【Geep】秒速でアノテーションソフト(Label Studio)を起動【Docker Compose】

Geep

はじめに

深層学習で自作のデータセットを扱う場合,アノテーションを付与する必要があります.そこで,アノテーションソフトのLabel Studioを秒速で構築していこうと思います.

環境

  • Windows11
  • Docker Desktop for Windows

前準備

ここから

Release Release 1.6.0 · heartexlabs/label-studio
Label Studio 1.6 Release Notes The Label Studio 1.6 open source release now supports video object tracking in general availability, making it the most popular o...

Source code(zip)をダウンロードします.

file

Label Studio 起動

docker-compose

ダウンロードしたフォルダの中でPowershellを起動します.

file

ここで下記コマンドを実行.必要なダウンロードとかは勝手にやってくれます.

PS C:\Users\Polaris2\Downloads\label-studio-1.6.0\label-studio-1.6.0> docker-compose up --build
[+] Building 5.6s (33/33) FINISHED
 => [internal] load build definition from Dockerfile                                                               0.1s
 => => transferring dockerfile: 32B                                                                                0.0s
 => [internal] load .dockerignore                                                                                  0.1s
 => => transferring context: 35B                                                                                   0.0s
 => resolve image config for docker.io/docker/dockerfile:1.3                                                       2.5s
 => [auth] docker/dockerfile:pull token for registry-1.docker.io                                                   0.0s
 => CACHED docker-image://docker.io/docker/dockerfile:1.3@sha256:42399d4635eddd7a9b8a24be879d2f9a930d0ed040a61324  0.0s
 => [internal] load build definition from Dockerfile                                                               0.0s
 => [internal] load .dockerignore                                                                                  0.0s
 => [internal] load metadata for docker.io/library/ubuntu:22.04                                                    2.3s
 => [internal] load metadata for docker.io/library/node:14                                                         2.0s
 => [auth] library/node:pull token for registry-1.docker.io                                                        0.0s
 => [auth] library/ubuntu:pull token for registry-1.docker.io                                                      0.0s
 => [frontend-builder 1/5] FROM docker.io/library/node:14@sha256:768de2ed03a469f086aeb737196d559427a7b313afa36359  0.0s
 => [internal] settings cache mount permissions                                                                    0.0s
 => [internal] load build context                                                                                  0.2s
 => => transferring context: 83.44kB                                                                               0.1s
 => [stage-1  1/14] FROM docker.io/library/ubuntu:22.04@sha256:4b1d0c4a2d2aaf63b37111f34eb9fa89fa1bf53dd6e4ca954d  0.0s
 => CACHED [stage-1  2/14] WORKDIR /label-studio                                                                   0.0s
 => CACHED [stage-1  3/14] RUN set -eux  && apt-get update  && apt-get install --no-install-recommends --no-insta  0.0s
 => CACHED [stage-1  4/14] RUN --mount=type=cache,target=/.cache,uid=1001,gid=0     pip3 install --upgrade pip se  0.0s
 => CACHED [stage-1  5/14] RUN set -eux;     curl -sSL https://nginx.org/keys/nginx_signing.key | apt-key add - &  0.0s
 => CACHED [stage-1  6/14] COPY --chown=1001:0 deploy/requirements-mw.txt .                                        0.0s
 => CACHED [stage-1  7/14] RUN --mount=type=cache,target=/.cache,uid=1001,gid=0     pip3 install -r requirements-  0.0s
 => CACHED [stage-1  8/14] COPY --chown=1001:0 deploy/requirements.txt .                                           0.0s
 => CACHED [stage-1  9/14] RUN --mount=type=cache,target=/.cache,uid=1001,gid=0     pip3 install -r requirements.  0.0s
 => CACHED [stage-1 10/14] COPY --chown=1001:0 . .                                                                 0.0s
 => CACHED [stage-1 11/14] RUN --mount=type=cache,target=/.cache,uid=1001,gid=0     pip3 install -e . &&     chow  0.0s
 => CACHED [stage-1 12/14] RUN rm -rf ./label_studio/frontend                                                      0.0s
 => CACHED [frontend-builder 2/5] WORKDIR /label-studio/label_studio/frontend                                      0.0s
 => CACHED [frontend-builder 3/5] COPY --chown=1001:0 label_studio/frontend .                                      0.0s
 => CACHED [frontend-builder 4/5] COPY --chown=1001:0 label_studio/__init__.py /label-studio/label_studio/__init_  0.0s
 => CACHED [frontend-builder 5/5] RUN --mount=type=cache,target=/.npm,uid=1001,gid=0     npm ci  && npm run build  0.0s
 => CACHED [stage-1 13/14] COPY --chown=1001:0 --from=frontend-builder /label-studio/label_studio/frontend/dist .  0.0s
 => CACHED [stage-1 14/14] RUN python3 label_studio/manage.py collectstatic --no-input &&     chown -R 1001:0 /la  0.0s
 => exporting to image                                                                                             0.1s
 => => exporting layers                                                                                            0.0s
 => => writing image sha256:9bc5c44443e8c6fbad3e16c2168010242ea5afddd92fc86475b7543ba6bc7b9b                       0.0s
 => => naming to docker.io/heartexlabs/label-studio:latest                                                         0.0s

サインイン

Docker Desktop for Windowsを開いて,label-studio-160の8080:80をクリックすると

file

Label Studioが開くのでメールアドレスとパスワードを登録したら完了です.

file

結論

無事にLabel Studioの環境を構築して起動することができました.

参考文献

GitHub - heartexlabs/label-studio: Label Studio is a multi-type data labeling and annotation tool with standardized output format
Label Studio is a multi-type data labeling and annotation tool with standardized output format - GitHub - heartexlabs/label-studio: Label Studio is a multi-type...

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