# Write a Trial Run on NNI¶

A Trial in NNI is an individual attempt at applying a configuration (e.g., a set of hyper-parameters) to a model.

To define an NNI trial, you need to first define the set of parameters (i.e., search space) and then update the model. NNI provides two approaches for you to define a trial: NNI API and NNI Python annotation. You could also refer to here for more trial examples.

## NNI API¶

### Step 1 - Prepare a SearchSpace parameters file.¶

An example is shown 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 SearchSpaceSpec.md to learn more about search spaces. Tuner will generate configurations from this search space, that is, choosing a value for each hyperparameter from the range.

### Step 2 - Update model code¶

• Import NNI

Include import nni in your trial code to use NNI APIs.

• Get configuration from Tuner

RECEIVED_PARAMS = nni.get_next_parameter()


RECEIVED_PARAMS is an object, for example:

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

• Report metric data periodically (optional)
nni.report_intermediate_result(metrics)


metrics can be any python object. If users use the NNI built-in tuner/assessor, metrics can only have two formats: 1) a number e.g., float, int, or 2) a dict object that has a key named default whose value is a number. These metrics are reported to assessor. Often, metrics includes the periodically evaluated loss or accuracy.

• Report performance of the configuration
nni.report_final_result(metrics)


metrics can also be any python object. If users use the NNI built-in tuner/assessor, metrics follows the same format rule as that in report_intermediate_result, the number indicates the model’s performance, for example, the model’s accuracy, loss etc. These metrics are reported to tuner.

### Step 3 - Enable NNI API¶

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

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


*Please refer to here for more APIs (e.g., nni.get_sequence_id()) provided by NNI.

## NNI Python Annotation¶

An alternative to writing a trial is to use NNI’s syntax for python. NNI annotations are simple, similar to comments. You don’t have to make structural changes to your existing code. With a few lines of NNI annotation, you will be able to:

• annotate the variables you want to tune
• specify the range in which you want to tune the variables
• annotate which variable you want to report as an intermediate result to assessor
• annotate which variable you want to report as the final result (e.g. model accuracy) to tuner.

Again, take MNIST as an example, it only requires 2 steps to write a trial with NNI Annotation.

### Step 1 - Update codes with annotations¶

The following is a TensorFlow code snippet for NNI Annotation where the highlighted four lines are annotations that:

1. tune batch_size and dropout_rate
2. report test_acc every 100 steps
3. lastly report test_acc as the final result.

It’s worth noting that, as these newly added codes are merely annotations, you can still run your code as usual in environments without NNI installed.

with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
+   """@nni.variable(nni.choice(50, 250, 500), name=batch_size)"""
batch_size = 128
for i in range(10000):
batch = mnist.train.next_batch(batch_size)
+       """@nni.variable(nni.choice(0.1, 0.5), name=dropout_rate)"""
dropout_rate = 0.5
mnist_network.train_step.run(feed_dict={mnist_network.images: batch[0],
mnist_network.labels: batch[1],
mnist_network.keep_prob: dropout_rate})
if i % 100 == 0:
test_acc = mnist_network.accuracy.eval(
feed_dict={mnist_network.images: mnist.test.images,
mnist_network.labels: mnist.test.labels,
mnist_network.keep_prob: 1.0})
+           """@nni.report_intermediate_result(test_acc)"""

test_acc = mnist_network.accuracy.eval(
feed_dict={mnist_network.images: mnist.test.images,
mnist_network.labels: mnist.test.labels,
mnist_network.keep_prob: 1.0})
+   """@nni.report_final_result(test_acc)"""


NOTE:

• @nni.variable will affect its following line which should be an assignment statement whose left-hand side must be the same as the keyword name in the @nni.variable statement.
• @nni.report_intermediate_result/@nni.report_final_result will send the data to assessor/tuner at that line.

### Step 2 - Enable NNI Annotation¶

In the YAML configure file, you need to set useAnnotation to true to enable NNI annotation:

useAnnotation: true


## Standalone mode for debugging¶

NNI supports a standalone mode for trial code to run without starting an NNI experiment. This is for finding out bugs in trial code more conveniently. NNI annotation natively supports standalone mode, as the added NNI related lines are comments. For NNI trial APIs, the APIs have changed behaviors in standalone mode, some APIs return dummy values, and some APIs do not really report values. Please refer to the following table for the full list of these APIs.

# NOTE: please assign default values to the hyperparameters in your trial code
nni.get_next_parameter # return {}
nni.report_final_result # have log printed on stdout, but does not report
nni.report_intermediate_result # have log printed on stdout, but does not report
nni.get_experiment_id # return "STANDALONE"
nni.get_trial_id # return "STANDALONE"
nni.get_sequence_id # return 0


You can try standalone mode with the mnist example. Simply run python3 mnist.py under the code directory. The trial code should successfully run with the default hyperparameter values.

## Where are my trials?¶

### Local Mode¶

In NNI, every trial has a dedicated directory for them to output their own data. In each trial, an environment variable called NNI_OUTPUT_DIR is exported. Under this directory, you can find each trial’s code, data, and other logs. In addition, each trial’s log (including stdout) will be re-directed to a file named trial.log under that directory.

If NNI Annotation is used, the trial’s converted code is in another temporary directory. You can check that in a file named run.sh under the directory indicated by NNI_OUTPUT_DIR. The second line (i.e., the cd command) of this file will change directory to the actual directory where code is located. Below is an example of run.sh:

#!/bin/bash
cd /tmp/user_name/nni/annotation/tmpzj0h72x6 #This is the actual directory
export NNI_PLATFORM=local
export NNI_SYS_DIR=/home/user_name/nni/experiments/$experiment_id$/trials/$trial_id$
export NNI_TRIAL_JOB_ID=nrbb2
export NNI_OUTPUT_DIR=/home/user_name/nni/experiments/$eperiment_id$/trials/$trial_id$
export NNI_TRIAL_SEQ_ID=1
export MULTI_PHASE=false
export CUDA_VISIBLE_DEVICES=
eval python3 mnist.py 2>/home/user_name/nni/experiments/$experiment_id$/trials/$trial_id$/stderr