--- title: Fly GPUs quickstart layout: docs toc: false nav: firecracker --- 1. You can use any base image for your Dockerfile, but it is convenient to base it on `ubuntu:22.04` and install libraries from NVIDIA's official apt repository: `RUN apt install -y cuda-nvcc-12-2 libcublas-12-2 libcudnn8` is usually enough. <div class = "note icon"> **Notes**: - Do not install meta packages like: `cuda-runtime-*` - `cuda-libraries-12-2` is good, but a bulky start. Once you know what libs are needed at build and runtime, please pick accordingly to optimize final image size. - Use multi-stage docker builds as much as possible. </div> 1. From flyctl, create an app using either `fly launch` or `fly apps create`. <div class="note icon"> **Note**: GPUs are not available in all regions. There are these GPU types available: Nvidia A10, L40S, A100-PCIe-40GB, and A100-SXM4-80GB. <br>Currently GPUs are available in the following regions: - `a10`: `ord` - `l40s`: `ord` - `a100-40gb`: `ord` - `a100-80gb`: `iad`, `sjc`, `syd`, `ams` </div> 1. Create or modify the `fly.toml` config file in the project source directory, replacing values with your own: ```toml app = "my-gpu-app" primary_region = "ord" vm.size = "a100-40gb" # Use a volume to store LLMs or any big file that doesn't fit in a Docker image [[mounts]] source = "data" destination = "/data" [http_service] internal_port = 8080 auto_stop_machines = false ``` <div class="note icon"> **Notes**: - Make sure `vm.size` is set in `fly.toml`, valid values are `a10`, `l40s`, `a100-40gb` and `a100-80gb`. - Make sure to include a `[[mounts]]` section in `fly.toml`. - The volume gets created automatically by `fly deploy`. - Use the volume to store the models and large files that can't be shipped as a docker image. </div> 1. Deploy your app: ```cmd fly deploy ``` That's pretty much it to get an app running with a Machine on a GPU. ## Volumes and GPU Machines <div class="important icon"> **Important**: If you create any additional volumes, they need to be created with the same constraints as your Machine. </div> Here's an example of creating a new one hundred gigabyte volume for storing ML models in the `ord` region, on a machine with a GPU: ```cmd fly volumes create models \ --size 100 \ --vm-gpu-kind a100-40gb \ --region ord ``` Example Dockerfile: ``` FROM ubuntu:22.04 as base RUN apt update -q && apt install -y ca-certificates wget && \ wget -qO /cuda-keyring.deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb && \ dpkg -i /cuda-keyring.deb && apt update -q FROM base as builder RUN apt install -y --no-install-recommends git cuda-nvcc-12-2 RUN git clone --depth=1 https://github.com/nvidia/cuda-samples.git /cuda-samples RUN cd /cuda-samples/Samples/1_Utilities/deviceQuery && \ make && install -m 755 deviceQuery /usr/local/bin FROM base as runtime #RUN apt install -y --no-install-recommends libcudnn8 libcublas-12-2 COPY --from=builder /usr/local/bin/deviceQuery /usr/local/bin/deviceQuery CMD ["sleep", "inf"] ``` ## Examples using Fly GPUs - Elixir Llama2-13b on Fly GPUs: https://gist.github.com/chrismccord/59a5e81f144a4dfb4bf0a8c3f2673131 - Github fly-apps repos with the `gpu` topic: https://github.com/orgs/fly-apps/repositories?q=topic%3Agpu - Fly.io CUDA example: https://gist.github.com/dangra/f8123001fe0f2453a8cd638b89738465 - Deploying CLIP on Fly.io: https://gist.github.com/simonw/52c7734e34cac2b26ea1378845674edc

fly volumes create models \
  --size 100 \
  --vm-gpu-kind a100-40gb \
  --region ord

FROM ubuntu:22.04 as base
RUN apt update -q && apt install -y ca-certificates wget && \
    wget -qO /cuda-keyring.deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-keyring_1.1-1_all.deb && \
    dpkg -i /cuda-keyring.deb && apt update -q

FROM base as builder
RUN apt install -y --no-install-recommends git cuda-nvcc-12-2
RUN git clone --depth=1 https://github.com/nvidia/cuda-samples.git /cuda-samples
RUN cd /cuda-samples/Samples/1_Utilities/deviceQuery && \
    make && install -m 755 deviceQuery /usr/local/bin

FROM base as runtime
#RUN apt install -y --no-install-recommends libcudnn8 libcublas-12-2
COPY --from=builder /usr/local/bin/deviceQuery /usr/local/bin/deviceQuery
CMD ["sleep", "inf"]