From cb1a8b27344aafeb00bdad4b60bd159f5d9324bd Mon Sep 17 00:00:00 2001 From: Wendy Gaultier Date: Fri, 23 Dec 2022 09:04:13 -0500 Subject: [PATCH 01/14] Add GAE Documentation (#1489) Co-authored-by: Wendy V Gaultier --- .../Geospatial-Analytical-Environment.md | 187 +++++++++++++++++ "docs/fr/4-Collaboration/Aper\303\247u.md" | 2 +- .../Environnement-Analyse-Geospatiale.md | 188 ++++++++++++++++++ mkdocs-en.yml | 1 + mkdocs-fr.yml | 1 + 5 files changed, 378 insertions(+), 1 deletion(-) create mode 100644 docs/en/4-Collaboration/Geospatial-Analytical-Environment.md create mode 100644 docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md diff --git a/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md b/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md new file mode 100644 index 000000000..497197c91 --- /dev/null +++ b/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md @@ -0,0 +1,187 @@ +# Geospatial Analytical Environment (GAE) - Cross Platform Access + +??? danger "Unprotected data only; SSI coming soon:" + At this time, our Geospatial server can only host and provide access to non-sensitive statistical information. + +## Getting Started + +??? success "Prerequisites" + 1. An onboarded project with access to DAS GAE ArcGIS Portal + 2. An ArcGIS Portal Client Id (API Key) + +The ArcGIS Enterprise Portal can be accessed in either the AAW or CAE using the API, from any service which leverages the Python programming language. + +For example, in AAW and the use of [Jupyter Notebooks](https://statcan.github.io/daaas/en/1-Experiments/Jupyter/) within the space, or in CAE the use of [Databricks](https://statcan.github.io/cae-eac/en/DataBricks/), DataFactory, etc. + +[The DAS GAE ArcGIS Enterprise Portal can be accessed directly here](https://geoanalytics.cloud.statcan.ca/portal) + +[For help with self-registering as a DAS Geospatial Portal user](https://statcan.github.io/daaas-dads-geo/english/portal/) + +
+ +## Using the ArcGIS API for Python + +### Connecting to ArcGIS Enterprise Portal using ArcGIS API + +1. Install packages: + + ```python + conda install -c esri arcgis + ``` + + or using Artifactory + + ```python3333 + conda install -c https://jfrog.aaw.cloud.statcan.ca/artifactory/api/conda/esri-remote arcgis + ``` + +2. Import the necessary libraries that you will need in the Notebook. + ```python + from arcgis.gis import GIS + from arcgis.gis import Item + ``` + +3. Access the Portal + Your project group will be provided with a Client ID upon onboarding. Paste the Client ID in between the quotations ```client_id='######'```. + + ```python + gis = GIS("https://geoanalytics.cloud.statcan.ca/portal", client_id=' ') + print("Successfully logged in as: " + gis.properties.user.username) + ``` + +4. - The output will redirect you to a login Portal. + - Use the StatCan Azure Login option, and your Cloud ID + - After successful login, you will receive a code to sign in using SAML. + - Paste this code into the output. + + ![OAuth2 Approval](images/OAuth2Key.png) + +
+ +### Display user information +Using the 'me' function, we can display various information about the user logged in. +```python +me = gis.users.me +username = me.username +description = me.description +display(me) +``` + +
+ +### Search for Content +Search for the content you have hosted on the DAaaS Geo Portal. Using the 'me' function we can search for all of the hosted content on the account. There are multiple ways to search for content. Two different methods are outlined below. + +**Search all of your hosted itmes in the DAaaS Geo Portal.** +```python +my_content = me.items() +my_content +``` +**Search for specific content you own in the DAaaS Geo Portal.** + +This is similar to the example above, however if you know the title of they layer you want to use, you can save it as a function. +```python +my_items = me.items() +for items in my_items: + print(items.title, " | ", items.type) + if items.title == "Flood in Sorel-Tracy": + flood_item = items + + else: + continue +print(flood_item) +``` + +**Search all content you have access to, not just your own.** + +```python +flood_item = gis.content.search("tags: flood", item_type ="Feature Service") +flood_item +``` + +
+ +### Get Content +We need to get the item from the DAaaS Geo Portal in order to use it in the Jupyter Notebook. This is done by providing the unique identification number of the item you want to use. Three examples are outlined below, all accessing the identical layer. +```python +item1 = gis.content.get(my_content[5].id) #from searching your content above +display(item1) + +item2 = gis.content.get(flood_item.id) #from example above -searching for specific content +display(item2) + +item3 = gis.content.get('edebfe03764b497f90cda5f0bfe727e2') #the actual content id number +display(item3) +``` + +
+ +### Perform Analysis +Once the layers are brought into the Jupyter notebook, we are able to perform similar types of analysis you would expect to find in a GIS software such as ArcGIS. There are many modules containing many sub-modules of which can perform multiple types of analyses. +
+ +Using the arcgis.features module, import the use_proximity submodule ```from arcgis.features import use_proximity```. This submodule allows us to '.create_buffers' - areas of equal distance from features. Here, we specify the layer we want to use, distance, units, and output name (you may also specify other characteristics such as field, ring type, end type, and others). By specifying an output name, after running the buffer command, a new layer will be automatically uploaded into the DAaaS GEO Portal containing the new feature you just created. +
+ +```python +buffer_lyr = use_proximity.create_buffers(item1, distances=[1], + units = "Kilometers", + output_name='item1_buffer') + +display(item1_buffer) +``` + +Some users prefer to work with Open-Source packages. Translating from ArcGIS to Spatial Dataframes is simple. +```python +# create a Spatially Enabled DataFrame object +sdf = pd.DataFrame.spatial.from_layer(feature_layer) +``` + +
+ +### Update Items +By getting the item as we did similar to the example above, we can use the '.update' function to update exisiting item within the DAaaS GEO Portal. We can update item properties, data, thumbnails, and metadata. +```python +item1_buffer = gis.content.get('c60c7e57bdb846dnbd7c8226c80414d2') +item1_buffer.update(item_properties={'title': 'Enter Title' + 'tags': 'tag1, tag2, tag3, tag4', + 'description': 'Enter description of item'} +``` + +
+ +### Visualize Your Data on an Interactive Map + +**Example: MatplotLib Library** +In the code below, we create an ax object, which is a map style plot. We then plot our data ('Population Change') change column on the axes +```python +import matplotlib.pyplot as plt +ax = sdf.boundary.plot(figsize=(10, 5)) +shape.plot(ax=ax, column='Population Change', legend=True) +plt.show() +``` + +**Example: ipyleaflet Library** +In this example we will use the library 'ipyleaflet' to create an interactive map. This map will be centered around Toronto, ON. The data being used will be outlined below. +Begin by pasting ```conda install -c conda-forge ipyleaflet``` allowing you to install ipyleaflet libraries in the Python environment. +
+Import the necessary libraries. +```python +import ipyleaflet +from ipyleaflet import * +``` +Now that we have imported the ipyleaflet module, we can create a simple map by specifying the latitude and longitude of the location we want, zoom level, and basemap [(more basemaps)](https://ipyleaflet.readthedocs.io/en/latest/map_and_basemaps/basemaps.html). Extra controls have been added such as layers and scale. +```python +toronto_map = Map(center=[43.69, -79.35], zoom=11, basemap=basemaps.Esri.WorldStreetMap) + +toronto_map.add_control(LayersControl(position='topright')) +toronto_map.add_control(ScaleControl(position='bottomleft')) +toronto_map +``` +
+ +##Learn More about the ArcGIS API for Python +[Full documentation for the ArGIS API can be located here](https://developers.arcgis.com/python/) + +##Learn More about DAS Geospatial Analytical Environment (GAE) and Services +[GAE Help Guide](https://statcan.github.io/daaas-dads-geo/) \ No newline at end of file diff --git "a/docs/fr/4-Collaboration/Aper\303\247u.md" "b/docs/fr/4-Collaboration/Aper\303\247u.md" index 4df480f4d..8eff89de9 100644 --- "a/docs/fr/4-Collaboration/Aper\303\247u.md" +++ "b/docs/fr/4-Collaboration/Aper\303\247u.md" @@ -109,5 +109,5 @@ possédez déjà via le Menu **Gérer les contributeurs** dans Kubeflow. ![Contributors Menu](../images/kubeflow_contributors.png) - info "Maintenant, vous et vos collègues pouvez partager l'accès à un serveur!" +!!! info "Maintenant, vous et vos collègues pouvez partager l'accès à un serveur!" Essayer le! diff --git a/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md new file mode 100644 index 000000000..0761e1403 --- /dev/null +++ b/docs/fr/4-Collaboration/Environnement-Analyse-Geospatiale.md @@ -0,0 +1,188 @@ +# Environnement d'Analyse Géospatiale (EAG) - Accès multiplateforme + + +??? danger "Données non protégées uniquement ; SSI bientôt disponible :" + À l'heure actuelle, notre serveur géospatial ne peut héberger et donner accès qu'à des informations statistiques non sensibles. + +## Démarrage + +??? success "Conditions préalables" + 1. Un projet intégré avec accès au portail DAS EAG ArcGIS + 2. Un identifiant client ArcGIS Portal (clé API) + +Le portail ArcGIS Enterprise est accessible dans AAW ou CAE à l'aide de l'API, à partir de n'importe quel service qui exploite le langage de programmation Python. + +Par exemple, dans AAW et l'utilisation de [Jupyter Notebooks](https://statcan.github.io/daaas/en/1-Experiments/Jupyter/) dans l'espace, ou pour CAE l'utilisation de [Databricks](https ://statcan.github.io/cae-eac/en/DataBricks/), DataFactory, etc. + +[Le portail DAS GAE ArcGIS Enterprise est accessible directement ici](https://geoanalytics.cloud.statcan.ca/portal) + +[Pour obtenir de l'aide sur l'auto-inscription en tant qu'utilisateur du portail géospatial DAS](https://statcan.github.io/daaas-dads-geo/english/portal/) + +
+ +## Utilisation de l'API ArcGIS pour Python + +### Connexion à ArcGIS Enterprise Portal à l'aide de l'API ArcGIS + +1. Installez les packages : + + ```python + conda install -c esri arcgis + ``` + + ou utilisez Artifactory + + ```python3333 + conda install -c https://jfrog.aaw.cloud.statcan.ca/artifactory/api/conda/esri-remote arcgis + ``` + +2. Importez les librairies nécessaires dont vous aurez besoin dans le Notebook. + ```python + from arcgis.gis import GIS + from arcgis.gis import Item + ``` + +3. Accéder au portail + Votre groupe de projet recevra un identifiant client lors de l'intégration. Collez l'ID client entre les guillemets```client_id='######'```. + + ```python + gis = GIS("https://geoanalytics.cloud.statcan.ca/portal", client_id=' ') + print("Successfully logged in as: " + gis.properties.user.username) + ``` + +4. - La sortie vous redirigera vers un portail de connexion. + - Utilisez l'option de connexion Azure de StatCan et votre identifiant Cloud + - Après une connexion réussie, vous recevrez un code pour vous connecter en utilisant SAML. + - Collez ce code dans la sortie. + + ![Approbation OAuth2](images/OAuth2Key.png) + +
+ +### Afficher les informations utilisateur +En utilisant la fonction "me", nous pouvons afficher diverses informations sur l'utilisateur connecté. +```python +me = gis.users.me +username = me.username +description = me.description +display(me) +``` + +
+ +### Rechercher du contenu +Recherchez le contenu que vous avez hébergé sur le portail géo DAaaS. En utilisant la fonction "me", nous pouvons rechercher tout le contenu hébergé sur le compte. Il existe plusieurs façons de rechercher du contenu. Deux méthodes différentes sont décrites ci-dessous. + +**Recherchez tous vos éléments hébergés dans le portail géographique DAaaS.** +```python +my_content = me.items() +my_content +``` +**Recherchez du contenu spécifique que vous possédez dans le portail géographique DAaaS.** + +Ceci est similaire à l'exemple ci-dessus, mais si vous connaissez le titre de la couche que vous souhaitez utiliser, vous pouvez l'enregistrer en tant que fonction. +```python +my_items = me.items() +for items in my_items: + print(items.title, " | ", items.type) + if items.title == "Flood in Sorel-Tracy": + flood_item = items + + else: + continue +print(flood_item) +``` + +**Recherchez tout le contenu auquel vous avez accès, pas seulement le vôtre.** + +```python +flood_item = gis.content.search("tags: flood", item_type ="Feature Service") +flood_item +``` + +
+ +### Obtenir du contenu +Nous devons obtenir l'élément du portail géographique DAaaS afin de l'utiliser dans le bloc-notes Jupyter. Cela se fait en fournissant le numéro d'identification unique de l'article que vous souhaitez utiliser. Trois exemples sont décrits ci-dessous, tous accédant à la même couche. +```python +item1 = gis.content.get(my_content[5].id) #from searching your content above +display(item1) + +item2 = gis.content.get(flood_item.id) #from example above -searching for specific content +display(item2) + +item3 = gis.content.get('edebfe03764b497f90cda5f0bfe727e2') #the actual content id number +display(item3) +``` + +
+ +### Effectuer une analyse +Une fois les couches importées dans le bloc-notes Jupyter, nous sommes en mesure d'effectuer des types d'analyse similaires à ceux que vous vous attendriez à trouver dans un logiciel SIG tel qu'ArcGIS. Il existe de nombreux modules contenant de nombreux sous-modules qui peuvent effectuer plusieurs types d'analyses. +
+ +À l'aide du module arcgis.features, importez le sous-module use_proximity ```from arcgis.features import use_proximity```. Ce sous-module nous permet de '.create_buffers' - des zones à égale distance des entités. Ici, nous spécifions la couche que nous voulons utiliser, la distance, les unités et le nom de sortie (vous pouvez également spécifier d'autres caractéristiques telles que le champ, le type d'anneau, le type de fin et autres). En spécifiant un nom de sortie, après avoir exécuté la commande buffer, une nouvelle couche sera automatiquement téléchargée dans le portail DAaaS GEO contenant la nouvelle fonctionnalité que vous venez de créer. +
+ +```python +buffer_lyr = use_proximity.create_buffers(item1, distances=[1], + units = "Kilometers", + output_name='item1_buffer') + +display(item1_buffer) +``` + +Certains utilisateurs préfèrent travailler avec des packages Open Source. La traduction d'ArcGIS vers Spatial Dataframes est simple. +```python +# create a Spatially Enabled DataFrame object +sdf = pd.DataFrame.spatial.from_layer(feature_layer) +``` + +
+ +### Mettre à jour les éléments +En obtenant l'élément comme nous l'avons fait similaire à l'exemple ci-dessus, nous pouvons utiliser la fonction '.update' pour mettre à jour l'élément existant dans le portail DAaaS GEO. Nous pouvons mettre à jour les propriétés, les données, les vignettes et les métadonnées des éléments. +```python +item1_buffer = gis.content.get('c60c7e57bdb846dnbd7c8226c80414d2') +item1_buffer.update(item_properties={'title': 'Enter Title' + 'tags': 'tag1, tag2, tag3, tag4', + 'description': 'Enter description of item'} +``` + +
+ +### Visualisez vos données sur une carte interactive + +**Exemple : Librairie MatplotLib** +Dans le code ci-dessous, nous créons un objet ax, qui est un tracé de style carte. Nous traçons ensuite notre colonne de changement de données ("Population Change") sur les axes +```python +import matplotlib.pyplot as plt +ax = sdf.boundary.plot(figsize=(10, 5)) +shape.plot(ax=ax, column='Population Change', legend=True) +plt.show() +``` + +**Exemple : librairie ipyleaflet** +Dans cet exemple, nous utiliserons la librairie 'ipyleaflet' pour créer une carte interactive. Cette carte sera centrée autour de Toronto, ON. Les données utilisées seront décrites ci-dessous. +Commencez par coller ```conda install -c conda-forge ipyleaflet``` vous permettant d'installer les librairies ipyleaflet dans l'environnement Python. +
+Import the necessary libraries. +```python +import ipyleaflet +from ipyleaflet import * +``` +Maintenant que nous avons importé le module ipyleaflet, nous pouvons créer une carte simple en spécifiant la latitude et la longitude de l'emplacement que nous voulons, le niveau de zoom et le fond de carte [(plus de fonds de carte)](https://ipyleaflet.readthedocs.io/en /latest/map_and_basemaps/basemaps.html). Des contrôles supplémentaires ont été ajoutés tels que les calques et l'échelle. +```python +toronto_map = Map(center=[43.69, -79.35], zoom=11, basemap=basemaps.Esri.WorldStreetMap) + +toronto_map.add_control(LayersControl(position='topright')) +toronto_map.add_control(ScaleControl(position='bottomleft')) +toronto_map +``` +
+ +##En savoir plus sur l'API ArcGIS pour Python +[La documentation complète de l'API ArGIS peut être trouvée ici](https://developers.arcgis.com/python/) + +##En savoir plus sur l'environnement analytique géospatial (GAE) et les services DAS +[Guide d'aide GAE](https://statcan.github.io/daaas-dads-geo/) \ No newline at end of file diff --git a/mkdocs-en.yml b/mkdocs-en.yml index 1454122fe..39126ddfd 100644 --- a/mkdocs-en.yml +++ b/mkdocs-en.yml @@ -60,6 +60,7 @@ nav: - 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 - Disks: 5-Storage/Disks.md diff --git a/mkdocs-fr.yml b/mkdocs-fr.yml index f08676e53..3957536ae 100644 --- a/mkdocs-fr.yml +++ b/mkdocs-fr.yml @@ -62,6 +62,7 @@ nav: - Collaboration: - Aperçu: 4-Collaboration/Aperçu.md - Demander un espace de nom: 4-Collaboration/Demander-EspaceDeNom.md + - EAG: Environnement-Analyse-Geospatiale.md - Stockage: - Aperçu: 5-Stockage/Aperçu.md - MinIo: 5-Stockage/MinIo.md From 17f8ce9ffb91269c24c68d8919a7a27bbd81a31b Mon Sep 17 00:00:00 2001 From: Wendy Gaultier Date: Fri, 30 Dec 2022 09:21:51 -0500 Subject: [PATCH 02/14] Feat add gae doc (#1492) * Add GAE Documentation * Add OAuth2Key.png to en doc * Add OAuth2Key.png to fr doc * Fix link to images Co-authored-by: Wendy V Gaultier --- .../Geospatial-Analytical-Environment.md | 5 ++++- docs/en/5-Storage/MinIO.md | 3 +++ docs/en/images/OAuth2Key.png | Bin 0 -> 4663 bytes .../Environnement-Analyse-Geospatiale.md | 5 +++-- docs/fr/images/OAuth2Key.png | Bin 0 -> 4663 bytes 5 files changed, 10 insertions(+), 3 deletions(-) create mode 100644 docs/en/images/OAuth2Key.png create mode 100644 docs/fr/images/OAuth2Key.png diff --git a/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md b/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md index 497197c91..208447cfb 100644 --- a/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md +++ b/docs/en/4-Collaboration/Geospatial-Analytical-Environment.md @@ -1,10 +1,12 @@ # Geospatial Analytical Environment (GAE) - Cross Platform Access + ??? danger "Unprotected data only; SSI coming soon:" At this time, our Geospatial server can only host and provide access to non-sensitive statistical information. ## Getting Started + ??? success "Prerequisites" 1. An onboarded project with access to DAS GAE ArcGIS Portal 2. An ArcGIS Portal Client Id (API Key) @@ -54,7 +56,8 @@ For example, in AAW and the use of [Jupyter Notebooks](https://statcan.github.io - After successful login, you will receive a code to sign in using SAML. - Paste this code into the output. - ![OAuth2 Approval](images/OAuth2Key.png) + + ![OAuth2 Approval](../images/OAuth2Key.png)
diff --git a/docs/en/5-Storage/MinIO.md b/docs/en/5-Storage/MinIO.md index 0cf328abd..213761ad4 100644 --- a/docs/en/5-Storage/MinIO.md +++ b/docs/en/5-Storage/MinIO.md @@ -16,6 +16,7 @@ S3 storage). Buckets are good at three things: ## MinIO Mounted Folders on a Notebook Server + !!! warning "MinIO mounts are not currently working on Protected B servers." Your MinIO storage are mounted as directories if you select the option @@ -104,6 +105,7 @@ This lets you browse, upload/download, delete, or share files. ## Browse Datasets + !!! warning "The link below is not currently working." Browse some [datasets](https://datasets.covid.cloud.statcan.ca) here. These data @@ -218,6 +220,7 @@ send to a collaborator! ## Get MinIO Credentials + !!! warning "The methods below have not been tested recently, since certain MinIO changes. These may require adjustment." diff --git a/docs/en/images/OAuth2Key.png b/docs/en/images/OAuth2Key.png new file mode 100644 index 0000000000000000000000000000000000000000..8336c826fb8efdc2950de678c208b3689ab5149d GIT binary patch literal 4663 zcmcJTXH-*Jzs7?)qM|58aHM2JL{K0iO-jgsC@^$T0jU$fs1S+~NPvVwm*Pj}r9XP>k8IcM!>J^$Z6&pP|D zi<8Zv1IG^l0Dwbww$^R{fJB@4I&|M&amziW3>Kdx5NX6a}N0N^vF zwtxRg+}|Hz>xBRSq(A;RBq-sPK>&bEubs8!6{H`#pe>_@EPYwPe)7bHbVzppeoK;s zy0vjJ009WiXaJxd zEN=z?&{b2!BcX@M(f~kng+iNXdm}vQh}BJdSJn}$eQ;2Uiq*bi_9gtqeJ2`;n$i1C zBsD9NrB5V%=EVZZ9GXAJ-&c2|XSP6)nP4is=|#c-XOQ!PsR>Ndkx|zy=!QVp<{9(~ z^H%gdLnzpx(54C*MX?T|R?%iuzG#q^fMr*cYcu5d{2T#r((|lg%Mv5*#{_FKgP>@V z@Cgv(6+oTYm-hJN9WKjPikk)D>SB%aC#&39)RG}{e$&4E za7dFd+rQqr*x;uoi%z#`*B4}tv5@VkFag2rlm&jRSvH*taJ*-p+Icv(M)P=P`j$|; zzKry^xPEM%T$Hup=!4t+kAzvpwlBt7bvIS2GUr{@F*=!VwU0@W!*=>OKHP1M=2h5W zH||uKN>lFbsp^EN*F44hl&*abKDfhgGRu)z{4K^Xw+?$z>>b*Ai%bO<8z6yySqYQtEfx0E+0LVAnG0KR=4kl+o$DXMi_7U%CSv1TEP9OF zcuLvs=XRQ)OuFVqjNrkG?Uht(6=Thyvw`{k3Z@8W??hj+4M?K`6b$*tkVXCN0<5-n z(dt`~H$Lv=XBAVa0+6>s(9=qIqE++xa%Kzm>{y4ugwnad9Tiu$IJ3qHZ}#n|TwMSP zUJeE~I!Q$FB7lrp=xI?>4Cd^jy?mF`%7TIxWvQY|{%S(XLp|P$c@hvJ`&M zSiEueKciVfKOx4?Pps&O)mSPtS z>|(kE;2qC}C0;FY|6#TKA09`&q!TB@b9#ile8`7g4Y|apxgC?7oE~=loX!dj%3YX^ zCHeSsaI3IQpZHdi4`^f2K7~d4nHdxFFnm?@_3GASNlR>43B63cLs{BGD^V*tuzj-^ z%DW8ZtENmO8d+ebKP?&VltGz=AadAIv<7Wu9L=T9Psp47Tt8f(jWpjYexW@pm)H%3 ziN(lRP(FTNR=pF^)utQ$F!LHqx_W82XT2!Dkn+pkZ`&G3URhJn)FsDI!SE0}pbKY_ ziyn{&O7Od%8SJoPsEvwmR*I2($y`Eaj`}MWc9LajHff59fgc7f2(Wkf;mxDMrM1^D zHz4wGd%-Ao1(HhlgxJTuluNub?e_|(W^w$qYEY3Lg>+QCb7#VnF?M_w@RaEL zah{KUDRS@_Nt2|y0LOp~kPhLwB0=2sdn>`}&DjyZ*!vR|%F z%dt2}(+RkvH&)F++tv^;nTksVujEK&ZLqnbiBp_9Sg3skelgy7*Os==ML8*Q?Ve9< zNHr4{%+BadFZ@8aUJFO?sr6pDs9wJgzVQWzn0eIsDEZ2Ex=~LQc2m?M8XclrLG9AThfsb}P;f@XsOwmZ~t11P$%r>D3$!{Vn97wRH1Bk`X>qcWU0@hGK1S^tGSs9Mxt*;LEJ%@Yj9ZV{7#* zEhLi|F5^8WxFN^;*-cV&HHWcj z%3#&Uyn{n)&2vKF)L1xtr!jf~GuvaSl%tKnT}~6F{rTj5Zd&@iXO=4IG%Do*%*b;u zgBWxLPMy6um#s&o?+s7;TK?>2V{Mv!>PCb{d+sA6kZ!+5od_tKHRUtE8Gd&4gc(LU zfdTP7FV^o5*^8oa9&mH9SGz*ov(fz|lOLk~<-Tg9BO$}o9VC4RN8vy3D;S;X29B z0%N0r+?<_0-4Rr60VH(=uABz%o6MhUPXnJSht;GUnp2)tot#zaeA)fJ-3CG;V#bWT zPs#}g_0m0oET&UoRRYh)_qx(o(_oxmg4YHfqr}vRkuFFSMvs3vLaXI43hUaMZMB7{ z=wWTF0P-lYS#Ld)<9adB`OOLTm(;kCrDJBFU`hqQA`~L2Q1e1=L?m>)unBM2cZ+7q zp6EL9jC+ZItU+Yhb3+fR(-hl&H853^#2tG|^j#E449QV<=*jjLR&aHNqmvW>jB?NNPagw_9^aD$Apccd*AUs%zt^EsR z;?Ws=!3K|1aL8X#ecPKH{+ayY$!pr}(rv+7?a{Dx64~Yyz0y#-FqU1V|!#-X)4N z+xxxuUR80L(+^CZUw%oKcz132#XTDm9UV*X2Mr5)Dz;J9%5en$l(0Giy*AW zhNb%l+N?5IkSpo+h=#&rl1^jK^66>RJGK;QGf0>D+QNmT%@FTv+pDy|{fn?6^9t2h z4bE=%Ld#+y{9v!BPSsb`@7c8b>CMUx*M8` z6wDGG-iJuFnpEG=8H0Ri_(D^VC&ER;> zw@1C)Ur!GU5%Y2sB{L!+b{sKp=+G;j@Q)nxDb~S>Zy6zzoR(4&n(ihTEn5cmz39HM zzHc*UuZpW*`zx;x_dZ)))NAbMH_Kdg9W_((>?t@Wx3)wl>lsM#JD*=)WUd?YQ=&;6z zZ=P1)G7+-CpM!-Bphlp8o@=xiQ@`T2HuSk;QSz<|N`n_am-!~to|jF36_uf{i1yQ7 zkCi-+iw(SXlRERRnf$h5*OAqw3fvlQS80i8#8_sEH@0=JrQ)MIIV|_2LfhQDvUKZ) zOFtxa&QJ1=c9^k@d-$7>tXpRBIWDYsfp~h^w<^Mg*2XR90Dd(x1wGA-(4%fmpHEuh zyIsg3Sg5LvTx}~O%10kEH0DW>@|#i}QDKHo{eJvqBL7x^1;&{GvbQ#HifuaP^%b@t?kz*@uNkr8O_zuGVu=5dJUTnP%1-M&w={wtv95|uX>X@qQsBuuDYf_H zpnG{5O2P}-sNadLT{A!7N`@9*7Yhd;+`7#Bs(l++guJ{MlH z+8RL3!aXb| zohTL}PhLY;R zV?!#yNZy?#%VesjagG_P;^BIHA>CT+m1N39-XlF!h-`-L_j?WwQ4(D9r@ef)+;1L6 z^Y$a?m#pB&=o_TD{#WQg*W2-{s>9|Tu$InIxlfp;!)X~yBjF4-JBN~MLs%cK4Tgk3 z3)x$TQn4fqH=>Q5c;VW$uDi=rwK@-WeP)%g=1B9~5a8WW24U^Myk$yTQ3aE}nfI2d zTtK-st(0`l%mWB zLLCA@{sufQk47`yDtpbJnFHHTvCCOn*;{O4ly1;3d}Qxq6|A0w+}A9F3i_Z$<<nkQItTD)E_SLUJQiT ??? danger "Données non protégées uniquement ; SSI bientôt disponible :" À l'heure actuelle, notre serveur géospatial ne peut héberger et donner accès qu'à des informations statistiques non sensibles. ## Démarrage + ??? success "Conditions préalables" 1. Un projet intégré avec accès au portail DAS EAG ArcGIS 2. Un identifiant client ArcGIS Portal (clé API) @@ -55,7 +56,7 @@ Par exemple, dans AAW et l'utilisation de [Jupyter Notebooks](https://statcan.gi - Après une connexion réussie, vous recevrez un code pour vous connecter en utilisant SAML. - Collez ce code dans la sortie. - ![Approbation OAuth2](images/OAuth2Key.png) + ![Approbation OAuth2](../images/OAuth2Key.png)
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Th - sqlite - many others... just ask :-) +### How much does the AAW cost? #### CPU Only From c6a988a20050661a07652dce1f20e27b5af23e25 Mon Sep 17 00:00:00 2001 From: StanHatko <38304828+StanHatko@users.noreply.github.com> Date: Mon, 23 Jan 2023 15:31:51 -0500 Subject: [PATCH 05/14] add warning that Kubeflow pipelines are going to be removed from AAW --- docs/en/3-Pipelines/Kubeflow-Pipelines.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/en/3-Pipelines/Kubeflow-Pipelines.md b/docs/en/3-Pipelines/Kubeflow-Pipelines.md index dab6fcf81..ef80639f5 100644 --- a/docs/en/3-Pipelines/Kubeflow-Pipelines.md +++ b/docs/en/3-Pipelines/Kubeflow-Pipelines.md @@ -1,5 +1,10 @@ # Overview + +!!! warning "Kubeflow pipelines are in the process of being removed from AAW." + No new development should use Kubeflow pipelines. If you have questions + about this removal, please speak with the AAW maintainers. + [Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/overview/) is a platform for building machine learning workflows for deployment in a Kubernetes environment. It enables authoring _pipelines_ that encapsulate From a9e84002060fca972abeb86741dab8be687fc6fc Mon Sep 17 00:00:00 2001 From: StanHatko <38304828+StanHatko@users.noreply.github.com> Date: Tue, 24 Jan 2023 10:05:23 -0500 Subject: [PATCH 06/14] SAS image --- docs/en/1-Experiments/Kubeflow.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/docs/en/1-Experiments/Kubeflow.md b/docs/en/1-Experiments/Kubeflow.md index ad75dd667..364f3b513 100644 --- a/docs/en/1-Experiments/Kubeflow.md +++ b/docs/en/1-Experiments/Kubeflow.md @@ -52,9 +52,11 @@ for your team. ## Image -You will need to choose an image. There are JupyterLab, RStudio, and Ubuntu remote -desktop images available. Select the drop down menu to select additional options -within these (for instance, CPU, PyTorch, and TensorFlow images for JupyterLab). +You will need to choose an image. There are JupyterLab, RStudio, Ubuntu remote +desktop, and SAS images available. The SAS image is only available for StatCan +employees (due to license limitations), the others are available for everyone. +Select the drop down menu to select additional options within these (for +instance, CPU, PyTorch, and TensorFlow images for JupyterLab). Check the name of the images and choose one that matches what you want to do. Don't know which one to choose? Check out your options [here](./Selecting-an-Image.md). 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z$9}vWHU~gCI|s~!Oz-u$!mt6|0T0ymd{#!UD+leA&@TKQGm~&!+hciNIa?s_o}Hcj zf#a8hk7ZOMzqqJ5H35!;Dd4Ml{c?+>556)w`X7I3YZ=T?8NIo?bjkkgpN_?0Z5DtF z!v)9QFj6qmi|KfV%plC@t8^(I6c7BVAvoh2tUoI;6!=ZT?N+Gv!E@`>{%i!M|0SFB jKR5rsLqf+-WE{a2zk2!oTKB_W From c95723e76c298313c9804d1dff37c1a8e07bdbf9 Mon Sep 17 00:00:00 2001 From: Stan Hatko Date: Tue, 24 Jan 2023 10:35:15 -0500 Subject: [PATCH 08/14] update CPU, RAM, GPU select --- docs/en/1-Experiments/Kubeflow.md | 54 ++++++++++++++++++------------ docs/en/images/cpu-ram-select.png | Bin 0 -> 43300 bytes 2 files changed, 32 insertions(+), 22 deletions(-) create mode 100644 docs/en/images/cpu-ram-select.png diff --git a/docs/en/1-Experiments/Kubeflow.md b/docs/en/1-Experiments/Kubeflow.md index 364f3b513..286d60aae 100644 --- a/docs/en/1-Experiments/Kubeflow.md +++ b/docs/en/1-Experiments/Kubeflow.md @@ -65,33 +65,43 @@ which one to choose? Check out your options [here](./Selecting-an-Image.md). ## CPU and Memory -- At the time of writing (December 23, 2021) there are two types of computers in - the cluster - - - **CPU:** `D16s v3` (16 CPU cores, 64 GiB memory; for user use 15 CPU cores - and 48 GiB memory are available; 1 CPU core and 16 GiB memory reserved for - system use). - - **GPU:** `NC6s_v3` (6 CPU cores, 112 GiB memory, 1 GPU; for user use 96 GiB - memory are available; 16 GiB memory reserved for system use). The available - GPU is the NVIDIA Tesla V100 GPU with specs - [here](https://images.nvidia.com/content/technologies/volta/pdf/volta-v100-datasheet-update-us-1165301-r5.pdf). - - When creating a notebook server, the system will limit you to the maximum - specifications above. For CPU notebook servers, you can specify the exact - amount of CPU and memory that you require. This allows you to meet your - compute needs while minimising cost. For a GPU notebook server, you will - always get the full server (6 CPU cores, 96 GiB accessible memory, and 1 GPU). - See below section on GPUs for information on how to select a GPU server. - - In the future there may be larger machines available, so you may have looser - restrictions. +At the time of writing (December 23, 2021) there are two types of computers in +the cluster + + - **CPU:** `D16s v3` (16 CPU cores, 64 GiB memory; for user use 15 CPU cores + and 48 GiB memory are available; 1 CPU core and 16 GiB memory reserved for + system use). + - **GPU:** `NC6s_v3` (6 CPU cores, 112 GiB memory, 1 GPU; for user use 96 GiB + memory are available; 16 GiB memory reserved for system use). The available + GPU is the NVIDIA Tesla V100 GPU with specs + [here](https://images.nvidia.com/content/technologies/volta/pdf/volta-v100-datasheet-update-us-1165301-r5.pdf). + +When creating a notebook server, the system will limit you to the maximum +specifications above. For CPU notebook servers, you can specify the exact +amount of CPU and memory that you require. This allows you to meet your +compute needs while minimising cost. For a GPU notebook server, you will +always get the full server (6 CPU cores, 96 GiB accessible memory, and 1 GPU). +See below section on GPUs for information on how to select a GPU server. + +In the advanced options, you can select a higher limit than the number of CPU cores and +RAM requested. The amount requested is the amount guaranteed to be available for your +notebook server and you will always pay for at least this much. If the limit is higher +than the amount requested, if additional RAM and CPU cores are available on that shared +server in the cluster your notebook server can use them as needed. One use case for this +is jobs that usually need only one CPU core but can benefit from multithreading to speed +up certain operations. By requesting one CPU core but a higher limit, you can pay much +less for the notebook server while allowing it to use spare unused CPU cores as needed +to speed up computations. + +![Select CPU and RAM](../images/cpu-ram-select.png) ## GPUs If you want a GPU server, select `1` as the number of GPUs and `NVIDIA` as the GPU vendor (the create button will be greyed out until the GPU vendor is selected if -you have a GPU specified). Multi-GPU servers are not currently supported on the -AAW system. +you have a GPU specified). Multi-GPU servers are currently supported on the AAW +system only on a special on-request basis, please contact the AAW maintainers if +you would like a multi-GPU server. ![GPU Configuration](../images/kubeflow_gpu_selection.jpg) diff --git a/docs/en/images/cpu-ram-select.png b/docs/en/images/cpu-ram-select.png new file mode 100644 index 0000000000000000000000000000000000000000..9ceaa1be0f045f5dd6977254ef854aa165fc68c8 GIT binary patch literal 43300 zcmbrlWmp_h*RDwt2o_v}OK=G876|U{60DKN-3jjQ?(Xgo+#QwjO%fcuSD;OWWQCxhYNHXK4Pf5u@U{|~4p30Yeg8fmhHOfVp`c1yB}If(T=kAu zk#sQbUbxQdF-4V`!U}%TB0aJi`bX_YH1#z84b_%jo^k4)rpfHHz66GA8z4VIIsD?) zpRp%oU2E|6pl-K!=DT4~Q0Lp{rA;>a9P9DF~ExUBzP z>~CfO=BL1{EF!=#9kJl&e=X@YD%Sj-j|au14H+2(0QBbL<6}86>l9y~%w-7Kge+3h zceZ{UseZDqZm&AR1uJqIz~4~L)U&gpFLTtF1TUqMp5DK`5d7y4@KVjIDPcw=W=6BYSuD3x$*U} zFYV9>-|AdN4Kui=1*()Ocgx|0>+Bss7{X8A(nJ%}^(?-H?3kMXjmO!hDh``LG_sfT z-9@8rd1xe=kxDJiPAy1J=V>BAPl6WjWb!a5biF>-gA;T51CxFlOk3iPNk? z2KUWU%;_~dkw8VE&EU^aP^GB0vix}-j>ug!x{fPp6!bV| 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