Update acap-documentation links to developer documentation (#208)

johan-hultberg-work · Stiv-work · web-flow · commit 2bcb8f4bf033 · 2025-03-06T10:38:14.000+01:00
Co-authored-by: Stiv Abdullwahed &lt;stiva@axis.com&gt;
diff --git a/README.md b/README.md
@@ -58,7 +58,7 @@ The examples support the following architectures:
 The example applications make use of two special components:
 
 * [Docker ACAP](https://github.com/AxisCommunications/docker-acap) needs to be installed and running on the Axis device. It's required to deploy and run the example application.
-* [ACAP runtime](https://github.com/AxisCommunications/acap-runtime) is used in a few examples to get access to [APIs](https://axiscommunications.github.io/acap-documentation/docs/api/computer-vision-sdk-apis.html). ACAP Runtime is a container image and the example applications will pull it from Docker Hub when starting.
+* [ACAP runtime](https://github.com/AxisCommunications/acap-runtime) is used in a few examples to get access to [APIs](https://developer.axis.com/acap/api/computer-vision-sdk-apis). ACAP Runtime is a container image and the example applications will pull it from Docker Hub when starting.
 
 ## Example applications
 
diff --git a/minimal-ml-inference/README.md b/minimal-ml-inference/README.md
@@ -8,7 +8,7 @@ This example demonstrates how to create a simple Python application that perform
 
 This example composes three different container images into an application that performs an inference using a deep learning model.
 
-The first container contains the actual program built in this example. It then uses [gRPC](https://grpc.io/)/[protobuf](https://developers.google.com/protocol-buffers) to call the second container, the *inference-server*, that is used to capture images from the camera and perform the actual inference by implementing the [TensorFlow Serving API](https://github.com/tensorflow/serving). You can find more documentation on the [Machine Learning API documentation page](https://axiscommunications.github.io/acap-documentation/docs/api/computer-vision-sdk-apis.html#machine-learning-api). This example uses a containerized version of the [ACAP Runtime](https://github.com/AxisCommunications/acap-runtime#containerized-version) as the *inference-server*.
+The first container contains the actual program built in this example. It then uses [gRPC](https://grpc.io/)/[protobuf](https://developers.google.com/protocol-buffers) to call the second container, the *inference-server*, that is used to capture images from the camera and perform the actual inference by implementing the [TensorFlow Serving API](https://github.com/tensorflow/serving). You can find more documentation on the [Machine Learning API documentation page](https://developer.axis.com/acap/api/computer-vision-sdk-apis/#machine-learning-api). This example uses a containerized version of the [ACAP Runtime](https://github.com/AxisCommunications/acap-runtime#containerized-version) as the *inference-server*.
 
 Lastly, there is a third container that holds the deep learning model, which is put into a volume that is accessible by the other two images. The layout of the Docker image containing the model is shown below. The *MODEL_PATH* variable in the configuration file you're using specifies what model to use.
 
@@ -125,7 +125,7 @@ The expected output from the application is the raw predictions from the model s
 ### Hardware acceleration
 
 The `./config` folder contains configuration files with the parameters to run the inference on different camera models, also giving the possibility to use the hardware accelerator.
-To achieve the best performance we recommend using DLPU (Deep Learning Processing Unit) equipped ARTPEC-8 cameras. See [ACAP Computer Vision SDK hardware and compatibility](https://axiscommunications.github.io/acap-documentation/docs/axis-devices-and-compatibility/#acap-computer-vision-sdk-hardware-compatibility)
+To achieve the best performance we recommend using DLPU (Deep Learning Processing Unit) equipped ARTPEC-8 cameras. See [ACAP Computer Vision SDK hardware and compatibility](https://developer.axis.com/acap/axis-devices-and-compatibility/#acap-computer-vision-solution-compatibility)
 
 ## License
 
diff --git a/object-detector-python/README.md b/object-detector-python/README.md
@@ -11,7 +11,7 @@ This example is written in Python and implements the following object detection
 
 This example composes three different container images into an application that performs object detection using a deep learning model.
 
-The first container contains the actual program built in this example. It uses [OpenCV](https://opencv.org/) to capture pictures from the camera and modifies them to fit the input required by the model. It then uses [gRPC](https://grpc.io/)/[protobuf](https://developers.google.com/protocol-buffers) to call the second container, the *inference-server*, that performs the actual inference by implementing the [TensorFlow Serving API](https://github.com/tensorflow/serving). You can find more documentation on the [Machine Learning API documentation page](https://axiscommunications.github.io/acap-documentation/docs/api/computer-vision-sdk-apis.html#machine-learning-api). This example uses a containerized version of the [ACAP Runtime](https://github.com/AxisCommunications/acap-runtime#containerized-version) as the *inference-server*.
+The first container contains the actual program built in this example. It uses [OpenCV](https://opencv.org/) to capture pictures from the camera and modifies them to fit the input required by the model. It then uses [gRPC](https://grpc.io/)/[protobuf](https://developers.google.com/protocol-buffers) to call the second container, the *inference-server*, that performs the actual inference by implementing the [TensorFlow Serving API](https://github.com/tensorflow/serving). You can find more documentation on the [Machine Learning API documentation page](https://developer.axis.com/acap/api/computer-vision-sdk-apis/#machine-learning-api). This example uses a containerized version of the [ACAP Runtime](https://github.com/AxisCommunications/acap-runtime#containerized-version) as the *inference-server*.
 
 Lastly, there is a third container that holds the deep learning model, which is put into a volume that is accessible by the other two images. The layout of the Docker image containing the model is shown below. The *MODEL_PATH* variable in the configuration file you're using specifies what model to use.
 
@@ -149,7 +149,7 @@ object-detector-python_1          | car
 ### Hardware acceleration
 
 The `./config` folder contains configuration files with the parameters to run the inference on different camera models, also giving the possibility to use the hardware accelerator.
-To achieve the best performance we recommend using DLPU (Deep Learning Processing Unit) equipped ARTPEC-8 cameras. See [ACAP Computer Vision SDK hardware and compatibility](https://axiscommunications.github.io/acap-documentation/docs/axis-devices-and-compatibility/#acap-computer-vision-sdk-hardware-compatibility)
+To achieve the best performance we recommend using DLPU (Deep Learning Processing Unit) equipped ARTPEC-8 cameras. See [ACAP Computer Vision SDK hardware and compatibility](https://developer.axis.com/acap/axis-devices-and-compatibility/#acap-computer-vision-solution-compatibility)
 
 ## License
 
diff --git a/pose-estimator-with-flask/README.md b/pose-estimator-with-flask/README.md
@@ -11,7 +11,7 @@ The model [MoveNet SinglePose Lightning](https://coral.ai/models/pose-estimation
 
 This example composes three different container images into an application that performs object detection using a deep learning model.
 
-The first container contains the actual program built in this example. It then uses [gRPC](https://grpc.io/)/[protobuf](https://developers.google.com/protocol-buffers) to call the second container, the *inference-server*, that is used to capture images from the camera and perform the actual inference by implementing the [TensorFlow Serving API](https://github.com/tensorflow/serving). You can find more documentation on the [Machine Learning API documentation page](https://axiscommunications.github.io/acap-documentation/docs/api/computer-vision-sdk-apis.html#machine-learning-api). This example uses a containerized version of the [ACAP Runtime](https://github.com/AxisCommunications/acap-runtime#containerized-version) as the *inference-server*.
+The first container contains the actual program built in this example. It then uses [gRPC](https://grpc.io/)/[protobuf](https://developers.google.com/protocol-buffers) to call the second container, the *inference-server*, that is used to capture images from the camera and perform the actual inference by implementing the [TensorFlow Serving API](https://github.com/tensorflow/serving). You can find more documentation on the [Machine Learning API documentation page](https://developer.axis.com/acap/api/computer-vision-sdk-apis/#machine-learning-api). This example uses a containerized version of the [ACAP Runtime](https://github.com/AxisCommunications/acap-runtime#containerized-version) as the *inference-server*.
 Lastly, there is a third container that holds the deep learning model, which is put into a volume that is accessible by the other two images. The layout of the Docker image containing the model is shown below. The *MODEL_PATH* variable in the configuration file you're using specifies what model to use.
 
 ```text
@@ -169,7 +169,7 @@ pose-estimator_1    |  0.02048427 0.01638742 0.15568045 0.07374337 0.05735596]
 ### Hardware acceleration
 
 The `./config` folder contains configuration files with the parameters to run the inference on different camera models, also giving the possibility to use the hardware accelerator.
-To achieve the best performance we recommend using DLPU (Deep Learning Processing Unit) equipped ARTPEC-8 cameras. See [ACAP Computer Vision SDK hardware and compatibility](https://axiscommunications.github.io/acap-documentation/docs/axis-devices-and-compatibility/#acap-computer-vision-sdk-hardware-compatibility)
+To achieve the best performance we recommend using DLPU (Deep Learning Processing Unit) equipped ARTPEC-8 cameras. See [ACAP Computer Vision SDK hardware and compatibility](https://developer.axis.com/acap/axis-devices-and-compatibility/#acap-computer-vision-solution-compatibility)
 
 ## License
 
