Merge pull request #8083 from ovh/mb-ai-update-docker-commands

[AI Guides & Tutorials] - Fix docker commands

Author: Y0Coss

pages/public_cloud/ai_machine_learning/deploy_tuto_01_streamlit/guide.de-de.md

@@ -1,7 +1,7 @@
 ---
 title: AI Deploy - Tutorial - Build & use a Streamlit image
 excerpt: How to build and use a custom Docker image containing a Streamlit application
-updated: 2023-11-27
+updated: 2025-06-27
 ---
 
 > [!primary]
@@ -17,7 +17,7 @@ The purpose of this tutorial is to provide a concrete example on how to build an
 
 ## Requirements
 
--   access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.de/&ovhSubsidiary=de)
+-   access to the [OVHcloud Control Panel](/links/manager)
 -   an **AI Deploy project** created inside a **Public Cloud** project
 -   a [user for AI Deploy](/pages/public_cloud/ai_machine_learning/gi_01_manage_users)
 -   [Docker](https://www.docker.com/get-started) installed on your local computer
@@ -98,27 +98,28 @@ WORKDIR /workspace
 -   More information about Dockerfiles can be found [here](https://docs.docker.com/engine/reference/builder/)
 -   Direct link to the full Dockerfile can be found here [here](https://github.com/ovh/ai-training-examples/tree/main/apps/streamlit/simple-app/Dockerfile)
 
-### Build the docker image from the dockerfile
+### Build the Docker image from the Dockerfile
 
-Launch the following command from the **Dockerfile** directory to build your application image.
+From the directory containing your **Dockerfile**, run one of the following commands to build your application image:
 
-``` {.console}
+```console
+# Build the image using your machine's default architecture
 docker build . -t streamlit-example:latest
+
+# Build image targeting the linux/amd64 architecture
+docker buildx build --platform linux/amd64 -t streamlit-example:latest .
 ```
 
-> [!primary]
->
-> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
+- The **first command** builds the image using your system’s default architecture. This may work if your machine already uses the `linux/amd64` architecture, which is required to run containers with our AI products. However, on systems with a different architecture (e.g. `ARM64` on `Apple Silicon`), the resulting image will not be compatible and cannot be deployed.
+
+- The **second command** explicitly targets the `linux/AMD64` architecture to ensure compatibility with our AI services. This requires `buildx`, which is not installed by default. If you haven’t used `buildx` before, you can install it by running: `docker buildx install`
 
 > [!primary]
 >
-> The `-t` argument allow you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **streamlit-example:latest**.
-
-> [!warning]
+> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
 >
-> Please make sure that the docker image you will push in order to run containers using AI products respects the **linux/AMD64** target architecture. You could, for instance, build your image using **buildx** as follows:
+> The `-t` argument allows you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **streamlit-example:latest**.
 >
-> `docker buildx build --platform linux/amd64 ...`
 
 ### Test it locally (optional)
 
@@ -192,7 +193,7 @@ Once the AI Deploy app is running you can access your Streamlit application dire
 - Do you want to use **Streamlit** to deploy an AI model for audio classification task? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification).
 - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_02_flask).
 
-If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/de/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.
+If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](/links/professional-services) to get a quote and ask our Professional Services experts for a custom analysis of your project.
 
 ## Feedback
 

pages/public_cloud/ai_machine_learning/deploy_tuto_01_streamlit/guide.en-asia.md

@@ -1,7 +1,7 @@
 ---
 title: AI Deploy - Tutorial - Build & use a Streamlit image
 excerpt: How to build and use a custom Docker image containing a Streamlit application
-updated: 2023-11-27
+updated: 2025-06-27
 ---
 
 > [!primary]
@@ -17,7 +17,7 @@ The purpose of this tutorial is to provide a concrete example on how to build an
 
 ## Requirements
 
--   access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/asia/&ovhSubsidiary=asia)
+-   access to the [OVHcloud Control Panel](/links/manager)
 -   an **AI Deploy project** created inside a **Public Cloud** project
 -   a [user for AI Deploy](/pages/public_cloud/ai_machine_learning/gi_01_manage_users)
 -   [Docker](https://www.docker.com/get-started) installed on your local computer
@@ -98,27 +98,28 @@ WORKDIR /workspace
 -   More information about Dockerfiles can be found [here](https://docs.docker.com/engine/reference/builder/)
 -   Direct link to the full Dockerfile can be found here [here](https://github.com/ovh/ai-training-examples/tree/main/apps/streamlit/simple-app/Dockerfile)
 
-### Build the docker image from the dockerfile
+### Build the Docker image from the Dockerfile
 
-Launch the following command from the **Dockerfile** directory to build your application image.
+From the directory containing your **Dockerfile**, run one of the following commands to build your application image:
 
-``` {.console}
+```console
+# Build the image using your machine's default architecture
 docker build . -t streamlit-example:latest
+
+# Build image targeting the linux/amd64 architecture
+docker buildx build --platform linux/amd64 -t streamlit-example:latest .
 ```
 
-> [!primary]
->
-> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
+- The **first command** builds the image using your system’s default architecture. This may work if your machine already uses the `linux/amd64` architecture, which is required to run containers with our AI products. However, on systems with a different architecture (e.g. `ARM64` on `Apple Silicon`), the resulting image will not be compatible and cannot be deployed.
+
+- The **second command** explicitly targets the `linux/AMD64` architecture to ensure compatibility with our AI services. This requires `buildx`, which is not installed by default. If you haven’t used `buildx` before, you can install it by running: `docker buildx install`
 
 > [!primary]
 >
-> The `-t` argument allow you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **streamlit-example:latest**.
-
-> [!warning]
+> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
 >
-> Please make sure that the docker image you will push in order to run containers using AI products respects the **linux/AMD64** target architecture. You could, for instance, build your image using **buildx** as follows:
+> The `-t` argument allows you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **streamlit-example:latest**.
 >
-> `docker buildx build --platform linux/amd64 ...`
 
 ### Test it locally (optional)
 
@@ -192,7 +193,7 @@ Once the AI Deploy app is running you can access your Streamlit application dire
 - Do you want to use **Streamlit** to deploy an AI model for audio classification task? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification).
 - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_02_flask).
 
-If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/asia/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.
+If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](/links/professional-services) to get a quote and ask our Professional Services experts for a custom analysis of your project.
 
 ## Feedback
 

pages/public_cloud/ai_machine_learning/deploy_tuto_01_streamlit/guide.en-au.md

@@ -1,7 +1,7 @@
 ---
 title: AI Deploy - Tutorial - Build & use a Streamlit image
 excerpt: How to build and use a custom Docker image containing a Streamlit application
-updated: 2023-11-27
+updated: 2025-06-27
 ---
 
 > [!primary]
@@ -17,7 +17,7 @@ The purpose of this tutorial is to provide a concrete example on how to build an
 
 ## Requirements
 
--   access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com.au/&ovhSubsidiary=au)
+-   access to the [OVHcloud Control Panel](/links/manager)
 -   an **AI Deploy project** created inside a **Public Cloud** project
 -   a [user for AI Deploy](/pages/public_cloud/ai_machine_learning/gi_01_manage_users)
 -   [Docker](https://www.docker.com/get-started) installed on your local computer
@@ -98,27 +98,28 @@ WORKDIR /workspace
 -   More information about Dockerfiles can be found [here](https://docs.docker.com/engine/reference/builder/)
 -   Direct link to the full Dockerfile can be found here [here](https://github.com/ovh/ai-training-examples/tree/main/apps/streamlit/simple-app/Dockerfile)
 
-### Build the docker image from the dockerfile
+### Build the Docker image from the Dockerfile
 
-Launch the following command from the **Dockerfile** directory to build your application image.
+From the directory containing your **Dockerfile**, run one of the following commands to build your application image:
 
-``` {.console}
+```console
+# Build the image using your machine's default architecture
 docker build . -t streamlit-example:latest
+
+# Build image targeting the linux/amd64 architecture
+docker buildx build --platform linux/amd64 -t streamlit-example:latest .
 ```
 
-> [!primary]
->
-> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
+- The **first command** builds the image using your system’s default architecture. This may work if your machine already uses the `linux/amd64` architecture, which is required to run containers with our AI products. However, on systems with a different architecture (e.g. `ARM64` on `Apple Silicon`), the resulting image will not be compatible and cannot be deployed.
+
+- The **second command** explicitly targets the `linux/AMD64` architecture to ensure compatibility with our AI services. This requires `buildx`, which is not installed by default. If you haven’t used `buildx` before, you can install it by running: `docker buildx install`
 
 > [!primary]
 >
-> The `-t` argument allow you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **streamlit-example:latest**.
-
-> [!warning]
+> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
 >
-> Please make sure that the docker image you will push in order to run containers using AI products respects the **linux/AMD64** target architecture. You could, for instance, build your image using **buildx** as follows:
+> The `-t` argument allows you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **streamlit-example:latest**.
 >
-> `docker buildx build --platform linux/amd64 ...`
 
 ### Test it locally (optional)
 
@@ -192,7 +193,7 @@ Once the AI Deploy app is running you can access your Streamlit application dire
 - Do you want to use **Streamlit** to deploy an AI model for audio classification task? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification).
 - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_02_flask).
 
-If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/en-au/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.
+If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](/links/professional-services) to get a quote and ask our Professional Services experts for a custom analysis of your project.
 
 ## Feedback
 

pages/public_cloud/ai_machine_learning/deploy_tuto_01_streamlit/guide.en-ca.md

@@ -1,7 +1,7 @@
 ---
 title: AI Deploy - Tutorial - Build & use a Streamlit image
 excerpt: How to build and use a custom Docker image containing a Streamlit application
-updated: 2023-11-27
+updated: 2025-06-27
 ---
 
 > [!primary]
@@ -17,7 +17,7 @@ The purpose of this tutorial is to provide a concrete example on how to build an
 
 ## Requirements
 
--   access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/ca/en/&ovhSubsidiary=ca)
+-   access to the [OVHcloud Control Panel](/links/manager)
 -   an **AI Deploy project** created inside a **Public Cloud** project
 -   a [user for AI Deploy](/pages/public_cloud/ai_machine_learning/gi_01_manage_users)
 -   [Docker](https://www.docker.com/get-started) installed on your local computer
@@ -98,27 +98,28 @@ WORKDIR /workspace
 -   More information about Dockerfiles can be found [here](https://docs.docker.com/engine/reference/builder/)
 -   Direct link to the full Dockerfile can be found here [here](https://github.com/ovh/ai-training-examples/tree/main/apps/streamlit/simple-app/Dockerfile)
 
-### Build the docker image from the dockerfile
+### Build the Docker image from the Dockerfile
 
-Launch the following command from the **Dockerfile** directory to build your application image.
+From the directory containing your **Dockerfile**, run one of the following commands to build your application image:
 
-``` {.console}
+```console
+# Build the image using your machine's default architecture
 docker build . -t streamlit-example:latest
+
+# Build image targeting the linux/amd64 architecture
+docker buildx build --platform linux/amd64 -t streamlit-example:latest .
 ```
 
-> [!primary]
->
-> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
+- The **first command** builds the image using your system’s default architecture. This may work if your machine already uses the `linux/amd64` architecture, which is required to run containers with our AI products. However, on systems with a different architecture (e.g. `ARM64` on `Apple Silicon`), the resulting image will not be compatible and cannot be deployed.
+
+- The **second command** explicitly targets the `linux/AMD64` architecture to ensure compatibility with our AI services. This requires `buildx`, which is not installed by default. If you haven’t used `buildx` before, you can install it by running: `docker buildx install`
 
 > [!primary]
 >
-> The `-t` argument allow you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **streamlit-example:latest**.
-
-> [!warning]
+> The dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory.
 >
-> Please make sure that the docker image you will push in order to run containers using AI products respects the **linux/AMD64** target architecture. You could, for instance, build your image using **buildx** as follows:
+> The `-t` argument allows you to choose the identifier to give to your image. Usually image identifiers are composed of a **name** and a **version tag** `<name>:<version>`. For this example we chose **streamlit-example:latest**.
 >
-> `docker buildx build --platform linux/amd64 ...`
 
 ### Test it locally (optional)
 
@@ -192,7 +193,7 @@ Once the AI Deploy app is running you can access your Streamlit application dire
 - Do you want to use **Streamlit** to deploy an AI model for audio classification task? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification).
 - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_02_flask).
 
-If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/en-ca/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.
+If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](/links/professional-services) to get a quote and ask our Professional Services experts for a custom analysis of your project.
 
 ## Feedback