**Tutorial: Create and Run an Experiment on local with NNI API**
===

In this tutorial, we will use the example in [~/examples/trials/mnist] to explain how to create and run an experiment on local with NNI API.

>Before starts

You have an implementation for MNIST classifer using convolutional layers, the Python code is in `mnist_before.py`.

>Step 1 - Update model codes

To enable NNI API, make the following changes:
~~~~
1.1 Declare NNI API
    Include `import nni` in your trial code to use NNI APIs.

1.2 Get predefined parameters
    Use the following code snippet:

        RECEIVED_PARAMS = nni.get_next_parameter()

    to get hyper-parameters' values assigned by tuner. `RECEIVED_PARAMS` is an object, for example:

        {"conv_size": 2, "hidden_size": 124, "learning_rate": 0.0307, "dropout_rate": 0.2029}

1.3 Report NNI results
    Use the API:

        `nni.report_intermediate_result(accuracy)`

    to send `accuracy` to assessor.

    Use the API:

        `nni.report_final_result(accuracy)`

    to send `accuracy` to tuner.
~~~~
We had made the changes and saved it to `mnist.py`.

**NOTE**:
~~~~
accuracy - The `accuracy` could be any python object, but  if you use NNI built-in tuner/assessor, `accuracy` should be a numerical variable (e.g. float, int).
assessor - The assessor will decide which trial should early stop based on the history performance of trial (intermediate result of one trial).
tuner    - The tuner will generate next parameters/architecture based on the explore history (final result of all trials).
~~~~

>Step 2 - Define SearchSpace

The hyper-parameters used in `Step 1.2 - Get predefined parameters` is defined in a `search_space.json` file like below:
```
{
    "dropout_rate":{"_type":"uniform","_value":[0.1,0.5]},
    "conv_size":{"_type":"choice","_value":[2,3,5,7]},
    "hidden_size":{"_type":"choice","_value":[124, 512, 1024]},
    "learning_rate":{"_type":"uniform","_value":[0.0001, 0.1]}
}
```
Refer to [define search space](../Tutorial/SearchSpaceSpec.md) to learn more about search space.

>Step 3 - Define Experiment

>>3.1 enable NNI API mode

To enable NNI API mode, you need to set useAnnotation to *false* and provide the path of SearchSpace file (you just defined in step 1):

```
useAnnotation: false
searchSpacePath: /path/to/your/search_space.json
```

To run an experiment in NNI, you only needed:

* Provide a runnable trial
* Provide or choose a tuner
* Provide a YAML experiment configure file
* (optional) Provide or choose an assessor

**Prepare trial**:
>A set of examples can be found in ~/nni/examples after your installation, run `ls ~/nni/examples/trials` to see all the trial examples.

Let's use a simple trial example, e.g. mnist, provided by NNI. After you installed NNI, NNI examples have been put in ~/nni/examples, run `ls ~/nni/examples/trials` to see all the trial examples. You can simply execute the following command to run the NNI mnist example:

      python ~/nni/examples/trials/mnist-annotation/mnist.py

This command will be filled in the YAML configure file below. Please refer to [here](../TrialExample/Trials.md) for how to write your own trial.

**Prepare tuner**: NNI supports several popular automl algorithms, including Random Search, Tree of Parzen Estimators (TPE), Evolution algorithm etc. Users can write their own tuner (refer to [here](../Tuner/CustomizeTuner.md)), but for simplicity, here we choose a tuner provided by NNI as below:

      tuner:
        builtinTunerName: TPE
        classArgs:
          optimize_mode: maximize

*builtinTunerName* is used to specify a tuner in NNI, *classArgs* are the arguments pass to the tuner (the spec of builtin tuners can be found [here](../Tuner/BuiltinTuner.md)), *optimization_mode* is to indicate whether you want to maximize or minimize your trial's result.

**Prepare configure file**: Since you have already known which trial code you are going to run and which tuner you are going to use, it is time to prepare the YAML configure file. NNI provides a demo configure file for each trial example, `cat ~/nni/examples/trials/mnist-annotation/config.yml` to see it. Its content is basically shown below:

```
authorName: your_name
experimentName: auto_mnist

# how many trials could be concurrently running
trialConcurrency: 1

# maximum experiment running duration
maxExecDuration: 3h

# empty means never stop
maxTrialNum: 100

# choice: local, remote
trainingServicePlatform: local

# choice: true, false
useAnnotation: true
tuner:
  builtinTunerName: TPE
  classArgs:
    optimize_mode: maximize
trial:
  command: python mnist.py
  codeDir: ~/nni/examples/trials/mnist-annotation
  gpuNum: 0
```

Here *useAnnotation* is true because this trial example uses our python annotation (refer to [here](../Tutorial/AnnotationSpec.md) for details). For trial, we should provide *trialCommand* which is the command to run the trial, provide *trialCodeDir* where the trial code is. The command will be executed in this directory. We should also provide how many GPUs a trial requires.

With all these steps done, we can run the experiment with the following command:

      nnictl create --config ~/nni/examples/trials/mnist-annotation/config.yml

You can refer to [here](../Tutorial/Nnictl.md) for more usage guide of *nnictl* command line tool.

## View experiment results
The experiment has been running now. Other than *nnictl*, NNI also provides WebUI for you to view experiment progress, to control your experiment, and some other appealing features.

## Using multiple local GPUs to speed up search
The following steps assume that you have 4 NVIDIA GPUs installed at local and [tensorflow with GPU support](https://www.tensorflow.org/install/gpu). The demo enables 4 concurrent trail jobs and each trail job uses 1 GPU.

**Prepare configure file**: NNI provides a demo configuration file for the setting above, `cat ~/nni/examples/trials/mnist-annotation/config_gpu.yml` to see it. The trailConcurrency and gpuNum are different from the basic configure file:

```
...

# how many trials could be concurrently running
trialConcurrency: 4

...

trial:
  command: python mnist.py
  codeDir: ~/nni/examples/trials/mnist-annotation
  gpuNum: 1
```

We can run the experiment with the following command:

      nnictl create --config ~/nni/examples/trials/mnist-annotation/config_gpu.yml

You can use *nnictl* command line tool or WebUI to trace the training progress. *nvidia_smi* command line tool can also help you to monitor the GPU usage during training.
