@@ -32,9 +32,31 @@ It is pretty simple to use multi-phase in trial code, an example is shown below:
# ...
```
__2. Modify experiment configuration__
__2. Experiment configuration__
To enable multi-phase, you should also add `multiPhase: true` in your experiment YAML configure file. If this line is not added, `nni.get_next_parameter()` would always return the same configuration. For all the built-in tuners/advisors, you can use multi-phase in your trial code without modification of tuner/advisor spec in the YAML configure file.
To enable multi-phase, you should also add `multiPhase: true` in your experiment YAML configure file. If this line is not added, `nni.get_next_parameter()` would always return the same configuration.
Multi-phase experiment configuration example:
```
authorName: default
experimentName: multiphase experiment
trialConcurrency: 2
maxExecDuration: 1h
maxTrialNum: 8
trainingServicePlatform: local
searchSpacePath: search_space.json
multiPhase: true
useAnnotation: false
tuner:
builtinTunerName: TPE
classArgs:
optimize_mode: maximize
trial:
command: python3 mytrial.py
codeDir: .
gpuNum: 0
```
### Write a tuner that leverages multi-phase:
...
...
@@ -48,6 +70,9 @@ trial_end
```
With this information, the tuner could know which trial is requesting a configuration, and which trial is reporting results. This information provides enough flexibility for your tuner to deal with different trials and different phases. For example, you may want to use the trial_job_id parameter of generate_parameters method to generate hyperparameters for a specific trial job.
Of course, to use your multi-phase tuner, __you should add `multiPhase: true` in your experiment YAML configure file__.
* check whether the version is consistent between nniManager and trialKeeper
*[Report final metrics for early stop job](https://github.com/Microsoft/nni/issues/776)
*[Report final metrics for early stop job](https://github.com/microsoft/nni/issues/776)
* If includeIntermediateResults is true, the last intermediate result of the trial that is early stopped by assessor is sent to tuner as final result. The default value of includeIntermediateResults is false.
***nni.report_final_result(result)** API supports more data types for result parameter.
...
...
@@ -278,20 +279,19 @@
docker pull msranni/nni:latest
```
* New trial example: [NNI Sklearn Example](https://github.com/Microsoft/nni/tree/master/examples/trials/sklearn)
* New competition example: [Kaggle Competition TGS Salt Example](https://github.com/Microsoft/nni/tree/master/examples/trials/kaggle-tgs-salt)
* New trial example: [NNI Sklearn Example](https://github.com/microsoft/nni/tree/master/examples/trials/sklearn)
* New competition example: [Kaggle Competition TGS Salt Example](https://github.com/microsoft/nni/tree/master/examples/trials/kaggle-tgs-salt)
### Others
* UI refactoring, refer to [WebUI doc](Tutorial/WebUI.md) for how to work with the new UI.
* Continuous Integration: NNI had switched to Azure pipelines
*[Known Issues in release 0.3.0](https://github.com/Microsoft/nni/labels/nni030knownissues).
## Release 0.2.0 - 9/29/2018
### Major Features
* Support [OpenPAI](https://github.com/Microsoft/pai) Training Platform (See [here](TrainingService/PaiMode.md) for instructions about how to submit NNI job in pai mode)
* Support [OpenPAI](https://github.com/microsoft/pai) Training Platform (See [here](TrainingService/PaiMode.md) for instructions about how to submit NNI job in pai mode)
* Support training services on pai mode. NNI trials will be scheduled to run on OpenPAI cluster
* NNI trial's output (including logs and model file) will be copied to OpenPAI HDFS for further debugging and checking
* Support [SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) tuner (See [here](Tuner/SmacTuner.md) for instructions about how to use SMAC tuner)
...
...
@@ -301,9 +301,6 @@
* Update ga squad example and related documentation
* WebUI UX small enhancement and bug fix
### Known Issues
[Known Issues in release 0.2.0](https://github.com/Microsoft/nni/labels/nni020knownissues).