Removed jetson-specific docker composes

sf-docker/README.md

@@ -1,8 +1,6 @@
 # SmartFace on Docker
 
-SmartFace docker images provide an easy way of deploying and scaling SmartFace with all the benefits of containerization. SmartFace platform is distributed as a number of linux docker images, some of which are specific for [Nvidia Jetson](https://developer.nvidia.com/embedded/jetson-developer-kits) platform.
-
-Note: _Supported Nvidia Jetson versions are Xavier NX and AGX._
+SmartFace docker images provide an easy way of deploying and scaling SmartFace with all the benefits of containerization. SmartFace platform is distributed as a number of linux docker images. Images are multiplatform and support both amd64 and arm64 architectures.
 
 # Deployment
 
@@ -16,7 +14,7 @@ Before deploying SF, you will need:
 
 In order to run SmartFace, you need a valid license.
 
-- Identify hardware id (hwid) for your machine with command `docker run registry.gitlab.com/innovatrics/smartface/license-manager:3.2.7`. For nvidia jetson device use command `docker run --privileged registry.gitlab.com/innovatrics/smartface/license-manager:3.2.7`. This process work for native linux, for `WSL2` eg. linux containers on Windows you need special license for which you need to contact our sales.
+- Identify hardware id (hwid) for your machine with command `docker run registry.gitlab.com/innovatrics/smartface/license-manager:3.2.7`. This process works for native linux, for `WSL2` eg. linux containers on Windows you need special license for which you need to contact our sales.
 - Obtain license for your hwid from our Customer Portal https://customerportal.innovatrics.com/
 - Copy the license file `iengine.lic` to the directory where `docker-compose.yml` is located
 
@@ -25,19 +23,16 @@ In order to run SmartFace, you need a valid license.
 To get up and running as fast as possible, these samples are available:
 - [`all-in-one`](./all-in-one/) - a All-in-One setup known from previous version. Contains all available services.
 - [`LFIS`](./LFIS/) - Lightweight Facial Identification Service deployment sample
-- [`nvidia-jetson`](./nvidia-jetson/) - launch demo deployment on [Nvidia Jetson](https://developer.nvidia.com/embedded/jetson-developer-kits) platform
-
-Note: _jetson docker containers need to be run in privileged mode. This is because we need specific system files available in the container to properly check license usage._
 
 # GPU acceleration
 
-Some services can benefit from GPU acceleration, which can be enabled in docker compose file, but also some prerequisites needs to be met on host machine.
+Note: _Gpu acceleration is supported only on amd64 platform with NVIDIA GPU_
 
-Please note that GPU acceleration is supported only on NVIDIA GPU.
+Some services can benefit from GPU acceleration, which can be enabled in docker compose file, but also some prerequisites needs to be met on host machine.
 
 To use GPU acceleration, you will need following on the docker host machine:
 
-- Nvidia GPU compatible with Cuda 11.6
+- Nvidia GPU compatible with Cuda 11.8
 - Nvidia driver of version >=510.47.03
 - Nvidia container toolkit https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#docker
 
@@ -46,7 +41,14 @@ When using the nvidia docker runtime SmartFace camera processes need gstreamer p
 
 Other services which could use GPU needs also uncomment environment variable `Gpu__GpuEnabled=true`. This is necessary for extractor, detector, pedestrian-detector and liveness service.
 
-For using specific neural networks runtime it is possible to uncomment environment variable `Gpu__GpuNeuralRuntime` which can have values `Default`, `Cuda` or `Tensor`. The GPU needs to support these neural runtimes. When using `Tensor` you can uncomment mapping `"/var/tmp/innovatrics/tensor-rt:/var/tmp/innovatrics/tensor-rt"` to retain TensorRT cache files in the host when container is recreated. This can be helpful as generating cache files is longer operation which needs to be performed before the first run of neural network. Setting neural network runtime is possible for camera, extractor, detector, pedestrian-detector and liveness services.
+For using specific neural networks runtime it is possible to uncomment environment variable `Gpu__GpuNeuralRuntime` which can have values `Default`, `Cuda` or `Tensor`. The GPU needs to support these neural runtimes. When using `Tensor` you can uncomment mapping `"/var/tmp/innovatrics/tensor-rt:/var/tmp/innovatrics/tensor-rt"` to retain TensorRT cache files in the host when container is recreated. This can be helpful as generating cache files is longer operation which needs to be performed before the first run of neural network. Setting neural network runtime is possible for following services:
+- camera
+- extractor
+- detector
+- pedestrian-detector
+- liveness
+- object-detector
+- pedestrian-extractor
 
 ## Production use