docs: sycl build in docker

README_sycl.md

@@ -44,7 +44,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 |Intel Data Center Flex Series| Support| Flex 170|
 |Intel Arc Series| Support| Arc 770|
 |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
-|Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7|
+|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7|
 
 
 ## Linux
@@ -59,7 +59,7 @@ Note: for iGPU, please install the client GPU driver.
 
 b. Add user to group: video, render.
 
-```
+```sh
 sudo usermod -aG render username
 sudo usermod -aG video username
 ```
@@ -68,7 +68,7 @@ Note: re-login to enable it.
 
 c. Check
 
-```
+```sh
 sudo apt install clinfo
 sudo clinfo -l
 ```
@@ -86,6 +86,7 @@ Platform #0: Intel(R) OpenCL HD Graphics
 
 2. Install Intel® oneAPI Base toolkit.
 
+Note: You can skip step this if you want to build inside docker container
 
 a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
 
@@ -95,7 +96,7 @@ Following guide use the default folder as example. If you use other folder, plea
 
 b. Check
 
-```
+```sh
 source /opt/intel/oneapi/setvars.sh
 
 sycl-ls
@@ -114,35 +115,48 @@ Output (example):
 
 2. Build locally:
 
+Note:
+- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
+- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
+
+Method using **docker**:
+
+```sh
+# For F16:
+#docker build -t llama-cpp-sycl:latest --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
+
+# Or, for F32:
+docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile .
+
+# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example
 ```
+
+or, without docker:
+
+```sh
 mkdir -p build
 cd build
 source /opt/intel/oneapi/setvars.sh
 
-#for FP16
+# For FP16:
-#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference
+#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
 
-#for FP32
+# Or, for FP32:
 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
-#build example/main only
+# Build example/main only
 #cmake --build . --config Release --target main
 
-#build all binary
+# Or, build all binary
 cmake --build . --config Release -v
-
 ```
 
 or
 
-```
+```sh
 ./examples/sycl/build.sh
 ```
 
-Note:
-
-- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
-
 ### Run
 
 1. Put model file to folder **models**
@@ -155,12 +169,14 @@ source /opt/intel/oneapi/setvars.sh
 
 3. List device ID
 
+(Skip this step if you're using docker)
+
 Run without parameter:
 
-```
+```sh
 ./build/bin/ls-sycl-device
 
-or
+# or running the "main" executable and look at the output log:
 
 ./build/bin/main
 ```
@@ -189,12 +205,24 @@ found 4 SYCL devices:
 
 Set device ID = 0 by **GGML_SYCL_DEVICE=0**
 
+Using docker image built from step 2:
+
+```sh
+# Firstly, find all the DRI cards:
+ls -la /dev/dri
+
+# Then, pick the card that you want to use. For example "/dev/dri/card1"
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
 ```
+
+or, without docker:
+
+```sh
 GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
 ```
 or run by script:
 
-```
+```sh
 ./examples/sycl/run_llama2.sh
 ```