feat(infr): add region selector step

pages/managed-inference/how-to/create-deployment.mdx

@@ -17,8 +17,9 @@ dates:
 - [Owner](/iam/concepts/#owner) status or [IAM permissions](/iam/concepts/#permission) allowing you to perform actions in the intended Organization
 
 1. Click the **AI** section of the [Scaleway console](https://console.scaleway.com/), and select **Managed Inference** from the side menu to access the Managed Inference dashboard.
-2. Click **Deploy a model** to launch the model deployment wizard.
-3. Provide the necessary information:
+2. From the drop-down menu, select the geographical region where you want to create your deployment.
+3. Click **Deploy a model** to launch the model deployment wizard.
+4. Provide the necessary information:
     - Select the desired model and quantization to use for your deployment [from the available options](/managed-inference/reference-content/).
         <Message type="important">
           Scaleway Managed Inference allows you to deploy various AI models, either from the Scaleway catalog or by importing a custom model. For detailed information about supported models, visit our [Supported models in Managed Inference](/managed-inference/reference-content/supported-models/) documentation.
@@ -28,12 +29,12 @@ dates:
         </Message>
     - Choose the geographical **region** for the deployment.
     - Specify the GPU Instance type to be used with your deployment.
-4. Enter a **name** for the deployment, and optional tags.
-5. Configure the **network connectivity** settings for the deployment:
+5. Enter a **name** for the deployment, and optional tags.
+6. Configure the **network connectivity** settings for the deployment:
     - Attach to a **Private Network** for secure communication and restricted availability. Choose an existing Private Network from the drop-down list, or create a new one.
     - Set up **Public connectivity** to access resources via the public internet. Authentication by API key is enabled by default.
     <Message type="important">
       - Enabling both private and public connectivity will result in two distinct endpoints (public and private) for your deployment.
       - Deployments must have at least one endpoint, either public or private.
     </Message>
-6. Click **Deploy model** to launch the deployment process. Once the model is ready, it will be listed among your deployments.
+7. Click **Deploy model** to launch the deployment process. Once the model is ready, it will be listed among your deployments.

pages/managed-inference/how-to/delete-deployment.mdx

@@ -22,10 +22,11 @@ Once you have finished your inference tasks you can delete your deployment. This
   - [Owner](/iam/concepts/#owner) status or [IAM permissions](/iam/concepts/#permission) allowing you to perform actions in the intended Organization
 
 1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
-2. Choose a deployment either by clicking its name or selecting **More info** from the drop-down menu represented by the icon <Icon name="more" /> to access the deployment dashboard.
-3. Click the **Settings** tab of your deployment to display additional settings.
-4. Click **Delete deployment**.
-5. Type **DELETE** to confirm and click **Delete deployment** to delete your deployment.
+2. From the drop-down menu, select the geographical region you want to manage.
+3. Choose a deployment either by clicking its name or selecting **More info** from the drop-down menu represented by the icon <Icon name="more" /> to access the deployment dashboard.
+4. Click the **Settings** tab of your deployment to display additional settings.
+5. Click **Delete deployment**.
+6. Type **DELETE** to confirm and click **Delete deployment** to delete your deployment.
 
 <Message type="important">
   Deleting a deployment is a permanent action and will erase all its associated data.

pages/managed-inference/how-to/import-custom-model.mdx

@@ -24,25 +24,26 @@ Scaleway provides a selection of common models for deployment from the Scaleway
 - [Owner](/iam/concepts/#owner) status or [IAM permissions](/iam/concepts/#permission) to perform actions in your Organization.
 
 1. Click **Managed Inference** in the **AI** section of the side menu in the [Scaleway console](https://console.scaleway.com/) to access the dashboard.
-2. Click **Deploy a model** to launch the model deployment wizard.
-3. In the **Choose a model** section, select **Custom model**. If you have no model yet, click **Import a model** to start the model import wizard.
-4. Choose an upload source:
+2. From the drop-down menu, select the geographical region you want to manage.
+3. Click **Deploy a model** to launch the model deployment wizard.
+4. In the **Choose a model** section, select **Custom model**. If you have no model yet, click **Import a model** to start the model import wizard.
+5. Choose an upload source:
    - **Hugging Face**: Pull the model from Hugging Face.
    - **Object Storage**: This feature is coming soon.
-5. Enter your Hugging Face access token, which must have READ access to the repository.
+6. Enter your Hugging Face access token, which must have READ access to the repository.
     <Message type="note">
       [Learn how to generate a Hugging Face access token](https://huggingface.co/docs/hub/security-tokens).
     </Message>
-6. Enter the name of the Hugging Face repository to pull the model from.
+7. Enter the name of the Hugging Face repository to pull the model from.
     <Message type="note">
       Ensure you have access to gated models if applicable. Refer to the [Hugging Face documentation](https://huggingface.co/docs/hub/en/models-gated) for details.
     </Message>
-7. Choose a name for your model. The name must be unique within your Organization and Project and cannot be changed later.
-8. Click **Verify import** to check your Hugging Face credentials and ensure model compatibility.
+8. Choose a name for your model. The name must be unique within your Organization and Project and cannot be changed later.
+9. Click **Verify import** to check your Hugging Face credentials and ensure model compatibility.
     <Message type="tip">
       For detailed information about supported models, visit our [Supported models in Managed Inference](/managed-inference/reference-content/supported-models/) documentation.
     </Message>
-9. Review the summary of your import, which includes:
+10. Review the summary of your import, which includes:
     - Context size by node type.
     - Quantization options.
     - Estimated cost.

pages/managed-inference/how-to/manage-allowed-ips.mdx

@@ -28,13 +28,14 @@ Allowed IPs restrict the IPs allowed to access your Managed Inference endpoints.
 ## How to allow an IP address to connect to a deployment
 
 1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
-2. Click a deployment name or <Icon name="more" /> > **More info** to access the deployment dashboard.
-3. Click the **Security** tab and navigate to the **Allowed IPs** section. A list of your allowed IP addresses displays.
-4. Click **Add allowed IP**. The IP can be a single IP or an IP block.
+2. From the drop-down menu, select the geographical region you want to manage.
+3. Click a deployment name or <Icon name="more" /> > **More info** to access the deployment dashboard.
+4. Click the **Security** tab and navigate to the **Allowed IPs** section. A list of your allowed IP addresses displays.
+5. Click **Add allowed IP**. The IP can be a single IP or an IP block.
     <Message type="note">
       The IP must be specified in CIDR format, i.e. `198.51.100.135/32` for a single IP or `198.51.100.0/24` for an IP block.
     </Message>
-5. Enter a single IP address or a subnetwork.
+6. Enter a single IP address or a subnetwork.
     <Message type="note">
       To restore initial settings and allow connections from all IPs, delete all allowed IPs from the list.
     </Message>

pages/managed-inference/how-to/managed-inference-with-private-network.mdx

@@ -29,25 +29,27 @@ Using a Private Network for communications between your Instances hosting your a
 ### Attaching a Private Network during deployment setup
 
 1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
-2. Navigate to the **Deployments** section and click **Create New Deployment**. The setup wizard displays.
-3. During the [setup process](/managed-inference/how-to/create-deployment/), you access the **Networking** section.
-4. You will be asked to **attach a Private Network**. Two options are available:
+2. From the drop-down menu, select the geographical region you want to manage.
+3. Navigate to the **Deployments** section and click **Create New Deployment**. The setup wizard displays.
+4. During the [setup process](/managed-inference/how-to/create-deployment/), you access the **Networking** section.
+5. You will be asked to **attach a Private Network**. Two options are available:
     - **Attach an existing Private Network**: Select from the list of available networks.
     - **Add a new Private Network**: Choose this option if you need to create a new network.
-5. **Confirm your selection** and complete the deployment setup process.
+6. **Confirm your selection** and complete the deployment setup process.
 
 ### Attaching a Private Network to an existing deployment
 
 1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
-2. Click a deployment name or <Icon name="more" /> > **More info** to access the deployment dashboard.
-3. Go to the **Overview** tab and locate the **Endpoints** section.
-4. Click **Attach Private Network**. Two options are available:
+2. From the drop-down menu, select the geographical region you want to manage.
+3. Click a deployment name or <Icon name="more" /> > **More info** to access the deployment dashboard.
+4. Go to the **Overview** tab and locate the **Endpoints** section.
+5. Click **Attach Private Network**. Two options are available:
     - **Attach an existing Private Network**: Select from the list of available networks.
     - **Add a new Private Network**: Choose this option if you need to create a new network.
     <Message type="tip">
       Alternatively, you can access the **Security tab** and attach a network from the **Private Network** section.
     </Message>
-5. **Save your changes** to apply the new network configuration.
+6. **Save your changes** to apply the new network configuration.
 
 ### Verifying the Private Network connection
 

pages/managed-inference/how-to/monitor-deployment.mdx

@@ -24,8 +24,9 @@ This documentation page shows you how to monitor your Managed Inference deployme
 ## How to monitor your LLM dashboard
 
 1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
-2. Click a deployment name or <Icon name="more" /> > **More info** to access the deployment dashboard.
-3. Click the **Monitoring** tab of your deployment. The Cockpit overview displays.
-4. Click **Open Grafana metrics dashboard** to open your Cockpit's Grafana interface.
-5. Authenticate with your [Grafana credentials](/cockpit/how-to/retrieve-grafana-credentials/). The Grafana dashboard displays.
-6. Select your Managed Inference dashboard from the [list of your preconfigured dashboards](/cockpit/how-to/access-grafana-and-managed-dashboards/) to visualize your metrics.
+2. From the drop-down menu, select the geographical region you want to manage.
+3. Click a deployment name or <Icon name="more" /> > **More info** to access the deployment dashboard.
+4. Click the **Monitoring** tab of your deployment. The Cockpit overview displays.
+5. Click **Open Grafana metrics dashboard** to open your Cockpit's Grafana interface.
+6. Authenticate with your [Grafana credentials](/cockpit/how-to/retrieve-grafana-credentials/). The Grafana dashboard displays.
+7. Select your Managed Inference dashboard from the [list of your preconfigured dashboards](/cockpit/how-to/access-grafana-and-managed-dashboards/) to visualize your metrics.

pages/managed-inference/quickstart.mdx

@@ -32,8 +32,9 @@ Here are some of the key features of Scaleway Managed Inference:
 ## How to create a Managed Inference deployment
 
 1. Navigate to the **AI** section of the [Scaleway console](https://console.scaleway.com/), and select **Managed Inference** from the side menu to access the Managed Inference dashboard.
-2. Click **Create deployment** to launch the deployment creation wizard.
-3. Provide the necessary information:
+2. From the drop-down menu, select the geographical region where you want to create your deployment.
+3. Click **Create deployment** to launch the deployment creation wizard.
+4. Provide the necessary information:
     - Select the desired model and the quantization to use for your deployment [from the available options](/managed-inference/reference-content/).
         <Message type="important">
           Scaleway Managed Inference allows you to deploy various AI models, either from the Scaleway catalog or by importing a custom model. For detailed information about supported models, visit our [Supported models in Managed Inference](/managed-inference/reference-content/supported-models/) documentation.
@@ -43,24 +44,25 @@ Here are some of the key features of Scaleway Managed Inference:
         </Message>
     - Choose the geographical **region** for the deployment.
     - Specify the GPU Instance type to be used with your deployment.
-4. Enter a **name** for the deployment, along with optional tags to aid in organization.
-5. Configure the **network** settings for the deployment:
+5. Enter a **name** for the deployment, along with optional tags to aid in organization.
+6. Configure the **network** settings for the deployment:
     - Enable **Private Network** for secure communication and restricted availability within Private Networks. Choose an existing Private Network from the drop-down list, or create a new one.
     - Enable **Public Network** to access resources via the public Internet. API key protection is enabled by default.
     <Message type="important">
       - Enabling both private and public networks will result in two distinct endpoints (public and private) for your deployment.
       - Deployments must have at least one endpoint, either public or private.
     </Message>
-6. Click **Create deployment** to launch the deployment process. Once the deployment is ready, it will be listed among your deployments.
+7. Click **Create deployment** to launch the deployment process. Once the deployment is ready, it will be listed among your deployments.
 
 ## How to access a Managed Inference deployment
 
 Managed Inference deployments have authentication enabled by default. As such, your endpoints expect a secret key generated with Scaleway's Identity and Access Management service (IAM) for authentication.
 
 1. Click **Managed Inference** in the **AI** section of the side menu. The Managed Inference dashboard displays.
-2. Click <Icon name="more" /> next to the deployment you want to edit. The deployment dashboard displays.
-3. Click **Generate key** in the **Deployment connection** section of the dashboard. The token creation wizard displays.
-4. Fill in the [required information for API key creation](/iam/how-to/create-api-keys/) and click **Generate API key**.
+2. From the drop-down menu, select the geographical region where you want to manage.
+3. Click <Icon name="more" /> next to the deployment you want to edit. The deployment dashboard displays.
+4. Click **Generate key** in the **Deployment connection** section of the dashboard. The token creation wizard displays.
+5. Fill in the [required information for API key creation](/iam/how-to/create-api-keys/) and click **Generate API key**.
 
 <Message type="tip">
   You have full control over authentication from the **Security** tab of your deployment. Authentication is enabled by default.
@@ -69,8 +71,9 @@ Managed Inference deployments have authentication enabled by default. As such, y
 ## How to interact with Managed Inference
 
 1. Click **Managed Inference** in the **AI** section of the side menu. The Managed Inference dashboard displays.
-2. Click <Icon name="more" /> next to the deployment you want to edit. The deployment dashboard displays.
-3. Click the **Inference** tab. Code examples in various environments display. Copy and paste them into your code editor or terminal.
+2. From the drop-down menu, select the geographical region where you want to manage.
+3. Click <Icon name="more" /> next to the deployment you want to edit. The deployment dashboard displays.
+4. Click the **Inference** tab. Code examples in various environments display. Copy and paste them into your code editor or terminal.
 
 <Message type="note">
   Prompt structure may vary from one model to another. Refer to the specific instructions for use in our [dedicated documentation](/managed-inference/reference-content/).
@@ -79,10 +82,11 @@ Managed Inference deployments have authentication enabled by default. As such, y
 ## How to delete a deployment
 
 1. Click **Managed Inference** in the **AI** section of the [Scaleway console](https://console.scaleway.com) side menu. A list of your deployments displays.
-2. Choose a deployment either by clicking its name or selecting **More info** from the drop-down menu represented by the icon <Icon name="more" /> to access the deployment dashboard.
-3. Click the **Settings** tab of your deployment to display additional settings.
-4. Click **Delete deployment**.
-5. Type **DELETE** to confirm and click **Delete deployment** to delete your deployment.
+2. From the drop-down menu, select the geographical region where you want to create your deployment.
+3. Choose a deployment either by clicking its name or selecting **More info** from the drop-down menu represented by the icon <Icon name="more" /> to access the deployment dashboard.
+4. Click the **Settings** tab of your deployment to display additional settings.
+5. Click **Delete deployment**.
+6. Type **DELETE** to confirm and click **Delete deployment** to delete your deployment.
 
 <Message type="important">
   Deleting a deployment is a permanent action, and will erase all its associated configuration and resources.