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* docs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Small enhancements Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Enhancements * Default to zen-dark Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
42 lines
1.5 KiB
Markdown
42 lines
1.5 KiB
Markdown
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disableToc = false
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title = "Running on Nvidia ARM64"
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weight = 27
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LocalAI can be run on Nvidia ARM64 devices, such as the Jetson Nano, Jetson Xavier NX, and Jetson AGX Xavier. The following instructions will guide you through building the LocalAI container for Nvidia ARM64 devices.
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## Prerequisites
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- Docker engine installed (https://docs.docker.com/engine/install/ubuntu/)
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- Nvidia container toolkit installed (https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-with-ap)
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## Build the container
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Build the LocalAI container for Nvidia ARM64 devices using the following command:
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```bash
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git clone https://github.com/mudler/LocalAI
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cd LocalAI
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docker build --build-arg SKIP_DRIVERS=true --build-arg BUILD_TYPE=cublas --build-arg BASE_IMAGE=nvcr.io/nvidia/l4t-jetpack:r36.4.0 --build-arg IMAGE_TYPE=core -t quay.io/go-skynet/local-ai:master-nvidia-l4t-arm64-core .
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```
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Otherwise images are available on quay.io and dockerhub:
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```bash
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docker pull quay.io/go-skynet/local-ai:master-nvidia-l4t-arm64-core
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```
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## Usage
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Run the LocalAI container on Nvidia ARM64 devices using the following command, where `/data/models` is the directory containing the models:
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```bash
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docker run -e DEBUG=true -p 8080:8080 -v /data/models:/models -ti --restart=always --name local-ai --runtime nvidia --gpus all quay.io/go-skynet/local-ai:master-nvidia-l4t-arm64-core
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```
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Note: `/data/models` is the directory containing the models. You can replace it with the directory containing your models.
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