diff --git a/docs/en/8-MicroSim/GettingStarted.md b/docs/en/8-MicroSim/GettingStarted.md
index c30367d3a..5b63d18c8 100644
--- a/docs/en/8-MicroSim/GettingStarted.md
+++ b/docs/en/8-MicroSim/GettingStarted.md
@@ -1,8 +1,7 @@
-# Getting started with Kubeflow Notebooks and OpenM++.
-
-This document is intended to act as an introduction to The AAWs Kubeflow Notebook servers and their use for the OpenM++ project.
+# Getting started with Kubeflow and OpenM++.
+This document is intended to act as an introduction to The AAW's Kubeflow Notebook servers and their use for the OpenM++ project.
## Starting
@@ -18,7 +17,7 @@ Select the account you wish to use and proceed with the authentication.
After your credentials are authenticated, you will be redirected to the AAW Kubeflow management panel.
-
+
## Create a notebook.
@@ -61,11 +60,11 @@ If the **Connect button** is disabled, click on the triangle (Start image) butto
## Your Kubeflow notebook.
-
+
To start the OpenM++ UI, click on the OpenM++ icon on the Notebooks page.
-
+
This will open a new window with the OpenM++ UI running.
@@ -78,8 +77,6 @@ Click on the Ellipses symbol on the upper Left corner to change language.
-
-
Click on the Hamburger Menu on the top right to open the sidebar.
@@ -122,4 +119,3 @@ You can then click the **Run the Model** to run the job.
This will run the Model and brings up the Model Run Results Panel which shows the results of the run.
-
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diff --git a/docs/en/index.md b/docs/en/index.md
index fdf7d091a..fa9c68d33 100644
--- a/docs/en/index.md
+++ b/docs/en/index.md
@@ -1,143 +1,136 @@
-# Welcome to the Advanced Analytics Workspace
+# The Advanced Analytics Workspace
-
+
-## The Advanced Analytics Workspace Documentation
+!!! 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.
-_Welcome to the world of data science and machine learning!_
+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).
-
-!!! 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.
+!!! Warning "Warning"
+ Many of the links on https://www.statcan.gc.ca/data-analytics-services/aaw are broken.
-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.
+## Getting Started
-
-!!! 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.
+- **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.
-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.
+## Creating Kubeflow Notebook Servers
-No matter what stage of your data science journey you're at, the Advanced Analytics Workspace has the resources you need to succeed.
+Follow these steps to create your first notebook server:
-## Getting Started with the AAW
+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.
-
-
-
+
+!!! 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.
-### The AAW Portal
+### Kubeflow Documentation
-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.
+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:
-
-!!! 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)
+- [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
-### Kubeflow Account
+Videos on Kubeflow have been developed by Google:
-
-!!! important "Attention External Users!"
- Users external to Statistics Canada will require a cloud account granted access by the business sponsor.
+- [Kubeflow 101](https://www.youtube.com/playlist?list=PLIivdWyY5sqLS4lN75RPDEyBgTro_YX7x) by Google Cloud Tech
-
-!!! 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/).
+## Working with Your Data
-
-!!! note ""
-
- [](https://kubeflow.aaw.cloud.statcan.ca/)
- **[👉 Click here to setup your Kubeflow account! 👈](https://kubeflow.aaw.cloud.statcan.ca/)**
-
+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).
-**[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.
+### Protected Data
-Kubeflow simplifies the following tasks:
+If your project requires protected data:
-- 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.
+- 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).
-
-!!! info "Kubeflow Dashboard"
- - [**Kubeflow Dashboard**](https://kubeflow.aaw.cloud.statcan.ca/) Use this link once you have your cloud account!
+### Unprotected Data
-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.
+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.
-### Slack
+## Working in JupyterLab
-
-[](https://statcan-aaw.slack.com/)
-
+Kubeflow creates and manages notebook servers running JupyterLab, which is the main interface in which you'll be doing your data science work.
-- **[Click here sign in to our Slack Support Workspace](https://statcan-aaw.slack.com/)**
+### Virtual Environments
-- **Use the _General_ Channel!**
+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).
-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.
+!!! 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).
-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.
+### JupyterLab Documentation
-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.
+- [Official Getting Started with JupyterLab Docs](https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html)
-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!
+### Example IPython Notebooks
-Click on the link, then choose "Create an account" in the upper right-hand corner.
+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.
-
-!!! note ""
-
- 
- Use your @statcan.gc.ca email address so that you will be automatically approved.
-
+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. [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)
-## 🧭 Getting Started
+## Need Help?
-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.
+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/).
-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.
+For comprehensive documentation and guidance, refer to the:
-## 🧰 Tools Offered
+- [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)
-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.
+!!! Info "Do you need help?"
+ **Need real-time assistance?** Join our [Slack Support Channel](https://statcan-aaw.slack.com).
-Here's a list of tools we offer:
+### Demos and Contributions
-- 📜 Languages:
- - 🐍 Python
- - 📈 R
- - 👩🔬 Julia
-- 🧮 Development environments:
- - VS Code
- - R Studio
- - Jupyter Notebooks
-- 🐧 Linux virtual desktops for additional tools (🧫 OpenM++, 🌏 QGIS etc.)
+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).
-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.
+## External Learning Resources
-### 💡 Help
+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:
-- Disk (also called Volumes on the Notebook Server creation screen)
-- Containers (Blob Storage)
-- Data Lakes (coming soon)
+### Data Science Resources (R and Python)
-- 📗 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)
+- [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/)
+- [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)
-## 🐱 Demos
+### Python Language Resources
-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).
+- [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)
-## Contributing
+### R Language Resources
-If you have any bugs to report or features to request please do so via https://github.com/StatCan/aaw.
+- [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)
diff --git a/docs/fr/8-MicroSim/demarrage.md b/docs/fr/8-MicroSim/demarrage.md
new file mode 100644
index 000000000..d5bbf22a0
--- /dev/null
+++ b/docs/fr/8-MicroSim/demarrage.md
@@ -0,0 +1,120 @@
+# Premiers pas avec Kubeflow et OpenM++.
+
+Ce document est destiné à servir d'introduction aux Bloc-note de Kubeflow sur la plateforme EAA et à leur utilisation pour le projet OpenM++.
+
+## Démarrage
+
+Pour accéder au portail Kubeflow de l'EAA, accédez au site Web suivant.
+
+[https://kubeflow.aaw-dev.cloud.statcan.ca/](https://kubeflow.aaw-dev.cloud.statcan.ca/)
+
+Cela vous redirigera vers une page de connexion Microsoft.
+
+
+
+Sélectionnez le compte que vous souhaitez utiliser et procédez à l'authentification.
+
+Une fois vos informations d'identification authentifiées, vous serez redirigé vers le panneau de gestion Kubeflow de l'EAA.
+
+
+
+## Créez un bloc-note.
+
+Cliquez sur le bouton Créer un nouveau serveur bloc-note.
+
+
+
+Cela fait apparaître le nouvel écran du bloc-notes.
+
+Pour créer un nouveau carnet, trois éléments doivent être définis :
+- Assurez-vous que le bon espace de noms est sélectionné.
+- Un nom unique.
+- Cliquez ensuite sur le type de carnet souhaité.
+ - Pour OpenM++, sélectionnez l'option JupyterLab.
+- Si vous travaillez avec des données protégées B, cochez la case **Exécuter dans le bloc-notes protégé B**.
+
+Faites défiler vers le bas pour voir le **Bouton de lancement.** Le bouton de lancement ne sera actif qu'une fois les options ci-dessus sélectionnées.
+
+
+
+Appuyez sur le bouton Lancer pour lancer votre nouveau bloc-notes.
+
+[Pour plus d'informations sur Kubeflow de l'EAA, cliquez ici.](https://statcan.github.io/aaw/en/1-Experiments/Kubeflow/)
+
+### Carnets existants
+
+Si vous avez déjà créé un carnet, vous pouvez le réutiliser en cliquant sur l'onglet Carnets.
+
+
+
+Cela fera apparaître une fenêtre avec tous vos blocs-notes existants.
+
+
+
+Pour démarrer un ordinateur portable existant, sélectionnez-le et appuyez sur le bouton CONNECT.
+
+Si le **bouton Connecter** est désactivé, cliquez sur le bouton triangle (Démarrer l'image) pour démarrer l'image, puis cliquez sur **connecter** lorsqu'il devient disponible.
+
+
+
+## Votre carnet Kubeflow.
+
+
+
+Pour démarrer l'interface utilisateur OpenM++, cliquez sur l'icône OpenM++ sur la page bloc-notes.
+
+
+
+Cela ouvrira une nouvelle fenêtre avec l'interface utilisateur OpenM++ en cours d'exécution.
+
+## Interface utilisateur OpenM++
+
+
+
+
+Cliquez sur le symbole Ellipses dans le coin supérieur gauche pour changer de langue.
+
+
+
+Cliquez sur le menu Hamburger en haut à droite pour ouvrir la barre latérale.
+
+
+
+Stockez les modèles et les données dans le bucket de stockage Blob monté sur le bloc-note.
+
+non classé : `/home/jovyan/buckets/aaw-unclassified/microsim/models`
+protégé-b : `/home/jovyan/buckets/protected-b/microsim/models`
+
+Les fichiers journaux doivent apparaître sous le répertoire parent « Microsim ».
+
+Veuillez consulter le lien suivant pour plus d'informations sur ce sujet :
+
+[Stockage Azure Blob](https://statcan.github.io/aaw/en/5-Storage/AzureBlobStorage/)
+
+Cliquez sur le modèle souhaité (panneau de gauche) pour le sélectionner.
+
+Cela fait apparaître le panneau d'exécution du modèle et active les onglets Scénarios d'entrée et Exécuter le modèle sur le panneau de droite.
+
+
+
+Les onglets horizontaux sont également actifs (mais grisés) à ce moment-là.
+
+
+
+Pour exécuter un modèle, vous devez d'abord saisir le nom du modèle. Cliquer dans la zone Nom du modèle générera un nom de modèle horodaté de manière unique pour l'exécution.
+
+
+
+Pour exécuter des modèles utilisant le multi-traitement (MPI), les opérations suivantes doivent être effectuées. Cela ne fonctionnera que pour les modèles compatibles MPI.
+
+Dans l'onglet **Options d'exécution du cluster**, assurez-vous que :
+- au moins un Processus est sélectionné dans le **MPI Nombre de Processus** et
+- le modèle sélectionné dans le **MPI Model Run Template** est : **mpi.ModelRun.template.txt**
+
+
+
+Vous pouvez ensuite cliquer sur **Exécuter le modèle** pour exécuter la tâche.
+
+Cela exécutera le modèle et affichera le panneau de résultats de l'exécution du modèle qui affiche les résultats de l'exécution.
+
+
diff --git a/mkdocs-en.yml b/mkdocs-en.yml
index 422feab70..16a57a125 100644
--- a/mkdocs-en.yml
+++ b/mkdocs-en.yml
@@ -66,6 +66,10 @@ extra:
# Page tree
nav:
- Getting Started: index.md
+ - Storage:
+ - Overview: 5-Storage/Overview.md
+ - Azure Blob Storage (Containers): 5-Storage/AzureBlobStorage.md
+ - Kubeflow Volumes (Disks): 5-Storage/KubeflowVolumes.md
- Analysis:
- Overview: 1-Experiments/Overview.md
- Virtual Environments: 1-Experiments/Virtual-Environments.md
@@ -82,6 +86,12 @@ nav:
- SweetViz: 1-Experiments/Notebooks/SweetViz_EN.ipynb
- VisualPython: 1-Experiments/Notebooks/VisualPython_EN.ipynb
- YData Profiling: 1-Experiments/Notebooks/YData-Profiling_EN.ipynb
+ - Collaboration:
+ - Overview: 4-Collaboration/Overview.md
+ - Request a Namespace: 4-Collaboration/Request-a-Namespace.md
+ - GEA: 4-Collaboration/Geospatial-Analytical-Environment.md
+ - Gitlab:
+ - Gitlab: 6-Gitlab/Gitlab.md
- Publishing:
- Overview: 2-Publishing/Overview.md
- R Shiny: 2-Publishing/R-Shiny.md
@@ -89,18 +99,6 @@ nav:
- Datasette: 2-Publishing/Datasette.md
- Power BI: 2-Publishing/PowerBI.md
- Custom: 2-Publishing/Custom.md
- - Pipelines:
- - Argo: 3-Pipelines/Argo.md
- - Collaboration:
- - Overview: 4-Collaboration/Overview.md
- - Request a Namespace: 4-Collaboration/Request-a-Namespace.md
- - GEA: 4-Collaboration/Geospatial-Analytical-Environment.md
- - Storage:
- - Overview: 5-Storage/Overview.md
- - Azure Blob Storage (Containers): 5-Storage/AzureBlobStorage.md
- - Kubeflow Volumes (Disks): 5-Storage/KubeflowVolumes.md
- - Gitlab:
- - Gitlab: 6-Gitlab/Gitlab.md
- MLOps:
- Overview: 7-MLOps/Overview.md
- Machine Learning Models: 7-MLOps/Machine-Learning.md
@@ -111,6 +109,8 @@ nav:
- Example Notebooks:
- Seldon Iris Classifier: 7-MLOps/sklearn_iris_jsondata.ipynb
- Integrate with External PaaS: 7-MLOps/PaaS-Integration.md
+ - Pipelines:
+ - Argo: 3-Pipelines/Argo.md
- MicroSim:
- Getting Started: 8-MicroSim/GettingStarted.md
- Adding new models: 8-MicroSim/Additions.md
diff --git a/mkdocs-fr.yml b/mkdocs-fr.yml
index 5efe5265a..fb27a512f 100644
--- a/mkdocs-fr.yml
+++ b/mkdocs-fr.yml
@@ -66,6 +66,10 @@ extra:
# Page tree
nav:
- Démarrage: index.md
+ - Stockage:
+ - Aperçu: 5-Stockage/Aperçu.md
+ - Azure Blob Storage (Conteneurs): 5-Stockage/StockageBlobAzure.md
+ - Kubeflow Volumes (Disques): 5-Stockage/KubeflowVolumes.md
- Analyse:
- Aperçu: 1-Experiences/Apercu.md
- Environnements virtuels: 1-Experiences/Environnements-virtuels.md
@@ -82,24 +86,18 @@ nav:
- SweetViz: 1-Experiences/bloc-notes/SweetViz_FR.ipynb
- VisualPython: 1-Experiences/bloc-notes/VisualPython_FR.ipynb
- YData Profiling: 1-Experiences/bloc-notes/YData-Profiling_FR.ipynb
- - Publication:
- - R Shiny: 2-Publication/R-Shiny.md
- - Dash: 2-Publication/Dash.md
- - Datasette: 2-Publication/Datasette.md
- - Power BI: 2-Publication/PowerBI.md
- - Sur mesure: 2-Publication/Sur-mesure.md
- - Pipelines:
- - Argo: 3-Pipelines/Argo.md
- Collaboration:
- Aperçu: 4-Collaboration/Aperçu.md
- Demander un espace de nom: 4-Collaboration/Demander-EspaceDeNom.md
- EAG: 4-Collaboration/Environnement-Analyse-Geospatiale.md
- - Stockage:
- - Aperçu: 5-Stockage/Aperçu.md
- - Azure Blob Storage (Conteneurs): 5-Stockage/StockageBlobAzure.md
- - Kubeflow Volumes (Disques): 5-Stockage/KubeflowVolumes.md
- Gitlab:
- Gitlab: 6-Gitlab/Gitlab.md
+ - Publication:
+ - R Shiny: 2-Publication/R-Shiny.md
+ - Dash: 2-Publication/Dash.md
+ - Datasette: 2-Publication/Datasette.md
+ - Power BI: 2-Publication/PowerBI.md
+ - Sur mesure: 2-Publication/Sur-mesure.md
- MLOps:
- Aperçu: 7-MLOps/Aperçu.md
- Modèles d'apprentissage automatique: 7-MLOps/Modèles-d'apprentissage-automatique.md
@@ -110,4 +108,8 @@ nav:
- Bloc-note d'exemple:
- Classificateur Seldon Iris : 7-MLOps/sklearn_iris_jsondata.ipynb
- Intégration avec PaaS externe: 7-MLOps/Integration-PaaS.md
+ - Pipelines:
+ - Argo: 3-Pipelines/Argo.md
+ - MicroSim:
+ - Démarrage: 8-MicroSim/demarrage.md
- Aide/Contactez-nous: Aide.md
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"