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Specify podman works in Container documentation

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In the RamaLama project we've been extensively using podman. We've
also been using docker. Both work resonably well with llama.cpp .
Highlighting this in the docmumentation

Signed-off-by: Eric Curtin <ecurtin@redhat.com>
This commit is contained in:
Eric Curtin
2025-02-05 13:46:03 +00:00
parent d774ab3acc
commit 947158ee52
1 changed files with 8 additions and 8 deletions
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docs/docker.md → docs/containers.md
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@@ -1,11 +1,11 @@
# Docker
# Containers
## Prerequisites
* Docker must be installed and running on your system.
* Docker or Podman must be installed and running on your system. Replace `docker` with `podman` if using Podman.
* Create a folder to store big models & intermediate files (ex. /llama/models)
## Images
We have three Docker images available for this project:
We have three container images available for this project:
1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
@@ -53,11 +53,11 @@ or with a server image:
docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
```
## Docker With CUDA
## Containers With CUDA
Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
## Building Docker locally
## Building Container images locally
```bash
docker build -t local/llama.cpp:full-cuda --target full -f .devops/cuda.Dockerfile .
@@ -88,11 +88,11 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /
docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
```
## Docker With MUSA
## Containers With MUSA
Assuming one has the [mt-container-toolkit](https://developer.mthreads.com/musa/native) properly installed on Linux, `muBLAS` should be accessible inside the container.
## Building Docker locally
## Building Container images locally
```bash
docker build -t local/llama.cpp:full-musa --target full -f .devops/musa.Dockerfile .
@@ -114,7 +114,7 @@ The resulting images, are essentially the same as the non-MUSA images:
## Usage
After building locally, Usage is similar to the non-MUSA examples, but you'll need to set `mthreads` as default Docker runtime. This can be done by executing `(cd /usr/bin/musa && sudo ./docker setup $PWD)` and verifying the changes by executing `docker info | grep mthreads` on the host machine. You will also want to use the `--n-gpu-layers` flag.
After building locally, Usage is similar to the non-MUSA examples, but you'll need to set `mthreads` as default container runtime. This can be done by executing `(cd /usr/bin/musa && sudo ./docker setup $PWD)` and verifying the changes by executing `docker info | grep mthreads` on the host machine. You will also want to use the `--n-gpu-layers` flag.
```bash
docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1