Merge pull request #121 from Microsoft/master

merge master

docs/tutorial_2_RemoteMachineMode.md

-**Tutorial: Run an experiment on multiple machines**
-===
-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. 
-
-e.g. Three machines and you login in with account `bob` (Note: the account is not necessarily the same on different machine): 
-
-| IP  | Username| Password |
-| -------- |---------|-------|
-| 10.1.1.1 | bob | bob123    |
-| 10.1.1.2 | bob | bob123    |
-| 10.1.1.3 | bob | bob123    |
-
-## Setup NNI environment
-Install NNI on each of your machines following the install guide [here](GetStarted.md).
-
-For remote machines that are used only to run trials but not the nnictl, you can just install python SDK:
-
-* __Install python SDK through pip__
-
-      python3 -m pip install --user --upgrade nni-sdk
-
-## Run an experiment
-Install NNI on another machine which has network accessibility to those three machines above, or you can just use any machine above to run nnictl command line tool.
-
-We use `examples/trials/mnist-annotation` as an example here. `cat ~/nni/examples/trials/mnist-annotation/config_remote.yml` to see the detailed configuration file: 
-```
-authorName: default
-experimentName: example_mnist
-trialConcurrency: 1
-maxExecDuration: 1h
-maxTrialNum: 10
-#choice: local, remote, pai
-trainingServicePlatform: remote
-#choice: true, false
-useAnnotation: true
-tuner:
-  #choice: TPE, Random, Anneal, Evolution, BatchTuner
-  #SMAC (SMAC should be installed through nnictl)
-  builtinTunerName: TPE
-  classArgs:
-    #choice: maximize, minimize
-    optimize_mode: maximize
-trial:
-  command: python3 mnist.py
-  codeDir: .
-  gpuNum: 0
-#machineList can be empty if the platform is local
-machineList:
-  - ip: 10.1.1.1
-    username: bob
-    passwd: bob123
-    #port can be skip if using default ssh port 22
-    #port: 22
-  - ip: 10.1.1.2
-    username: bob
-    passwd: bob123
-  - ip: 10.1.1.3
-    username: bob
-    passwd: bob123
-```
-Simply filling the `machineList` section and then run:
-```
-nnictl create --config ~/nni/examples/trials/mnist-annotation/config_remote.yml
-```
-to start the experiment. 

docs/tutorial_3_tryTunersAndAssessors.md

-# Tutorial - Try different Tuners and Assessors
-
-NNI provides an easy to adopt approach to set up parameter tuning algorithms as well as early stop policies, we call them **Tuners** and **Assessors**.
-  
-**Tuner** specifies the algorithm you use to generate hyperparameter sets for each trial. In NNI, we support two approaches to set the tuner. 
-1. Directly use tuner provided by nni sdk
-
-        required fields: builtinTunerName and classArgs. 
-
-2. Customize your own tuner file
-
-        required fields: codeDirectory, classFileName, className and classArgs.
-
-### **Learn More about tuners**
-* For detailed defintion and usage about the required field, please refer to [Config an experiment](ExperimentConfig.md)
-* [Tuners in the latest NNI release](HowToChooseTuner.md)
-* [How to implement your own tuner](howto_2_CustomizedTuner.md)
-
-
-**Assessor** specifies the algorithm you use to apply early stop policy. In NNI, there are two approaches to set the assessor.
-1. Directly use assessor provided by nni sdk
-
-        required fields: builtinAssessorName and classArgs. 
-
-2. Customize your own assessor file
-
-        required fields: codeDirectory, classFileName, className and classArgs.
-
-### **Learn More about assessor**
-* For detailed defintion and usage aobut the required field, please refer to [Config an experiment](ExperimentConfig.md)
-* Find more about the detailed instruction about [enable assessor](EnableAssessor.md)
-* [How to implement your own assessor](../examples/assessors/README.md)
-
-## **Learn More**
-* [How to run an experiment on local (with multiple GPUs)?](tutorial_1_CR_exp_local_api.md)
-* [How to run an experiment on multiple machines?](tutorial_2_RemoteMachineMode.md)
-* [How to run an experiment on OpenPAI?](PAIMode.md)

examples/trials/ga_squad/README.md

@@ -88,7 +88,7 @@ nnictl create --config ~/nni/examples/trials/ga_squad/config.yml
 Due to the memory limitation of upload, we only upload the source code and complete the data download and training on OpenPAI. This experiment requires sufficient memory that `memoryMB >= 32G`, and the training may last for several hours.
 
 ### Update configuration
-Modify `nni/examples/trials/ga_squad/config_pai.yaml`, here is the default configuration:
+Modify `nni/examples/trials/ga_squad/config_pai.yml`, here is the default configuration:
 
 ```
 authorName: default
@@ -114,18 +114,18 @@ trial:
   gpuNum: 0
   cpuNum: 1
   memoryMB: 32869
-  #The docker image to run nni job on pai
+  #The docker image to run NNI job on OpenPAI
   image: msranni/nni:latest
-  #The hdfs directory to store data on pai, format 'hdfs://host:port/directory'
+  #The hdfs directory to store data on OpenPAI, format 'hdfs://host:port/directory'
   dataDir: hdfs://10.10.10.10:9000/username/nni
-  #The hdfs directory to store output data generated by nni, format 'hdfs://host:port/directory'
+  #The hdfs directory to store output data generated by NNI, format 'hdfs://host:port/directory'
   outputDir: hdfs://10.10.10.10:9000/username/nni
 paiConfig:
-  #The username to login pai
+  #The username to login OpenPAI
   userName: username
-  #The password to login pai
+  #The password to login OpenPAI
   passWord: password
-  #The host of restful server of pai
+  #The host of restful server of OpenPAI
   host: 10.10.10.10
 ```
 

examples/tuners/enas_nni/README.md

  **Run ENAS in NNI**	
  ===	
  
-  Now we have an enas example [enas-nni](https://github.com/countif/enas_nni) run in nni from our contributors.	
+  Now we have an enas example [enas-nni](https://github.com/countif/enas_nni) run in NNI from our contributors.	
  Thanks our lovely contributors. 	
  And welcome more and more people to join us!

src/nni_manager/common/log.ts

@@ -138,7 +138,7 @@ class Logger {
     private log(level: string, param: any[]): void {
         const buffer: WritableStreamBuffer = new WritableStreamBuffer();
         buffer.write(`[${(new Date()).toISOString()}] ${level} `);
-        buffer.write(format(null, param));
+        buffer.write(format(param));
         buffer.write('\n');
         buffer.end();
         this.bufferSerialEmitter.feed(buffer.getContents());