docs: Restructure image folder and add alt for SEO (#5150)

Restructure image folder and add alt for seo Signed-off-by: Sherlock113 <[email protected]>
bentoml · Dec 25, 2024 · 749f49e · 749f49e
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diff --git a/...ce/_static/img/guides/autoscaling/hpa.png → ...static/img/bentocloud/autoscaling/hpa.png b/...ce/_static/img/guides/autoscaling/hpa.png → ...static/img/bentocloud/autoscaling/hpa.png
diff --git a/...atic/img/guides/asgi/inside-the-class.png → ...ld-with-bentoml/asgi/inside-the-class.png b/...atic/img/guides/asgi/inside-the-class.png → ...ld-with-bentoml/asgi/inside-the-class.png
diff --git a/...tic/img/guides/asgi/outside-the-class.png → ...d-with-bentoml/asgi/outside-the-class.png b/...tic/img/guides/asgi/outside-the-class.png → ...d-with-bentoml/asgi/outside-the-class.png
diff --git a/...g/guides/asgi/two-asgi-fastapi-routes.png → ...-bentoml/asgi/two-asgi-fastapi-routes.png b/...g/guides/asgi/two-asgi-fastapi-routes.png → ...-bentoml/asgi/two-asgi-fastapi-routes.png
diff --git a/...-inference/gpu-inference-architecture.png → ...-inference/gpu-inference-architecture.png b/...-inference/gpu-inference-architecture.png → ...-inference/gpu-inference-architecture.png
diff --git a/...c/img/guides/gradio/gradio-ui-bentoml.png → ...with-bentoml/gradio/gradio-ui-bentoml.png b/...c/img/guides/gradio/gradio-ui-bentoml.png → ...with-bentoml/gradio/gradio-ui-bentoml.png
diff --git a/...and-management/hf-model-on-bentocloud.png → ...and-management/hf-model-on-bentocloud.png b/...and-management/hf-model-on-bentocloud.png → ...and-management/hf-model-on-bentocloud.png
diff --git a/...servability/metrics/grafana-bentoml-1.png → ...servability/metrics/grafana-bentoml-1.png b/...servability/metrics/grafana-bentoml-1.png → ...servability/metrics/grafana-bentoml-1.png
diff --git a/...servability/metrics/grafana-bentoml-2.png → ...servability/metrics/grafana-bentoml-2.png b/...servability/metrics/grafana-bentoml-2.png → ...servability/metrics/grafana-bentoml-2.png
diff --git a/...bservability/metrics/prome-ui-bentoml.png → ...bservability/metrics/prome-ui-bentoml.png b/...bservability/metrics/prome-ui-bentoml.png → ...bservability/metrics/prome-ui-bentoml.png
diff --git a/...ity/tracing/jaeger-ui-tracing-bentoml.png → ...ity/tracing/jaeger-ui-tracing-bentoml.png b/...ity/tracing/jaeger-ui-tracing-bentoml.png → ...ity/tracing/jaeger-ui-tracing-bentoml.png
diff --git a/...ity/tracing/zipkin-ui-tracing-bentoml.png → ...ity/tracing/zipkin-ui-tracing-bentoml.png b/...ity/tracing/zipkin-ui-tracing-bentoml.png → ...ity/tracing/zipkin-ui-tracing-bentoml.png
diff --git a/...mg/guides/workers/workers-models-gpus.png → ...h-bentoml/workers/workers-models-gpus.png b/...mg/guides/workers/workers-models-gpus.png → ...h-bentoml/workers/workers-models-gpus.png
diff --git a/...ptive-batching/multi-service-batching.png → ...ptive-batching/multi-service-batching.png b/...ptive-batching/multi-service-batching.png → ...ptive-batching/multi-service-batching.png
diff --git a/...tive-batching/single-service-batching.png → ...tive-batching/single-service-batching.png b/...tive-batching/single-service-batching.png → ...tive-batching/single-service-batching.png
diff --git a/...img/get-started/quickstart/service-ui.png → ...mg/get-started/hello-world/service-ui.png b/...img/get-started/quickstart/service-ui.png → ...mg/get-started/hello-world/service-ui.png
diff --git a/...static/img/guides/tasks/task-workflow.png → ...c/img/get-started/tasks/task-workflow.png b/...static/img/guides/tasks/task-workflow.png → ...c/img/get-started/tasks/task-workflow.png
diff --git a/docs/source/_static/img/guides/deployment/deployment-replica.png b/docs/source/_static/img/guides/deployment/deployment-replica.png
diff --git a/docs/source/_static/img/guides/model-composition/model-composition-blip-2.png b/docs/source/_static/img/guides/model-composition/model-composition-blip-2.png
diff --git a/...s/adaptive-batching/batch-dim-example.png → ...ference/bentoml/sdk/batch-dim-example.png b/...s/adaptive-batching/batch-dim-example.png → ...ference/bentoml/sdk/batch-dim-example.png
diff --git a/docs/source/build-with-bentoml/asgi.rst b/docs/source/build-with-bentoml/asgi.rst
@@ -131,17 +131,20 @@ The following is a more practical example of mounting FastAPI onto the Summariza
 
 After you start the BentoML Service, which is accessible at `http://localhost:3000 <http://localhost:3000/>`_, you can find two additional endpoints ``hello-inside`` and ``hello-outside`` exposed.
 
-.. image:: ../../_static/img/guides/asgi/two-asgi-fastapi-routes.png
+.. image:: ../../_static/img/build-with-bentoml/asgi/two-asgi-fastapi-routes.png
+   :alt: Two API endpoints defined in BentoML
 
 By sending a ``GET`` request, you can receive the corresponding output from both endpoints.
 
 FastAPI route inside the Service class:
 
-.. image:: ../../_static/img/guides/asgi/inside-the-class.png
+.. image:: ../../_static/img/build-with-bentoml/asgi/inside-the-class.png
+   :alt: FastAPI route inside the BentoML Service class
 
 FastAPI route outside the Service class:
 
-.. image:: ../../_static/img/guides/asgi/outside-the-class.png
+.. image:: ../../_static/img/build-with-bentoml/asgi/outside-the-class.png
+   :alt: FastAPI route outside the BentoML Service class
 
 Quart
 ^^^^^

diff --git a/docs/source/build-with-bentoml/gpu-inference.rst b/docs/source/build-with-bentoml/gpu-inference.rst
@@ -42,9 +42,10 @@ To use a specific GPU:
 
 This image explains how different models use the GPUs assigned to them.
 
-.. image:: ../../_static/img/guides/gpu-inference/gpu-inference-architecture.png
+.. image:: ../../_static/img/build-with-bentoml/gpu-inference/gpu-inference-architecture.png
     :width: 400px
     :align: center
+    :alt: BentoML GPU inference architecture
 
 .. note::
 

diff --git a/docs/source/build-with-bentoml/gradio.rst b/docs/source/build-with-bentoml/gradio.rst
@@ -96,6 +96,7 @@ Follow the steps below to integrate Gradio with a BentoML Service.
 
         bentoml serve service:Summarization
 
-   .. image:: ../../_static/img/guides/gradio/gradio-ui-bentoml.png
+   .. image:: ../../_static/img/build-with-bentoml/gradio/gradio-ui-bentoml.png
+      :alt: Gradio UI for a BentoML Service
 
 Visit this `example <https://github.com/bentoml/BentoML/tree/main/examples>`_ to view the full demo code.
diff --git a/docs/source/build-with-bentoml/model-loading-and-management.rst b/docs/source/build-with-bentoml/model-loading-and-management.rst
@@ -83,7 +83,8 @@ BentoML provides an efficient mechanism for loading AI models to accelerate mode
 
       If you deploy the HF model to BentoCloud, you can view and verify it within your Bento on the details page. It is indicated with the HF icon. Clicking it redirects you to the model page on HF.
 
-      .. image:: ../../_static/img/guides/model-loading-and-management/hf-model-on-bentocloud.png
+      .. image:: ../../_static/img/build-with-bentoml/model-loading-and-management/hf-model-on-bentocloud.png
+         :alt: Hugging Face model marked with an icon on BentoCloud console
 
    .. tab-item:: From the Model Store or BentoCloud
 

diff --git a/docs/source/build-with-bentoml/observability/metrics.rst b/docs/source/build-with-bentoml/observability/metrics.rst
@@ -394,7 +394,8 @@ You can integrate Prometheus to scrape and visualize both default and custom met
 
         histogram_quantile(0.99, rate(bentoml_service_request_duration_seconds_bucket{endpoint="/encode"}[1m]))
 
-   .. image:: ../../_static/img/guides/observability/metrics/prome-ui-bentoml.png
+   .. image:: ../../_static/img/build-with-bentoml/observability/metrics/prome-ui-bentoml.png
+      :alt: Prometheus UI for BentoML metrics
 
 Create a Grafana dashboard
 --------------------------
@@ -424,10 +425,12 @@ Grafana is an analytics platform that allows you to create dynamic and informati
 4. Access the Grafana web UI at ``http://localhost:4000/`` (use your own port). Log in with the default credentials (``admin``/``admin``).
 5. In the Grafana search box at the top, enter ``Data sources`` and add Prometheus as an available option. In **Connection**, set the URL to the address of your running Prometheus instance, such as ``http://localhost:9090``. Save the configuration and test the connection to ensure Grafana can retrieve data from Prometheus.
 
-   .. image:: ../../_static/img/guides/observability/metrics/grafana-bentoml-1.png
+   .. image:: ../../_static/img/build-with-bentoml/observability/metrics/grafana-bentoml-1.png
+      :alt: Add Prometheus in Grafana
 
 6. With Prometheus configured as a data source, you can create a new dashboard. Start by adding a panel and selecting a metric to visualize, such as ``bentoml_service_request_duration_seconds_bucket``. Grafana offers a wide array of visualization options, from simple line graphs to more complex representations like heatmaps or gauges.
 
-   .. image:: ../../_static/img/guides/observability/metrics/grafana-bentoml-2.png
+   .. image:: ../../_static/img/build-with-bentoml/observability/metrics/grafana-bentoml-2.png
+      :alt: Grafana UI for BentoML metrics
 
    For detailed instructions on dashboard creation and customization, read the `Grafana documentation <https://grafana.com/docs/grafana/latest/dashboards/>`_.
diff --git a/docs/source/build-with-bentoml/observability/tracing.rst b/docs/source/build-with-bentoml/observability/tracing.rst
@@ -126,7 +126,8 @@ With your BentoML Service configured, `run Zipkin <https://zipkin.io/pages/quick
 
 Start your BentoML Service and send some requests to it. You can then visit the Zipkin UI at ``http://localhost:9411/`` to view the traces:
 
-.. image:: ../../_static/img/guides/observability/tracing/zipkin-ui-tracing-bentoml.png
+.. image:: ../../_static/img/build-with-bentoml/observability/tracing/zipkin-ui-tracing-bentoml.png
+   :alt: Zipkin UI for BentoML traces
 
 Jaeger
 ^^^^^^
@@ -197,7 +198,8 @@ With your BentoML Service configured, run Jaeger before starting the Service. Fo
 
 Start your BentoML Service and send some requests to it. You can then visit the Jaeger UI at ``http://localhost:16686/`` to view the traces:
 
-.. image:: ../../_static/img/guides/observability/tracing/jaeger-ui-tracing-bentoml.png
+.. image:: ../../_static/img/build-with-bentoml/observability/tracing/jaeger-ui-tracing-bentoml.png
+   :alt: Jaeger UI for BentoML traces
 
 OTLP exporter
 ^^^^^^^^^^^^^

diff --git a/docs/source/build-with-bentoml/parallelize-requests.rst b/docs/source/build-with-bentoml/parallelize-requests.rst
@@ -71,9 +71,10 @@ Here is an example:
 
 This Service dynamically determines the GPU device to use for the model by creating a ``torch.device`` object. The device ID is set by ``bentoml.server_context.worker_index - 1`` to allocate a specific GPU to each worker process. Worker 1 (``worker_index = 1``) uses GPU 0 and worker 2 (``worker_index = 2``) uses GPU 1. See the figure below for details.
 
-.. image:: ../../_static/img/guides/workers/workers-models-gpus.png
+.. image:: ../../_static/img/build-with-bentoml/workers/workers-models-gpus.png
     :width: 400px
     :align: center
+    :alt: GPUs allocated to different BentoML workers for serving models
 
 When determining which device ID to assign to each worker for tasks such as loading models onto GPUs, this 1-indexing approach means you need to subtract 1 from the ``worker_index`` to get the 0-based device ID. This is because hardware devices like GPUs are usually indexed starting from 0. For more information, see :doc:`/build-with-bentoml/gpu-inference`.
 

diff --git a/docs/source/get-started/adaptive-batching.rst b/docs/source/get-started/adaptive-batching.rst
@@ -18,15 +18,17 @@ Adaptive batching is implemented on the server side. This is advantageous as opp
 
 Specifically, there is a dispatcher within a BentoML Service that oversees collecting requests into a batch until the conditions of the batch window or batch size are met, at which point the batch is sent to the model for inference.
 
-.. image:: ../../_static/img/guides/adaptive-batching/single-service-batching.png
+.. image:: ../../_static/img/get-started/adaptive-batching/single-service-batching.png
     :width: 65%
     :align: center
+    :alt: Adaptive batching in a single BentoML Service
 
 For multiple Services, the Service responsible for running model inference (``ServiceTwo`` in the diagram below) collects requests from the intermediary Service (``ServiceOne``) and forms batches based on optimal latency.
 
-.. image:: ../../_static/img/guides/adaptive-batching/multi-service-batching.png
+.. image:: ../../_static/img/get-started/adaptive-batching/multi-service-batching.png
     :width: 100%
     :align: center
+    :alt: Adaptive batching in multiple BentoML Services
 
 .. note::
 

diff --git a/docs/source/get-started/async-task-queues.rst b/docs/source/get-started/async-task-queues.rst
@@ -18,9 +18,10 @@ Waiting synchronously for such tasks could lead to inefficiencies, with the call
 
 Here is the general workflow of using BentoML tasks:
 
-.. image:: ../../_static/img/guides/tasks/task-workflow.png
+.. image:: ../../_static/img/get-started/tasks/task-workflow.png
     :width: 400px
     :align: center
+    :alt: BentoML task workflow
 
 Define a task endpoint
 ----------------------

diff --git a/docs/source/get-started/hello-world.rst b/docs/source/get-started/hello-world.rst
@@ -131,7 +131,8 @@ Serve the model locally
 
            Visit `http://localhost:3000 <http://localhost:3000/>`_, scroll down to **Service APIs**, and click **Try it out**. In the **Request body** box, enter your prompt and click **Execute**.
 
-           .. image:: ../_static/img/get-started/quickstart/service-ui.png
+           .. image:: ../_static/img/get-started/hello-world/service-ui.png
+              :alt: BentoML hello world example Swagger UI
 
    Expected output:
 

diff --git a/docs/source/reference/bentoml/sdk.rst b/docs/source/reference/bentoml/sdk.rst
@@ -29,7 +29,8 @@ Note that when you enable batching, ``batch_dim`` can be a tuple or a single val
 
     This image illustrates the concept of ``batch_dim`` in the context of processing 2-D arrays.
 
-    .. image:: ../../_static/img/guides/adaptive-batching/batch-dim-example.png
+    .. image:: ../../_static/img/reference/bentoml/sdk/batch-dim-example.png
+       :alt: Batching dimension explanation
 
     On the left side, there are two 2-D arrays of size 5x2, represented by blue and green boxes. The arrows show two different paths that these arrays can take depending on the ``batch_dim`` configuration:
 

diff --git a/docs/source/scale-with-bentocloud/scaling/autoscaling.rst b/docs/source/scale-with-bentocloud/scaling/autoscaling.rst
@@ -51,7 +51,8 @@ For instance, consider a scenario where ``concurrency`` is set to 32 and the Ser
 
 In general, the autoscaler will scale the number of replicas based on the following formula, permitted by the ``min_replicas`` and ``max_replicas`` settings in the deployment:
 
-.. image:: ../../_static/img/guides/autoscaling/hpa.png
+.. image:: ../../_static/img/bentocloud/autoscaling/hpa.png
+   :alt: HPA algorithm
 
 Key points about concurrency: