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