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move installation to book

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@ -10,65 +10,13 @@ and ease of use. Try our online demos:
[LLaMA2](https://huggingface.co/spaces/lmz/candle-llama2),
[yolo](https://huggingface.co/spaces/lmz/candle-yolo).
## Installation
- **With Cuda support**:
1. First, make sure that Cuda is correctly installed.
- `nvcc --version` should print your information about your Cuda compiler driver.
- `nvidia-smi --query-gpu=compute_cap --format=csv` should print your GPUs compute capability, e.g. something
like:
```
compute_cap
8.9
```
If any of the above commands errors out, please make sure to update your Cuda version.
2. Create a new app and add [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) with Cuda support
Start by creating a new cargo:
```bash
cargo new myapp
cd myapp
```
Make sure to add the `candle-core` crate with the cuda feature:
```
cargo add --git https://github.com/huggingface/candle.git candle-core --features "cuda"
```
Run `cargo build` to make sure everything can be correctly built.
```
cargo run
```
**Without Cuda support**:
Create a new app and add [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) as follows:
```
cargo new myapp
cd myapp
cargo add --git https://github.com/huggingface/candle.git candle-core
```
Run `cargo build` to make sure everything can be correctly built.
```
cargo run
```
## Get started
Having installed `candle-core` as described in [Installation](#Installation), we can now
run a simple matrix multiplication.
Make sure that you have [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) correctly installed as described in [**Installation**](https://huggingface.github.io/candle/guide/installation.html).
We will need the [`anyhow`](https://docs.rs/anyhow/latest/anyhow/) package for our example,
so let's add it to our app.
Let's see how to run a simple matrix multiplication.
We will need the [`anyhow`](https://docs.rs/anyhow/latest/anyhow/) package for our example, so let's also add it to our app.
```
cd myapp
@ -103,7 +51,7 @@ Having installed `candle` with Cuda support, simply define the `device` to be on
+ let device = Device::new_cuda(0)?;
```
For more advanced examples, please have a look at the following sections.
For more advanced examples, please have a look at the following section.
## Check out our examples

40
candle-book/src/guide/installation.md
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@ -1,5 +1,44 @@
# Installation
- **With Cuda support**:
1. First, make sure that Cuda is correctly installed.
- `nvcc --version` should print your information about your Cuda compiler driver.
- `nvidia-smi --query-gpu=compute_cap --format=csv` should print your GPUs compute capability, e.g. something
like:
```
compute_cap
8.9
```
If any of the above commands errors out, please make sure to update your Cuda version.
2. Create a new app and add [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) with Cuda support
Start by creating a new cargo:
```bash
cargo new myapp
cd myapp
```
Make sure to add the `candle-core` crate with the cuda feature:
```
cargo add --git https://github.com/huggingface/candle.git candle-core --features "cuda"
```
Run `cargo build` to make sure everything can be correctly built.
```
cargo run
```
**Without Cuda support**:
Create a new app and add [`candle-core`](https://github.com/huggingface/candle/tree/main/candle-core) as follows:
Start by creating a new app:
```bash
@ -20,5 +59,6 @@ You can check everything works properly:
cargo build
```
**With mkl support**
You can also see the `mkl` feature which could be interesting to get faster inference on CPU. [Using mkl](./advanced/mkl.md)