diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.de-de.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.de-de.md index 6fdb5805c6a..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.de-de.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.de-de.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.de/&ovhSubsidiary=de). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/de/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-asia.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-asia.md index d5cb88b279a..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-asia.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-asia.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/asia/&ovhSubsidiary=asia). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/asia/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-au.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-au.md index 2b486abf335..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-au.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-au.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com.au/&ovhSubsidiary=au). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/en-au/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-ca.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-ca.md index a5107270298..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-ca.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-ca.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/ca/en/&ovhSubsidiary=ca). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/en-ca/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-gb.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-gb.md index f24a5ee5701..03ed11ffc03 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-gb.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-gb.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-ie.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-ie.md index 3a88c5b7828..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-ie.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-ie.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.ie/&ovhSubsidiary=ie). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/en-ie/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -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-ie/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-sg.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-sg.md index f1905c0cb39..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-sg.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-sg.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/sg/&ovhSubsidiary=sg). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/en-sg/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -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-sg/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-us.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-us.md index 46870343651..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-us.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.en-us.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/world/&ovhSubsidiary=we). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/en/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -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/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.es-es.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.es-es.md index 05b8e10e521..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.es-es.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.es-es.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.es/&ovhSubsidiary=es). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/es-es/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/es-es/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.es-us.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.es-us.md index 2802c81bf70..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.es-us.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.es-us.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/world/&ovhSubsidiary=ws). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/es/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/es/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.fr-ca.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.fr-ca.md index 13771755dc2..66e1364b268 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.fr-ca.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.fr-ca.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutoriel - Déployer une app Gradio pour de la reconnaissance de schémas (EN) excerpt: Comment construire et utiliser votre propre image Docker contenant une application Gradio -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://ca.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/ca/fr/&ovhSubsidiary=qc). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/fr-ca/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/fr-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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.fr-fr.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.fr-fr.md index d93d85a06fc..66e1364b268 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.fr-fr.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.fr-fr.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutoriel - Déployer une app Gradio pour de la reconnaissance de schémas (EN) excerpt: Comment construire et utiliser votre propre image Docker contenant une application Gradio -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.com/fr/&ovhSubsidiary=fr). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/fr/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/fr/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.it-it.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.it-it.md index c70b8f52ec2..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.it-it.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.it-it.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.it/&ovhSubsidiary=it). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/it/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/it/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.pl-pl.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.pl-pl.md index a73728c5d84..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.pl-pl.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.pl-pl.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.pl/&ovhSubsidiary=pl). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/pl/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/pl/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) - diff --git a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.pt-pt.md b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.pt-pt.md index a6f141d5252..e089b95a8bb 100644 --- a/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.pt-pt.md +++ b/pages/public_cloud/ai_machine_learning/deploy_tuto_05_gradio_sketch_recognition/guide.pt-pt.md @@ -1,7 +1,7 @@ --- title: AI Deploy - Tutorial - Deploy a Gradio app for sketch recognition excerpt: How to build and use a custom Docker image containing a Gradio application -updated: 2023-11-27 +updated: 2025-06-25 --- > [!primary] @@ -23,8 +23,8 @@ Overview of the app: ## Requirements -- Access to the [OVHcloud Control Panel](https://www.ovh.com/auth/?action=gotomanager&from=https://www.ovh.pt/&ovhSubsidiary=pt). -- An AI Deploy project created inside a [Public Cloud project](https://www.ovhcloud.com/pt/public-cloud/) in your OVHcloud account. +- Access to the [OVHcloud Control Panel](/links/manager). +- An AI Deploy project created inside a [Public Cloud project](/links/public-cloud/public-cloud) in your OVHcloud account. - 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. - Some knowledge about building image and [Dockerfile](https://docs.docker.com/engine/reference/builder/). @@ -87,7 +87,7 @@ Load the previously trained model for handwritten digits classification. > ```python -model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5") +model = tf.keras.models.load_model("model/sketch_recognition_numbers_model.h5", compile=False) ``` Create the function that recognizes the written number. @@ -158,12 +158,20 @@ CMD [ "python3" , "/workspace/app.py" ] ### 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 +# Build the image using your machine's default architecture docker build . -t gradio_app:latest + +# Build image targeting the linux/amd64 architecture +docker buildx build --platform linux/amd64 -t gradio_app:latest . ``` +- 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 dot `.` argument indicates that your build context (place of the **Dockerfile** and other needed files) is the current directory. @@ -171,13 +179,6 @@ docker build . -t gradio_app:latest > 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** `:`. For this example we chose **gradio_app:latest**. > -> [!warning] -> -> 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: -> -> `docker buildx build --platform linux/amd64 ...` -> - ### Push the image into the shared registry > [!warning] @@ -211,7 +212,7 @@ The following command starts a new AI Deploy app running your Gradio application ```console ovhai app run \ --cpu 1 \ - --volume @/:/workspace/model:RO \ + --volume @/model/:/workspace/model:RO \ /gradio_app:latest ``` @@ -241,11 +242,10 @@ If you want your **AI Deploy app** to be accessible without the need to authenti - You can imagine deploying an AI model with an other tool: **Flask**. Refer to this [tutorial](/pages/public_cloud/ai_machine_learning/deploy_tuto_06_flask_hugging_face). - Do you want to use **Streamlit** to create a audio classification app? [Here it is](/pages/public_cloud/ai_machine_learning/deploy_tuto_03_streamlit_sounds_classification). -If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://www.ovhcloud.com/pt/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 Please send us your questions, feedback and suggestions to improve the service: - On the OVHcloud [Discord server](https://discord.gg/ovhcloud) -