* Open the `Web UI url` in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. [Here](docs/WebUI.md) are more Web UI pages.
* Open the `Web UI url` in your browser, you can view detail information of the experiment and all the submitted trial jobs as shown below. [Here](docs/en_US/WebUI.md) are more Web UI pages.
@@ -169,27 +169,27 @@ You can use these commands to get more information about the experiment
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@@ -169,27 +169,27 @@ You can use these commands to get more information about the experiment
</table>
</table>
## **Documentation**
## **Documentation**
*[NNI overview](docs/Overview.md)
*[NNI overview](docs/en_US/Overview.md)
*[Quick start](docs/QuickStart.md)
*[Quick start](docs/en_US/QuickStart.md)
## **How to**
## **How to**
*[Install NNI](docs/Installation.md)
*[Install NNI](docs/en_US/Installation.md)
*[Use command line tool nnictl](docs/NNICTLDOC.md)
*[Use command line tool nnictl](docs/en_US/NNICTLDOC.md)
*[Use NNIBoard](docs/WebUI.md)
*[Use NNIBoard](docs/en_US/WebUI.md)
*[How to define search space](docs/SearchSpaceSpec.md)
*[How to define search space](docs/en_US/SearchSpaceSpec.md)
*[How to define a trial](docs/Trials.md)
*[How to define a trial](docs/en_US/Trials.md)
*[How to choose tuner/search-algorithm](docs/Builtin_Tuner.md)
*[How to choose tuner/search-algorithm](docs/en_US/Builtin_Tuner.md)
*[Config an experiment](docs/ExperimentConfig.md)
*[Config an experiment](docs/en_US/ExperimentConfig.md)
*[How to use annotation](docs/Trials.md#nni-python-annotation)
*[How to use annotation](docs/en_US/Trials.md#nni-python-annotation)
## **Tutorials**
## **Tutorials**
*[Run an experiment on local (with multiple GPUs)?](docs/tutorial_1_CR_exp_local_api.md)
*[Run an experiment on local (with multiple GPUs)?](docs/en_US/tutorial_1_CR_exp_local_api.md)
*[Run an experiment on multiple machines?](docs/RemoteMachineMode.md)
*[Run an experiment on multiple machines?](docs/en_US/RemoteMachineMode.md)
*[Run an experiment on OpenPAI?](docs/PAIMode.md)
*[Run an experiment on OpenPAI?](docs/en_US/PAIMode.md)
*[Run an experiment on Kubeflow?](docs/KubeflowMode.md)
*[Run an experiment on Kubeflow?](docs/en_US/KubeflowMode.md)
*[Try different tuners](docs/tuners.rst)
*[Try different tuners](docs/en_US/tuners.rst)
*[Try different assessors](docs/assessors.rst)
*[Try different assessors](docs/en_US/assessors.rst)
*[Implement a customized tuner](docs/Customize_Tuner.md)
*[Implement a customized tuner](docs/en_US/Customize_Tuner.md)
*[Implement a customized assessor](docs/Customize_Assessor.md)
*[Implement a customized assessor](docs/en_US/Customize_Assessor.md)
*[Use Genetic Algorithm to find good model architectures for Reading Comprehension task](examples/trials/ga_squad/README.md)
*[Use Genetic Algorithm to find good model architectures for Reading Comprehension task](examples/trials/ga_squad/README.md)
## **Contribute**
## **Contribute**
...
@@ -197,11 +197,11 @@ This project welcomes contributions and suggestions, we use [GitHub issues](http
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@@ -197,11 +197,11 @@ This project welcomes contributions and suggestions, we use [GitHub issues](http
Issues with the **good first issue** label are simple and easy-to-start ones that we recommend new contributors to start with.
Issues with the **good first issue** label are simple and easy-to-start ones that we recommend new contributors to start with.
To set up environment for NNI development, refer to the instruction: [Set up NNI developer environment](docs/SetupNNIDeveloperEnvironment.md)
To set up environment for NNI development, refer to the instruction: [Set up NNI developer environment](docs/en_US/SetupNNIDeveloperEnvironment.md)
Before start coding, review and get familiar with the NNI Code Contribution Guideline: [Contributing](docs/CONTRIBUTING.md)
Before start coding, review and get familiar with the NNI Code Contribution Guideline: [Contributing](docs/en_US/CONTRIBUTING.md)
We are in construction of the instruction for [How to Debug](docs/HowToDebug.md), you are also welcome to contribute questions or suggestions on this area.
We are in construction of the instruction for [How to Debug](docs/en_US/HowToDebug.md), you are also welcome to contribute questions or suggestions on this area.
## **License**
## **License**
The entire codebase is under [MIT license](LICENSE)
The entire codebase is under [MIT license](LICENSE)
Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in [search space spec](../../../../../docs/SearchSpaceSpec.md).
Batch tuner allows users to simply provide several configurations (i.e., choices of hyper-parameters) for their trial code. After finishing all the configurations, the experiment is done. Batch tuner only supports the type choice in [search space spec](../../../../../docs/en_US/SearchSpaceSpec.md).
Suggested sceanrio: If the configurations you want to try have been decided, you can list them in searchspace file (using choice) and run them using batch tuner.
Suggested sceanrio: If the configurations you want to try have been decided, you can list them in searchspace file (using choice) and run them using batch tuner.
Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](../../../../../docs/SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
Grid Search performs an exhaustive searching through a manually specified subset of the hyperparameter space defined in the searchspace file. Note that the only acceptable types of search space are `choice`, `quniform`, `qloguniform`. **The number `q` in `quniform` and `qloguniform` has special meaning (different from the spec in [search space spec](../../../../../docs/en_US/SearchSpaceSpec.md)). It means the number of values that will be sampled evenly from the range `low` and `high`.**
[SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO, in order to handle categorical parameters. The SMAC supported by nni is a wrapper on [the SMAC3 github repo](https://github.com/automl/SMAC3).
[SMAC](https://www.cs.ubc.ca/~hutter/papers/10-TR-SMAC.pdf) is based on Sequential Model-Based Optimization (SMBO). It adapts the most prominent previously used model class (Gaussian stochastic process models) and introduces the model class of random forests to SMBO, in order to handle categorical parameters. The SMAC supported by nni is a wrapper on [the SMAC3 github repo](https://github.com/automl/SMAC3).
Note that SMAC on nni only supports a subset of the types in [search space spec](../../../../../docs/SearchSpaceSpec.md), including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`.
Note that SMAC on nni only supports a subset of the types in [search space spec](../../../../../docs/en_US/SearchSpaceSpec.md), including `choice`, `randint`, `uniform`, `loguniform`, `quniform(q=1)`.
