ts

@@ -566,7 +566,7 @@ class NNIManager implements Manager {
             assert(this.status.status === 'RUNNING' ||
                 this.status.status === 'DONE' ||
                 this.status.status === 'NO_MORE_TRIAL' ||
-                this.status.status === 'TUNER_NO_MORE_TRIAL');
+                this.status.status === 'TUNER_NO_MORE_TRIAL', `Actual status: ${this.status.status}`);
             if (this.experimentProfile.execDuration > this.experimentProfile.params.maxExecDuration ||
                 this.currSubmittedTrialNum >= this.experimentProfile.params.maxTrialNum) {
                 if (this.status.status !== 'DONE') {

src/nni_manager/training_service/kubernetes/kubernetesData.ts

@@ -47,10 +47,10 @@ export NNI_EXP_ID={4}
 export NNI_CODE_DIR={5}
 export NNI_TRIAL_SEQ_ID={6}
 {7}
-mkdir -p $NNI_SYS_DIR
+mkdir -p $NNI_SYS_DIR/code
 mkdir -p $NNI_OUTPUT_DIR
-cp -r $NNI_CODE_DIR/. $NNI_SYS_DIR
-cd $NNI_SYS_DIR
-sh install_nni.sh
+cp -r $NNI_CODE_DIR/. $NNI_SYS_DIR/code
+sh $NNI_SYS_DIR/install_nni.sh
+cd $NNI_SYS_DIR/code
 python3 -m nni_trial_tool.trial_keeper --trial_command '{8}' --nnimanager_ip {9} --nnimanager_port {10} \
 --nni_manager_version '{11}' --log_collection '{12}' 1>$NNI_OUTPUT_DIR/trialkeeper_stdout 2>$NNI_OUTPUT_DIR/trialkeeper_stderr`;

src/nni_manager/training_service/local/localTrainingService.ts

@@ -477,16 +477,14 @@ class LocalTrainingService implements TrainingService {
     private getScript(localTrialConfig: TrialConfig, workingDirectory: string): string[] {
         const script: string[] = [];
         if (process.platform === 'win32') {
-            script.push(`Copy-Item $env:NNI_CODE_DIR\\* -Destination $env:NNI_SYS_DIR -Recurse`);
-            script.push(`cd $env:NNI_SYS_DIR`);
+            script.push(`cd $env:NNI_CODE_DIR`);
             script.push(
                 `cmd.exe /c ${localTrialConfig.command} 2>"${path.join(workingDirectory, 'stderr')}"`,
                 `$NOW_DATE = [int64](([datetime]::UtcNow)-(get-date "1/1/1970")).TotalSeconds`,
                 `$NOW_DATE = "$NOW_DATE" + (Get-Date -Format fff).ToString()`,
                 `Write $LASTEXITCODE " " $NOW_DATE  | Out-File "${path.join(workingDirectory, '.nni', 'state')}" -NoNewline -encoding utf8`);
         } else {
-            script.push(`cp -r $NNI_CODE_DIR/. $NNI_SYS_DIR`);
-            script.push(`cd $NNI_SYS_DIR`);
+            script.push(`cd $NNI_CODE_DIR`);
             script.push(`eval ${localTrialConfig.command} 2>"${path.join(workingDirectory, 'stderr')}"`);
             if (process.platform === 'darwin') {
                 // https://superuser.com/questions/599072/how-to-get-bash-execution-time-in-milliseconds-under-mac-os-x

src/nni_manager/training_service/pai/paiK8S/paiK8SData.ts

@@ -31,6 +31,6 @@ fi`;
 
 export const PAI_K8S_TRIAL_COMMAND_FORMAT: string =
 `export NNI_PLATFORM=pai NNI_SYS_DIR={0} NNI_OUTPUT_DIR={1} NNI_TRIAL_JOB_ID={2} NNI_EXP_ID={3} NNI_TRIAL_SEQ_ID={4} MULTI_PHASE={5} \
-&& NNI_CODE_DIR={6} && cp -r $NNI_CODE_DIR/. $NNI_SYS_DIR && cd $NNI_SYS_DIR && sh install_nni.sh \
-&& python3 -m nni_trial_tool.trial_keeper --trial_command '{7}' --nnimanager_ip '{8}' --nnimanager_port '{9}' \
+&& NNI_CODE_DIR={6} && mkdir -p $NNI_SYS_DIR/code && cp -r $NNI_CODE_DIR/. $NNI_SYS_DIR/code && sh $NNI_SYS_DIR/install_nni.sh \
+&& cd $NNI_SYS_DIR/code && python3 -m nni_trial_tool.trial_keeper --trial_command '{7}' --nnimanager_ip '{8}' --nnimanager_port '{9}' \
 --nni_manager_version '{10}' --log_collection '{11}'`;

src/nni_manager/training_service/pai/paiK8S/paiK8STrainingService.ts

@@ -54,7 +54,7 @@ const yaml = require('js-yaml');
 class PAIK8STrainingService extends PAITrainingService {
     protected paiTrialConfig: NNIPAIK8STrialConfig | undefined;
     private copyExpCodeDirPromise?: Promise<void>;
-    private paiJobConfig: undefined;
+    private paiJobConfig: any;
     private nniVersion: string | undefined;
     constructor() {
         super();
@@ -190,7 +190,7 @@ class PAIK8STrainingService extends PAITrainingService {
 
         let nniJobConfig: any = undefined;
         if (this.paiTrialConfig.paiConfigPath) {
-            nniJobConfig = this.paiJobConfig;
+            nniJobConfig = JSON.parse(JSON.stringify(this.paiJobConfig)); //Trick for deep clone in Typescript
             nniJobConfig.name = jobName;
             // Each taskRole will generate new command in NNI's command format
             // Each command will be formatted to NNI style
@@ -290,8 +290,6 @@ class PAIK8STrainingService extends PAITrainingService {
             await this.writeParameterFile(trialJobDetail.logPath, trialJobDetail.form.hyperParameters);
         }
 
-        //Copy codeDir files to local working folder
-        await execCopydir(this.paiTrialConfig.codeDir, trialJobDetail.logPath);
         //Generate Job Configuration in yaml format
         const paiJobConfig = this.generateJobConfigInYamlFormat(trialJobDetail);
         this.log.debug(paiJobConfig);

src/nni_manager/training_service/remote_machine/extends/linuxCommands.ts

@@ -22,10 +22,10 @@ class LinuxCommands extends OsCommands {
             export NNI_PLATFORM=remote NNI_SYS_DIR=${workingDirectory} NNI_OUTPUT_DIR=${workingDirectory} NNI_TRIAL_JOB_ID=${trialJobId} \
             NNI_EXP_ID=${experimentId} NNI_TRIAL_SEQ_ID=${trialSequenceId} NNI_CODE_DIR=${codeDir}
             export MULTI_PHASE=${isMultiPhase}
-
-            cp -r $NNI_CODE_DIR/. $NNI_SYS_DIR
-            cd $NNI_SYS_DIR
-            sh install_nni.sh
+            mkdir -p $NNI_SYS_DIR/code
+            cp -r $NNI_CODE_DIR/. $NNI_SYS_DIR/code
+            sh $NNI_SYS_DIR/install_nni.sh
+            cd $NNI_SYS_DIR/code
             python3 -m nni_trial_tool.trial_keeper --trial_command '${cudaVisibleSetting} ${command}' --nnimanager_ip '${nniManagerAddress}' \
                 --nnimanager_port '${nniManagerPort}' --nni_manager_version '${nniManagerVersion}' \
                 --job_id_file ${jobIdFileName} \
@@ -93,9 +93,9 @@ class LinuxCommands extends OsCommands {
         return result;
     }
 
-    public killChildProcesses(pidFileName: string): string {
+    public killChildProcesses(pidFileName: string, killSelf: boolean): string {
         // prevent trialkeeper to be killed, so it can save exit code.
-        const command = `list_descendants ()
+        let command = `list_descendants ()
                 {
                 local children=$(ps -o pid= --ppid "$1")
 
@@ -107,6 +107,9 @@ class LinuxCommands extends OsCommands {
                 echo "$children"
                 }
             kill $(list_descendants \`cat '${pidFileName}'\`)`
+        if (killSelf) {
+            command += `\nkill \`cat '${pidFileName}'\``
+        }
         return command;
     }
 

src/nni_manager/training_service/remote_machine/extends/windowsCommands.ts

@@ -28,9 +28,9 @@ class WindowsCommands extends OsCommands {
             set MULTI_PHASE=${isMultiPhase}
             set NNI_CODE_DIR=${codeDir}
             ${cudaVisibleSetting !== "" ? "set " + cudaVisibleSetting : ""}
-
-            robocopy /s %NNI_CODE_DIR%/. %NNI_SYS_DIR%
-            cd %NNI_SYS_DIR%
+            md %NNI_SYS_DIR%/code
+            robocopy /s %NNI_CODE_DIR%/. %NNI_SYS_DIR%/code
+            cd %NNI_SYS_DIR%/code
             python -c "import nni" 2>nul
             if not %ERRORLEVEL% EQU 0 (
                 echo installing NNI as exit code of "import nni" is %ERRORLEVEL%
@@ -102,11 +102,14 @@ class WindowsCommands extends OsCommands {
         return result;
     }
 
-    public killChildProcesses(pidFileName: string): string {
-        const command = `powershell "$ppid=(type ${pidFileName}); function Kill-Tree {Param([int]$subppid);` +
+    public killChildProcesses(pidFileName: string, killSelf: boolean): string {
+        let command = `powershell "$ppid=(type ${pidFileName}); function Kill-Tree {Param([int]$subppid);` +
             `Get-CimInstance Win32_Process | Where-Object { $_.ParentProcessId -eq $subppid } | ForEach-Object { Kill-Tree $_.ProcessId }; ` +
-            `if ($subppid -ne $ppid){Stop-Process -Id $subppid}}` +
+            `if ($subppid -ne $ppid){Stop-Process -Id $subppid -Force"}}` +
             `kill-tree $ppid"`;
+        if (killSelf){
+            command += `;Stop-Process -Id $ppid`;
+        }
         return command;
     }
 

src/nni_manager/training_service/remote_machine/osCommands.ts

@@ -25,7 +25,7 @@ abstract class OsCommands {
     public abstract readLastLines(fileName: string, lineCount: number): string;
     public abstract isProcessAliveCommand(pidFileName: string): string;
     public abstract isProcessAliveProcessOutput(result: RemoteCommandResult): boolean;
-    public abstract killChildProcesses(pidFileName: string): string;
+    public abstract killChildProcesses(pidFileName: string, killSelf: boolean): string;
     public abstract extractFile(tarFileName: string, targetFolder: string): string;
     public abstract executeScript(script: string, isFile: boolean): string;
 

src/nni_manager/training_service/remote_machine/remoteMachineTrainingService.ts

@@ -96,8 +96,8 @@ class RemoteMachineTrainingService implements TrainingService {
                 }
             }
             if (restServer.getErrorMessage !== undefined) {
-                throw new Error(restServer.getErrorMessage);
                 this.stopping = true;
+                throw new Error(restServer.getErrorMessage);
             }
             await delay(3000);
         }
@@ -394,7 +394,7 @@ class RemoteMachineTrainingService implements TrainingService {
                 if (executor !== undefined) {
                     this.log.info(`killing gpu metric collector on ${executor.name}`);
                     const gpuJobPidPath: string = executor.joinPath(executor.getRemoteScriptsPath(getExperimentId()), 'pid');
-                    await executor.killChildProcesses(gpuJobPidPath);
+                    await executor.killChildProcesses(gpuJobPidPath, true);
                 }
                 executorManager.releaseAllExecutor();
             }
@@ -460,6 +460,10 @@ class RemoteMachineTrainingService implements TrainingService {
                             this.timer.unsubscribe(disposable);
                         }
                     }
+                    if (this.stopping){
+                        this.timer.unsubscribe(disposable);
+                        this.log.debug(`Stopped GPU collector on ${rmMeta.ip}, since experiment is exiting.`);
+                    }
                     collectingCount.pop();
                 }
             }

src/nni_manager/training_service/remote_machine/shellExecutor.ts

@@ -230,8 +230,8 @@ class ShellExecutor {
         return result !== undefined ? result : false;
     }
 
-    public async killChildProcesses(pidFileName: string): Promise<boolean> {
-        const commandText = this.osCommands && this.osCommands.killChildProcesses(pidFileName);
+    public async killChildProcesses(pidFileName: string, killSelf: boolean = false): Promise<boolean> {
+        const commandText = this.osCommands && this.osCommands.killChildProcesses(pidFileName, killSelf);
         const commandResult = await this.execute(commandText);
         return commandResult.exitCode == 0;
     }

src/sdk/pynni/nni/nas/tensorflow/enas/trainer.py

@@ -4,7 +4,6 @@
 import logging
 
 import tensorflow as tf
-from tensorflow.data import Dataset
 from tensorflow.keras.optimizers import Adam
 
 from nni.nas.tensorflow.utils import AverageMeterGroup, fill_zero_grads
@@ -39,9 +38,9 @@ def __init__(self, model, loss, metrics, reward_function, optimizer, batch_size,
 
         x, y = dataset_train
         split = int(len(x) * 0.9)
-        self.train_set = Dataset.from_tensor_slices((x[:split], y[:split]))
-        self.valid_set = Dataset.from_tensor_slices((x[split:], y[split:]))
-        self.test_set = Dataset.from_tensor_slices(dataset_valid)
+        self.train_set = tf.data.Dataset.from_tensor_slices((x[:split], y[:split]))
+        self.valid_set = tf.data.Dataset.from_tensor_slices((x[split:], y[split:]))
+        self.test_set = tf.data.Dataset.from_tensor_slices(dataset_valid)
 
         self.mutator = EnasMutator(model)
         self.mutator_optim = Adam(learning_rate=mutator_lr)
@@ -151,9 +150,9 @@ def validate_one_epoch(self, epoch):
 
 
     def _create_train_loader(self):
-        train_set = self.train_set.shuffle(1000000).batch(self.batch_size)
-        test_set = self.test_set.shuffle(1000000).batch(self.batch_size)
+        train_set = self.train_set.shuffle(1000000).repeat().batch(self.batch_size)
+        test_set = self.test_set.shuffle(1000000).repeat().batch(self.batch_size)
         return iter(train_set), iter(test_set)
 
     def _create_validate_loader(self):
-        return iter(self.test_set.shuffle(1000000).batch(self.batch_size))
+        return iter(self.test_set.shuffle(1000000).repeat().batch(self.batch_size))

tools/nni_gpu_tool/gpu_metrics_collector.py

@@ -11,31 +11,9 @@
 from xml.dom import minidom
 
 
-def check_ready_to_run():
-    if sys.platform == 'win32':
-        pgrep_output = subprocess.check_output(
-            'wmic process where "CommandLine like \'%nni_gpu_tool.gpu_metrics_collector%\' and name like \'%python%\'" get processId')
-        pidList = pgrep_output.decode("utf-8").strip().split()
-        pidList.pop(0)  # remove the key word 'ProcessId'
-        pidList = list(map(int, pidList))
-        pidList.remove(os.getpid())
-        return not pidList
-    else:
-        pgrep_output = subprocess.check_output('pgrep -afu "$(whoami)" \'python3 -m nni_gpu_tool.gpu_metrics_collector\'', shell=True)
-        pidList = []
-        for pid in pgrep_output.splitlines():
-            pid = pid.decode()
-            if "pgrep " in pid or pid.startswith('%s ' % os.getpid()) or pid.startswith('%s ' % os.getppid()):
-                continue
-            pidList.append(pid)
-        return not pidList
-
-
 def main(argv):
     metrics_output_dir = os.environ['METRIC_OUTPUT_DIR']
-    if check_ready_to_run() == False:
-        print("GPU metrics collector is already running. exiting...")
-        exit(2)
+
     cmd = 'nvidia-smi -q -x'.split()
     while(True):
         try: