メモ書きとしてWIndowsにDockerを入れて、docker-composeを導入する手順、jupyterを起動するまでの手順を残す
基本的にDockerはコンテナ仮想化を提供するプラットフォームアプリで、下記のような手順で実施可能
１
Dockerfileを作成し、docker buildでコンテナイメージを作成。
２
docker runでそのイメージからコンテナを起動し、必要に応じてdocker execでコンテナ内でコマンドを実行してアプリを操作。
上記の他にはdocker-composeという仕組みがあり
docker-compose.yml ファイルを使用して、複数のコンテナの設定を一つのファイルにまとめて定義します。このファイルには、各サービス（コンテナ）のイメージ、ビルド手順、環境変数、ボリュームマウント、ネットワーキング設定などを記述できます。
Docker for DesktopをWindowsに入れる
適当に公式ページからインストール
https://www.docker.com/products/docker-desktop/
docker-composeをPower Shellから導入
なぜかデスクトップ用のDockerの入れてもdocker-composeが入っていなかったのでPowerSHellから導入。
Invoke-WebRequest "https://github.com/docker/compose/releases/download/v2.24.6/docker-compose-windows-x86_64.exe"
-OutFile C:\Program Files\Docker\Docker
docker-composeでymlファイルを実行
docker-compose.yml
version: "3"
services:
jupyter:
build: .
volumes:
- "C:\\Users:/tmp/working"
working_dir: /tmp/working
ports:
- "8888:8888"
command: jupyter notebook --ip=0.0.0.0 --allow-root --no-browser
インストールログはこちら。
PS C:xxxxxxxxx python-kaggle-start-book-master\python-kaggle-start-book-master> docker-compose.exe up --build
time="2024-08-27T09:50:50+09:00" level=warning msg="C:xxxxxxxxxxxxxx python-kaggle-start-book-master\\python-kaggle-start-book-master\\docker-compose.yml: the attribute `version` is obsolete, it will be ignored, please remove it to avoid potential confusion"
[+] Building 755.0s (6/6) FINISHED docker:desktop-linux
=> [jupyter internal] load build definition from Dockerfile 0.0s
=> => transferring dockerfile: 74B 0.0s
=> [jupyter internal] load metadata for gcr.io/kaggle-images/python:v68 1.9s
=> [jupyter internal] load .dockerignore 0.0s
=> => transferring context: 2B 0.0s
=> [jupyter 1/1] FROM gcr.io/kaggle-images/python:v68@sha256:7a54af952b186b6135e2e6876ee89fffc5e018383ce91f6fd 752.6s
=> => resolve gcr.io/kaggle-images/python:v68@sha256:7a54af952b186b6135e2e6876ee89fffc5e018383ce91f6fdb91a255f7b 0.0s
=> => sha256:c5e155d5a1d130a7f8a3e24cee0d9e1349bff13f90ec6a941478e558fde53c14 45.34MB / 45.34MB 16.4s
=> => sha256:7a54af952b186b6135e2e6876ee89fffc5e018383ce91f6fdb91a255f7b658d2 7.07kB / 7.07kB 0.0s
=> => sha256:38107095ca92aa4322ef8b6851dea976921a1f0b599276e617844659165ec726 33.02kB / 33.02kB 0.0s
=> => sha256:5764e90b1fae3f6050c1b56958da5e94c0d0c2a5211955f579958fcbe6a679fd 1.57GB / 1.57GB 481.9s
=> => sha256:86534c0d13b7196a49d52a65548f524b744d48ccaf89454659637bee4811d312 95.10MB / 95.10MB 33.0s
=> => extracting sha256:c5e155d5a1d130a7f8a3e24cee0d9e1349bff13f90ec6a941478e558fde53c14 2.0s
=> => sha256:ba67f7304613606a1d577e2fc5b1e6bb14b764bcc8d07021779173bcc6a8d4b6 1.08MB / 1.08MB 18.4s
=> => sha256:10d355ad7c8c7ec86943c9bc6647bc77ef87a59c9901ddbf4338d328eacc21ad 213B / 213B 18.8s
=> => sha256:16ad5b44117a2fca535f611053d375afbcd5ce91212ff21825ba9d5a383537ab 525B / 525B 19.2s
=> => sha256:4def8145903fa388b44bdfe99e061e25c756e8a85071988230fbcd2d8406a5ce 457B / 457B 19.8s
=> => sha256:b318a6db4c687ab828a06300402bcfda4df9df6e351c11e98ea145e5adeefab5 13.09MB / 13.09MB 26.2s
=> => sha256:444bc9f8f83c3c6457e2257d84aeac9f47cfb211f407756cf1be27c650eeb265 571.84MB / 571.84MB 210.7s
=> => sha256:22b6483a5759b34af4ff11a0db4f5cb5fd227ce4a4023d34d5720c42f4c24858 953.23MB / 953.23MB 303.0s
=> => extracting sha256:86534c0d13b7196a49d52a65548f524b744d48ccaf89454659637bee4811d312 3.2s
=> => sha256:3a247f79636f01846b77de9820a40c24cab3276724276c0204bf4f4eb6c964d2 79.10MB / 79.10MB 236.3s
=> => sha256:a323e7c22c178c86d28e1a1f95a99f24409d612c2dfa363dcac945d69bfc3039 93.99MB / 93.99MB 259.4s
=> => sha256:52ca83457f627d2bdb01a56ef4efb22644ec1527f6b19d5928df2ec2fec45c67 1.15GB / 1.15GB 595.1s
=> => sha256:0caf9e9942fecefcb6629fbc5344fee90b08cd16c8ae3c86d68004975b3d3981 371.52MB / 371.52MB 406.6s
=> => sha256:061ec0743e8bc182ee50b953b0c3b3260fea72279e9786f85aae256b94cad51b 145.89MB / 145.89MB 458.2s
=> => sha256:4eb09dffe364526524f185f0ef0b6948a6f67e13a4e2c8a5c945f4d1aa4163af 63.25MB / 63.25MB 489.2s
=> => extracting sha256:5764e90b1fae3f6050c1b56958da5e94c0d0c2a5211955f579958fcbe6a679fd 50.3s
=> => sha256:c5282e066b17f86c7e037fdcab5300c8d9e19d94cc607956538def2b808b8ab1 372.31MB / 372.31MB 571.4s
=> => sha256:4d6b11a35c61823ec4e76573a77b67fb1cc55f24c0bedc3de02b36e7bbdaccc0 91.38MB / 91.38MB 521.6s
=> => sha256:c4e7c70c716c9ab1c39604076d5193d4f3219b69918d3c02f29a096cbc51d931 115.24MB / 115.24MB 566.2s
=> => extracting sha256:ba67f7304613606a1d577e2fc5b1e6bb14b764bcc8d07021779173bcc6a8d4b6 0.0s
=> => extracting sha256:10d355ad7c8c7ec86943c9bc6647bc77ef87a59c9901ddbf4338d328eacc21ad 0.0s
=> => extracting sha256:16ad5b44117a2fca535f611053d375afbcd5ce91212ff21825ba9d5a383537ab 0.0s
=> => extracting sha256:4def8145903fa388b44bdfe99e061e25c756e8a85071988230fbcd2d8406a5ce 0.0s
=> => extracting sha256:b318a6db4c687ab828a06300402bcfda4df9df6e351c11e98ea145e5adeefab5 0.1s
=> => extracting sha256:444bc9f8f83c3c6457e2257d84aeac9f47cfb211f407756cf1be27c650eeb265 34.8s
=> => sha256:3610cd6de3daa0a7b04d0c696319600ab39517c639f991f6283ed81d0e9e8330 66.71MB / 66.71MB 584.8s
=> => extracting sha256:22b6483a5759b34af4ff11a0db4f5cb5fd227ce4a4023d34d5720c42f4c24858 10.0s
=> => sha256:81e49fa7885b9774361729aeba6926d10bf3241136a4deb2a00580bd5fd127cd 12.75MB / 12.75MB 577.8s
=> => sha256:0cd90ca0bb5315533ddd29fc827df87f512db435ef77355f0a1d1fb82e36e359 611.52MB / 611.52MB 722.5s
=> => extracting sha256:3a247f79636f01846b77de9820a40c24cab3276724276c0204bf4f4eb6c964d2 0.5s
=> => extracting sha256:a323e7c22c178c86d28e1a1f95a99f24409d612c2dfa363dcac945d69bfc3039 4.1s
=> => sha256:8cdcc412f2bfc13af935863fd277409d191ff90cdb352b15ba0bd9f793773903 21.74MB / 21.74MB 596.0s
=> => extracting sha256:52ca83457f627d2bdb01a56ef4efb22644ec1527f6b19d5928df2ec2fec45c67 19.3s
=> => sha256:78a72602749e101fcfa99322c86a49f3d878ffb210d1fc16afa993a8ed8008f7 163.49MB / 163.49MB 628.1s
=> => sha256:6fc4ff181a1de96f828fdcaec501f8b27eb6c0875ce5320e63fc4353ec277df0 19.09kB / 19.09kB 597.0s
=> => sha256:9268a2f3969f9a4701de9aabf05dde69168db6d6e25c817713f78a90b73a3de7 55.06MB / 55.06MB 621.4s
=> => extracting sha256:0caf9e9942fecefcb6629fbc5344fee90b08cd16c8ae3c86d68004975b3d3981 8.9s
=> => sha256:149e7ce48019565994388a04e5b3a28acf0e605e6588b44529e66568ea7514d4 3.08kB / 3.08kB 622.9s
=> => sha256:b223eca929d16d722e56e8cce14583273a7a2c85ee9eae6a73704c9d0f68168c 2.08kB / 2.08kB 624.4s
=> => extracting sha256:061ec0743e8bc182ee50b953b0c3b3260fea72279e9786f85aae256b94cad51b 4.7s
=> => sha256:ff5df7baf16080ed5274ff3cec017443eecd891f5580d96562e4540eb4c18b97 2.05kB / 2.05kB 625.9s
=> => sha256:2a5ec7dba5069c470f7392ac6afed0f3da405b09c7d323ae2cfdaca27b9468c1 873B / 873B 627.4s
=> => sha256:5188298d15aaf83916628e388441cb845ef9c2f7e7a950d4468781e868d20990 73.99kB / 73.99kB 629.0s
=> => sha256:ebbdeaaec044b6f4c48249297b5c2ae67e428bde25fc7502d7a36fb0ce1bd7a6 1.11kB / 1.11kB 629.6s
=> => extracting sha256:4eb09dffe364526524f185f0ef0b6948a6f67e13a4e2c8a5c945f4d1aa4163af 1.3s
=> => extracting sha256:c5282e066b17f86c7e037fdcab5300c8d9e19d94cc607956538def2b808b8ab1 20.2s
=> => extracting sha256:4d6b11a35c61823ec4e76573a77b67fb1cc55f24c0bedc3de02b36e7bbdaccc0 6.4s
=> => extracting sha256:c4e7c70c716c9ab1c39604076d5193d4f3219b69918d3c02f29a096cbc51d931 3.5s
=> => extracting sha256:3610cd6de3daa0a7b04d0c696319600ab39517c639f991f6283ed81d0e9e8330 2.2s
=> => extracting sha256:81e49fa7885b9774361729aeba6926d10bf3241136a4deb2a00580bd5fd127cd 0.8s
=> => extracting sha256:0cd90ca0bb5315533ddd29fc827df87f512db435ef77355f0a1d1fb82e36e359 11.8s
=> => extracting sha256:8cdcc412f2bfc13af935863fd277409d191ff90cdb352b15ba0bd9f793773903 0.4s
=> => extracting sha256:78a72602749e101fcfa99322c86a49f3d878ffb210d1fc16afa993a8ed8008f7 8.9s
=> => extracting sha256:6fc4ff181a1de96f828fdcaec501f8b27eb6c0875ce5320e63fc4353ec277df0 0.0s
=> => extracting sha256:9268a2f3969f9a4701de9aabf05dde69168db6d6e25c817713f78a90b73a3de7 6.9s
=> => extracting sha256:149e7ce48019565994388a04e5b3a28acf0e605e6588b44529e66568ea7514d4 0.0s
=> => extracting sha256:b223eca929d16d722e56e8cce14583273a7a2c85ee9eae6a73704c9d0f68168c 0.0s
=> => extracting sha256:ff5df7baf16080ed5274ff3cec017443eecd891f5580d96562e4540eb4c18b97 0.0s
=> => extracting sha256:2a5ec7dba5069c470f7392ac6afed0f3da405b09c7d323ae2cfdaca27b9468c1 0.0s
=> => extracting sha256:5188298d15aaf83916628e388441cb845ef9c2f7e7a950d4468781e868d20990 0.0s
=> => extracting sha256:ebbdeaaec044b6f4c48249297b5c2ae67e428bde25fc7502d7a36fb0ce1bd7a6 0.0s
=> [jupyter] exporting to image 0.4s
=> => exporting layers 0.0s
=> => writing image sha256:2c99a373a41fa4b2b24f07ebf89df08ff15e8b1852cf533cce86798b15a65a0e 0.0s
=> => naming to docker.io/library/python-kaggle-start-book-master-jupyter 0.0s
=> [jupyter] resolving provenance for metadata file 0.0s
[+] Running 2/2
✔ Network python-kaggle-start-book-master_default Creat... 0.0s
✔ Container python-kaggle-start-book-master-jupyter-1 C... 0.8s
Attaching to jupyter-1
Gracefully stopping... (press Ctrl+C again to force)
[+] Stopping 1/0
✔ Container python-kaggle-start-book-master-jupyter-1 S... 0.0s
Error response from daemon: driver failed programming external connectivity on endpoint python-kaggle-start-book-master-jupyter-1 (4fc62d234720dcb7f38318944b67440a7b9baff7748140a24e7c8b9e67dc9f72): Bind for 0.0.0.0:8888 failed: port is already allocated
docker-composeによるイメージのビルドが完了して、プロセスが立ち上がったようなので管理コンソールからコンテナを見てみましょうか。
↓ ありますね。新たに仮想化コンテナプロセスが作られています。
下記のDockerのコンソールから、ログを確認してjupyternotebookにアクセスしてみます。
ローカルホストの8888のTCPポートでアプリケーションプロセスとしてjupyternotebookが起動していますね！
改めてコンテナ仮想化は、従来のハイパーバイザー仮想型(ホストOS型)に比べて非常に軽量と実感しました。。