[doc] recommend pip instead of conda (vllm-project#8446)

docs/source/getting_started/installation.rst

@@ -26,6 +26,10 @@ You can install vLLM using pip:
  $ # Install vLLM with CUDA 12.1.
  $ pip install vllm
 
+.. note::
+
+ Although we recommend using ``conda`` to create and manage Python environments, it is highly recommended to use ``pip`` to install vLLM. This is because ``pip`` can install ``torch`` with separate library packages like ``NCCL``, while ``conda`` installs ``torch`` with statically linked ``NCCL``. This can cause issues when vLLM tries to use ``NCCL``. See `this issue <https://github.com/vllm-project/vllm/issues/8420>`_ for more details.
+
 .. note::
 
  As of now, vLLM's binaries are compiled with CUDA 12.1 and public PyTorch release versions by default.
@@ -34,7 +38,7 @@ You can install vLLM using pip:
  .. code-block:: console
 
  $ # Install vLLM with CUDA 11.8.
- $ export VLLM_VERSION=0.4.0
+ $ export VLLM_VERSION=0.6.1.post1
  $ export PYTHON_VERSION=310
  $ pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSION}/vllm-${VLLM_VERSION}+cu118-cp${PYTHON_VERSION}-cp${PYTHON_VERSION}-manylinux1_x86_64.whl --extra-index-url https://download.pytorch.org/whl/cu118
 
@@ -48,7 +52,7 @@ You can install vLLM using pip:
 
  .. code-block:: console
 
- $ export VLLM_VERSION=0.5.4 # vLLM's main branch version is currently set to latest released tag
+ $ export VLLM_VERSION=0.6.1.post1 # vLLM's main branch version is currently set to latest released tag
  $ pip install https://vllm-wheels.s3.us-west-2.amazonaws.com/nightly/vllm-${VLLM_VERSION}-cp38-abi3-manylinux1_x86_64.whl
  $ # You can also access a specific commit
  $ # export VLLM_COMMIT=...
@@ -80,11 +84,11 @@ You can also build and install vLLM from source:
 
 .. tip::
 
- Building from source requires quite a lot compilation. If you are building from source for multiple times, it is beneficial to cache the compilation results. For example, you can install `ccache <https://github.com/ccache/ccache>`_ via either `conda install ccache` or `apt install ccache` . As long as `which ccache` command can find the `ccache` binary, it will be used automatically by the build system. After the first build, the subsequent builds will be much faster.
+ Building from source requires quite a lot compilation. If you are building from source for multiple times, it is beneficial to cache the compilation results. For example, you can install `ccache <https://github.com/ccache/ccache>`_ via either ``conda install ccache`` or ``apt install ccache`` . As long as ``which ccache`` command can find the ``ccache`` binary, it will be used automatically by the build system. After the first build, the subsequent builds will be much faster.
 
 .. tip::
  To avoid your system being overloaded, you can limit the number of compilation jobs
- to be run simultaneously, via the environment variable `MAX_JOBS`. For example:
+ to be run simultaneously, via the environment variable ``MAX_JOBS``. For example:
 
  .. code-block:: console
 
@@ -99,7 +103,7 @@ You can also build and install vLLM from source:
  $ # Use `--ipc=host` to make sure the shared memory is large enough.
  $ docker run --gpus all -it --rm --ipc=host nvcr.io/nvidia/pytorch:23.10-py3
 
- If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from `the official website <https://developer.nvidia.com/cuda-toolkit-archive>`_. After installation, set the environment variable `CUDA_HOME` to the installation path of CUDA Toolkit, and make sure that the `nvcc` compiler is in your `PATH`, e.g.:
+ If you don't want to use docker, it is recommended to have a full installation of CUDA Toolkit. You can download and install it from `the official website <https://developer.nvidia.com/cuda-toolkit-archive>`_. After installation, set the environment variable ``CUDA_HOME`` to the installation path of CUDA Toolkit, and make sure that the ``nvcc`` compiler is in your ``PATH``, e.g.:
 
  .. code-block:: console