├── images ├── spec.png ├── open_material.png ├── referral_code.PNG ├── usage │ ├── signin.PNG │ ├── mfa-mobile.png │ ├── after-login.png │ ├── run_notebook.png │ ├── use-template.PNG │ ├── copy_to_project.png │ ├── extension-lsp.PNG │ ├── restart-jupyter.PNG │ ├── start_runtime.png │ ├── copy_from_github.png │ ├── enable-extension.PNG │ ├── clone_git_repository.png │ ├── open_in_studio_lab.png │ ├── create_conda_environment.png │ └── create_environment_in_terminal.png ├── what_is_studio_lab.png ├── studio_lab_to_sagemaker.png ├── open-in-studio-lab-1-url.PNG └── open-in-studio-lab-2-preview.PNG ├── README_button.md ├── contributing.md ├── .gitignore ├── code-of-conduct.md ├── README_usage.md ├── README.md └── LICENSE /images/spec.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/aws-sagemaker-jp/awesome-studio-lab-jp/HEAD/images/spec.png 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3 | GitHubに"Open in Studio Lab"ボタンを設置することで、1 clickでGitHubをStudio Labにインポートすることができるようになります。 4 | 5 | (※以下のボタンはサンプルで、押しても何も開きません※) 6 | 7 | [![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github///blob//) 8 | 9 | ## 1. "Open in Studio Lab"ボタンで開きたいJupyter NotebookのURLを確認する。 10 | 11 | GitHubでJupyter Notebookを開いたときのURLをコピーします。次の画像は[機械学習帳の例](https://github.com/chokkan/mlnote/blob/main/regression/01sra.ipynb)です。現在Markdownのファイルを開くことはできません。 12 | 13 | ![open-in-studio-lab-1-url.PNG](./images/open-in-studio-lab-1-url.PNG) 14 | 15 | ## 2. "Open in Studio Lab"ボタンのMarkdown記述をコピーする。 16 | 17 | "Open in Studio Lab"ボタンを設置したいMarkdownのファイル(README.mdなど)に次の記述をコピーしてください。 18 | 19 | ``` 20 | [![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github///blob//) 21 | ``` 22 | 23 | ## 3. "Open in Studio Lab"ボタンのURLを編集する。 24 | 25 | コピーしたベース記述の以下のパートを、1で確認したURLの内容をもとに編集します。 26 | 27 | * `` 28 | * `` 29 | * `` 30 | * `` 31 | 32 | 機械学習帳の例では次のようになります 33 | 34 | | パラメータ| 値| 35 | |:---|:---| 36 | |your-org | chokkan| 37 | |your-repo-name|mlnote| 38 | |your-branch-name|main| 39 | |path-to-your-notebook.ipynb|regression/01sra.ipynb| 40 | 41 | パラメーターを埋めたボタンは次のようになります(↓のボタンは実際に押せます)。 42 | 43 | [![Open in SageMaker Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/chokkan/mlnote/blob/main/regression/01sra.ipynb) 44 | 45 | 公開する時AWSアカウントへアクセスするためのCredentialなど機微な情報が含まれていないか注意してください。 46 | 47 | ボタンを設置したら、ぜひ[Issues](https://github.com/aws-studiolab-jp/awesome-studio-lab-jp/issues)/[Pull Requests](https://github.com/aws-studiolab-jp/awesome-studio-lab-jp/pulls)からご連絡ください！本リポジトリに掲載させていただきます。 48 | 49 | ## "Open in Studio Lab"ボタンを押すとどうなるのか? 50 | 51 | ボタンを押すと次のようなプレビュー画面が開きます。**プレビューを開くのにStudio LabのアカウントもAWSアカウントも必要ありません**。プレビューで開いた画面を自分のStudio Labアカウントにコピーしてくるのにアカウントが必要です。コピーしてくる方法は["機械学習を学ぶ教材"をご参照ください](https://github.com/aws-studiolab-jp/awesome-studio-lab-jp#%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92%E3%82%92%E5%AD%A6%E3%81%B6%E6%95%99%E6%9D%90)。なお、リポジトリがプライベートである場合はもちろんプレビューを開くのに認証が必要になります。 52 | 53 | コピーする対象はNotebook単体、リポジトリ全体いずれも選択できます。複数のNotebookから構成される教材ではリポジトリ全体の方が便利ですが、大きなデータセットが含まれる場合はNotebookのみの方が好ましいかもしれません。Studio Labの静的ストレージは15Gです。 54 | ![open-in-studio-lab-2-preview.PNG](./images/open-in-studio-lab-2-preview.PNG) 55 | 56 | ## Studio Labのアカウントを作成するには? 57 | 58 | アカウントの申し込みは次のフォームから行えます。 59 | 60 | **[アカウント作成フォーム](https://bit.ly/3kIjuZL)** 61 | 62 | 利用方法は[Amazon SageMaker Studio Lab の使い方](./README_usage.md)を参照してください。 63 | -------------------------------------------------------------------------------- /contributing.md: -------------------------------------------------------------------------------- 1 | # Contributing Guidelines 2 | 3 | Thank you for your interest in contributing to our project. Whether it's a bug report, new feature, correction, or additional 4 | documentation, we greatly value feedback and contributions from our community. 5 | 6 | Please read through this document before submitting any issues or pull requests to ensure we have all the necessary 7 | information to effectively respond to your bug report or contribution. 8 | 9 | 10 | ## Reporting Bugs/Feature Requests 11 | 12 | We welcome you to use the GitHub issue tracker to report bugs or suggest features. 13 | 14 | When filing an issue, please check existing open, or recently closed, issues to make sure somebody else hasn't already 15 | reported the issue. Please try to include as much information as you can. Details like these are incredibly useful: 16 | 17 | * A reproducible test case or series of steps 18 | * The version of our code being used 19 | * Any modifications you've made relevant to the bug 20 | * Anything unusual about your environment or deployment 21 | 22 | ## Ask Questions and Share Knowledge 23 | 24 | We welcome you to ask questions about contents and share your knowledge in discussion. 25 | Please try to include as much information as you can. Details like these are incredibly useful: 26 | 27 | * The link to the content you ask. 28 | * The situation that you use this content. 29 | 30 | ## Contributing via Pull Requests 31 | Contributions via pull requests are much appreciated. Before sending us a pull request, please ensure that: 32 | 33 | 1. You are working against the latest source on the *main* branch. 34 | 2. You check existing open, and recently merged, pull requests to make sure someone else hasn't addressed the problem already. 35 | 3. You open an issue to discuss any significant work - we would hate for your time to be wasted. 36 | 37 | To send us a pull request, please: 38 | 39 | 1. Fork the repository. 40 | 2. Modify the source; please focus on the specific change you are contributing. If you also reformat all the code, it will be hard for us to focus on your change. 41 | 3. Ensure local tests pass. 42 | 4. Commit to your fork using clear commit messages. 43 | 5. Send us a pull request, answering any default questions in the pull request interface. 44 | 6. Pay attention to any automated CI failures reported in the pull request, and stay involved in the conversation. 45 | 46 | GitHub provides additional document on [forking a repository](https://help.github.com/articles/fork-a-repo/) and 47 | [creating a pull request](https://help.github.com/articles/creating-a-pull-request/). 48 | 49 | 50 | ## Finding contributions to work on 51 | Looking at the existing issues is a great way to find something to contribute on. As our projects, by default, use the default GitHub issue labels (enhancement/bug/duplicate/help wanted/invalid/question/wontfix), looking at any 'help wanted' issues is a great place to start. 52 | 53 | 54 | ## Code of Conduct 55 | This project has adopted the [Code of Conduct](code-of-conduct.md). 56 | 57 | ## Licensing 58 | 59 | See the [LICENSE](LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution. 60 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | 162 | -------------------------------------------------------------------------------- /code-of-conduct.md: -------------------------------------------------------------------------------- 1 | # Contributor Covenant Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | We as members, contributors, and leaders pledge to make participation in our 6 | community a harassment-free experience for everyone, regardless of age, body 7 | size, visible or invisible disability, ethnicity, sex characteristics, gender 8 | identity and expression, level of experience, education, socio-economic status, 9 | nationality, personal appearance, race, caste, color, religion, or sexual 10 | identity and orientation. 11 | 12 | We pledge to act and interact in ways that contribute to an open, welcoming, 13 | diverse, inclusive, and healthy community. 14 | 15 | ## Our Standards 16 | 17 | Examples of behavior that contributes to a positive environment for our 18 | community include: 19 | 20 | * Demonstrating empathy and kindness toward other people 21 | * Being respectful of differing opinions, viewpoints, and experiences 22 | * Giving and gracefully accepting constructive feedback 23 | * Accepting responsibility and apologizing to those affected by our mistakes, 24 | and learning from the experience 25 | * Focusing on what is best not just for us as individuals, but for the overall 26 | community 27 | 28 | Examples of unacceptable behavior include: 29 | 30 | * The use of sexualized language or imagery, and sexual attention or advances of 31 | any kind 32 | * Trolling, insulting or derogatory comments, and personal or political attacks 33 | * Public or private harassment 34 | * Publishing others' private information, such as a physical or email address, 35 | without their explicit permission 36 | * Other conduct which could reasonably be considered inappropriate in a 37 | professional setting 38 | 39 | ## Enforcement Responsibilities 40 | 41 | Community leaders are responsible for clarifying and enforcing our standards of 42 | acceptable behavior and will take appropriate and fair corrective action in 43 | response to any behavior that they deem inappropriate, threatening, offensive, 44 | or harmful. 45 | 46 | Community leaders have the right and responsibility to remove, edit, or reject 47 | comments, commits, code, wiki edits, issues, and other contributions that are 48 | not aligned to this Code of Conduct, and will communicate reasons for moderation 49 | decisions when appropriate. 50 | 51 | ## Scope 52 | 53 | This Code of Conduct applies within all community spaces, and also applies when 54 | an individual is officially representing the community in public spaces. 55 | Examples of representing our community include using an official e-mail address, 56 | posting via an official social media account, or acting as an appointed 57 | representative at an online or offline event. 58 | 59 | ## Enforcement 60 | 61 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 62 | reported to the community leaders responsible for enforcement at icoxfog417@yahoo.co.jp . 63 | All complaints will be reviewed and investigated promptly and fairly. 64 | 65 | All community leaders are obligated to respect the privacy and security of the 66 | reporter of any incident. 67 | 68 | ## Enforcement Guidelines 69 | 70 | Community leaders will follow these Community Impact Guidelines in determining 71 | the consequences for any action they deem in violation of this Code of Conduct: 72 | 73 | ### 1. Correction 74 | 75 | **Community Impact**: Use of inappropriate language or other behavior deemed 76 | unprofessional or unwelcome in the community. 77 | 78 | **Consequence**: A private, written warning from community leaders, providing 79 | clarity around the nature of the violation and an explanation of why the 80 | behavior was inappropriate. A public apology may be requested. 81 | 82 | ### 2. Warning 83 | 84 | **Community Impact**: A violation through a single incident or series of 85 | actions. 86 | 87 | **Consequence**: A warning with consequences for continued behavior. No 88 | interaction with the people involved, including unsolicited interaction with 89 | those enforcing the Code of Conduct, for a specified period of time. This 90 | includes avoiding interactions in community spaces as well as external channels 91 | like social media. Violating these terms may lead to a temporary or permanent 92 | ban. 93 | 94 | ### 3. Temporary Ban 95 | 96 | **Community Impact**: A serious violation of community standards, including 97 | sustained inappropriate behavior. 98 | 99 | **Consequence**: A temporary ban from any sort of interaction or public 100 | communication with the community for a specified period of time. No public or 101 | private interaction with the people involved, including unsolicited interaction 102 | with those enforcing the Code of Conduct, is allowed during this period. 103 | Violating these terms may lead to a permanent ban. 104 | 105 | ### 4. Permanent Ban 106 | 107 | **Community Impact**: Demonstrating a pattern of violation of community 108 | standards, including sustained inappropriate behavior, harassment of an 109 | individual, or aggression toward or disparagement of classes of individuals. 110 | 111 | **Consequence**: A permanent ban from any sort of public interaction within the 112 | community. 113 | 114 | ## Attribution 115 | 116 | This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), 117 | version 2.1, available at [here](https://www.contributor-covenant.org/version/2/1/code_of_conduct/). 118 | -------------------------------------------------------------------------------- /README_usage.md: -------------------------------------------------------------------------------- 1 | # Amazon SageMaker Studio Lab の使い方 2 | 3 | Amazon SageMaker Studio Labを使い教材を学ぶためのステップを解説します。 4 | 5 | 1. アカウントを作成する 6 | 2. ログインする 7 | 3. Jupyter Labを起動する 8 | 4. 教材を開く 9 | 5. Studio Labをより便利に使う 10 | * 日本語化する 11 | * JupyterLabの拡張を導入する 12 | * Jupyter Notebookでコード補完を行う 13 | * Jupyter Notebookでコードフォーマットを行う 14 | * Python以外の言語のKernelを追加する 15 | * AWSへ接続する 16 | * データサイエンスのリポジトリを作成する 17 | 6. 参考資料 18 | 19 | ハンズオンなどでこの資料を見ている時は、1 から 4 までを実行するとハンズオンで使用するNotebookが開けるはずです。手順の質問をするときは、手順の番号を伝えてください。(例: 1番のアカウント作成の手順3番目のapprovedのメールが届かない・・・など)。 20 | 21 | ## 1. アカウントを作成する 22 | 23 | 1. [アカウント作成フォーム](https://bit.ly/3kIjuZL)からアカウントの申し込みを行う。 24 | * リファラルコードをお持ちの場合は、アカウント作成フォームで忘れずに入力ください。リファラルコードの詳細は[大規模な講座などでStudio Labのアカウントを発行するにはどうすればよいですか?](https://github.com/aws-sagemaker-jp/awesome-studio-lab-jp/discussions/8)をご参照ください。 25 | * ![referral_code.PNG](./images/referral_code.PNG) 26 | 2. `Account request confirmed ...`のメールを受信する。 27 | * アカウントの申し込みが受け付けられた連絡です。リクエストの受付はすぐにメールが届きます。 28 | 3. `Account request approved ...`のメールを受信し、メール内のリンクからアカウントを作成する。 29 | * 申し込みが承認された連絡です。承認から 7 日以内にメール内のリンクからアカウント作成を行ってください。 30 | * 承認は 5 営業日以内に結果が通知されます。リファラルコードを利用している場合は 2~3 分以内に結果が届きます。 31 | * この連絡がなかなか来ない、という場合は[問い合わせフォーム](https://pages.awscloud.com/GLOBAL_PM_PA_amazon-sagemaker_20211116_7014z000000rjq2-registration.html)から連絡してください。 32 | * リファラルコードを使用してもメールがすぐに届かない場合は、直接 https://studiolab.sagemaker.aws/signup へアクセスしてアカウント登録を試してみてください。 33 | 4. `Verify your email ...`のメールを受信し、メール内のリンクからメールアドレスを認証する。 34 | * アカウント作成後にメールアドレスの認証を行います。メール内のリンクからメールアドレスを認証してください。 35 | 5. `Your account is ready ...`のメールを受信する。 36 | * お待たせしました！利用開始いただけます。 37 | 38 | ## 2. ログインする 39 | 40 | Studio Labへのログインは、[Studio Lab のランディングページ](https://studiolab.sagemaker.aws/)から行います。 41 | 42 | 1. 右上の "Sign in" ボタンを押す。 43 | * ![signin.PNG](images/usage/signin.PNG) 44 | 2. Eメールアドレス/ユーザー名、パスワードを入力する。 45 | 3. "Sign in" を押しプロジェクトのページを開く。 46 | * ![after-login.png](images/usage/after-login.png) 47 | 48 | ## 3. Jupyter Labを起動する 49 | 50 | Studio LabではCPU/GPUのいずれかでJupyter Notebookを実行することができます。CPUは4時間/セッション (1日8時間まで)、GPUは4時間/セッション (1日4時間まで)です。 51 | 52 | 1. 「My Project」の「Select compute type」から CPUかGPU を選択する。 53 | * 通常のハンズオンはCPUで十分です。 54 | 2. 「Start runtime」を押す。 55 | * ![start_runtime.png](images/usage/start_runtime.png) 56 | * 起動時に“There is no runtime available right now.”と表示された場合は何回かボタンを押してみてください。 57 | 3. 起動時に多要素認証を求められた場合、使用可能なデバイスで認証を行います。 58 | * ![mfa-mobile.png](images/usage/mfa-mobile.png) 59 | * 入力した電話番号に届いたコードを入力し認証してください。 60 | 4. ランタイムが開始したら「Open project」を押す。 61 | * JupyterLab 環境が起動します。 62 | 63 | ## 4. 教材を開く 64 | 65 | 「Open in Studio Lab」のボタンが付いた教材を開く手順は次の通りです。 66 | 67 | 1. 教材の「Open Studio Lab」ボタンを押す(※↓のボタンは画像です)。 68 | * ![open_in_studio_lab.png](images/usage/open_in_studio_lab.png) 69 | 2. 「Copy to Project」を押す。 70 | * ![copy_to_project.png](images/usage/copy_to_project.png) 71 | 3. 「Clone Entire Repo」 か 「Copy Notebook Only」を押す。 72 | * Clone Entire Repo は、Studio Labのプロジェクト内に教材をすべてコピーします。Copy Notebook Onlyは開いているノートブックのみコピーします。 73 | * ![copy_from_github.png](images/usage/copy_from_github.png) 74 | * ![clone_git_repository.png](images/usage/clone_git_repository.png) 75 | 4. "Confirm you want to build..."が出たら「OK」を押す。 76 | * ![create_conda_environment.png](images/usage/create_conda_environment.png) 77 | * OKを押し忘れたら`environment.yml`を右クリックし「Build Conda Environment」を実行してください。 78 | * `environment.yml`がリポジトリに含まれない場合、このポップアップは登場しません。その際はリポジトリが指定する方法で環境を構築してください。 79 | * 起動したターミナルで実行されたコマンドが終了したら環境構築は完了です。「done」とコンソール上に表示され、環境を有効化するためのコマンド表示されます。 80 | * ![create_environment_in_terminal.png](images/usage/create_environment_in_terminal.png) 81 | 5. 教材のNotebookを開く(ハンズオンの場合、どのNotebookを開くかは講師から指示があるはずです)。 82 | * Notebookを開いて、右上のメニューから作成したKernelを選択し、作成した環境を選択します。 83 | * ![run_notebook.png](images/usage/run_notebook.png) 84 | 85 | ## 5. Studio Labをより便利に使う 86 | 87 | ### 日本語化する 88 | 89 | [JupyterLabの言語パック](https://anaconda.org/search?q=jupyterlab-language-pack)をインストールすることで、部分的に日本語化することができます。 90 | 91 | ターミナルを起動し、次のコマンドを実行してください。 92 | 93 | ``` 94 | conda install -c conda-forge jupyterlab-language-pack-ja-jp 95 | ``` 96 | 97 | Settings > Languageから「日本語」が選択できるようになります。 98 | 99 | 全部日本語にしたいんだよ!!という場合は、次の記事を参考にしてください。 100 | 101 | [【AWS Expert Online】SageMaker Studio Lab](https://zenn.dev/shigeru_oda/articles/4d35453eb1a01ffae95c#%E5%88%9D%E6%9C%9F%E7%94%BB%E9%9D%A2) 102 | 103 | ### JupyterLabの拡張を導入する 104 | 105 | JupyterLabの拡張を入れることで開発環境をより便利にカスタマイズできます。拡張を有効にするには、拡張管理のセクションで拡張を"Enable"にしてください。 106 | 107 | ![enable-extension.PNG](images/usage/enable-extension.PNG) 108 | 109 | 拡張をインストールした後は、JupyterLabを再起動してください。 110 | 111 | JupyterLabを再起動します。 112 | 113 | ![restart-jupyter](images/usage/restart-jupyter.PNG) 114 | 115 | #### Jupyter Notebookでコード補完を行う 116 | 117 | [jupyterlab-lsp](https://github.com/jupyter-lsp/jupyterlab-lsp)の拡張を導入することで、Notebookでコード補完が行われるようにできます。 118 | 119 | ターミナルを起動し、次のコマンドを実行してください。 120 | 121 | ``` 122 | conda install -c conda-forge nodejs jupyterlab-lsp python-lsp-server 123 | ``` 124 | 125 | JupyterLab再起動後、Notebookでコード補完が効くようになっているはずです。 126 | 127 | ![extension-lsp.PNG](images/usage/extension-lsp.PNG) 128 | 129 | 130 | #### Jupyter Notebookでコードフォーマットを行う 131 | 132 | [jupyterlab_code_formatter](https://github.com/ryantam626/jupyterlab_code_formatter)を導入することでJupyterNotebook上のコードを成形することができます。`black`と`isort`を使いフォーマットができます。 133 | 134 | ``` 135 | conda install -c conda-forge jupyterlab_code_formatter black isort 136 | ``` 137 | 138 | 保存したときに自動的に成形されるようにするには、Settings > Advanced Settings Editor > Jupyterlab Code Formatterから`Auto format config`にチェックを入れてください。 139 | 140 | ### Python以外の言語のKernelを追加する 141 | 142 | JupyterはKernelを追加することで他の言語を動かすことができます。以下はその言語の一覧です。リストにある言語は理論上Studio Labで動かすことができます。 143 | 144 | [Jupyter kernels](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels) 145 | 146 | 各言語の動かし方はQ&Aを参照してください。 147 | 148 | * [C++をSageMaker Studio Labで使いたい](https://github.com/aws-sagemaker-jp/awesome-studio-lab-jp/discussions/13) 149 | 150 | ### AWSへ接続する 151 | 152 | Studio Labで用意されているCPUやGPU、ストレージが足りなくなった場合AWSと接続することで移行できます。 153 | 154 | * Amazon S3: Amazon S3に接続することで15G以上のストレージが利用可能です。詳細は[Amazon S3 に接続](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab-use-external.html#studio-lab-use-external-s3)をご参照ください。 155 | * SageMaker Studio: SageMaker Studioに移行することで、Studio Labと同様の使い勝手でAWS上のGPUインスタンスを利用することができます。詳細は[Amazon SageMaker Studio への移行](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab-use-migrate.html) を参照してください。 156 | 157 | ### データサイエンスのリポジトリを作成する 158 | 159 | 新しくリポジトリを作成する時は、テンプレートを使うことで品質の高いリポジトリを手早く作成できます。[datascience-template](https://github.com/icoxfog417/datascience-template)はそのうちの一つです。 160 | 161 | ![use-template.PNG](./images/usage/use-template.PNG) 162 | 163 | 他にも、[Cookiecutter Data Science](https://drivendata.github.io/cookiecutter-data-science/)などプロジェクトのテンプレートを生成してくれるツールがあります。新規にプロジェクトを開始する際は、こうしたベストプラクティスに沿ってリポジトリを作るとよいでしょう。 164 | 165 | ## 6. 参考資料 166 | 167 | * [公式ドキュメント](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-lab.html) 168 | * [公式FAQ](https://studiolab.sagemaker.aws/faq) 169 | * [Studio Lab日本コミュニティQA](https://github.com/aws-sagemaker-jp/awesome-studio-lab-jp/discussions) 170 | * 使い方に関する質問があればこちらのDiscussionに投稿をお願いします！ 171 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # SageMaker Studio Lab Community 2 | 3 |

4 | 5 |

6 | 7 | **[Amazon SageMaker Studio Lab](https://aws.amazon.com/jp/builders-flash/202205/awsgeek-sagemaker-studio-lab/)は、無料かつ簡単にデータサイエンスを学び始めることができる環境です。** 8 | 9 | 🆓 **Free** 10 | * Amazon SageMaker Studio Labは無料で利用ができます。必要なのはメールアドレスのみです。 11 | 12 | :octocat: **Open** 13 | * Studio Labはオープンソースの[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/)をベースにしています。 14 | * コードの補完や見出しの作成など、コミュニティで開発された便利な拡張機能も利用できます。 15 | 16 | 🔰 **Easy** 17 | * Pythonが実行できる、Jupyter Notebookが作成できる環境があらかじめ構築されています。 18 | * Gitが標準でインストールされており、GUIからも使用できます。 19 | 20 | 📚 **Community** 21 | * コミュニティを通じて、Studio Labでデータサイエンスが学べる教材やデータ分析の実装が共有されています。 22 | * [GitHub](https://github.com/topics/amazon-sagemaker-lab)で検索して見つけることもできます。 23 | 24 | 25 | 🚀 **SageMakerへの移行が可能** 26 | * Studio Labで作成したプロジェクトは、AWSのSageMakerへ移行することができます。 27 | * **学ぶだけでなく活用したい**方にとって必要なサービス機能と学習教材を提供しています。 28 | 29 |

30 |

"Amazon SageMaker Studio Lab の使い方"から利用を開始できます。

31 |

32 | 33 | ## Studio Labで学べる教材 34 | 35 | 本リポジトリでは、[データサイエンティスト協会の定義](https://www.datascientist.or.jp/dskentei/)を参照し**データサイエンス**、**データエンジニアリング**、**ビジネス**の3つのカテゴリに分けて教材を紹介します。 36 | 37 | Studio LabでJupyterLabで動かせる教材はStudio Labで動かせますが、特に「Open in Studio Lab」のボタンがあると簡単にStudio Labで開くことができます。Open in Studio Labボタンの設置方法は["Open in Studio Lab ボタンの設置方法"](./README_button.md)をご参照ください。 38 | 39 | ### データサイエンス 40 | 41 | #### [Python早見帳](https://chokkan.github.io/python/index.html) 42 | 43 | 東京工業大学 情報理工学院で使用されている機械学習の教材です。Pythonの基本的な文法はもちろん、Numpy、Matplotlibといった機械学習に欠かせないライブラリの使い方についても解説されています。 44 | 45 | 教材の詳細とStudio Labでの学び方は、[Python 早見帳が SageMaker Studio Lab からすぐに学べるようになりました](https://aws.amazon.com/jp/blogs/news/python-hayamicho-is-available-in-sagemaker-studio-lab/)をぜひ参照してください。 46 | 47 | #### [機械学習帳](https://chokkan.github.io/mlnote/index.html) 48 | 49 | Python早見帳と同じく、東京工業大学 情報理工学院で使用されている機械学習の教材です。教材内容の解説と確認問題の実装を[mlnote-note](https://github.com/icoxfog417/mlnote-note)で公開しています。 50 | 51 | 教材の詳細とStudio Labでの学び方は、[機械学習帳が SageMaker Studio Lab からすぐに学べるようになりました](https://aws.amazon.com/jp/blogs/news/mlnote-sagemaker-studio-lab/)をぜひ参照してください。 52 | 53 | #### [ゼロからはじめるデータサイエンス入門（講談社）](https://github.com/taroyabuki/fromzero) 54 | 55 | プログラミングの基本、統計入門、前処理、機械学習などを、R・Python対訳・対照で学ぶ書籍です。書籍に掲載されたコードのほかに、[Amazon SageMaker Studio Labのための仮想環境構築法](https://github.com/taroyabuki/fromzero/tree/main/addendum/sagemaker)もサポートサイトで公開されています。GPUを使った深層学習もAmazon SageMaker Studio Labで動作確認済みです。 56 | 57 | #### [Machine Learning University](https://aws.amazon.com/jp/machine-learning/mlu/) 58 | 59 | Amazonが社内の機械学習教育で使用している教材です。自然言語処理、テーブルデータ、画像、決定木の4コースで、それぞれApplied Scientistらによる解説動画、スライド、Notebookが提供されてます。 60 | 61 | * [自然言語処理](https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp) 62 | * [テーブルデータ](https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab) 63 | * [画像](https://github.com/aws-samples/aws-machine-learning-university-accelerated-cv) 64 | * [決定木](https://github.com/aws-samples/aws-machine-learning-university-dte) 65 | 66 | #### [PRML](https://github.com/ctgk/PRML) 67 | 68 | [パターン認識と機械学習](https://www.amazon.co.jp/%E3%83%91%E3%82%BF%E3%83%BC%E3%83%B3%E8%AA%8D%E8%AD%98%E3%81%A8%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92-%E4%B8%8A-C-M-%E3%83%93%E3%82%B7%E3%83%A7%E3%83%83%E3%83%97/dp/4621061224)の書籍で登場するアルゴリズムの実装を行っているリポジトリです。かなり難しい書籍なので、[awesome-prml-ja](https://github.com/tsg-ut/awesome-prml-ja)で紹介されている各大学の輪講資料も活用して学ぶことをお勧めします。 69 | 70 | #### 他教材 71 | 72 | * 自然言語処理 73 | * [BERTによる自然言語処理入門: Transformersを使った実践プログラミング](https://github.com/stockmarkteam/bert-book) 74 | * [Natural Language Processing with Transformers](https://github.com/manuelyhvh/nlp-with-transformers) 75 | * [Hugging Face Quick tour](https://huggingface.co/docs/transformers/quicktour) 76 | * 強化学習 77 | * [ゼロから作るDeep Learning ❹ 強化学習編](https://github.com/oreilly-japan/deep-learning-from-scratch-4) 78 | 79 | ### データエンジニアリング 80 | 81 | #### [データサイエンス100本ノック](https://github.com/The-Japan-DataScientist-Society/100knocks-preprocess) 82 | 83 | 小売のPOSデータを題材に、SQLを使用したデータの抽出方法を学べる教材です。SQL以外にも、Pythonの表計算ライブラリであるpandasやRによる実装方法も解説されています。[データサイエンス100本ノック構造化データ加工編ガイドブック](https://www.amazon.co.jp/dp/4802613563)として解説の書籍も発売されています。 84 | 85 | 教材の詳細とStudio Labでの学び方は、[データサイエンス100本ノックがStudio Labからすぐに学べるようになりました](https://aws.amazon.com/jp/blogs/news/100knocks-preprocess-sagemaker-studio-lab/)をぜひ参照してください。 86 | 87 | #### [入門 機械学習パイプライン](https://github.com/oreilly-japan/building-ml-pipelines-ja) 88 | 89 | 本番環境で機械学習モデルを運用するために、学習データの取り込みからモデルのデプロイまで一連のプロセスを自動化するパイプラインを構築する方法を解説した書籍です(モデルの構築よりはデータに絡むため、こちらのセクションで紹介しています)。TensorFlow Extendedを使用してパイプラインを実装する方法、またクラウド環境にデプロイする方法が学べます。付録ではUberやSpotify、Netflixなど著名な企業のパイプラインの構築事例を参照することができます。 90 | 91 | 教材の詳細とStudio Labでの学び方は、[「入門機械学習パイプライン」にSagemaker Studio Labで入門する](https://aws.amazon.com/jp/blogs/news/intro-to-mlops-sagemaker-studio-lab/)をぜひ参照してください。 92 | 93 | #### データサイエンティストのための Git 入門 [Part1](https://aws.amazon.com/jp/builders-flash/202207/git-introduction-for-data-schientist/?awsf.filter-name=*all) / [Part2](https://aws.amazon.com/jp/builders-flash/202209/git-introduction-for-data-schientist-2/?awsf.filter-name=*all) 94 | 95 | 開発したモデルをサービスへ組み込むときアプリケーション開発チームと協力することになりますが、協力するうえで知っておくべきチーム開発のためのGitの機能を重点的に紹介した記事です。Part1ではGitの基本的な使い方、Part2ではPull Requestの送り方などを中心に解説しています。[いちばんやさしいGit&GitHubの教本](https://www.amazon.co.jp/%E3%81%84%E3%81%A1%E3%81%B0%E3%82%93%E3%82%84%E3%81%95%E3%81%97%E3%81%84Git-GitHub%E3%81%AE%E6%95%99%E6%9C%AC-%E4%BA%BA%E6%B0%97%E8%AC%9B%E5%B8%AB%E3%81%8C%E6%95%99%E3%81%88%E3%82%8B%E3%83%90%E3%83%BC%E3%82%B8%E3%83%A7%E3%83%B3%E7%AE%A1%E7%90%86-%E5%85%B1%E6%9C%89%E5%85%A5%E9%96%80-%E3%80%8C%E3%81%84%E3%81%A1%E3%81%B0%E3%82%93%E3%82%84%E3%81%95%E3%81%97%E3%81%84%E6%95%99%E6%9C%AC%E3%80%8D%E3%82%B7%E3%83%AA%E3%83%BC%E3%82%BA/dp/4295013617)著者による、アニメーション画像を使ったわかりやすい記事になっています。 96 | 97 | * Part1: [データサイエンティストのための Git 入門](https://aws.amazon.com/jp/builders-flash/202207/git-introduction-for-data-schientist/?awsf.filter-name=*all) 98 | * Part2: [データサイエンティストのための Git 入門 チーム開発編](https://aws.amazon.com/jp/builders-flash/202209/git-introduction-for-data-schientist-2/?awsf.filter-name=*all) 99 | 100 | ### ビジネス 101 | 102 | #### [ML Enablement Workshop](https://github.com/aws-samples/aws-ml-enablement-workshop) 103 | 104 | プロダクトを開発するチームが、課題解決の選択肢として機械学習を選択できるようになることをゴールとしたワークショップです。プロダクトマネージャー向けの機械学習入門、ソフトウェア開発者向けハンズオン、プロダクトマネージャー/ソフトウェア開発者/データサイエンティストを交えて行う機械学習のユースケース発見アイデアソンの3つから構成されます。資料はすべてGitHubで公開されています。 105 | 106 | #### 金融業界での活用 107 | 108 | ##### [ESG評価に対する自然言語処理の活用Workshop](https://github.com/aws-samples/aws-esg-evaluation-handson) 109 | 110 | 企業の開示情報から、ESG評価に関わる記載を抽出するのに自然言語処理を活用する方法を学べるワークショップです。ESG評価の動向と、実際手を動かすハンズオンの2つから構成されています。MSCIやFTSE、RobecoSAMなど代表的な評価機関で採用されているチェックリストを持ちたESG評価手法をベースにしています。 111 | 112 | ##### [決算短信セグメント情報のデータ抽出ハンズオン](https://github.com/JapanExchangeGroup/FinancialResultsHTML-DataExtraction) 113 | 114 | HTML化された決算短信からセグメント情報を抽出する方法が学べるハンズオンです。HTML化された決算短信は、[適時開示情報閲覧サービス](https://www.release.tdnet.info/inbs/I_main_00.html)や、[東証上場会社情報サービス](https://www.jpx.co.jp/listing/co-search/index.html)で公開されています。BeautifulSoupを用いたHTMLからの情報抽出を基礎から学ぶことができます。 115 | 116 | ## Studio LabからSageMakerへの移行 117 | 118 | 大規模なデータの前処理や学習が必要になっときは、Studio LabからSageMakerへ移行することができます。 119 | 120 | ![studio_lab_to_sagemaker.png](./images/studio_lab_to_sagemaker.png) 121 | 122 | * [Export Amazon SageMaker Studio Lab environment to Amazon SageMaker Studio](https://docs.amazonaws.cn/en_us/sagemaker/latest/dg/studio-lab-use-migrate.html) 123 | * ※日本語版作成中 124 | 125 | ## 採用事例 126 | 127 | Studio Labを採用頂いている授業や事例を紹介します。 128 | 129 | * [Juliaで学ぶ最適化と機械学習（2024）](https://matsui528.github.io/julia_opt_ml_2024/) 130 | * [4840-1054: Media Computing in Practice (Summer 2022)](https://media-comp.github.io/2022/) 131 | * [NLP若手の会 (YANS) 第17回シンポジウム ハッカソン](https://yans.anlp.jp/entry/yans2022hackathon) 132 | * [岐阜大学　2022実践データサイエンティスト育成事業](https://sites.google.com/db.info.gifu-u.ac.jp/ds2022/%E3%83%9B%E3%83%BC%E3%83%A0/aws%E8%AC%9B%E7%BF%92%E4%BC%9A%E9%AB%98%E5%BA%A6%E3%83%97%E3%83%AD%E3%82%B0%E3%83%A9%E3%83%9F%E3%83%B3%E3%82%B0?authuser=0) 133 | 134 | ## 関連記事検索 135 | 136 | * [Qiita](https://qiita.com/tags/sagemakerstudiolab) 137 | * [Zenn](https://zenn.dev/topics/sagemaker) 138 | * [Twitter](https://twitter.com/search?q=lang%3Aja%20SageMaker%20Studio%20Lab&src=typed_query&f=live) 139 | 140 | ## リンク 141 | 142 | * [aws/studio-lab-examples](https://github.com/aws/studio-lab-examples) 143 | 144 | ## Disclaimer 145 | 146 | 本リポジトリのメンテナンスを行っているメンバーはAWSに所属していますが、コミュニティ活動の一環として行っておりAWSの事業とかかわりはありません。 147 | 148 | 教材の追加やリポジトリ内のコンテンツの修正は[Issues](https://github.com/aws-studiolab-jp/awesome-studio-lab-jp/issues)、また[Pull Requests](https://github.com/aws-studiolab-jp/awesome-studio-lab-jp/pulls)よりお送りください。 149 | 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