Add quickstart section to READMEs

README_GAUDI.md

@@ -4,7 +4,7 @@ This README provides instructions on running vLLM with Intel Gaudi devices.
 
 # Requirements and Installation
 
-Please follow the instructions provided in the [Gaudi Installation Guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html) to set up the environment. To achieve the best performance, please follow the methods outlined in the [Optimizing Training Platform Guide](https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Optimization_in_Training_Platform.html).
+Please follow the instructions provided in the [Gaudi Installation Guide](https://docs.habana.ai/en/latest/Installation_Guide/index.html) to set up the execution environment. To achieve the best performance, please follow the methods outlined in the [Optimizing Training Platform Guide](https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Optimization_in_Training_Platform.html).
 
 ## Requirements
 
@@ -13,18 +13,31 @@ Please follow the instructions provided in the [Gaudi Installation Guide](https:
 - Intel Gaudi accelerator
 - Intel Gaudi software version 1.18.0
 
+## Quick start using Dockerfile
+```
+$ docker build -f Dockerfile.hpu -t vllm-hpu-env  .
+$ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --rm vllm-hpu-env
+```
+
+> [!TIP]
+> If you're facing the following error: `docker: Error response from daemon: Unknown runtime specified habana.`, please refer to "Install Using Containers" section of [Intel Gaudi Software Stack and Driver Installation](https://docs.habana.ai/en/v1.18.0/Installation_Guide/Bare_Metal_Fresh_OS.html). Make sure you have `habana-container-runtime` package installed and that `habana` container runtime is registered correctly.
+
+
+## Build from source
+
+### Environment verification
 To verify that the Intel Gaudi software was correctly installed, run:
 
 ```{.console}
 $ hl-smi # verify that hl-smi is in your PATH and each Gaudi accelerator is visible
-$ apt list --installed | grep habana # verify that habanalabs-firmware-tools, habanalabs-graph, habanalabs-rdma-core and habanalabs-thunk are installed
+$ apt list --installed | grep habana # verify that habanalabs-firmware-tools, habanalabs-graph, habanalabs-rdma-core, habanalabs-thunk and habanalabs-container-runtime are installed
 $ pip list | grep habana # verify that habana-torch-plugin, habana-torch-dataloader, habana-pyhlml and habana-media-loader are installed
 $ pip list | grep neural # verify that neural-compressor is installed
 ```
 
 Refer to [Intel Gaudi Software Stack Verification](https://docs.habana.ai/en/latest/Installation_Guide/SW_Verification.html#platform-upgrade) for more details.
 
-## Run Docker Image
+### Run Docker Image
 
 It is highly recommended to use the latest Docker image from Intel Gaudi vault. Refer to the [Intel Gaudi documentation](https://docs.habana.ai/en/latest/Installation_Guide/Bare_Metal_Fresh_OS.html#pull-prebuilt-containers) for more details.
 
@@ -35,7 +48,7 @@ $ docker pull vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch
 $ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --ipc=host vault.habana.ai/gaudi-docker/1.18.0/ubuntu22.04/habanalabs/pytorch-installer-2.4.0:latest
 ```
 
-## Build and Install vLLM
+### Build and Install vLLM-fork
 
 Currently, the latest features and performance optimizations are developed in Gaudi's [vLLM-fork](https://github.com/HabanaAI/vllm-fork) and we periodically upstream them to vLLM main repo. To install latest [HabanaAI/vLLM-fork](https://github.com/HabanaAI/vllm-fork), run the following:
 

docs/source/getting_started/gaudi-installation.rst

@@ -8,8 +8,8 @@ Requirements and Installation
 
 Please follow the instructions provided in the `Gaudi Installation
 Guide <https://docs.habana.ai/en/latest/Installation_Guide/index.html>`__
-to set up the environment. To achieve the best performance, please
-follow the methods outlined in the `Optimizing Training Platform
+to set up the execution environment. To achieve the best performance,
+please follow the methods outlined in the `Optimizing Training Platform
 Guide <https://docs.habana.ai/en/latest/PyTorch/Model_Optimization_PyTorch/Optimization_in_Training_Platform.html>`__.
 
 Requirements
@@ -20,12 +20,31 @@ Requirements
 -  Intel Gaudi accelerator
 -  Intel Gaudi software version 1.18.0
 
+
+Quick start using Dockerfile
+============================
+.. code:: console
+
+   $ docker build -f Dockerfile.hpu -t vllm-hpu-env  .
+   $ docker run -it --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e OMPI_MCA_btl_vader_single_copy_mechanism=none --cap-add=sys_nice --net=host --rm vllm-hpu-env
+
+
+.. tip::
+   If you're observing the following error: ``docker: Error response from daemon: Unknown runtime specified habana.``, please refer to "Install Using Containers" section of `Intel Gaudi Software Stack and Driver Installation <https://docs.habana.ai/en/v1.18.0/Installation_Guide/Bare_Metal_Fresh_OS.html`__. Make sure you have ``habana-container-runtime`` package installed and that ```habana`` container runtime is registered.
+
+
+Build from source
+=================
+
+Environment verification
+------------------------
+
 To verify that the Intel Gaudi software was correctly installed, run:
 
 .. code:: console
 
    $ hl-smi # verify that hl-smi is in your PATH and each Gaudi accelerator is visible
-   $ apt list --installed | grep habana # verify that habanalabs-firmware-tools, habanalabs-graph, habanalabs-rdma-core and habanalabs-thunk are installed
+   $ apt list --installed | grep habana # verify that habanalabs-firmware-tools, habanalabs-graph, habanalabs-rdma-core, habanalabs-thunk and habanalabs-container-runtime are installed
    $ pip list | grep habana # verify that habana-torch-plugin, habana-torch-dataloader, habana-pyhlml and habana-media-loader are installed
    $ pip list | grep neural # verify that neural_compressor is installed
 
@@ -210,7 +229,7 @@ As an example, if a request of 3 sequences, with max sequence length of 412 come
    Bucketing is transparent to a client - padding in sequence length dimension is never returned to the client, and padding in batch dimension does not create new requests.
 
 Warmup
-------------
+------
 
 Warmup is an optional, but highly recommended step occurring before vLLM server starts listening. It executes a forward pass for each bucket with dummy data. The goal is to pre-compile all graphs and not incur any graph compilation overheads within bucket boundaries during server runtime. Each warmup step is logged during vLLM startup: