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<h1 id="pytorch-cheatsheet"><a class="header" href="#pytorch-cheatsheet">Pytorch cheatsheet</a></h1>
<p>Cheatsheet:</p>
<div class="table-wrapper"><table><thead><tr><th></th><th>Using PyTorch</th><th>Using Candle</th></tr></thead><tbody>
<tr><td>Creation</td><td><code>torch.Tensor([[1, 2], [3, 4]])</code></td><td><code>Tensor::new(&amp;[[1f32, 2.], [3., 4.]], &amp;Device::Cpu)?</code></td></tr>
<tr><td>Creation</td><td><code>torch.zeros((2, 2))</code></td><td><code>Tensor::zeros((2, 2), DType::F32, &amp;Device::Cpu)?</code></td></tr>
<tr><td>Indexing</td><td><code>tensor[:, :4]</code></td><td><code>tensor.i((.., ..4))?</code></td></tr>
<tr><td>Operations</td><td><code>tensor.view((2, 2))</code></td><td><code>tensor.reshape((2, 2))?</code></td></tr>
<tr><td>Operations</td><td><code>a.matmul(b)</code></td><td><code>a.matmul(&amp;b)?</code></td></tr>
<tr><td>Arithmetic</td><td><code>a + b</code></td><td><code>&amp;a + &amp;b</code></td></tr>
<tr><td>Device</td><td><code>tensor.to(device="cuda")</code></td><td><code>tensor.to_device(&amp;Device::new_cuda(0)?)?</code></td></tr>
<tr><td>Dtype</td><td><code>tensor.to(dtype=torch.float16)</code></td><td><code>tensor.to_dtype(&amp;DType::F16)?</code></td></tr>
<tr><td>Saving</td><td><code>torch.save({"A": A}, "model.bin")</code></td><td><code>candle::safetensors::save(&amp;HashMap::from([("A", A)]), "model.safetensors")?</code></td></tr>
<tr><td>Loading</td><td><code>weights = torch.load("model.bin")</code></td><td><code>candle::safetensors::load("model.safetensors", &amp;device)</code></td></tr>
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<h1 id="hello-world"><a class="header" href="#hello-world">Hello world!</a></h1>
<p>We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.</p>
<p>Open <code>src/main.rs</code> and fill in this content:</p>
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
</span>use candle_core::{Device, Result, Tensor};
struct Model {
first: Tensor,
second: Tensor,
}
impl Model {
fn forward(&amp;self, image: &amp;Tensor) -&gt; Result&lt;Tensor&gt; {
let x = image.matmul(&amp;self.first)?;
let x = x.relu()?;
x.matmul(&amp;self.second)
}
}
fn main() -&gt; Result&lt;()&gt; {
// Use Device::new_cuda(0)?; to use the GPU.
let device = Device::Cpu;
let first = Tensor::randn(0f32, 1.0, (784, 100), &amp;device)?;
let second = Tensor::randn(0f32, 1.0, (100, 10), &amp;device)?;
let model = Model { first, second };
let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &amp;device)?;
let digit = model.forward(&amp;dummy_image)?;
println!("Digit {digit:?} digit");
Ok(())
}</code></pre></pre>
<p>Everything should now run with:</p>
<pre><code class="language-bash">cargo run --release
</code></pre>
<h2 id="using-a-linear-layer"><a class="header" href="#using-a-linear-layer">Using a <code>Linear</code> layer.</a></h2>
<p>Now that we have this, we might want to complexify things a bit, for instance by adding <code>bias</code> and creating
the classical <code>Linear</code> layer. We can do as such</p>
<pre><pre class="playground"><code class="language-rust"><span class="boring">#![allow(unused)]
</span><span class="boring">fn main() {
</span><span class="boring">extern crate candle_core;
</span><span class="boring">use candle_core::{Device, Result, Tensor};
</span>struct Linear{
weight: Tensor,
bias: Tensor,
}
impl Linear{
fn forward(&amp;self, x: &amp;Tensor) -&gt; Result&lt;Tensor&gt; {
let x = x.matmul(&amp;self.weight)?;
x.broadcast_add(&amp;self.bias)
}
}
struct Model {
first: Linear,
second: Linear,
}
impl Model {
fn forward(&amp;self, image: &amp;Tensor) -&gt; Result&lt;Tensor&gt; {
let x = self.first.forward(image)?;
let x = x.relu()?;
self.second.forward(&amp;x)
}
}
<span class="boring">}</span></code></pre></pre>
<p>This will change the model running code into a new function</p>
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
</span><span class="boring">use candle_core::{Device, Result, Tensor};
</span><span class="boring">struct Linear{
</span><span class="boring"> weight: Tensor,
</span><span class="boring"> bias: Tensor,
</span><span class="boring">}
</span><span class="boring">impl Linear{
</span><span class="boring"> fn forward(&amp;self, x: &amp;Tensor) -&gt; Result&lt;Tensor&gt; {
</span><span class="boring"> let x = x.matmul(&amp;self.weight)?;
</span><span class="boring"> x.broadcast_add(&amp;self.bias)
</span><span class="boring"> }
</span><span class="boring">}
</span><span class="boring">
</span><span class="boring">struct Model {
</span><span class="boring"> first: Linear,
</span><span class="boring"> second: Linear,
</span><span class="boring">}
</span><span class="boring">
</span><span class="boring">impl Model {
</span><span class="boring"> fn forward(&amp;self, image: &amp;Tensor) -&gt; Result&lt;Tensor&gt; {
</span><span class="boring"> let x = self.first.forward(image)?;
</span><span class="boring"> let x = x.relu()?;
</span><span class="boring"> self.second.forward(&amp;x)
</span><span class="boring"> }
</span><span class="boring">}
</span>fn main() -&gt; Result&lt;()&gt; {
// Use Device::new_cuda(0)?; to use the GPU.
// Use Device::Cpu; to use the CPU.
let device = Device::cuda_if_available(0)?;
// Creating a dummy model
let weight = Tensor::randn(0f32, 1.0, (784, 100), &amp;device)?;
let bias = Tensor::randn(0f32, 1.0, (100, ), &amp;device)?;
let first = Linear{weight, bias};
let weight = Tensor::randn(0f32, 1.0, (100, 10), &amp;device)?;
let bias = Tensor::randn(0f32, 1.0, (10, ), &amp;device)?;
let second = Linear{weight, bias};
let model = Model { first, second };
let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &amp;device)?;
// Inference on the model
let digit = model.forward(&amp;dummy_image)?;
println!("Digit {digit:?} digit");
Ok(())
}</code></pre></pre>
<p>Now it works, it is a great way to create your own layers.
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>
<h2 id="using-candle_nn"><a class="header" href="#using-candle_nn">Using <code>candle_nn</code>.</a></h2>
<p>For instance <a href="https://github.com/huggingface/candle/blob/main/candle-nn/src/linear.rs">Linear</a> is already there.
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>
<p>So instead we can simplify our example:</p>
<pre><code class="language-bash">cargo add --git https://github.com/huggingface/candle.git candle-nn
</code></pre>
<p>And rewrite our examples using it</p>
<pre><pre class="playground"><code class="language-rust"><span class="boring">extern crate candle_core;
</span><span class="boring">extern crate candle_nn;
</span>use candle_core::{Device, Result, Tensor};
use candle_nn::{Linear, Module};
struct Model {
first: Linear,
second: Linear,
}
impl Model {
fn forward(&amp;self, image: &amp;Tensor) -&gt; Result&lt;Tensor&gt; {
let x = self.first.forward(image)?;
let x = x.relu()?;
self.second.forward(&amp;x)
}
}
fn main() -&gt; Result&lt;()&gt; {
// Use Device::new_cuda(0)?; to use the GPU.
let device = Device::Cpu;
// This has changed (784, 100) -&gt; (100, 784) !
let weight = Tensor::randn(0f32, 1.0, (100, 784), &amp;device)?;
let bias = Tensor::randn(0f32, 1.0, (100, ), &amp;device)?;
let first = Linear::new(weight, Some(bias));
let weight = Tensor::randn(0f32, 1.0, (10, 100), &amp;device)?;
let bias = Tensor::randn(0f32, 1.0, (10, ), &amp;device)?;
let second = Linear::new(weight, Some(bias));
let model = Model { first, second };
let dummy_image = Tensor::randn(0f32, 1.0, (1, 784), &amp;device)?;
let digit = model.forward(&amp;dummy_image)?;
println!("Digit {digit:?} digit");
Ok(())
}</code></pre></pre>
<p>Feel free to modify this example to use <code>Conv2d</code> to create a classical convnet instead.</p>
<p>Now that we have the running dummy code we can get to more advanced topics:</p>
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<li><a href="../guide/cheatsheet.html">For PyTorch users</a></li>
<li><a href="../inference/inference.html">Running existing models</a></li>
<li><a href="../training/training.html">Training models</a></li>
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<h1 id="installation"><a class="header" href="#installation">Installation</a></h1>
<p><strong>With Cuda support</strong>:</p>
<ol>
<li>First, make sure that Cuda is correctly installed.</li>
</ol>
<ul>
<li><code>nvcc --version</code> should print information about your Cuda compiler driver.</li>
<li><code>nvidia-smi --query-gpu=compute_cap --format=csv</code> should print your GPUs compute capability, e.g. something
like:</li>
</ul>
<pre><code class="language-bash">compute_cap
8.9
</code></pre>
<p>You can also compile the Cuda kernels for a specific compute cap using the
<code>CUDA_COMPUTE_CAP=&lt;compute cap&gt;</code> environment variable.</p>
<p>If any of the above commands errors out, please make sure to update your Cuda version.</p>
<ol start="2">
<li>Create a new app and add <a href="https://github.com/huggingface/candle/tree/main/candle-core"><code>candle-core</code></a> with Cuda support.</li>
</ol>
<p>Start by creating a new cargo:</p>
<pre><code class="language-bash">cargo new myapp
cd myapp
</code></pre>
<p>Make sure to add the <code>candle-core</code> crate with the cuda feature:</p>
<pre><code class="language-bash">cargo add --git https://github.com/huggingface/candle.git candle-core --features "cuda"
</code></pre>
<p>Run <code>cargo build</code> to make sure everything can be correctly built.</p>
<pre><code class="language-bash">cargo build
</code></pre>
<p><strong>Without Cuda support</strong>:</p>
<p>Create a new app and add <a href="https://github.com/huggingface/candle/tree/main/candle-core"><code>candle-core</code></a> as follows:</p>
<pre><code class="language-bash">cargo new myapp
cd myapp
cargo add --git https://github.com/huggingface/candle.git candle-core
</code></pre>
<p>Finally, run <code>cargo build</code> to make sure everything can be correctly built.</p>
<pre><code class="language-bash">cargo build
</code></pre>
<p><strong>With mkl support</strong></p>
<p>You can also see the <code>mkl</code> feature which could be interesting to get faster inference on CPU. <a href="./advanced/mkl.html">Using mkl</a></p>
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