Starting the book.

candle-book/src/guide/cheatsheet.md

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+# Pytorch cheatsheet
+
+{{#include ../../../README.md:cheatsheet}}

candle-book/src/guide/hello_world.md

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-# PyTorch cheatsheet
+# Hello world !
+
+We will now create the hello world of the ML world, building a model capable of solving MNIST dataset.
+
+Open `src/main.rs` and fill in with these contents:
+
+```rust
+# extern crate candle;
+use candle::{DType, Device, Result, Tensor};
+
+struct Model {
+    first: Tensor,
+    second: Tensor,
+}
+
+impl Model {
+    fn forward(&self, image: &Tensor) -> Result<Tensor> {
+        let x = image.matmul(&self.first)?;
+        let x = x.relu()?;
+        x.matmul(&self.second)
+    }
+}
+
+fn main() -> Result<()> {
+    // Use Device::new_cuda(0)?; to use the GPU.
+    let device = Device::Cpu;
+
+    let first = Tensor::zeros((784, 100), DType::F32, &device)?;
+    let second = Tensor::zeros((100, 10), DType::F32, &device)?;
+    let model = Model { first, second };
+
+    let dummy_image = Tensor::zeros((1, 784), DType::F32, &device)?;
+
+    let digit = model.forward(&dummy_image)?;
+    println!("Digit {digit:?} digit");
+    Ok(())
+}
+```
+
+Everything should now run with:
+
+```bash
+cargo run --release
+```
+
+Now that we have the running dummy code we can get to more advanced topics:
+
+
+- [For PyTorch users](./guide/cheatsheet.md)
+- [Running existing models](./inference/README.md)
+- [Training models](./training/README.md)
+
+

candle-book/src/guide/installation.md

@@ -1 +1,24 @@
 # Installation
+
+Start by creating a new app:
+
+```bash
+cargo new myapp
+cd myapp
+cargo add --git https://github.com/LaurentMazare/candle.git candle
+```
+
+At this point, candle will be built **without** CUDA support.
+To get CUDA support use the feature `cuda`
+```bash
+cargo add --git https://github.com/LaurentMazare/candle.git candle --features cuda
+```
+
+You can check everything works properly:
+
+```bash
+cargo build
+```
+
+
+You can also see feature `mkl` which could be interesting to get faster inference on CPU. [Using mkl](./advanced/mkl.md)