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<main>
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<h1 id="hello-world"><a class="header" href="#hello-world">Hello world!</a></h1>
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<p>We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.</p>
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<p>Open <code>src/main.rs</code> and fill in this content:</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
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</span>use candle_core::{Device, Result, Tensor};
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struct Model {
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first: Tensor,
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second: Tensor,
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}
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impl Model {
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fn forward(&self, image: &Tensor) -> Result<Tensor> {
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let x = image.matmul(&self.first)?;
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let x = x.relu()?;
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x.matmul(&self.second)
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}
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}
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fn main() -> Result<()> {
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// Use Device::new_cuda(0)?; to use the GPU.
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let device = Device::Cpu;
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let first = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
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let second = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
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let model = Model { first, second };
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let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
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let digit = model.forward(&dummy_image)?;
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println!("Digit {digit:?} digit");
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Ok(())
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}</code></pre></pre>
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<p>Everything should now run with:</p>
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<pre><code class="language-bash">cargo run --release
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</code></pre>
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<h2 id="using-a-linear-layer"><a class="header" href="#using-a-linear-layer">Using a <code>Linear</code> layer.</a></h2>
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<p>Now that we have this, we might want to complexify things a bit, for instance by adding <code>bias</code> and creating
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the classical <code>Linear</code> layer. We can do as such</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
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</span><span class="boring">fn main() {
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</span><span class="boring">extern crate candle_core;
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</span><span class="boring">use candle_core::{Device, Result, Tensor};
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</span>struct Linear{
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weight: Tensor,
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bias: Tensor,
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}
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impl Linear{
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fn forward(&self, x: &Tensor) -> Result<Tensor> {
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let x = x.matmul(&self.weight)?;
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x.broadcast_add(&self.bias)
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}
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}
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struct Model {
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first: Linear,
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second: Linear,
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}
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impl Model {
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fn forward(&self, image: &Tensor) -> Result<Tensor> {
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let x = self.first.forward(image)?;
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let x = x.relu()?;
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self.second.forward(&x)
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}
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}
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<span class="boring">}</span></code></pre></pre>
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<p>This will change the model running code into a new function</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
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</span><span class="boring">use candle_core::{Device, Result, Tensor};
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</span><span class="boring">struct Linear{
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</span><span class="boring"> weight: Tensor,
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</span><span class="boring"> bias: Tensor,
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</span><span class="boring">}
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</span><span class="boring">impl Linear{
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</span><span class="boring"> fn forward(&self, x: &Tensor) -> Result<Tensor> {
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</span><span class="boring"> let x = x.matmul(&self.weight)?;
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</span><span class="boring"> x.broadcast_add(&self.bias)
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</span><span class="boring"> }
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</span><span class="boring">}
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</span><span class="boring">
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</span><span class="boring">struct Model {
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</span><span class="boring"> first: Linear,
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</span><span class="boring"> second: Linear,
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</span><span class="boring">}
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</span><span class="boring">
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</span><span class="boring">impl Model {
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</span><span class="boring"> fn forward(&self, image: &Tensor) -> Result<Tensor> {
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</span><span class="boring"> let x = self.first.forward(image)?;
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</span><span class="boring"> let x = x.relu()?;
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</span><span class="boring"> self.second.forward(&x)
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</span><span class="boring"> }
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</span><span class="boring">}
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</span>fn main() -> Result<()> {
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// Use Device::new_cuda(0)?; to use the GPU.
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// Use Device::Cpu; to use the CPU.
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let device = Device::cuda_if_available(0)?;
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// Creating a dummy model
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let weight = Tensor::randn(0f32, 1.0, (784, 100), &device)?;
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let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
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let first = Linear{weight, bias};
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let weight = Tensor::randn(0f32, 1.0, (100, 10), &device)?;
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let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
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let second = Linear{weight, bias};
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let model = Model { first, second };
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let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
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// Inference on the model
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let digit = model.forward(&dummy_image)?;
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println!("Digit {digit:?} digit");
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Ok(())
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}</code></pre></pre>
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<p>Now it works, it is a great way to create your own layers.
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But most of the classical layers are already implemented in <a href="https://github.com/huggingface/candle/tree/main/candle-nn">candle-nn</a>.</p>
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<h2 id="using-candle_nn"><a class="header" href="#using-candle_nn">Using <code>candle_nn</code>.</a></h2>
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<p>For instance <a href="https://github.com/huggingface/candle/blob/main/candle-nn/src/linear.rs">Linear</a> is already there.
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This Linear is coded with PyTorch layout in mind, to reuse better existing models out there, so it uses the transpose of the weights and not the weights directly.</p>
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<p>So instead we can simplify our example:</p>
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<pre><code class="language-bash">cargo add --git https://github.com/huggingface/candle.git candle-nn
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</code></pre>
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<p>And rewrite our examples using it</p>
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<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
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</span><span class="boring">extern crate candle_nn;
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</span>use candle_core::{Device, Result, Tensor};
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use candle_nn::{Linear, Module};
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struct Model {
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first: Linear,
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second: Linear,
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}
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impl Model {
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fn forward(&self, image: &Tensor) -> Result<Tensor> {
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let x = self.first.forward(image)?;
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let x = x.relu()?;
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self.second.forward(&x)
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}
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}
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fn main() -> Result<()> {
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// Use Device::new_cuda(0)?; to use the GPU.
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let device = Device::Cpu;
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// This has changed (784, 100) -> (100, 784) !
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let weight = Tensor::randn(0f32, 1.0, (100, 784), &device)?;
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let bias = Tensor::randn(0f32, 1.0, (100, ), &device)?;
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let first = Linear::new(weight, Some(bias));
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let weight = Tensor::randn(0f32, 1.0, (10, 100), &device)?;
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let bias = Tensor::randn(0f32, 1.0, (10, ), &device)?;
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let second = Linear::new(weight, Some(bias));
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let model = Model { first, second };
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let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &device)?;
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let digit = model.forward(&dummy_image)?;
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println!("Digit {digit:?} digit");
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Ok(())
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}</code></pre></pre>
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<p>Feel free to modify this example to use <code>Conv2d</code> to create a classical convnet instead.</p>
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<p>Now that we have the running dummy code we can get to more advanced topics:</p>
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<ul>
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<li><a href="../guide/cheatsheet.html">For PyTorch users</a></li>
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<li><a href="../inference/inference.html">Running existing models</a></li>
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<li><a href="../training/training.html">Training models</a></li>
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</ul>
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