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+![Statistics](images/statistics-on-the-moon.jpg) +
+ +## The Advanced Analytics Workspace Documentation + +_Welcome to the world of data science and machine learning!_ + + +!!! info "What is the AAW?" + **[Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw)** is an open source platform designed for data scientists, data stewards, analysts and researchers familiar with open source tools and coding. Developed by data scientists for data scientists, AAW provides a flexible environment that enables advanced practitioners to get their work done with ease. + +The AAW is a comprehensive solution for data science and data analytics. With the AAW, you can customize notebook server deployments to suit your specific data science needs. We have a small number of custom Docker images made by our team. + + +!!! info "What is Kubeflow?" + The AAW is based on [Kubeflow](https://www.kubeflow.org/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. + +Whether you're just getting started or already knee-deep in data analysis, the Advanced Analytics Workspace has everything you need to take your work to the next level. From powerful tools for data pipelines to cloud storage for your datasets, our platform has it all. Need to collaborate with colleagues or publish your results? No problem. We offer seamless collaboration features that make it easy to work together and share your work with others. + +No matter what stage of your data science journey you're at, the Advanced Analytics Workspace has the resources you need to succeed. + +## Getting Started with the AAW + +
+![AAW icon](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) +
+ +### The AAW Portal + +The AAW portal homepage is available for internal users only. However, external users with a cloud account granted access by the business sponsor can access the platform through the analytics-platform URL. + + +!!! info annotate "AAW Portal Homepage" + - [**Portal Homepage for Statistics Canada Employees**](https://www.statcan.gc.ca/data-analytics-service/aaw) + - [**Portal Homepage for External Users**](https://www.statcan.gc.ca/data-analytics-services/overview) + +### Kubeflow Account + + +!!! important "Attention External Users!" + Users external to Statistics Canada will require a cloud account granted access by the business sponsor. + + +!!! important "Attention Statistics Canada Employees!" + Users internal to Statistics Canada can get started right away without any additional sign up procedures, just head to [https://kubeflow.aaw.cloud.statcan.ca/](https://kubeflow.aaw.cloud.statcan.ca/). + + +!!! note "" +
+ [![Kubeflow is the Heart of the AAW!](./images/Kubeflow.PNG)](https://kubeflow.aaw.cloud.statcan.ca/) +

**[👉 Click here to setup your Kubeflow account! 👈](https://kubeflow.aaw.cloud.statcan.ca/)**

+
+ +**[Kubeflow](1-Experiments/Kubeflow/)** is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. + +Kubeflow simplifies the following tasks: + +- Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). +- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. + + +!!! info "Kubeflow Dashboard" + - [**Kubeflow Dashboard**](https://kubeflow.aaw.cloud.statcan.ca/) Use this link once you have your cloud account! + +Getting started with the Advanced Analytics Workspace (AAW) is easy and quick. First, you'll want to login to Kubeflow to create your first notebook server running JupyterLab, RStudio or Ubuntu Desktop. We encourage you to join our Slack channel to connect with other data scientists and analysts, ask questions, and share your experiences with the AAW platform. + +### Slack + +
+[![Ask Platform Related Questions on Slack!](images/SlackAAW.PNG)](https://statcan-aaw.slack.com/) +
+ +- **[Click here sign in to our Slack Support Workspace](https://statcan-aaw.slack.com/)** + +- **Use the _General_ Channel!** + +At StatCan, we understand that embarking on a new project can be overwhelming, and you're likely to have many platform-related questions along the way. That's why we've created a dedicated **[Slack channel](https://statcan-aaw.slack.com/)** to provide you with the support you need. Our team of experts is standing by to answer your questions, address any concerns, and guide you through every step of the process. + +To join our **[Slack channel](https://statcan-aaw.slack.com/)**, simply click on the link provided and follow the instructions. You'll be prompted to create an account in the upper right-hand corner of the page. If you have an `@statcan.gc.ca` email address, use it when signing up as this will ensure that you are automatically approved and can start engaging with our community right away. + +Once you've created your account, you'll have access to a wealth of resources and information, as well as the opportunity to connect with other users who are working on similar projects. Our **[Slack channel](https://statcan-aaw.slack.com/)** is the perfect place to ask questions, share insights, and collaborate with your peers in real-time. Whether you're just getting started with a new project or you're looking for expert advice on a complex issue, our team is here to help. + +So don't hesitate - join our **[Slack channel](https://statcan-aaw.slack.com/)** today and start getting the answers you need to succeed. We look forward to welcoming you to our community! + +Click on the link, then choose "Create an account" in the upper right-hand corner. + + +!!! note "" +
+ ![Use your @statcan.gc.ca email!](images/SlackAAW2.png) +

Use your @statcan.gc.ca email address so that you will be automatically approved.

+
+ +## 🧭 Getting Started + +To access AAW services, you need to log in to Kubeflow with your StatCan guest cloud account. Once logged in, select Notebook Servers and click the "New Server" button to get started. + +1. Login to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) with your StatCan guest cloud account. You will be prompted to authenticate the account. +2. Select Notebook Servers. +3. Click the "➕ New Server" button. + +## 🧰 Tools Offered + +AAW is a flexible platform for data analysis and machine learning. It offers a range of languages, including Python, R, and Julia. AAW also supports development environments such as VS Code, R Studio, and Jupyter Notebooks. Additionally, Linux virtual desktops are available for users who require additional tools such as OpenM++ and QGIS. + +Here's a list of tools we offer: + +- 📜 Languages: + - 🐍 Python + - 📈 R + - 👩‍🔬 Julia +- 🧮 Development environments: + - VS Code + - R Studio + - Jupyter Notebooks +- 🐧 Linux virtual desktops for additional tools (🧫 OpenM++, 🌏 QGIS etc.) + +Sharing code, disks, and workspaces (e.g.: two people sharing the same virtual machine) is described in more detail in the [Collaboration](4-Collaboration/Overview.md) section. Sharing data through buckets is described in more detail in the **[Azure Blob Storage](./5-Storage/AzureBlobStorage.md)** +section. + +### 💡 Help + +- Disk (also called Volumes on the Notebook Server creation screen) +- Containers (Blob Storage) +- Data Lakes (coming soon) + +- 📗 AAW Portal Documentation + - [https://statcan.github.io/aaw/](https://statcan.github.io/aaw/) +- 📘 Kubeflow Documentation + - [https://www.kubeflow.org/docs/](https://www.kubeflow.org/docs/) +- 🤝 Slack Support Channel + - [https://statcan-aaw.slack.com](https://statcan-aaw.slack.com) + +## 🐱 Demos + +If you require a quick onboarding demo session, need help, or have any questions, please reach out to us through our [🤝 Slack Support Channel](https://statcan-aaw.slack.com). + +## Contributing + +If you have any bugs to report or features to request please do so via https://github.com/StatCan/aaw. diff --git a/docs/en/index.md b/docs/en/index.md index fdf7d091a..6a2b8c87e 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -1,143 +1,77 @@ # Welcome to the Advanced Analytics Workspace -
-![Statistics](images/statistics-on-the-moon.jpg) -
+![Statistics](images/statistics-on-the-moon-small.jpg) -## The Advanced Analytics Workspace Documentation +## Introduction -_Welcome to the world of data science and machine learning!_ +Embark on an exciting journey into the realm of data science and machine learning with the Advanced Analytics Workspace (AAW). Developed by Data Analytics Services (DAS) at Statistics Canada, AAW is an open-source platform specifically crafted for data scientists, analysts, and researchers proficient in open-source tools and coding. - -!!! info "What is the AAW?" - **[Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw)** is an open source platform designed for data scientists, data stewards, analysts and researchers familiar with open source tools and coding. Developed by data scientists for data scientists, AAW provides a flexible environment that enables advanced practitioners to get their work done with ease. +## What is the AAW? -The AAW is a comprehensive solution for data science and data analytics. With the AAW, you can customize notebook server deployments to suit your specific data science needs. We have a small number of custom Docker images made by our team. +The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw) (AAW) stands as a comprehensive and open-source solution designed to cater to the diverse needs of data scientists. It offers a flexible environment that empowers advanced practitioners to seamlessly conduct their work. AAW is a versatile platform, providing solutions for data science and analytics across various domains. - -!!! info "What is Kubeflow?" - The AAW is based on [Kubeflow](https://www.kubeflow.org/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. +## What is Kubeflow? -Whether you're just getting started or already knee-deep in data analysis, the Advanced Analytics Workspace has everything you need to take your work to the next level. From powerful tools for data pipelines to cloud storage for your datasets, our platform has it all. Need to collaborate with colleagues or publish your results? No problem. We offer seamless collaboration features that make it easy to work together and share your work with others. +[![Kubeflow is the Heart of the AAW!](./images/Kubeflow-small.png)](https://kubeflow.aaw.cloud.statcan.ca/) -No matter what stage of your data science journey you're at, the Advanced Analytics Workspace has the resources you need to succeed. +**[👉 Click here to set up your Kubeflow account! 👈](https://kubeflow.aaw.cloud.statcan.ca/)** -## Getting Started with the AAW - -
-![AAW icon](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) -
- -### The AAW Portal - -The AAW portal homepage is available for internal users only. However, external users with a cloud account granted access by the business sponsor can access the platform through the analytics-platform URL. - - -!!! info annotate "AAW Portal Homepage" - - [**Portal Homepage for Statistics Canada Employees**](https://www.statcan.gc.ca/data-analytics-service/aaw) - - [**Portal Homepage for External Users**](https://www.statcan.gc.ca/data-analytics-services/overview) - -### Kubeflow Account - - -!!! important "Attention External Users!" - Users external to Statistics Canada will require a cloud account granted access by the business sponsor. - - -!!! important "Attention Statistics Canada Employees!" - Users internal to Statistics Canada can get started right away without any additional sign up procedures, just head to [https://kubeflow.aaw.cloud.statcan.ca/](https://kubeflow.aaw.cloud.statcan.ca/). - - -!!! note "" -
- [![Kubeflow is the Heart of the AAW!](./images/Kubeflow.PNG)](https://kubeflow.aaw.cloud.statcan.ca/) -

**[👉 Click here to setup your Kubeflow account! 👈](https://kubeflow.aaw.cloud.statcan.ca/)**

-
- -**[Kubeflow](1-Experiments/Kubeflow/)** is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. - -Kubeflow simplifies the following tasks: +The AAW is based on [Kubeflow](1-Experiments/Kubeflow/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. [Kubeflow](1-Experiments/Kubeflow/) is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. Kubeflow simplifies the following tasks: - Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). - Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. + - -!!! info "Kubeflow Dashboard" - - [**Kubeflow Dashboard**](https://kubeflow.aaw.cloud.statcan.ca/) Use this link once you have your cloud account! -Getting started with the Advanced Analytics Workspace (AAW) is easy and quick. First, you'll want to login to Kubeflow to create your first notebook server running JupyterLab, RStudio or Ubuntu Desktop. We encourage you to join our Slack channel to connect with other data scientists and analysts, ask questions, and share your experiences with the AAW platform. -### Slack +## Getting Started -
-[![Ask Platform Related Questions on Slack!](images/SlackAAW.PNG)](https://statcan-aaw.slack.com/) -
+### AAW Portal -- **[Click here sign in to our Slack Support Workspace](https://statcan-aaw.slack.com/)** +Whether you're an internal user from Statistics Canada or an external collaborator, the AAW Portal is your gateway to a world of possibilities. -- **Use the _General_ Channel!** +- **Internal Users**: Access the [Portal Homepage for Statistics Canada Employees](https://www.statcan.gc.ca/data-analytics-service/aaw). +- **External Users**: Explore the [Portal Homepage for External Users](https://www.statcan.gc.ca/data-analytics-services/overview). -At StatCan, we understand that embarking on a new project can be overwhelming, and you're likely to have many platform-related questions along the way. That's why we've created a dedicated **[Slack channel](https://statcan-aaw.slack.com/)** to provide you with the support you need. Our team of experts is standing by to answer your questions, address any concerns, and guide you through every step of the process. - -To join our **[Slack channel](https://statcan-aaw.slack.com/)**, simply click on the link provided and follow the instructions. You'll be prompted to create an account in the upper right-hand corner of the page. If you have an `@statcan.gc.ca` email address, use it when signing up as this will ensure that you are automatically approved and can start engaging with our community right away. - -Once you've created your account, you'll have access to a wealth of resources and information, as well as the opportunity to connect with other users who are working on similar projects. Our **[Slack channel](https://statcan-aaw.slack.com/)** is the perfect place to ask questions, share insights, and collaborate with your peers in real-time. Whether you're just getting started with a new project or you're looking for expert advice on a complex issue, our team is here to help. +### Kubeflow Account -So don't hesitate - join our **[Slack channel](https://statcan-aaw.slack.com/)** today and start getting the answers you need to succeed. We look forward to welcoming you to our community! +#### External Users -Click on the link, then choose "Create an account" in the upper right-hand corner. +1. External users need a cloud account granted access by the business sponsor. +2. Visit [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/). - -!!! note "" -
- ![Use your @statcan.gc.ca email!](images/SlackAAW2.png) -

Use your @statcan.gc.ca email address so that you will be automatically approved.

-
+#### Statistics Canada Employees -## 🧭 Getting Started +1. Internal users at Statistics Canada can directly access [https://kubeflow.aaw.cloud.statcan.ca/](https://kubeflow.aaw.cloud.statcan.ca/). -To access AAW services, you need to log in to Kubeflow with your StatCan guest cloud account. Once logged in, select Notebook Servers and click the "New Server" button to get started. +## Creating a Kubeflow Notebook Server -1. Login to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) with your StatCan guest cloud account. You will be prompted to authenticate the account. +1. Log in to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) with your StatCan guest cloud account. 2. Select Notebook Servers. 3. Click the "➕ New Server" button. +4. Follow the intructions here: ... -## 🧰 Tools Offered +## Importing/Accessing Your Data -AAW is a flexible platform for data analysis and machine learning. It offers a range of languages, including Python, R, and Julia. AAW also supports development environments such as VS Code, R Studio, and Jupyter Notebooks. Additionally, Linux virtual desktops are available for users who require additional tools such as OpenM++ and QGIS. +Dive into the world of data with our [Example Notebooks](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/DrawData_EN.html). Explore different data sets and learn how to interact with them using AAW. -Here's a list of tools we offer: +## Examples in JupyterLab -- 📜 Languages: - - 🐍 Python - - 📈 R - - 👩‍🔬 Julia -- 🧮 Development environments: - - VS Code - - R Studio - - Jupyter Notebooks -- 🐧 Linux virtual desktops for additional tools (🧫 OpenM++, 🌏 QGIS etc.) +Unlock the potential of AAW in JupyterLab, offering support for Python, R, Julia, and more. -Sharing code, disks, and workspaces (e.g.: two people sharing the same virtual machine) is described in more detail in the [Collaboration](4-Collaboration/Overview.md) section. Sharing data through buckets is described in more detail in the **[Azure Blob Storage](./5-Storage/AzureBlobStorage.md)** -section. +List of sources of examples: -### 💡 Help +1. one +2. two -- Disk (also called Volumes on the Notebook Server creation screen) -- Containers (Blob Storage) -- Data Lakes (coming soon) +## Need Help? -- 📗 AAW Portal Documentation - - [https://statcan.github.io/aaw/](https://statcan.github.io/aaw/) -- 📘 Kubeflow Documentation - - [https://www.kubeflow.org/docs/](https://www.kubeflow.org/docs/) -- 🤝 Slack Support Channel - - [https://statcan-aaw.slack.com](https://statcan-aaw.slack.com) +Join our vibrant community on the [Slack channel](https://statcan-aaw.slack.com/) to connect with fellow users, ask questions, and share experiences. -## 🐱 Demos +For comprehensive documentation and guidance, refer to the [AAW Portal Documentation](https://statcan.github.io/aaw/) and the [Kubeflow Documentation](https://www.kubeflow.org/docs/). Need real-time assistance? Join our [Slack Support Channel](https://statcan-aaw.slack.com). -If you require a quick onboarding demo session, need help, or have any questions, please reach out to us through our [🤝 Slack Support Channel](https://statcan-aaw.slack.com). +## Demos and Contributions -## Contributing +For in-depth demos, personalized assistance, or to contribute to the AAW community, reach out to us on [Slack](https://statcan-aaw.slack.com). Contribute to the platform's development and report issues or feature requests on [GitHub](https://github.com/StatCan/aaw). -If you have any bugs to report or features to request please do so via https://github.com/StatCan/aaw. +Embark on your data science journey with AAW — where possibilities are limitless, and innovation knows no bounds. From d268f62efe09a3742d87bdf88da7ef6b987cad54 Mon Sep 17 00:00:00 2001 From: Bryan Paget <8212170+bryanpaget@users.noreply.github.com> Date: Wed, 13 Dec 2023 21:21:38 +0000 Subject: [PATCH 2/7] feat(index.md): Getting Started section --- package.json | 2 +- yarn.lock | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/package.json b/package.json index d45dfaf3d..e91b9f6d5 100644 --- a/package.json +++ b/package.json @@ -15,6 +15,6 @@ "devDependencies": { "husky": "^4.2.5", "markdown-spellcheck": "https://github.com/brendangadd/node-markdown-spellcheck.git#45cf81bfb56f298d0928461133aa9f264047dd49", - "prettier": "3.1.0" + "prettier": "3.1.1" } } diff --git a/yarn.lock b/yarn.lock index 3144efdd7..f5f0df1b3 100644 --- a/yarn.lock +++ b/yarn.lock @@ -573,10 +573,10 @@ please-upgrade-node@^3.2.0: dependencies: semver-compare "^1.0.0" -prettier@3.1.0: - version "3.1.0" - resolved "https://registry.yarnpkg.com/prettier/-/prettier-3.1.0.tgz#c6d16474a5f764ea1a4a373c593b779697744d5e" - integrity sha512-TQLvXjq5IAibjh8EpBIkNKxO749UEWABoiIZehEPiY4GNpVdhaFKqSTu+QrlU6D2dPAfubRmtJTi4K4YkQ5eXw== +prettier@3.1.1: + version "3.1.1" + resolved "https://registry.yarnpkg.com/prettier/-/prettier-3.1.1.tgz#6ba9f23165d690b6cbdaa88cb0807278f7019848" + integrity sha512-22UbSzg8luF4UuZtzgiUOfcGM8s4tjBv6dJRT7j275NXsy2jb4aJa4NNveul5x4eqlF1wuhuR2RElK71RvmVaw== queue-microtask@^1.2.2: version "1.2.3" From 529817acf3851706fd6ce1d2773db2abb0811a71 Mon Sep 17 00:00:00 2001 From: Bryan Paget <8212170+bryanpaget@users.noreply.github.com> Date: Wed, 13 Dec 2023 21:44:42 +0000 Subject: [PATCH 3/7] feat(index.md): add links to resources --- docs/en/index.md | 34 +++++++++++++++++++++++++++++++--- 1 file changed, 31 insertions(+), 3 deletions(-) diff --git a/docs/en/index.md b/docs/en/index.md index 6a2b8c87e..5f2ef5e74 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -16,14 +16,20 @@ The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-serv **[👉 Click here to set up your Kubeflow account! 👈](https://kubeflow.aaw.cloud.statcan.ca/)** +### Videos + +- [Kubeflow 101](https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x) by Google Cloud Tech, a series dedicated to helping you get started with Kubeflow! Learn more about what Kubeflow is, the different parts of Kubeflow, and how to get started with simple, portable, and scalable machine learning. + +### Documentation + +- [Official](https://www.kubeflow.org/) +- [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) +- [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf) The AAW is based on [Kubeflow](1-Experiments/Kubeflow/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. [Kubeflow](1-Experiments/Kubeflow/) is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. Kubeflow simplifies the following tasks: - Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). - Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. - - - ## Getting Started ### AAW Portal @@ -64,6 +70,28 @@ List of sources of examples: 1. one 2. two +## Learning Resources + +- https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/ + +### Data Preparation + +- https://machinelearningmastery.com/start-here/#dataprep + + +### Machine Learning + +- https://machinelearningmastery.com/start-here/#imbalanced + +### Python + +- https://machinelearningmastery.com/start-here/#pythonskills +- https://www.youtube.com/watch?v=t8pPdKYpowI + +### R + +- https://www.youtube.com/playlist?list=PLLOxZwkBK52C6_Nkmp0nFCreLfnfJgUL7 + ## Need Help? Join our vibrant community on the [Slack channel](https://statcan-aaw.slack.com/) to connect with fellow users, ask questions, and share experiences. 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zyn1F#$iG7i2rbiB1UX!Vep8=waU}3-_QTu`x{P@33wZTaUuC+atR;^c}V{(i~qkm^v4~>sYuroH*O)N2fz<1r35LJF#i1i E0hFjN1^@s6 literal 0 HcmV?d00001 diff --git a/docs/en/index.md b/docs/en/index.md index 5f2ef5e74..895387e5a 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -12,9 +12,21 @@ The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-serv ## What is Kubeflow? -[![Kubeflow is the Heart of the AAW!](./images/Kubeflow-small.png)](https://kubeflow.aaw.cloud.statcan.ca/) +**[Click here to set up your Kubeflow account!](https://kubeflow.aaw.cloud.statcan.ca/)** -**[👉 Click here to set up your Kubeflow account! 👈](https://kubeflow.aaw.cloud.statcan.ca/)** +Kubeflow is an advanced, container-based data science and machine learning workflow system that runs on Kubernetes. + +Kubeflow simplifies the following tasks: + +- Creating customizable environments to work with data +- Managing and tracking, experiments, machine learning jobs and analytical workloads over time + +![AAW Architecture](images/kubeflow-stack.png){: style="height:150px;width:150px"} + +The AAW is based on [Kubeflow](1-Experiments/Kubeflow/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. [Kubeflow](1-Experiments/Kubeflow/) is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. Kubeflow simplifies the following tasks: + +- Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). +- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. ### Videos @@ -25,10 +37,6 @@ The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-serv - [Official](https://www.kubeflow.org/) - [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) - [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf) -The AAW is based on [Kubeflow](1-Experiments/Kubeflow/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. [Kubeflow](1-Experiments/Kubeflow/) is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. Kubeflow simplifies the following tasks: - -- Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). -- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. ## Getting Started From 94a66371ffa0abfd528e4ec125503b6d9ad6b27a Mon Sep 17 00:00:00 2001 From: Bryan Paget <8212170+bryanpaget@users.noreply.github.com> Date: Thu, 14 Dec 2023 21:58:52 +0000 Subject: [PATCH 5/7] feat(index.md): updates to getting started - need to think more about the user story and the step-by-step instructions to go from new to experienced - need to ask for help on storage related stuff, want to make sure I understand the steps to get your protected data into the aaw --- docs/en/images/kubeflow-favourite.png | Bin 0 -> 251889 bytes docs/en/images/kubeflow-stack.png | Bin 99609 -> 49986 bytes docs/en/images/kubeflow-stack_384x384.png | Bin 0 -> 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zc>VR)$;!&ACzEt^baZq8GBY!I_uY3HIB+1z$;tJ;eL>Uyw|d!NFv!lr+FF#Y=VJ0tu0pB(@|I2^7t*5z_x zv)O3YtQjdODM*r@hp(ffqjMqzK_C*(m{oJWYFNL*YTF)=Z8=+J@0#6(Oc(;3}oy*hyE zsH& -![Statistics](images/statistics-on-the-moon.jpg) - - -## The Advanced Analytics Workspace Documentation - -_Welcome to the world of data science and machine learning!_ - - -!!! info "What is the AAW?" - **[Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw)** is an open source platform designed for data scientists, data stewards, analysts and researchers familiar with open source tools and coding. Developed by data scientists for data scientists, AAW provides a flexible environment that enables advanced practitioners to get their work done with ease. - -The AAW is a comprehensive solution for data science and data analytics. With the AAW, you can customize notebook server deployments to suit your specific data science needs. We have a small number of custom Docker images made by our team. - - -!!! info "What is Kubeflow?" - The AAW is based on [Kubeflow](https://www.kubeflow.org/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. - -Whether you're just getting started or already knee-deep in data analysis, the Advanced Analytics Workspace has everything you need to take your work to the next level. From powerful tools for data pipelines to cloud storage for your datasets, our platform has it all. Need to collaborate with colleagues or publish your results? No problem. We offer seamless collaboration features that make it easy to work together and share your work with others. - -No matter what stage of your data science journey you're at, the Advanced Analytics Workspace has the resources you need to succeed. - -## Getting Started with the AAW - -
    -![AAW icon](https://user-images.githubusercontent.com/8212170/158243976-0ee25082-f3dc-4724-b8c3-1430c7f2a461.png) -
    - -### The AAW Portal - -The AAW portal homepage is available for internal users only. However, external users with a cloud account granted access by the business sponsor can access the platform through the analytics-platform URL. - - -!!! info annotate "AAW Portal Homepage" - - [**Portal Homepage for Statistics Canada Employees**](https://www.statcan.gc.ca/data-analytics-service/aaw) - - [**Portal Homepage for External Users**](https://www.statcan.gc.ca/data-analytics-services/overview) - -### Kubeflow Account - - -!!! important "Attention External Users!" - Users external to Statistics Canada will require a cloud account granted access by the business sponsor. - - -!!! important "Attention Statistics Canada Employees!" - Users internal to Statistics Canada can get started right away without any additional sign up procedures, just head to [https://kubeflow.aaw.cloud.statcan.ca/](https://kubeflow.aaw.cloud.statcan.ca/). - - -!!! note "" -
    - [![Kubeflow is the Heart of the AAW!](./images/Kubeflow.PNG)](https://kubeflow.aaw.cloud.statcan.ca/) -

    **[👉 Click here to setup your Kubeflow account! 👈](https://kubeflow.aaw.cloud.statcan.ca/)**

    -
    - -**[Kubeflow](1-Experiments/Kubeflow/)** is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. - -Kubeflow simplifies the following tasks: - -- Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). -- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. - - -!!! info "Kubeflow Dashboard" - - [**Kubeflow Dashboard**](https://kubeflow.aaw.cloud.statcan.ca/) Use this link once you have your cloud account! - -Getting started with the Advanced Analytics Workspace (AAW) is easy and quick. First, you'll want to login to Kubeflow to create your first notebook server running JupyterLab, RStudio or Ubuntu Desktop. We encourage you to join our Slack channel to connect with other data scientists and analysts, ask questions, and share your experiences with the AAW platform. - -### Slack - -
    -[![Ask Platform Related Questions on Slack!](images/SlackAAW.PNG)](https://statcan-aaw.slack.com/) -
    - -- **[Click here sign in to our Slack Support Workspace](https://statcan-aaw.slack.com/)** - -- **Use the _General_ Channel!** - -At StatCan, we understand that embarking on a new project can be overwhelming, and you're likely to have many platform-related questions along the way. That's why we've created a dedicated **[Slack channel](https://statcan-aaw.slack.com/)** to provide you with the support you need. Our team of experts is standing by to answer your questions, address any concerns, and guide you through every step of the process. - -To join our **[Slack channel](https://statcan-aaw.slack.com/)**, simply click on the link provided and follow the instructions. You'll be prompted to create an account in the upper right-hand corner of the page. If you have an `@statcan.gc.ca` email address, use it when signing up as this will ensure that you are automatically approved and can start engaging with our community right away. - -Once you've created your account, you'll have access to a wealth of resources and information, as well as the opportunity to connect with other users who are working on similar projects. Our **[Slack channel](https://statcan-aaw.slack.com/)** is the perfect place to ask questions, share insights, and collaborate with your peers in real-time. Whether you're just getting started with a new project or you're looking for expert advice on a complex issue, our team is here to help. - -So don't hesitate - join our **[Slack channel](https://statcan-aaw.slack.com/)** today and start getting the answers you need to succeed. We look forward to welcoming you to our community! - -Click on the link, then choose "Create an account" in the upper right-hand corner. - - -!!! note "" -
    - ![Use your @statcan.gc.ca email!](images/SlackAAW2.png) -

    Use your @statcan.gc.ca email address so that you will be automatically approved.

    -
    - -## 🧭 Getting Started - -To access AAW services, you need to log in to Kubeflow with your StatCan guest cloud account. Once logged in, select Notebook Servers and click the "New Server" button to get started. - -1. Login to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) with your StatCan guest cloud account. You will be prompted to authenticate the account. -2. Select Notebook Servers. -3. Click the "➕ New Server" button. - -## 🧰 Tools Offered - -AAW is a flexible platform for data analysis and machine learning. It offers a range of languages, including Python, R, and Julia. AAW also supports development environments such as VS Code, R Studio, and Jupyter Notebooks. Additionally, Linux virtual desktops are available for users who require additional tools such as OpenM++ and QGIS. - -Here's a list of tools we offer: - -- 📜 Languages: - - 🐍 Python - - 📈 R - - 👩‍🔬 Julia -- 🧮 Development environments: - - VS Code - - R Studio - - Jupyter Notebooks -- 🐧 Linux virtual desktops for additional tools (🧫 OpenM++, 🌏 QGIS etc.) - -Sharing code, disks, and workspaces (e.g.: two people sharing the same virtual machine) is described in more detail in the [Collaboration](4-Collaboration/Overview.md) section. Sharing data through buckets is described in more detail in the **[Azure Blob Storage](./5-Storage/AzureBlobStorage.md)** -section. - -### 💡 Help - -- Disk (also called Volumes on the Notebook Server creation screen) -- Containers (Blob Storage) -- Data Lakes (coming soon) - -- 📗 AAW Portal Documentation - - [https://statcan.github.io/aaw/](https://statcan.github.io/aaw/) -- 📘 Kubeflow Documentation - - [https://www.kubeflow.org/docs/](https://www.kubeflow.org/docs/) -- 🤝 Slack Support Channel - - [https://statcan-aaw.slack.com](https://statcan-aaw.slack.com) - -## 🐱 Demos - -If you require a quick onboarding demo session, need help, or have any questions, please reach out to us through our [🤝 Slack Support Channel](https://statcan-aaw.slack.com). - -## Contributing - -If you have any bugs to report or features to request please do so via https://github.com/StatCan/aaw. diff --git a/docs/en/index.md b/docs/en/index.md index 895387e5a..e90c6d983 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -1,104 +1,125 @@ # Welcome to the Advanced Analytics Workspace +
    ![Statistics](images/statistics-on-the-moon-small.jpg) +
    -## Introduction - -Embark on an exciting journey into the realm of data science and machine learning with the Advanced Analytics Workspace (AAW). Developed by Data Analytics Services (DAS) at Statistics Canada, AAW is an open-source platform specifically crafted for data scientists, analysts, and researchers proficient in open-source tools and coding. +_Embark on an exciting journey into the realm of data science and machine learning with the Advanced Analytics Workspace (AAW). Developed by Data Analytics Services (DAS) at Statistics Canada, AAW is an open-source platform specifically crafted for data scientists, analysts, and researchers proficient in open-source tools and coding._ ## What is the AAW? -The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw) (AAW) stands as a comprehensive and open-source solution designed to cater to the diverse needs of data scientists. It offers a flexible environment that empowers advanced practitioners to seamlessly conduct their work. AAW is a versatile platform, providing solutions for data science and analytics across various domains. - -## What is Kubeflow? - -**[Click here to set up your Kubeflow account!](https://kubeflow.aaw.cloud.statcan.ca/)** - -Kubeflow is an advanced, container-based data science and machine learning workflow system that runs on Kubernetes. +
    +![AAW Architecture](images/kubeflow-stack.png) +
    -Kubeflow simplifies the following tasks: +The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw) (AAW) stands as a comprehensive and open-source solution designed to cater to the diverse needs of data scientists. It offers a flexible environment that empowers advanced practitioners to seamlessly conduct their work. AAW is a versatile platform, providing solutions for data science and analytics across various domains. -- Creating customizable environments to work with data -- Managing and tracking, experiments, machine learning jobs and analytical workloads over time +More information about the AAW can be found on [the DAS Portal](https://www.statcan.gc.ca/data-analytics-services/aaw). -![AAW Architecture](images/kubeflow-stack.png){: style="height:150px;width:150px"} +TODO: Many of the links on https://www.statcan.gc.ca/data-analytics-services/aaw are broken. -The AAW is based on [Kubeflow](1-Experiments/Kubeflow/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. [Kubeflow](1-Experiments/Kubeflow/) is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. Kubeflow simplifies the following tasks: +## Getting Started -- Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). -- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. +### AAW Portal -### Videos +Whether you're an internal user from Statistics Canada or an external collaborator, the AAW Portal is your gateway to a world of possibilities. -- [Kubeflow 101](https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x) by Google Cloud Tech, a series dedicated to helping you get started with Kubeflow! Learn more about what Kubeflow is, the different parts of Kubeflow, and how to get started with simple, portable, and scalable machine learning. +- **Internal Users**: Access the [Portal Homepage for Statistics Canada Employees](https://www.statcan.gc.ca/data-analytics-service/aaw) and click Kubeflow to enter your Kubeflow dashboard. +- **External Users**: Explore the [Portal Homepage for External Users](https://www.statcan.gc.ca/data-analytics-services/overview) and click getting started to open the onboarding form. -### Documentation +### Kubeflow -- [Official](https://www.kubeflow.org/) -- [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) -- [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf) - -## Getting Started +[Click here to set up your Kubeflow account!](https://kubeflow.aaw.cloud.statcan.ca/) -### AAW Portal +
    +![Your Favourite Software](images/kubeflow-favourite.png) +
    -Whether you're an internal user from Statistics Canada or an external collaborator, the AAW Portal is your gateway to a world of possibilities. +The AAW is based on [Kubeflow](1-Experiments/Kubeflow/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. [Kubeflow](1-Experiments/Kubeflow/) is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. Kubeflow simplifies the following tasks: -- **Internal Users**: Access the [Portal Homepage for Statistics Canada Employees](https://www.statcan.gc.ca/data-analytics-service/aaw). -- **External Users**: Explore the [Portal Homepage for External Users](https://www.statcan.gc.ca/data-analytics-services/overview). +- Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). +- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. ### Kubeflow Account +To create a namespace, go to [the DAS Portal](https://www.statcan.gc.ca/data-analytics-services/overview) where you'll find a link to [the onboarding form](https://forms.office.com/pages/responsepage.aspx?id=mR-PJT3ux0K_xXrxsjQ-ApbSRgATuERFkV3XLiwQ8xFUQ1NHRzBVWEFYUktZRzBJRDIwUDZFSDQ0USQlQCN0PWcu). + #### External Users -1. External users need a cloud account granted access by the business sponsor. -2. Visit [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/). +1. External users need a StatCan Cloud account granted by the business sponsor. +2. Visit [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) once the StatCan Cloud account has been created. #### Statistics Canada Employees 1. Internal users at Statistics Canada can directly access [https://kubeflow.aaw.cloud.statcan.ca/](https://kubeflow.aaw.cloud.statcan.ca/). -## Creating a Kubeflow Notebook Server +### Creating a Kubeflow Notebook Server. 1. Log in to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) with your StatCan guest cloud account. 2. Select Notebook Servers. 3. Click the "➕ New Server" button. -4. Follow the intructions here: ... +4. Follow [the instructions here](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html#setup). + +#### Kubeflow Documentation + +- [Official Kubeflow Documentation](https://www.kubeflow.org/docs/started/introduction/) + +#### Kubeflow Videos + +- [Kubeflow 101](https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x) by Google Cloud Tech. -## Importing/Accessing Your Data +## Your Data -Dive into the world of data with our [Example Notebooks](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/DrawData_EN.html). Explore different data sets and learn how to interact with them using AAW. +Instructions on how to add storage to your notebook server can be found here on [the documentation page for storage](https://statcan.github.io/aaw/en/5-Storage/Disks.html). -## Examples in JupyterLab +If your project requires protected data, cloud storage buckets will be created for you at the time of your projects onboarding. Access protected data by opening the buckets folder, see [the documentation](https://statcan.github.io/aaw/en/5-Storage/AzureBlobStorage.html). + +Uploading data into JupyterLab is simple, [the official JupyterLab documentation](https://jupyterlab.readthedocs.io/en/stable/user/files.html#uploading-and-downloading) has a section on uploading and downloading files from the JupyterLab web interface. + +## JupyterLab Unlock the potential of AAW in JupyterLab, offering support for Python, R, Julia, and more. +When conducting data science experiments, it's a best practice to utilize Python and/or conda virtual environments to manage your project dependencies. It is common to create a dedicated environment for each project or, in some cases, separate environments for different features or aspects of your work (for instance, one environment for general projects and an additional environment tailored for GPU-accelerated deep learning tasks). + +### JupyterLab Documentation + +- [Official Getting Started with JupyterLab Docs.](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html) + +If you find yourself frequently switching between environments and desire a more convenient way to access them within JupyterLab, you can follow [these instructions](https://statcan.github.io/aaw/en/1-Experiments/Virtual-Environments.html#creating-and-adding-environments-to-the-jupyterlab-launcher). + List of sources of examples: -1. one -2. two +1. [Visual Python: Simplifying Data Analysis for Python Learners](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/VisualPython_EN.html) +2. [YData Profiling: Streamlining Data Analysis](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/YData-Profiling_EN.html) -## Learning Resources +## External Learning Resources -- https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/ +- [A Gentle Introduction to SciKit Learn (Python)](https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/) +- [Official SciKit Learn Tutorials](https://scikit-learn.org/stable/tutorial/index.html) ### Data Preparation -- https://machinelearningmastery.com/start-here/#dataprep +- [Machine Learning Mastery's Data Preparation Course](https://machinelearningmastery.com/start-here/#dataprep) ### Machine Learning -- https://machinelearningmastery.com/start-here/#imbalanced +- [How to Handle Imbalanced Datasets](https://machinelearningmastery.com/start-here/#imbalanced) ### Python -- https://machinelearningmastery.com/start-here/#pythonskills -- https://www.youtube.com/watch?v=t8pPdKYpowI +- [Real Python's Introduction to Python](https://realpython.com/learning-paths/python3-introduction/) +- [W3School's Introduction to Python](https://www.w3schools.com/python/python_intro.asp) +- [Google Developers' Introduction to Python](https://developers.google.com/edu/python) +- [Machine Learning Mastery's Python Skills](https://machinelearningmastery.com/start-here/#pythonskills) +- [TechWorld with Nana's Python Tutorial for Beginners](https://www.youtube.com/watch?v=t8pPdKYpowI) ### R -- https://www.youtube.com/playlist?list=PLLOxZwkBK52C6_Nkmp0nFCreLfnfJgUL7 +- [Videos on R](https://www.youtube.com/playlist?list=PLLOxZwkBK52C6_Nkmp0nFCreLfnfJgUL7) +- [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) +- [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf) ## Need Help? From c529842f30bd979745929de18b8106cead267ab6 Mon Sep 17 00:00:00 2001 From: Bryan Paget <8212170+bryanpaget@users.noreply.github.com> Date: Fri, 15 Dec 2023 19:26:46 +0000 Subject: [PATCH 6/7] feat(index.md): updates to getting started --- docs/en/index.md | 143 +++++++++++++++++++++++------------------------ 1 file changed, 70 insertions(+), 73 deletions(-) diff --git a/docs/en/index.md b/docs/en/index.md index e90c6d983..d7d398c4d 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -1,113 +1,122 @@ -# Welcome to the Advanced Analytics Workspace +# The Advanced Analytics Workspace + +_The AAW is an open-source platform specifically crafted for data scientists, analysts, and researchers proficient in open-source tools and coding._
    ![Statistics](images/statistics-on-the-moon-small.jpg)
    -_Embark on an exciting journey into the realm of data science and machine learning with the Advanced Analytics Workspace (AAW). Developed by Data Analytics Services (DAS) at Statistics Canada, AAW is an open-source platform specifically crafted for data scientists, analysts, and researchers proficient in open-source tools and coding._ - ## What is the AAW? -
    -![AAW Architecture](images/kubeflow-stack.png) -
    - -The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw) (AAW) stands as a comprehensive and open-source solution designed to cater to the diverse needs of data scientists. It offers a flexible environment that empowers advanced practitioners to seamlessly conduct their work. AAW is a versatile platform, providing solutions for data science and analytics across various domains. - -More information about the AAW can be found on [the DAS Portal](https://www.statcan.gc.ca/data-analytics-services/aaw). +The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw) (AAW) stands as a comprehensive and open-source solution designed to cater to the diverse needs of data scientists. It offers a flexible environment that empowers advanced practitioners to seamlessly conduct their work. AAW is a versatile platform, providing solutions for data science and analytics across various domains. More information about the AAW and Data Analytics Services (DAS) can be found on [the DAS Portal](https://www.statcan.gc.ca/data-analytics-services/aaw). -TODO: Many of the links on https://www.statcan.gc.ca/data-analytics-services/aaw are broken. +**TODO:** Many of the links on https://www.statcan.gc.ca/data-analytics-services/aaw are broken. ## Getting Started -### AAW Portal - -Whether you're an internal user from Statistics Canada or an external collaborator, the AAW Portal is your gateway to a world of possibilities. - -- **Internal Users**: Access the [Portal Homepage for Statistics Canada Employees](https://www.statcan.gc.ca/data-analytics-service/aaw) and click Kubeflow to enter your Kubeflow dashboard. -- **External Users**: Explore the [Portal Homepage for External Users](https://www.statcan.gc.ca/data-analytics-services/overview) and click getting started to open the onboarding form. +- **StatCan Users**: Access the [Kubeflow Dashboard](https://kubeflow.aaw.cloud.statcan.ca/) to get started +- **External Users and Collaborators**: Fill out [the DAS Onboarding Form](https://forms.office.com/r/RPrgDDkU9T) to tell us about your project needs. Once completed, a DAS representative will contact you to discuss the next steps and begin the onboarding process. Note: External users need a StatCan Cloud account granted by the business sponsor ### Kubeflow -[Click here to set up your Kubeflow account!](https://kubeflow.aaw.cloud.statcan.ca/) - -
    -![Your Favourite Software](images/kubeflow-favourite.png) -
    - -The AAW is based on [Kubeflow](1-Experiments/Kubeflow/), an open source comprehensive solution for deploying and managing end-to-end ML workflows. [Kubeflow](1-Experiments/Kubeflow/) is a powerful and flexible open source platform that allows for dynamic leverage of cloud compute, with users having the ability to control compute, memory, and storage resources used. Kubeflow simplifies the following tasks: +The AAW is based on [Kubeflow](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html), an open source comprehensive solution for deploying and managing end-to-end ML workflows. Kubeflow simplifies the following tasks: -- Creating customizable environments to work with data with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). -- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS for Statistics Canada employees. +- Creating customizable compute environments with user-controlled resource provisioning (custom CPU, GPU, RAM and storage) +- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS -### Kubeflow Account +#### Creating Kubeflow Notebook Servers -To create a namespace, go to [the DAS Portal](https://www.statcan.gc.ca/data-analytics-services/overview) where you'll find a link to [the onboarding form](https://forms.office.com/pages/responsepage.aspx?id=mR-PJT3ux0K_xXrxsjQ-ApbSRgATuERFkV3XLiwQ8xFUQ1NHRzBVWEFYUktZRzBJRDIwUDZFSDQ0USQlQCN0PWcu). +Follow these steps to create your first notebook server: -#### External Users - -1. External users need a StatCan Cloud account granted by the business sponsor. -2. Visit [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) once the StatCan Cloud account has been created. - -#### Statistics Canada Employees - -1. Internal users at Statistics Canada can directly access [https://kubeflow.aaw.cloud.statcan.ca/](https://kubeflow.aaw.cloud.statcan.ca/). - -### Creating a Kubeflow Notebook Server. - -1. Log in to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) with your StatCan guest cloud account. -2. Select Notebook Servers. -3. Click the "➕ New Server" button. -4. Follow [the instructions here](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html#setup). +1. Log in to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) +2. Click "Notebooks" from the sidebar on the left (you may need to select a namespace from the "Select namespace" dropdown menu in the upper left-hand corner) +3. Click the "+ New Notebook" button (upper right-hand corder) +4. Follow [the instructions here](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html#setup) to configure the notebook server #### Kubeflow Documentation - [Official Kubeflow Documentation](https://www.kubeflow.org/docs/started/introduction/) +- [AAW Kubeflow Documentation](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html) #### Kubeflow Videos -- [Kubeflow 101](https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x) by Google Cloud Tech. +- [Kubeflow 101](https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x) by Google Cloud Tech + +## Working with Your Data -## Your Data +Once your notebook server has been created, you may want to import data or access shared data from cloud storage. Instructions on how to add storage to your notebook server can be found on [the documentation page for storage](https://statcan.github.io/aaw/en/5-Storage/Disks.html). -Instructions on how to add storage to your notebook server can be found here on [the documentation page for storage](https://statcan.github.io/aaw/en/5-Storage/Disks.html). +### Protected Data -If your project requires protected data, cloud storage buckets will be created for you at the time of your projects onboarding. Access protected data by opening the buckets folder, see [the documentation](https://statcan.github.io/aaw/en/5-Storage/AzureBlobStorage.html). +If your project requires protected data: -Uploading data into JupyterLab is simple, [the official JupyterLab documentation](https://jupyterlab.readthedocs.io/en/stable/user/files.html#uploading-and-downloading) has a section on uploading and downloading files from the JupyterLab web interface. +- Cloud storage buckets will be created for you at the time of your projects onboarding. +- Accessing protected data is done by opening the buckets folder, see [the documentation on Azure Blob Storage](https://statcan.github.io/aaw/en/5-Storage/AzureBlobStorage.html). + +### Unprotected Data + +If you want to upload data into your notebook server ([on a Data Volume](https://statcan.github.io/aaw/en/5-Storage/Disks.html#setup), for instance), you can upload data into JupyterLab by following [the official JupyterLab documentation](https://jupyterlab.readthedocs.io/en/stable/user/files.html#uploading-and-downloading), which has a section on uploading and downloading files from the JupyterLab web interface. ## JupyterLab -Unlock the potential of AAW in JupyterLab, offering support for Python, R, Julia, and more. +Kubeflow creates and manages notebook servers running JupyterLab, which is the main interface in which you'll be doing your data science work. + +### Virtual Environments When conducting data science experiments, it's a best practice to utilize Python and/or conda virtual environments to manage your project dependencies. It is common to create a dedicated environment for each project or, in some cases, separate environments for different features or aspects of your work (for instance, one environment for general projects and an additional environment tailored for GPU-accelerated deep learning tasks). +#### Virtual Environments and the Launcher + +If you find yourself frequently switching between environments and desire a more convenient way to access them within JupyterLab, you can follow [these instructions](https://statcan.github.io/aaw/en/1-Experiments/Virtual-Environments.html#creating-and-adding-environments-to-the-jupyterlab-launcher). + ### JupyterLab Documentation -- [Official Getting Started with JupyterLab Docs.](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html) +- [Official Getting Started with JupyterLab Docs](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html) -If you find yourself frequently switching between environments and desire a more convenient way to access them within JupyterLab, you can follow [these instructions](https://statcan.github.io/aaw/en/1-Experiments/Virtual-Environments.html#creating-and-adding-environments-to-the-jupyterlab-launcher). +### Example IPython Notebooks -List of sources of examples: +You can download these notebooks and upload them to your notebook server. These notebooks can also be run from Visual Studio Code if you prefer. 1. [Visual Python: Simplifying Data Analysis for Python Learners](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/VisualPython_EN.html) 2. [YData Profiling: Streamlining Data Analysis](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/YData-Profiling_EN.html) +3. [](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/DrawData_EN.html) +4. [](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/DTale_EN.html) -## External Learning Resources +## Need Help? -- [A Gentle Introduction to SciKit Learn (Python)](https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/) -- [Official SciKit Learn Tutorials](https://scikit-learn.org/stable/tutorial/index.html) +Join our vibrant community! Connect with AAW developers and fellow users, ask questions, and share experiences all on the [Slack Support Channel](https://statcan-aaw.slack.com/). -### Data Preparation +For comprehensive documentation and guidance, refer to the: -- [Machine Learning Mastery's Data Preparation Course](https://machinelearningmastery.com/start-here/#dataprep) +- [AAW Documentation](https://statcan.github.io/aaw/) +- [Official Kubeflow Documentation](https://www.kubeflow.org/docs/) +- [Official JupyterLab Documentation](https://jupyterlab.readthedocs.io/en/stable/user/index.html) + +**Need real-time assistance?** Join our [Slack Support Channel](https://statcan-aaw.slack.com). + +### Demos and Contributions + +For in-depth demos, personalized assistance, or to contribute to the AAW community, reach out to us on [Slack Support Channel](https://statcan-aaw.slack.com). You can contribute to the platform's development and report issues or feature requests on [GitHub](https://github.com/StatCan/aaw). + +## External Learning Resources +Some of the AAW Developers are also data scientists! So we have a lot of material to share on data science tooling and best practices. Below are some useful and interested data science learning resources: -### Machine Learning +### Data Science Resources (R and Python) +- [Machine Learning Mastery's Data Preparation Course](https://machinelearningmastery.com/start-here/#dataprep) +- [A Gentle Introduction to SciKit Learn (Python)](https://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/) +- [Official SciKit Learn Tutorials](https://scikit-learn.org/stable/tutorial/index.html) - [How to Handle Imbalanced Datasets](https://machinelearningmastery.com/start-here/#imbalanced) +- [Quarto Themes](https://quarto.org/docs/output-formats/html-themes.html) +- [Tidy Models Resampling Techniques](https://www.tidymodels.org/start/resampling/ +- [EasyStats for R](https://github.com/easystats) +- [EasyStats Model Performance Evaluation Package](https://easystats.github.io/performance/) +- [Tidy Modelling with R](https://www.tmwr.org/) +- [](https://modeva.r-forge.r-project.org/modEvA-tutorial.html) +- [](https://scikit-learn.org/stable/modules/model_evaluation.html) -### Python +### Python Language Resources - [Real Python's Introduction to Python](https://realpython.com/learning-paths/python3-introduction/) - [W3School's Introduction to Python](https://www.w3schools.com/python/python_intro.asp) @@ -115,20 +124,8 @@ List of sources of examples: - [Machine Learning Mastery's Python Skills](https://machinelearningmastery.com/start-here/#pythonskills) - [TechWorld with Nana's Python Tutorial for Beginners](https://www.youtube.com/watch?v=t8pPdKYpowI) -### R +### R Language Resources - [Videos on R](https://www.youtube.com/playlist?list=PLLOxZwkBK52C6_Nkmp0nFCreLfnfJgUL7) - [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) -- [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf) - -## Need Help? - -Join our vibrant community on the [Slack channel](https://statcan-aaw.slack.com/) to connect with fellow users, ask questions, and share experiences. - -For comprehensive documentation and guidance, refer to the [AAW Portal Documentation](https://statcan.github.io/aaw/) and the [Kubeflow Documentation](https://www.kubeflow.org/docs/). Need real-time assistance? Join our [Slack Support Channel](https://statcan-aaw.slack.com). - -## Demos and Contributions - -For in-depth demos, personalized assistance, or to contribute to the AAW community, reach out to us on [Slack](https://statcan-aaw.slack.com). Contribute to the platform's development and report issues or feature requests on [GitHub](https://github.com/StatCan/aaw). - -Embark on your data science journey with AAW — where possibilities are limitless, and innovation knows no bounds. +- [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf From 9fbf28dc1ef20b92a930a6427d4f839395ed049a Mon Sep 17 00:00:00 2001 From: Bryan Paget <8212170+bryanpaget@users.noreply.github.com> Date: Mon, 18 Dec 2023 15:38:27 +0000 Subject: [PATCH 7/7] feat(index.md): updates to getting started --- docs/en/index.md | 67 ++++++++++++++++++++++++++---------------------- 1 file changed, 36 insertions(+), 31 deletions(-) diff --git a/docs/en/index.md b/docs/en/index.md index d7d398c4d..fa9c68d33 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -1,45 +1,46 @@ # The Advanced Analytics Workspace -_The AAW is an open-source platform specifically crafted for data scientists, analysts, and researchers proficient in open-source tools and coding._ -
    ![Statistics](images/statistics-on-the-moon-small.jpg)
    -## What is the AAW? +!!! Info "Open source and made for you!" + The AAW is an open-source platform specifically crafted for data scientists, analysts, and researchers proficient in open-source tools and coding. -The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw) (AAW) stands as a comprehensive and open-source solution designed to cater to the diverse needs of data scientists. It offers a flexible environment that empowers advanced practitioners to seamlessly conduct their work. AAW is a versatile platform, providing solutions for data science and analytics across various domains. More information about the AAW and Data Analytics Services (DAS) can be found on [the DAS Portal](https://www.statcan.gc.ca/data-analytics-services/aaw). +The [Advanced Analytics Workspace](https://www.statcan.gc.ca/data-analytics-services/aaw) (AAW) stands as a comprehensive and open-source solution designed to cater to the diverse needs of data scientists. It offers a flexible environment that empowers practitioners to seamlessly conduct their work. More information about the AAW and Data Analytics Services (DAS) can be found on [the DAS Portal](https://www.statcan.gc.ca/data-analytics-services/aaw). -**TODO:** Many of the links on https://www.statcan.gc.ca/data-analytics-services/aaw are broken. +!!! Warning "Warning" + Many of the links on https://www.statcan.gc.ca/data-analytics-services/aaw are broken. ## Getting Started -- **StatCan Users**: Access the [Kubeflow Dashboard](https://kubeflow.aaw.cloud.statcan.ca/) to get started -- **External Users and Collaborators**: Fill out [the DAS Onboarding Form](https://forms.office.com/r/RPrgDDkU9T) to tell us about your project needs. Once completed, a DAS representative will contact you to discuss the next steps and begin the onboarding process. Note: External users need a StatCan Cloud account granted by the business sponsor - -### Kubeflow +- **StatCan Users**: Access the [Kubeflow Dashboard](https://kubeflow.aaw.cloud.statcan.ca/) to get started. +- **External Users and Collaborators**: Fill out [the DAS Onboarding Form](https://forms.office.com/r/RPrgDDkU9T) to tell us about your project needs. Once completed, a DAS representative will contact you to discuss the next steps and begin the onboarding process. Note: External users need a StatCan Cloud account granted by the business sponsor. -The AAW is based on [Kubeflow](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html), an open source comprehensive solution for deploying and managing end-to-end ML workflows. Kubeflow simplifies the following tasks: +## Creating Kubeflow Notebook Servers -- Creating customizable compute environments with user-controlled resource provisioning (custom CPU, GPU, RAM and storage) -- Managing notebook servers including Ubuntu Desktop (via noVNC), R Studio, JupyterLab with Python, R, Julia and SAS +Follow these steps to create your first notebook server: -#### Creating Kubeflow Notebook Servers +1. Log in to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/); +2. Click **Notebooks** from the sidebar on the left (you may need to select a namespace from the **Select namespace** dropdown menu in the upper left-hand corner); +3. Click the **+ New Notebook** button (upper right-hand corder); +4. Follow [the instructions here](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html#setup) to configure the notebook server. -Follow these steps to create your first notebook server: + +!!! Hint "Need help creating a notebook server?" + We have [a Slideshow](https://docs.google.com/presentation/d/12yTDlbMCmbg0ccdea2h0vwhs5YTa_GHm_3DieG5A-k8/edit?usp=sharing) with instructions on how to create a notebook server. -1. Log in to [Kubeflow](https://kubeflow.aaw.cloud.statcan.ca/) -2. Click "Notebooks" from the sidebar on the left (you may need to select a namespace from the "Select namespace" dropdown menu in the upper left-hand corner) -3. Click the "+ New Notebook" button (upper right-hand corder) -4. Follow [the instructions here](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html#setup) to configure the notebook server +### Kubeflow Documentation -#### Kubeflow Documentation +The AAW is based on [Kubeflow](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html), an open source comprehensive solution for deploying and managing end-to-end ML workflows. Kubeflow simplifies the creation and management of customizable compute environments with user-controlled resource provisioning (custom CPU, GPU, RAM and storage). For more information on Kubeflow, please visit: -- [Official Kubeflow Documentation](https://www.kubeflow.org/docs/started/introduction/) - [AAW Kubeflow Documentation](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow.html) +- [Official Kubeflow Documentation](https://www.kubeflow.org/docs/started/introduction/) #### Kubeflow Videos +Videos on Kubeflow have been developed by Google: + - [Kubeflow 101](https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x) by Google Cloud Tech ## Working with Your Data @@ -57,7 +58,7 @@ If your project requires protected data: If you want to upload data into your notebook server ([on a Data Volume](https://statcan.github.io/aaw/en/5-Storage/Disks.html#setup), for instance), you can upload data into JupyterLab by following [the official JupyterLab documentation](https://jupyterlab.readthedocs.io/en/stable/user/files.html#uploading-and-downloading), which has a section on uploading and downloading files from the JupyterLab web interface. -## JupyterLab +## Working in JupyterLab Kubeflow creates and manages notebook servers running JupyterLab, which is the main interface in which you'll be doing your data science work. @@ -65,9 +66,8 @@ Kubeflow creates and manages notebook servers running JupyterLab, which is the m When conducting data science experiments, it's a best practice to utilize Python and/or conda virtual environments to manage your project dependencies. It is common to create a dedicated environment for each project or, in some cases, separate environments for different features or aspects of your work (for instance, one environment for general projects and an additional environment tailored for GPU-accelerated deep learning tasks). -#### Virtual Environments and the Launcher - -If you find yourself frequently switching between environments and desire a more convenient way to access them within JupyterLab, you can follow [these instructions](https://statcan.github.io/aaw/en/1-Experiments/Virtual-Environments.html#creating-and-adding-environments-to-the-jupyterlab-launcher). +!!! Info "Virtual Environments and the Launcher" + If you find yourself frequently switching between environments and desire a more convenient way to access them within JupyterLab, you can follow [these instructions](https://statcan.github.io/aaw/en/1-Experiments/Virtual-Environments.html#creating-and-adding-environments-to-the-jupyterlab-launcher). ### JupyterLab Documentation @@ -79,8 +79,12 @@ You can download these notebooks and upload them to your notebook server. These 1. [Visual Python: Simplifying Data Analysis for Python Learners](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/VisualPython_EN.html) 2. [YData Profiling: Streamlining Data Analysis](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/YData-Profiling_EN.html) -3. [](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/DrawData_EN.html) -4. [](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/DTale_EN.html) +3. [Draw Data: Creating Synthetic Datasets with Ease](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/DrawData_EN.html) +4. [D-Tale: A Seamless Data Exploration Tool for Python](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/DTale_EN.html) +5. [Mito Sheet: Excel-Like Spreadsheets in JupyterLab](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/MitoSheet_EN.html) +6. [PyGWalker: Simplifying Exploratory Data Analysis with Python](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/PyGWalker_EN.html) +7. [ReRun: Fast and Powerful Multimodal Data Visualization](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/ReRun_EN.html) +8. [SweetViz: Streamlining EDA with Elegant Visualizations](https://statcan.github.io/aaw/en/1-Experiments/Notebooks/SweetViz_EN.html) ## Need Help? @@ -92,7 +96,8 @@ For comprehensive documentation and guidance, refer to the: - [Official Kubeflow Documentation](https://www.kubeflow.org/docs/) - [Official JupyterLab Documentation](https://jupyterlab.readthedocs.io/en/stable/user/index.html) -**Need real-time assistance?** Join our [Slack Support Channel](https://statcan-aaw.slack.com). +!!! Info "Do you need help?" + **Need real-time assistance?** Join our [Slack Support Channel](https://statcan-aaw.slack.com). ### Demos and Contributions @@ -109,12 +114,12 @@ Some of the AAW Developers are also data scientists! So we have a lot of materia - [Official SciKit Learn Tutorials](https://scikit-learn.org/stable/tutorial/index.html) - [How to Handle Imbalanced Datasets](https://machinelearningmastery.com/start-here/#imbalanced) - [Quarto Themes](https://quarto.org/docs/output-formats/html-themes.html) -- [Tidy Models Resampling Techniques](https://www.tidymodels.org/start/resampling/ +- [Tidy Models Resampling Techniques](https://www.tidymodels.org/start/resampling/) - [EasyStats for R](https://github.com/easystats) - [EasyStats Model Performance Evaluation Package](https://easystats.github.io/performance/) - [Tidy Modelling with R](https://www.tmwr.org/) -- [](https://modeva.r-forge.r-project.org/modEvA-tutorial.html) -- [](https://scikit-learn.org/stable/modules/model_evaluation.html) +- [Model evaluation and analysis: the modEvA R package in a nutshell](https://modeva.r-forge.r-project.org/modEvA-tutorial.html) +- [Metrics and scoring: quantifying the quality of predictions](https://scikit-learn.org/stable/modules/model_evaluation.html) ### Python Language Resources @@ -128,4 +133,4 @@ Some of the AAW Developers are also data scientists! So we have a lot of materia - [Videos on R](https://www.youtube.com/playlist?list=PLLOxZwkBK52C6_Nkmp0nFCreLfnfJgUL7) - [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) -- [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf +- [R Data Import/Export](https://cran.r-project.org/doc/manuals/r-release/R-data.pdf)