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compose实例

• Docker Compose examples: https://github.com/Haxxnet/Compose-Examples

ethereum实例

• 实例:

# Build Geth in a stock Go builder container
FROM golang:1.10-alpine as builder

RUN apk add --no-cache make gcc musl-dev linux-headers

ADD . /go-ethereum
RUN cd /go-ethereum && make geth

# Pull Geth into a second stage deploy alpine container
FROM alpine:latest

RUN apk add --no-cache ca-certificates
COPY --from=builder /go-ethereum/build/bin/geth /usr/local/bin/

EXPOSE 8545 8546 30303 30303/udp 30304/udp
ENTRYPOINT ["geth"]

Docker工具-netshoot

• a Docker + Kubernetes network trouble-shooting swiss-army container: https://github.com/nicolaka/netshoot

FROM debian:stable-slim as fetcher
COPY build/fetch_binaries.sh /tmp/fetch_binaries.sh

RUN apt-get update && apt-get install -y \
  curl \
  wget

RUN /tmp/fetch_binaries.sh

FROM alpine:3.18.0

RUN set -ex \
    && echo "http://dl-cdn.alpinelinux.org/alpine/edge/main" >> /etc/apk/repositories \
    && echo "http://dl-cdn.alpinelinux.org/alpine/edge/testing" >> /etc/apk/repositories \
    && echo "http://dl-cdn.alpinelinux.org/alpine/edge/community" >> /etc/apk/repositories \
    && apk update \
    && apk upgrade \
    && apk add --no-cache \
    apache2-utils \
    bash \
    bind-tools \
    bird \
    bridge-utils \
    busybox-extras \
    conntrack-tools \
    curl \
    dhcping \
    drill \
    ethtool \
    file\
    fping \
    iftop \
    iperf \
    iperf3 \
    iproute2 \
    ipset \
    iptables \
    iptraf-ng \
    iputils \
    ipvsadm \
    httpie \
    jq \
    libc6-compat \
    liboping \
    ltrace \
    mtr \
    net-snmp-tools \
    netcat-openbsd \
    nftables \
    ngrep \
    nmap \
    nmap-nping \
    nmap-scripts \
    openssl \
    py3-pip \
    py3-setuptools \
    scapy \
    socat \
    speedtest-cli \
    openssh \
    oh-my-zsh \
    strace \
    tcpdump \
    tcptraceroute \
    tshark \
    util-linux \
    vim \
    git \
    zsh \
    websocat \
    swaks \
    perl-crypt-ssleay \
    perl-net-ssleay

# Installing ctop - top-like container monitor
COPY --from=fetcher /tmp/ctop /usr/local/bin/ctop

# Installing calicoctl
COPY --from=fetcher /tmp/calicoctl /usr/local/bin/calicoctl

# Installing termshark
COPY --from=fetcher /tmp/termshark /usr/local/bin/termshark

# Installing grpcurl
COPY --from=fetcher /tmp/grpcurl /usr/local/bin/grpcurl

# Installing fortio
COPY --from=fetcher /tmp/fortio /usr/local/bin/fortio

# Setting User and Home
USER root
WORKDIR /root
ENV HOSTNAME netshoot

# ZSH Themes
RUN curl -fsSL https://raw.githubusercontent.com/ohmyzsh/ohmyzsh/master/tools/install.sh | sh
RUN git clone https://github.com/zsh-users/zsh-autosuggestions ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-autosuggestions
RUN git clone --depth=1 https://github.com/romkatv/powerlevel10k.git ${ZSH_CUSTOM:-$HOME/.oh-my-zsh/custom}/themes/powerlevel10k
COPY zshrc .zshrc
COPY motd motd

# Fix permissions for OpenShift and tshark
RUN chmod -R g=u /root
RUN chown root:root /usr/bin/dumpcap

# Running ZSH
CMD ["zsh"]

guoxudongdocker/kubectl

FROM alpine

LABEL maintainer="sunnydog0826@gmail.com"

ENV KUBE_LATEST_VERSION="v1.14.1"

RUN apk add --update ca-certificates \
 && apk add --update -t deps curl \
 && curl -L https://storage.googleapis.com/kubernetes-release/release/${KUBE_LATEST_VERSION}/bin/linux/amd64/kubectl -o /usr/local/bin/kubectl \
 && chmod +x /usr/local/bin/kubectl \
 && curl -L https://github.com/kubernetes-sigs/kustomize/releases/download/v2.0.3/kustomize_2.0.3_linux_amd64 -o /usr/local/bin/kustomize \
 && chmod +x /usr/local/bin/kustomize \
 && curl -L https://dl.bintray.com/flant/kubedog/v0.2.0/kubedog-linux-amd64-v0.2.0 -o /usr/local/bin/kubedog \
 && chmod +x /usr/local/bin/kubedog \
 && apk del --purge deps \
 && rm /var/cache/apk/*


WORKDIR /root
ENTRYPOINT ["kubectl"]
CMD ["help"]

# syntax = docker/dockerfile:experimental
#
# NOTE: To build this you will need a docker version > 18.06 with
#       experimental enabled and DOCKER_BUILDKIT=1
#
#       If you do not use buildkit you are not going to have a good time
#
#       For reference:
#           https://docs.docker.com/develop/develop-images/build_enhancements/
# From: https://github.com/pytorch/pytorch/blob/main/Dockerfile

ARG BASE_IMAGE=ubuntu:20.04
ARG PYTHON_VERSION=3.8

FROM ${BASE_IMAGE} as dev-base
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        build-essential \
        ca-certificates \
        ccache \
        cmake \
        curl \
        git \
        libjpeg-dev \
        libpng-dev && \
    rm -rf /var/lib/apt/lists/*
RUN /usr/sbin/update-ccache-symlinks
RUN mkdir /opt/ccache && ccache --set-config=cache_dir=/opt/ccache
ENV PATH /opt/conda/bin:$PATH

FROM dev-base as conda
ARG PYTHON_VERSION=3.8
# Automatically set by buildx
ARG TARGETPLATFORM
# translating Docker's TARGETPLATFORM into miniconda arches
RUN case ${TARGETPLATFORM} in \
         "linux/arm64")  MINICONDA_ARCH=aarch64  ;; \
         *)              MINICONDA_ARCH=x86_64   ;; \
    esac && \
    curl -fsSL -v -o ~/miniconda.sh -O  "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-${MINICONDA_ARCH}.sh"
COPY requirements.txt .
# Manually invoke bash on miniconda script per https://github.com/conda/conda/issues/10431
RUN chmod +x ~/miniconda.sh && \
    bash ~/miniconda.sh -b -p /opt/conda && \
    rm ~/miniconda.sh && \
    /opt/conda/bin/conda install -y python=${PYTHON_VERSION} cmake conda-build pyyaml numpy ipython && \
    /opt/conda/bin/python -mpip install -r requirements.txt && \
    /opt/conda/bin/conda clean -ya

FROM dev-base as submodule-update
WORKDIR /opt/pytorch
COPY . .
RUN git submodule update --init --recursive

FROM conda as build
ARG CMAKE_VARS
WORKDIR /opt/pytorch
COPY --from=conda /opt/conda /opt/conda
COPY --from=submodule-update /opt/pytorch /opt/pytorch
RUN make triton
RUN --mount=type=cache,target=/opt/ccache \
    export eval ${CMAKE_VARS} && \
    TORCH_CUDA_ARCH_LIST="3.5 5.2 6.0 6.1 7.0+PTX 8.0" TORCH_NVCC_FLAGS="-Xfatbin -compress-all" \
    CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" \
    python setup.py install

FROM conda as conda-installs
ARG PYTHON_VERSION=3.8
ARG CUDA_VERSION=12.1
ARG CUDA_CHANNEL=nvidia
ARG INSTALL_CHANNEL=pytorch-nightly
# Automatically set by buildx
# Note conda needs to be pinned to 23.5.2 see: https://github.com/pytorch/pytorch/issues/106470
RUN /opt/conda/bin/conda install -c "${INSTALL_CHANNEL}" -y python=${PYTHON_VERSION} conda=23.5.2
ARG TARGETPLATFORM

# On arm64 we can only install wheel packages.
RUN case ${TARGETPLATFORM} in \
         "linux/arm64")  pip install --extra-index-url https://download.pytorch.org/whl/cpu/ torch torchvision torchaudio ;; \
         *)              /opt/conda/bin/conda install -c "${INSTALL_CHANNEL}" -c "${CUDA_CHANNEL}" -y "python=${PYTHON_VERSION}" pytorch torchvision torchaudio "pytorch-cuda=$(echo $CUDA_VERSION | cut -d'.' -f 1-2)"  ;; \
    esac && \
    /opt/conda/bin/conda clean -ya
RUN /opt/conda/bin/pip install torchelastic

FROM ${BASE_IMAGE} as official
ARG PYTORCH_VERSION
ARG TRITON_VERSION
ARG TARGETPLATFORM
ARG CUDA_VERSION
LABEL com.nvidia.volumes.needed="nvidia_driver"
RUN apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
        ca-certificates \
        libjpeg-dev \
        libpng-dev \
        && rm -rf /var/lib/apt/lists/*
COPY --from=conda-installs /opt/conda /opt/conda
RUN if test -n "${TRITON_VERSION}" -a "${TARGETPLATFORM}" != "linux/arm64"; then \
        DEBIAN_FRONTEND=noninteractive apt install -y --no-install-recommends gcc; \
        rm -rf /var/lib/apt/lists/*; \
    fi
ENV PATH /opt/conda/bin:$PATH
ENV NVIDIA_VISIBLE_DEVICES all
ENV NVIDIA_DRIVER_CAPABILITIES compute,utility
ENV LD_LIBRARY_PATH /usr/local/nvidia/lib:/usr/local/nvidia/lib64
ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
ENV PYTORCH_VERSION ${PYTORCH_VERSION}
WORKDIR /workspace

FROM official as dev
# Should override the already installed version from the official-image stage
COPY --from=build /opt/conda /opt/conda

LLamaFactory

docker 命令:

docker build -f ./docker/docker-cuda/Dockerfile \
    --build-arg INSTALL_BNB=false \
    --build-arg INSTALL_VLLM=false \
    --build-arg INSTALL_DEEPSPEED=false \
    --build-arg INSTALL_FLASHATTN=false \
    --build-arg PIP_INDEX=https://pypi.org/simple \
    -t llamafactory:latest .

docker run -dit --gpus=all \
    -v ./hf_cache:/root/.cache/huggingface \
    -v ./ms_cache:/root/.cache/modelscope \
    -v ./data:/app/data \
    -v ./output:/app/output \
    -p 7860:7860 \
    -p 8000:8000 \
    --shm-size 16G \
    --name llamafactory \
    llamafactory:latest

docker exec -it llamafactory bash

docker-compose 命令:

docker compose up -d
docker compose exec llamafactory bash

# Use the NVIDIA official image with PyTorch 2.3.0
# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-24-02.html
FROM nvcr.io/nvidia/pytorch:24.02-py3

# Define environments
ENV MAX_JOBS=4
ENV FLASH_ATTENTION_FORCE_BUILD=TRUE
ENV VLLM_WORKER_MULTIPROC_METHOD=spawn

# Define installation arguments
ARG INSTALL_BNB=false
ARG INSTALL_VLLM=false
ARG INSTALL_DEEPSPEED=false
ARG INSTALL_FLASHATTN=false
ARG PIP_INDEX=https://mirrors.aliyun.com/pypi/simple

# Set the working directory
WORKDIR /app

# Install the requirements
COPY requirements.txt /app
RUN pip config set global.index-url "$PIP_INDEX" && \
    pip config set global.extra-index-url "$PIP_INDEX" && \
    python -m pip install --upgrade pip && \
    python -m pip install -r requirements.txt

# Copy the rest of the application into the image
COPY . /app

# Install the LLaMA Factory
RUN EXTRA_PACKAGES="metrics"; \
    if [ "$INSTALL_BNB" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},bitsandbytes"; \
    fi; \
    if [ "$INSTALL_VLLM" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},vllm"; \
    fi; \
    if [ "$INSTALL_DEEPSPEED" == "true" ]; then \
        EXTRA_PACKAGES="${EXTRA_PACKAGES},deepspeed"; \
    fi; \
    pip install -e ".[$EXTRA_PACKAGES]"

# Rebuild flash attention
RUN pip uninstall -y transformer-engine flash-attn && \
    if [ "$INSTALL_FLASHATTN" == "true" ]; then \
        pip uninstall -y ninja && pip install ninja && \
        pip install --no-cache-dir flash-attn --no-build-isolation; \
    fi

# Set up volumes
VOLUME [ "/root/.cache/huggingface", "/root/.cache/modelscope", "/app/data", "/app/output" ]

# Expose port 7860 for the LLaMA Board
ENV GRADIO_SERVER_PORT 7860
EXPOSE 7860

# Expose port 8000 for the API service
ENV API_PORT 8000
EXPOSE 8000

services:
  llamafactory:
    build:
      dockerfile: ./docker/docker-cuda/Dockerfile
      context: ../..
      args:
        INSTALL_BNB: false
        INSTALL_VLLM: false
        INSTALL_DEEPSPEED: false
        INSTALL_FLASHATTN: false
        PIP_INDEX: https://pypi.org/simple
    container_name: llamafactory
    volumes:
      - ../../hf_cache:/root/.cache/huggingface
      - ../../ms_cache:/root/.cache/modelscope
      - ../../data:/app/data
      - ../../output:/app/output
    ports:
      - "7860:7860"
      - "8000:8000"
    ipc: host
    tty: true
    stdin_open: true
    command: bash
    deploy:
      resources:
        reservations:
          devices:
          - driver: nvidia
            count: "all"
            capabilities: [gpu]
    restart: unless-stopped

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