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Very Deep Variational Auto Encoder(VDVAE)を手軽に試せる様にクジラとタコの力を借りてみた

Last updated at Posted at 2022-05-27

VAE(Variational Auto Encoder : 変分Auto Encoder)の総数を深く(Very Deep)にしたモデルにVDVAE(Very Deep Variational Auto Encoder)が有ります.OpenAIによるPyTorchを用いた実装がGitHubに公開されていますが,兎に角依存関係がシビアであり,環境構築時に依存関係エラーに翻弄されてしまいます.よってOpenAIが公式に動作を保証している環境を構築するために,クジラ(Docker)とタコ(Docker Compose)の力を借りて構築してみた所,とても楽に環境構築が出来たので備忘録を残しておこうと思います.

  • 2022/5現在 OpenAIが動作を保証している環境

    • Ubuntu 18.04
    • CUDA 10.1
    • PyTorch 1.6, Numpy 1.16
  • 公式リポジトリ

【参考】 依存関係エラー
$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
/opt/conda/lib/python3.8/site-packages/pip/_internal/commands/install.py:232: UserWarning: Disabling all use of wheels due to the use of --build-option / --global-option / --install-option.
  cmdoptions.check_install_build_global(options)
Using pip 21.1.3 from /opt/conda/lib/python3.8/site-packages/pip (python 3.8)
Non-user install because site-packages writeable
Created temporary directory: /tmp/pip-ephem-wheel-cache-fyxzb17o
Created temporary directory: /tmp/pip-req-tracker-i_0ln_a9
Initialized build tracking at /tmp/pip-req-tracker-i_0ln_a9
Created build tracker: /tmp/pip-req-tracker-i_0ln_a9
Entered build tracker: /tmp/pip-req-tracker-i_0ln_a9
Created temporary directory: /tmp/pip-install-7a69s0cm
Processing /home/yano/work/vdvae/apex
  Created temporary directory: /tmp/pip-req-build-3zevkle7
  DEPRECATION: A future pip version will change local packages to be built in-place without first copying to a temporary directory. We recommend you use --use-feature=in-tree-build to test your packages with this new behavior before it becomes the default.
   pip 21.3 will remove support for this functionality. You can find discussion regarding this at https://github.com/pypa/pip/issues/7555.
  Added file:///home/yano/work/vdvae/apex to build tracker '/tmp/pip-req-tracker-i_0ln_a9'
    Running setup.py (path:/tmp/pip-req-build-3zevkle7/setup.py) egg_info for package from file:///home/yano/work/vdvae/apex
    Created temporary directory: /tmp/pip-pip-egg-info-3a3q4z6h
    Running command python setup.py egg_info


    torch.__version__  = 1.11.0+cu102


    running egg_info
    creating /tmp/pip-pip-egg-info-3a3q4z6h/apex.egg-info
    writing /tmp/pip-pip-egg-info-3a3q4z6h/apex.egg-info/PKG-INFO
    writing dependency_links to /tmp/pip-pip-egg-info-3a3q4z6h/apex.egg-info/dependency_links.txt
    writing top-level names to /tmp/pip-pip-egg-info-3a3q4z6h/apex.egg-info/top_level.txt
    writing manifest file '/tmp/pip-pip-egg-info-3a3q4z6h/apex.egg-info/SOURCES.txt'
    reading manifest file '/tmp/pip-pip-egg-info-3a3q4z6h/apex.egg-info/SOURCES.txt'
    writing manifest file '/tmp/pip-pip-egg-info-3a3q4z6h/apex.egg-info/SOURCES.txt'
    /tmp/pip-req-build-3zevkle7/setup.py:109: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies!
      warnings.warn("Option --pyprof not specified. Not installing PyProf dependencies!")
  Source in /tmp/pip-req-build-3zevkle7 has version 0.1, which satisfies requirement apex==0.1 from file:///home/yano/work/vdvae/apex
  Removed apex==0.1 from file:///home/yano/work/vdvae/apex from build tracker '/tmp/pip-req-tracker-i_0ln_a9'
Created temporary directory: /tmp/pip-unpack-nmoac9pn
Skipping wheel build for apex, due to binaries being disabled for it.
Installing collected packages: apex
  Created temporary directory: /tmp/pip-record-siimy0x7
    Running command /opt/conda/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-3zevkle7/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-3zevkle7/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-siimy0x7/install-record.txt --single-version-externally-managed --compile --install-headers /opt/conda/include/python3.8/apex


    torch.__version__  = 1.11.0+cu102


    /tmp/pip-req-build-3zevkle7/setup.py:109: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies!
      warnings.warn("Option --pyprof not specified. Not installing PyProf dependencies!")

    Compiling cuda extensions with
    nvcc: NVIDIA (R) Cuda compiler driver
    Copyright (c) 2005-2021 NVIDIA Corporation
    Built on Mon_Oct_11_21:27:02_PDT_2021
    Cuda compilation tools, release 11.4, V11.4.152
    Build cuda_11.4.r11.4/compiler.30521435_0
    from /usr/local/cuda/bin

    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/tmp/pip-req-build-3zevkle7/setup.py", line 177, in <module>
        check_cuda_torch_binary_vs_bare_metal(CUDA_HOME)
      File "/tmp/pip-req-build-3zevkle7/setup.py", line 34, in check_cuda_torch_binary_vs_bare_metal
        raise RuntimeError(
    RuntimeError: Cuda extensions are being compiled with a version of Cuda that does not match the version used to compile Pytorch binaries.  Pytorch binaries were compiled with Cuda 10.2.
    In some cases, a minor-version mismatch will not cause later errors:  https://github.com/NVIDIA/apex/pull/323#discussion_r287021798.  You can try commenting out this check (at your own risk).
    Running setup.py install for apex ... error
ERROR: Command errored out with exit status 1: /opt/conda/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-3zevkle7/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-3zevkle7/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-siimy0x7/install-record.txt --single-version-externally-managed --compile --install-headers /opt/conda/include/python3.8/apex Check the logs for full command output.
Exception information:
Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/req/req_install.py", line 811, in install
    success = install_legacy(
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/operations/install/legacy.py", line 81, in install
    raise LegacyInstallFailure
pip._internal.operations.install.legacy.LegacyInstallFailure

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/cli/base_command.py", line 180, in _main
    status = self.run(options, args)
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/cli/req_command.py", line 205, in wrapper
    return func(self, options, args)
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/commands/install.py", line 393, in run
    installed = install_given_reqs(
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/req/__init__.py", line 77, in install_given_reqs
    requirement.install(
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/req/req_install.py", line 829, in install
    six.reraise(*exc.parent)
  File "/opt/conda/lib/python3.8/site-packages/pip/_vendor/six.py", line 703, in reraise
    raise value
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/operations/install/legacy.py", line 69, in install
    runner(
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/utils/subprocess.py", line 274, in runner
    call_subprocess(
  File "/opt/conda/lib/python3.8/site-packages/pip/_internal/utils/subprocess.py", line 244, in call_subprocess
    raise InstallationSubprocessError(proc.returncode, command_desc)
pip._internal.exceptions.InstallationSubprocessError: Command errored out with exit status 1: /opt/conda/bin/python -u -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-3zevkle7/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-3zevkle7/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-siimy0x7/install-record.txt --single-version-externally-managed --compile --install-headers /opt/conda/include/python3.8/apex Check the logs for full command output.
1 location(s) to search for versions of pip:
* https://pypi.org/simple/pip/
Fetching project page and analyzing links: https://pypi.org/simple/pip/
Getting page https://pypi.org/simple/pip/
Found index url https://pypi.org/simple
Starting new HTTPS connection (1): pypi.org:443
https://pypi.org:443 "GET /simple/pip/ HTTP/1.1" 200 18918
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  Found link https://files.pythonhosted.org/packages/ca/bf/4133a0e05eac641ec270bbcef30512b5ad307d7838adb994acd652cc30e3/pip-21.2.3-py3-none-any.whl#sha256=895df6014c2f02f9d278a8ad6e31cdfd312952b4a93c3068d0556964f4490057 (from https://pypi.org/simple/pip/) (requires-python:>=3.6), version: 21.2.3
  Found link https://files.pythonhosted.org/packages/e1/63/7c0e553ae0513ebf1858f08030158ff5998324013e0ba4c2e1c00b85df79/pip-21.2.3.tar.gz#sha256=91e66f2a2702e7d2dcc092ed8c5ebe923e69b9997ea28ba25823943bcd3bf820 (from https://pypi.org/simple/pip/) (requires-python:>=3.6), version: 21.2.3
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  Found link https://files.pythonhosted.org/packages/00/5f/d6959d6f25f202e3e68e3a53b815af42d770c829c19382d0acbf2c3e2112/pip-21.3.tar.gz#sha256=741a61baab1dbce2d8ca415effa48a2b6a964564f81a9f4f1fce4c433346c034 (from https://pypi.org/simple/pip/) (requires-python:>=3.6), version: 21.3
  Found link https://files.pythonhosted.org/packages/a4/6d/6463d49a933f547439d6b5b98b46af8742cc03ae83543e4d7688c2420f8b/pip-21.3.1-py3-none-any.whl#sha256=deaf32dcd9ab821e359cd8330786bcd077604b5c5730c0b096eda46f95c24a2d (from https://pypi.org/simple/pip/) (requires-python:>=3.6), version: 21.3.1
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  Found link https://files.pythonhosted.org/packages/4a/ca/e72b3b399d7a8cb34311aa8f52924108591c013b09f0268820afb4cd96fb/pip-22.0.tar.gz#sha256=d3fa5c3e42b33de52bddce89de40268c9a263cd6ef7c94c40774808dafb32c82 (from https://pypi.org/simple/pip/) (requires-python:>=3.7),
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  Found link https://files.pythonhosted.org/packages/83/b5/df8640236faa5a3cb80bfafd68e9fb4b22578208b8398c032ccff803f9e0/pip-22.0.2-py3-none-any.whl#sha256=682eabc4716bfce606aca8dab488e9c7b58b0737e9001004eb858cdafcd8dbdd (from https://pypi.org/simple/pip/) (requires-python:>=3.7), version: 22.0.2
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  Found link https://files.pythonhosted.org/packages/4d/16/0a14ca596f30316efd412a60bdfac02a7259bf8673d4d917dc60b9a21812/pip-22.0.4-py3-none-any.whl#sha256=c6aca0f2f081363f689f041d90dab2a07a9a07fb840284db2218117a52da800b (from https://pypi.org/simple/pip/) (requires-python:>=3.7), version: 22.0.4
  Found link https://files.pythonhosted.org/packages/33/c9/e2164122d365d8f823213a53970fa3005eb16218edcfc56ca24cb6deba2b/pip-22.0.4.tar.gz#sha256=b3a9de2c6ef801e9247d1527a4b16f92f2cc141cd1489f3fffaf6a9e96729764 (from https://pypi.org/simple/pip/) (requires-python:>=3.7), version: 22.0.4
Skipping link: not a file: https://pypi.org/simple/pip/
Given no hashes to check 191 links for project 'pip': discarding no candidates
Removed build tracker: '/tmp/pip-req-tracker-i_0ln_a9'

クジラで環境構築準備

DockerとDocker Composeを用いて環境構築を行います.(これら2つのセットアップは既に完了しているものとします.)本家のリポジトリにPull Requestを出すことを見越して,本家のリポジトリでは各種スクリプトファイルを積極的に利用していることを踏まえ,本記事の環境構築でもスクリプトファイルを作成し,作成したスクリプトファイルを実行して環境構築という方法を採用します.
:warning: 各スクリプトファイル内のディレクトリパス等は筆者の環境での値です.利用される場合は適宜変更して下さい.

Dockerfileの作成

今回の環境構築に用いるコンテナの中身を定義したDockerfileを作成します.ファイルの内容は以下の様に作成しました.ファイル冒頭リポジトリリストやリポジトリキーに関する操作が記載されていますが,これは4/28に実施されたNVIDIAによるリポジトリキーの更新に対応させるための操作です.今後公式のCUDAコンテナイメージが更新された場合は不要になる可能性が有ります.加えてapt-keyコマンドはUbuntuでは非推奨コマンドに指定されていますが,wgetcurlがインストールされていないため,やむを得ず利用しました.

2022/6/28更新: Decoderの一部の機能が正常に動作しないという症状に見舞われたため,Dockerfile/docker-compose.ymlの内容を見直し,更新しました.

FROM nvidia/cuda:10.2-cudnn8-devel-ubuntu18.04

ENV PIP_ROOT_USER_ACTION=ignore

RUN apt-key del 7fa2af80 && rm /etc/apt/sources.list.d/cuda.list
RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub

RUN apt-get update && apt-get upgrade -y
RUN apt-get install -y curl git vim wget
RUN apt-get install -y libjpeg-dev libopenmpi-dev zlib1g-dev

RUN apt-get install -y software-properties-common && add-apt-repository ppa:deadsnakes/ppa
RUN apt-get update && apt-get install -y python3.7 python3-pip python3.7-dev python3.7-distutils
RUN ln -fs /usr/bin/python3.7 /usr/bin/python3 && ln -fs /usr/bin/python3 /usr/bin/python
RUN python -m pip install -U pip && ln -fs /usr/bin/pip3 /usr/bin/pip && pip install -U setuptools

RUN pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 -f https://download.pytorch.org/whl/torch_stable.html
RUN pip install numpy==1.20.2 imageio==2.19.2 scikit-learn
RUN python -m pip install mpi4py==3.1.3
RUN git clone https://github.com/NVIDIA/apex
RUN cd apex && pip install -t/usr/local/lib/python3.7/dist-packages -v --disable-pip-version-check \
        --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .
  • 参考

docker-compose.ymlを作成

Docker Composeを利用するためdocker-compose.ymlファイルを作成しました.ファイルの内容は以下の様に作成しました.

version: "3.3"
services:
    vdvae_cuda102:
        build:
            context: .
            dockerfile: Dockerfile
        deploy:
            resources:
                reservations:
                    devices:
                        - capabilities:
                            - gpu
        environment:
            - NVIDIA_VISIBLE_DEVICES=all
            - NVIDIA_DRIVER_CAPABILITIES=all
        volumes:
            - $PWD:/root/work
        working_dir: /root/work/
        restart: always
        tty: true

コンテナ環境立ち上げ・ログイン用スクリプトファイルの作成

コンテナの起動とログインに用いるスクリプトファイルを作成します.ファイル内容は以下の様に作成しました.

コンテナ起動用スクリプト(start_container.sh)
#!/usr/bin/env bash                                                                                                                                        

docker-compose -f docker/docker-compose.yml up -d
コンテナログイン用スクリプト(login_container.sh)
#!/usr/bin/env bash                                                                                                                                          

docker-compose -f docker/docker-compose.yml exec vdvae_cuda101 /bin/bash

コンテナの起動とログイン

コンテナの起動

コンテナを起動するスクリプトを以下の様に実行します.初回のみイメージファイルのビルド処理が実行されます.

./start_container.sh

以下の様にコンテナが起動していることを確認出来ました.

$ docker-compose -f docker/docker-compose.yml ps
NAME                     COMMAND             SERVICE             STATUS              PORTS
docker-vdvae_cuda101-1   "bash"              vdvae_cuda101       running
【参考】 実行結果
$ ./start_container.sh 
[+] Building 2001.5s (16/16) FINISHED                                                                                                                        
 => [internal] load build definition from Dockerfile                                                                                                    0.0s
 => => transferring dockerfile: 32B                                                                                                                     0.0s
 => [internal] load .dockerignore                                                                                                                       0.0s
 => => transferring context: 2B                                                                                                                         0.0s
 => [internal] load metadata for docker.io/nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04                                                                    0.0s
 => [ 1/12] FROM docker.io/nvidia/cuda:10.1-cudnn7-devel-ubuntu18.04                                                                                    0.0s
 => CACHED [ 2/12] RUN apt-key del 7fa2af80                                                                                                             0.0s
 => CACHED [ 3/12] RUN rm /etc/apt/sources.list.d/nvidia-ml.list                                                                                        0.0s
 => CACHED [ 4/12] RUN rm /etc/apt/sources.list.d/cuda.list                                                                                             0.0s
 => CACHED [ 5/12] RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub                 0.0s
 => [ 6/12] RUN apt-get update && apt-get upgrade -y                                                                                                   57.0s
 => [ 7/12] RUN apt-get install -y curl git python3-pip vim wget                                                                                      115.7s
 => [ 8/12] RUN apt-get install -y libjpeg-dev libopenmpi-dev zlib1g-dev                                                                                9.8s 
 => [ 9/12] RUN pip3 install torch==1.6+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html                          904.5s 
 => [10/12] RUN pip3 install numpy==1.16 imageio mpi4py sklearn                                                                                       112.8s 
 => [11/12] RUN git clone https://github.com/NVIDIA/apex                                                                                               18.1s 
 => [12/12] RUN cd apex && pip3 install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .      777.8s 
 => exporting to image                                                                                                                                  5.8s 
 => => exporting layers                                                                                                                                 5.8s 
 => => writing image sha256:185335a54425d48460e0fbf23a1dd48795c304aa731b2ac0095388c347a38ba7                                                            0.0s 
 => => naming to docker.io/library/docker_vdvae_cuda101                                                                                                 0.0s 
                                                                                                                                                             
Use 'docker scan' to run Snyk tests against images to find vulnerabilities and learn how to fix them                                                         
[+] Running 2/2
 ⠿ Network docker_default            Created                                                                                                            0.0s
 ⠿ Container docker-vdvae_cuda101-1  Started

コンテナにログイン

コンテナにログインするスクリプトを以下の様に実行します.

./login_container.sh

以下の様にコンテナにログイン出来ました.lsコマンドを実行すると,working_dir上のファイルが表示されています.

$ ./login_container.sh 
root@8017c6abb55c:~/work# ls
LICENSE.md           cifar-10-python.tar.gz  files_to_npy.py   login_container.sh  setup_ffhq256.sh    train.py          vae.py
README.md            data.py                 header-image.png  setup_cifar10.sh    setup_imagenet.sh   train_helpers.py  vae_helpers.py
cifar-10-batches-py  docker                  hps.py            setup_ffhq1024.sh   start_container.sh  utils.py

まとめ

依存関係の厳しいフレームワークもクジラとタコのコンビを活用することで,ストレスフリーの環境構築が実現できました.また,実際に業務で利用していますが,実装元が保証している条件で環境構築を実行したこともあり,今までの所トラブルも発生せずに利用出来ています.今までに紹介したファイルをCommitしてPull Requestを本家に出していますが,2022/5/27現在の状況として,レビュー,コメント,マージ何れのステージにも移れていません...:scream: :scream_cat: 公式リポジトリの状況を見ると,2年前を最後に開発が止まっている様にも見えます...

2022/5/28追記: 本家の開発の現状

バグ修正等のPull Requestを出してみましたが,1ヶ月程何も進展が有りませんでした.しかし,実装者からのコメントが...

image.png

どうやら実装者がOpenAIを離れてしまった様です.Forkしたリポジトリで開発を進めることを勧められました.今後の開発の成果は筆者のリポジトリのmainリポジトリに反映していこうと思います.

Reference

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