docs: Update arc instructions, add troubleshooting (intel-retail#497)

* docs: Update arc instructions, add troubleshooting Closes: intel-retail#366, intel-retail#367 Signed-off-by: Elizabeth J Lee <[email protected]>
jim-wang-intel · Feb 12, 2024 · 9e76631 · 9e76631
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diff --git a/docs_src/hardwaresetup.md b/docs_src/hardwaresetup.md
@@ -4,17 +4,18 @@
 
 To build the Intel® Automated Self-Checkout Reference Package, you need:
 
-- [Ubuntu LTS](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview) ([22.04](https://releases.ubuntu.com/22.04/) or [20.04](https://releases.ubuntu.com/focal/))
+- [Ubuntu LTS](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview) ([22.04](https://releases.ubuntu.com/22.04/))
 - [Docker](https://docs.docker.com/engine/install/ubuntu/) (Tested on version >= 23.0.0)
 - [Docker Compose v2](https://docs.docker.com/compose/) (Required, if using docker compose feature)
 - [Git](https://git-scm.com/download/linux)
+- Make - install with `apt install make`
 
 Click the links for corresponding set up instructions.
 
 ## Hardware Dependent Installation
 
-=== "11th/12th Gen Intel® Core™ Processor"
-    1. Download [Ubuntu 20.04](https://releases.ubuntu.com/focal/) and follow these [installation steps](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview).
+=== "11th/12th Gen Intel® Core™ Processor, Intel® Arc™, Intel® Xeon® Processor"
+    1. Download [Ubuntu 22.04](https://releases.ubuntu.com/22.04/) and follow these [installation steps](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview).
 
     2. Install [Docker* Engine](https://docs.docker.com/engine/install/ubuntu/)
 
@@ -27,35 +28,8 @@ Click the links for corresponding set up instructions.
 
     3. [Set up the pipeline](./OVMS/pipelinesetup.md)
 
-=== "Intel® Xeon® Processor"
-    1. Download [Ubuntu 22.04](https://releases.ubuntu.com/22.04/) and follow these [installation steps](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview).
-
-    2. Install [Docker Engine](https://docs.docker.com/engine/install/ubuntu/)
-
-    3. [**Optional**] Install [Docker Compose v2](https://docs.docker.com/compose/), if using the docker compose feature
-
-    4. Install [Git](https://git-scm.com/download/linux)
-
-    3. [Set up the pipeline](./OVMS/pipelinesetup.md)
-
 === "Intel® Data Center GPU Flex 140/170"
-    1. Download [Ubuntu 22.04](https://releases.ubuntu.com/22.04/) and follow these [installation steps](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview).
-
-    2. Update the [Kernel](https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-jammy-dc.html)
-
-        !!! Warning
-            After the kernel is updated, `apt-get install` might not work due to the unsupported kernel versions that were installed. To resolve this issue, do the following:
-           
-            1. Find all the installed kernels
-       
-                ```bash
-                    dpkg --list | grep -E -i --color 'linux-image|linux-headers'
-                ```
-            2. Then remove the unsupported kernels. The example below will remove the installed kernel 5.19:
-       
-                ```bash
-                    sudo apt-get purge -f 'linux--5.19'
-                ```
+    1. Download [Ubuntu 22.04](https://releases.ubuntu.com/22.04/) and follow these [installation steps](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).
 
     3. Install [Docker Engine](https://docs.docker.com/engine/install/ubuntu/)
 
@@ -64,31 +38,3 @@ Click the links for corresponding set up instructions.
     4. Install [Git](https://git-scm.com/download/linux)
 
     4. [Set up the pipeline](./OVMS/pipelinesetup.md)
-
-=== "Intel® Arc™"
-
-    1. Download [Ubuntu 20.04](https://releases.ubuntu.com/focal/) and follow these [installation steps](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview).
-
-    2. Update the [Kernel](https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-focal-arc.html)
-
-        !!! Warning
-            After the kernel is updated, `apt-get install` might not work due to the unsupported kernel versions that were installed. To resolve this issue, do the following:
-
-            1. Find all the installed kernels
-
-                ```bash
-                    dpkg --list | grep -E -i --color 'linux-image|linux-headers'
-                ```
-            2. Then remove the unsupported kernels. The example below will remove the installed kernel 5.19:
-
-                ```bash
-                    sudo apt-get purge -f 'linux--5.19'
-                ```
-
-    3. Install [Docker Engine](https://docs.docker.com/engine/install/ubuntu/)
-
-    3. [**Optional**] Install [Docker Compose v2](https://docs.docker.com/compose/), if using the docker compose feature
-
-    4. Install [Git](https://git-scm.com/download/linux)
-
-    4. [Set up the pipeline](./OVMS/pipelinesetup.md)
diff --git a/docs_src/troubleshooting.md b/docs_src/troubleshooting.md
@@ -1,8 +1,8 @@
 # Troubleshooting
 
-Q: Why is the performance sometimes on CPU better than on GPU, when running pipeline benchmarking like stream density ?
+Q: Why is the performance sometimes on CPU better than on GPU, when running pipeline benchmarking like stream density?
 
-A: The performance of pipeline benchmarking strongly depends on the models.  Specifically for `yolov5s` object detection, it is recommended to use the model precision FP32 when it is running on device `GPU`.  If supported, then you can change the model precision by going to the folder `configs/opencv-ovms/models/2022` from the root of project folder and editing the `base_path` for that particular model in the `config_template.json` file.  For example, you can change the the base_path of `FP16` to `FP32` assuming the precision `FP32` of the model yolov5s is available:  
+A: The performance of pipeline benchmarking strongly depends on the models.  Specifically for `yolov5s` object detection, it is recommended to use the model precision FP32 when it is running on device `GPU`.  If supported, then you can change the model precision by going to the folder `configs/opencv-ovms/models/2022` from the root of project folder and editing the `base_path` for that particular model in the `config_template.json` file.  For example, you can change the base_path of `FP16` to `FP32` assuming the precision `FP32` of the model yolov5s is available:  
 
 ```json
     ...
@@ -13,3 +13,12 @@ A: The performance of pipeline benchmarking strongly depends on the models.  Spe
     ...
     }
 ```
+
+Q: What happens if the system keeps crashing when building the `dlstreamer-realsense` image?
+
+A: Some systems may run into issues with memory when building the `dlstreamer-realsense` image. In the `Dockerfile.dlstreamer` file, change the make command to not use the `-j` threading option.
+
+```diff
+- make -j"$(cat < /proc/cpuinfo |grep -c proc)" &&
++ make &&
+```