By: Edward Krueger and Douglas Franklin.

作者： 愛德華·克魯格 ( Edward Krueger)和道格拉斯·富蘭克林 ( Douglas Franklin) 。

In this article, we will cover how to set up a cloud computing instance to run Python with or without Jupyter Notebook. Then we show how to connect that instance to Github for a smooth cloud workflow.

在本文中，我們將介紹如何設置云計算實例以在有或沒有Jupyter Notebook的情況下運行Python。 然后，我們展示了如何將該實例連接到Github，以實現流暢的云工作流程。

We utilize cloud computing instances to get flexible Python and Jupyter environments while maintaining the reproducibility of enterprise data science platforms.

我們利用云計算實例來獲得靈活的Python和Jupyter環境，同時保持企業數據科學平臺的可重復性。

These AI platform notebooks come configured with many data science and analytics packages, including NumPy, Pandas, Scikit-learn and TensorFlow. Typically, we would discourage the use of bloated virtual machines. However, package bloat on our analytics machine isn’t as much of a problem because we only save the result (model, data, report) for later use. Needing only this result and the few packages needed to run our model allows us to disregard the numerous packages on the VM.

這些AI平臺筆記本配置了許多數據科學和分析軟件包，包括NumPy，Pandas，Scikit-learn和TensorFlow。 通常，我們不鼓勵使用of腫的虛擬機。 但是，由于我們只保存結果(模型，數據，報告)供以后使用，因此我們的分析機上的軟件包膨脹并不是什么大問題。 只需要這個結果和運行模型所需的幾個軟件包，就可以忽略VM上的眾多軟件包。

For example, in this Medium article, we push an NLP mode to the cloud without having to worry about dependencies.

例如，在這篇中型文章中，我們將NLP模式推到了云端，而不必擔心依賴關系。

Note that AI platform notebooks have all of the client packages for GCP services installed and are already authenticated to allow easy access to anything within the same GCP project. Additionally, this platform gives us not just access to Jupyter Notebooks, but also a Python console and a CLI where we can run BASH commands.

請注意，AI平臺筆記本電腦已安裝了所有用于GCP服務的客戶端軟件包，并且已經過身份驗證，可以輕松訪問同一GCP項目中的任何內容。 此外，該平臺使我們不僅可以訪問Jupyter Notebook，而且還可以使用Python控制臺和CLI來運行BASH命令。

取得GCP帳戶 (Getting a GCP account)

Google’s AI Platform Notebooks offer a JupyterLab and Python environment for data scientists and machine learning developers to experiment, develop, and deploy models into production. Users can create instances running JupyterLab that come pre-installed with common packages.

Google的AI平臺筆記本為數據科學家和機器學習開發人員提供JupyterLab和Python環境，以進行實驗，開發并將模型部署到生產中。 用戶可以創建預裝有通用軟件包的運行JupyterLab的實例。

Before we can set up an AI Platform Notebook, we will have to set up an account and billing, don’t worry new users get $300 in free credits!

在我們設置AI Platform Notebook之前，我們必須先設置一個帳戶并進行結算，不要擔心新用戶將獲得300美元的免費積分！

Visit GCP AI Platform and click ‘go to console.’

訪問GCP AI平臺 ，然后單擊“轉到控制臺”。

Be sure to click ‘Enable API’ below to access notebooks.

確保單擊下面的“啟用API”以訪問筆記本。

Once we have billing set up, we can start a project.

設置好帳單后，我們可以開始一個項目。

啟動您的第一個GCP AI Platform Notebook實例 (Starting up your first GCP AI Platform Notebook Instance)

Now we need to select the hardware we want our virtual machine to run on. Be sure to set up the cheapest machine possible if you are testing this out!

現在，我們需要選擇要在其上運行虛擬機的硬件。 如果要進行測試，請務必設置最便宜的機器！

Once we have the API enabled, the popup selections will change to those seen below, click ‘Go to instances page’ to get started.

啟用API后，彈出式菜單選擇將變為以下所示，單擊“轉到實例頁面”開始使用。

The instances page might have you select ‘Enable API’ another time, be sure to do so. Then click on the ‘New Instance’ button and select ‘Python 2 and 3.’

實例頁面可能會讓您再次選擇“啟用API”，請務必選擇。 然后點擊“新實例”按鈕并選擇“ Python 2和3”。

This will open up an options menu where you’ll input the region you’d like to use. Note that different regions can have different pricing. Once you have a region selected, you will want to click ‘Customize’ and select the machine with the least RAM to have the lowest cost. In our case, it is the ‘n1-standard-1’ VM with 3.75GB of RAM.

這將打開一個選項菜單，您可以在其中輸入要使用的區域。 請注意，不同地區的定價可能不同。 選定區域后，將需要單擊“自定義”，然后選擇RAM最少的機器以降低成本。 在我們的案例中，它是具有3.75GB RAM的“ n1-standard-1” VM。

This instance will only generate fees when it is running and can be easily paused at any time! If needed, you can swap out hardware with the dropdown menus seen below while the instance is paused.

該實例僅在運行時才會產生費用，并且可以隨時輕松暫停！ 如果需要，您可以在實例暫停時通過下面顯示的下拉菜單交換硬件。

Now we can use SSH to connect our VM to GitHub to allow us to push and pull to our repositories with ease.

現在，我們可以使用SSH將虛擬機連接到GitHub，從而使我們可以輕松地push存儲庫push和pull 。

設置SSH (Setting Up SSH)

Be aware you will only have to do this once per instance.

請注意，每個實例只需執行一次。

使用SSH連接到GitHub (Connecting to GitHub with ssh)

1. Generate an ssh key by running ssh-keygen and accepting the defaults by leaving them blank and pressing the enter key. This command generates files at user/.ssh/id_rsa that you’ll need to enter into GitHub.

   通過運行ssh-keygen生成ssh密鑰，并通過將其保留為空白并按Enter鍵來接受默認值。 此命令在user/.ssh/id_rsa處生成文件，您需要將這些文件輸入GitHub。

2. Copy your public key to your clipboard. One way to do this is by running cat ~/.ssh/id_rsa.pub to return the public key text into your console, display its contents, and then copy with the mouse and keyboard.

2.將您的公鑰復制到剪貼板。 一種方法是運行cat ~/.ssh/id_rsa.pub將公鑰文本返回到控制臺，顯示其內容，然后使用鼠標和鍵盤進行復制。

3. Go to github.com and sign in.

3.轉到gi??thub.com并登錄。

4. Click your profile image in the top right and then click “Settings.”

4.單擊右上角的個人資料圖片，然后單擊“設置”。

5. On the left-hand side, click “SSH and GPG keys.”

5.在左側，單擊“ SSH和GPG密鑰”。

6. On the top right, click “New SSH key.”

6.在右上方，單擊“新建SSH密鑰”。

7. Set the title to whatever you like. The “Title” is your choice, but it will help you identify what computer this authorization authorizes. Paste the copied key into the “Key” field and press “Add SSH key.”

7.將標題設置為任何您喜歡的名稱。 您可以選擇“標題”，但這將幫助您確定此授權授權的計算機。 將復制的密鑰粘貼到“密鑰”字段中，然后按“添加SSH密鑰”。

8. Go back to your computer and run eval 'ssh-agent -s' to start your ssh authentication agent.

8.返回計算機并運行eval 'ssh-agent -s'以啟動ssh身份驗證代理。

9. Run ssh-addto add your private key so that the agent can authenticate the public key.

9.運行ssh-add添加您的私鑰，以便代理可以驗證公鑰。

10. Set your git configuration so that GitHub knows who you are by running git config --global user.email you@email.com and git config --global user.name username, where the email and username are those attached to your GitHub account.

10.設置您的git配置，以便GitHub通過運行git config --global user.email you@email.com和git config --global user.name username知道您的git config --global user.name username ，其中電子郵件和用戶名是附加到GitHub上的電子郵件和用戶名帳戶。

Now you can git clone any repository you have access too right onto the VM, make changes to the code, and push them back to the repository!

現在，您可以git clone任何有權訪問的存儲庫直接git clone到VM上，對代碼進行更改，然后將其推回到存儲庫中！

結論 (Conclusion)

We’ve discussed how to set up a cloud computing instance to run Python, BASH, and Jupyter Notebooks and how to connect that instance to Github for an easy and secure cloud workflow.

我們已經討論了如何設置一個云計算實例來運行Python，BASH和Jupyter Notebook，以及如何將該實例連接到Github，以實現簡單而安全的云工作流程。

This workflow is great because it is so reproducible! Teams using VMs like this will encounter less of the ‘it works on my machine’ bugs. Using ssh to connect the cloud VM and our remote repositories provide a safe connection to protect your data. Additionally, if you want to run code on expensive hardware, you don’t have to buy that hardware! Instead, run what you need and pause your instance to save costs.

這個工作流程很棒，因為它是如此的可復制！ 使用此類VM的團隊將遇到較少的“在我的計算機上運行”錯誤。 使用ssh連接云VM和我們的遠程存儲庫可提供安全的連接來保護您的數據。 此外，如果您想在昂貴的硬件上運行代碼，則不必購買該硬件！ 而是運行所需的內容并暫停實例以節省成本。

We hope this guide has been helpful and that your coding skills are leveling up with us!

我們希望本指南對您有所幫助，并且您的編碼技能正在與我們一起發展！