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<h1 id="getting-started">Getting Started</h1>
<p>NimbleEdge has exposed two classes for the clients to use and integrate with their application.</p>
<h2 id="class-descriptions">Class Descriptions</h2>
<p><a
href="https://docs.nimbleedgehq.ai/nimblenet-android/ai.nimbleedge/-nimble-net-config/index.html">NimbleNetConfig</a>
Class</p>
<ul>
<li>This class instantiates the object used as input to the main NimbleNet class. It sets up the
config parameters for the NimbleNet class to instantiate with.</li>
<li>The config store items such as <code>Client ID, Device ID, Host, Port, and Model ID's</code>.</li>
<li>Detailed descriptions of the above variables to be provided in the constructor are at the
following link</li>
</ul>
<p><a href="https://docs.nimbleedgehq.ai/nimblenet-android/ai.nimbleedge/-nimble-net/index.html">NimbleNet</a> class
</p>
<ul>
<li>This is the main class for the NimbleNet interface - used for model inference and federated
training.</li>
<li>NimbleNet always creates a singleton instance, which is active for the entire duration of the user
session. The instance should be created in main view of the application.</li>
<li>The object of this class holds methods and attributes which help in obtaining inference</li>
</ul>
<h2 id="implementation">Implementation</h2>
<p>In the code for your application, broadly, we will import the modules, instantiate the object with
required class parameters, and then call the inference function as per the requirement.</p>
<h3 id="step-0--adding-the-dependencies">Step 0 : s dependencies</h3>
<pre class="hljs"><code><div>dependencies {
<span class="hljs-comment">// ...... Other dependencies ......</span>
implementation <span class="hljs-string">'ai.nimbleedge:nimblenet_android:1.0.0'</span>
<span class="hljs-comment">// ...... Other dependencies .......</span>
}
<span class="hljs-comment">// add the path to the repositories</span>
repositories {
maven {
url <span class="hljs-string">"s3://nimblebuilds.s3.ap-south-1.amazonaws.com"</span>
credentials(AwsCredentials) {
accessKey project.properties[<span class="hljs-string">"aws_access_key_id"</span>].toString()
secretKey project.properties[<span class="hljs-string">"aws_secret_access_key"</span>].toString()
}
}
}
</div></code></pre>
<h3 id="step-1--initializing-nimblenet">Step 1 : Initializing NimbleNet</h3>
<p>The NimbleNet must be initialized at start of the mobile application. Override the <code>onCreate</code> method
of Application Class and add the following lines</p>
<pre class="hljs"><code><div><span class="hljs-comment">// Import nimbleedge interface into your main application context</span>
<span class="hljs-keyword">import</span> ai.nimbleedge.NimbleNet
<span class="hljs-keyword">import</span> ai.nimbleedge.NimbleNetConfig
<span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">DemoApplication</span> : <span class="hljs-type">Application</span></span>() {
<span class="hljs-keyword">override</span> <span class="hljs-function"><span class="hljs-keyword">fun</span> <span class="hljs-title">onCreate</span><span class="hljs-params">()</span></span> {
<span class="hljs-keyword">super</span>.onCreate()
<span class="hljs-comment">// In this example, ClientID is "client123", modelID(s) is "model123" </span>
<span class="hljs-comment">// 43.205.11.213 is the IP address for connecting to NimbleEdge cloud. you will be provided with a URL for NimbleEdge cloud.</span>
<span class="hljs-keyword">val</span> nimbleNetConfig = NimbleNetConfig(
<span class="hljs-string">"client123"</span>,
arrayOf(<span class="hljs-string">"model123"</span>),
<span class="hljs-string">"http://43.205.11.213"</span>,
<span class="hljs-number">8080</span>,
<span class="hljs-string">"device_1"</span>
)
Log.d(<span class="hljs-string">"Demo Application"</span>, <span class="hljs-string">"NimbleNet interface demonstration"</span>)
}
}
</div></code></pre>
<p>Initialize the NimbleNet object</p>
<pre class="hljs"><code><div><span class="hljs-comment">// Import nimbleedge interface into your main application context</span>
<span class="hljs-keyword">import</span> ai.nimbleedge.NimbleNet
<span class="hljs-keyword">import</span> ai.nimbleedge.NimbleNetConfig
<span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">DemoApplication</span> : <span class="hljs-type">Application</span></span>() {
<span class="hljs-keyword">override</span> <span class="hljs-function"><span class="hljs-keyword">fun</span> <span class="hljs-title">onCreate</span><span class="hljs-params">()</span></span> {
<span class="hljs-keyword">super</span>.onCreate()
<span class="hljs-comment">// In this example, ClientID is "client123", modelID(s) is "model123" </span>
<span class="hljs-comment">// 43.205.11.213 is the IP address for connecting to NimbleEdge cloud. you will be provided with a URL for NimbleEdge cloud.</span>
<span class="hljs-keyword">val</span> nimbleNetConfig = NimbleNetConfig(
<span class="hljs-string">"client123"</span>,
arrayOf(<span class="hljs-string">"model123"</span>),
<span class="hljs-string">"http://43.205.11.213"</span>,
<span class="hljs-number">8080</span>,
<span class="hljs-string">"device_1"</span>
)
NimbleNet.initialize()
Log.d(<span class="hljs-string">"Demo Application"</span>, <span class="hljs-string">"NimbleNet interface demonstration"</span>)
}
}
</div></code></pre>
<h3 id="step-2-calling-the-prediction-request">Step 2 Calling the Prediction Request</h3>
<p>You can call the predict method of the SDK from within the Fragment or ViewModel. You also have
asynchronous methods for parallel execution. Please refer to API docs for detailed information.</p>
<pre class="hljs"><code><div><span class="hljs-comment">// Here, we have hardcoded the input to the predict call</span>
<span class="hljs-comment">// You can feed the input as needed, example from the DB</span>
<span class="hljs-comment">// "input" is the name of the input as specified in the planFeatures file</span>
<span class="hljs-keyword">val</span> result = NimbleNet.predict(
<span class="hljs-string">"model123"</span>,
<span class="hljs-string">"input"</span> to floatArrayOf(<span class="hljs-number">1.0f</span>, <span class="hljs-number">2.0f</span>, <span class="hljs-number">3.0f</span>, <span class="hljs-number">4.0f</span>)
)
<span class="hljs-comment">// You can access "data", "shape" and "type" elements of any output </span>
<span class="hljs-comment">// using the name provided in the planFeatures</span>
<span class="hljs-comment">// Example: result["probabilities"]?.data will return the "probabilities" data. Now, Use the output received from the model as needed</span>
</div></code></pre>
<p>A demo fragment that calls predict and shows the responses on TextView looks like this</p>
<pre class="hljs"><code><div><span class="hljs-class"><span class="hljs-keyword">class</span> <span class="hljs-title">FirstFragment</span> : <span class="hljs-type">Fragment</span></span>() {
<span class="hljs-keyword">override</span> <span class="hljs-function"><span class="hljs-keyword">fun</span> <span class="hljs-title">onViewCreated</span><span class="hljs-params">(view: <span class="hljs-type">View</span>, savedInstanceState: <span class="hljs-type">Bundle</span>?)</span></span> {
<span class="hljs-keyword">super</span>.onViewCreated(view, savedInstanceState)
binding.predict.setOnClickListener {
<span class="hljs-comment">// predictUsingCallback()</span>
predictUsingFlow()
}
}
<span class="hljs-keyword">private</span> <span class="hljs-function"><span class="hljs-keyword">fun</span> <span class="hljs-title">predictUsingFlow</span><span class="hljs-params">()</span></span> {
<span class="hljs-keyword">val</span> input = List(<span class="hljs-number">4</span>) { Random.nextFloat() * <span class="hljs-number">4</span> }.toFloatArray()
lifecycleScope.launch {
NimbleNet.predictAsync(
<span class="hljs-string">"plan1"</span>,
mapOf(<span class="hljs-string">"input"</span> to input)
).onSuccess { <span class="hljs-keyword">data</span> ->
Log.d(NE_LOG_TAG, <span class="hljs-string">"predictAsync:onSuccess"</span> + Thread.currentThread().name)
binding.parray.text =
<span class="hljs-keyword">data</span>[<span class="hljs-string">"probabilities"</span>]?.<span class="hljs-keyword">data</span>?.joinToString(<span class="hljs-string">","</span>)
binding.larray.text = <span class="hljs-keyword">data</span>[<span class="hljs-string">"label"</span>]?.<span class="hljs-keyword">data</span>.contentToString()
}.onFailure {
Log.d(NE_LOG_TAG, <span class="hljs-string">"predictAsync:onFallback"</span> + Thread.currentThread().name)
}.onFallback {
Log.d(NE_LOG_TAG, <span class="hljs-string">"predictAsync:onFailure"</span> + Thread.currentThread().name)
}
}
}
<span class="hljs-keyword">private</span> <span class="hljs-function"><span class="hljs-keyword">fun</span> <span class="hljs-title">predictUsingCallback</span><span class="hljs-params">()</span></span> {
<span class="hljs-keyword">val</span> input = List(<span class="hljs-number">4</span>) { Random.nextFloat() * <span class="hljs-number">4</span> }.toFloatArray()
NimbleNet.predictAsync(
<span class="hljs-string">"plan1"</span>,
mapOf(<span class="hljs-string">"input"</span> to input),
<span class="hljs-keyword">object</span> : PredictionLoadCallback {
<span class="hljs-keyword">override</span> <span class="hljs-function"><span class="hljs-keyword">fun</span> <span class="hljs-title">onSuccess</span><span class="hljs-params">(output: <span class="hljs-type">ModelOutput</span>)</span></span> {
Log.d(NE_LOG_TAG, <span class="hljs-string">"predictAsync:onSuccess=>"</span> + Thread.currentThread().name)
binding.parray.text =
output[<span class="hljs-string">"probabilities"</span>]?.<span class="hljs-keyword">data</span>?.joinToString(<span class="hljs-string">","</span>) {
String.format(
<span class="hljs-string">"%.2f"</span>,
it
)
}
binding.larray.text = output[<span class="hljs-string">"label"</span>]?.<span class="hljs-keyword">data</span>.contentToString()
}
<span class="hljs-keyword">override</span> <span class="hljs-function"><span class="hljs-keyword">fun</span> <span class="hljs-title">onFailure</span><span class="hljs-params">(failure: <span class="hljs-type">Failure</span>)</span></span> {
Log.d(NE_LOG_TAG, <span class="hljs-string">"predictAsync:onFailure"</span> + Thread.currentThread().name)
}
}
)
}
}
</div></code></pre>
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