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[CI/Build] drop support for Python 3.8 EOL (#8464)

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Signed-off-by: Aaron Pham <contact@aarnphm.xyz>
This commit is contained in:
Aaron Pham
2024-11-06 02:11:55 -05:00
committed by GitHub
parent 4be3a45158
commit 21063c11c7
115 changed files with 239 additions and 321 deletions
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10
docs/source/getting_started/installation.rst
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@@ -10,7 +10,7 @@ Requirements
============
* OS: Linux
* Python: 3.8 - 3.12
* Python: 3.9 -- 3.12
* GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, H100, etc.)
Install released versions
@@ -148,7 +148,7 @@ If you want to modify C++ or CUDA code, you'll need to build vLLM from source. T
.. tip::
Building from source requires a lot of compilation. If you are building from source repeatedly, it's more efficient to cache the compilation results.
For example, you can install `ccache <https://github.com/ccache/ccache>`_ using ``conda install ccache`` or ``apt install ccache`` .
For example, you can install `ccache <https://github.com/ccache/ccache>`_ using ``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, subsequent builds will be much faster.
@@ -181,8 +181,8 @@ to be run simultaneously, via the environment variable ``MAX_JOBS``. For example
$ export MAX_JOBS=6
$ pip install -e .
This is especially useful when you are building on less powerful machines. For example, when you use WSL it only `assigns 50% of the total memory by default <https://learn.microsoft.com/en-us/windows/wsl/wsl-config#main-wsl-settings>`_, so using ``export MAX_JOBS=1`` can avoid compiling multiple files simultaneously and running out of memory.
A side effect is a much slower build process.
This is especially useful when you are building on less powerful machines. For example, when you use WSL it only `assigns 50% of the total memory by default <https://learn.microsoft.com/en-us/windows/wsl/wsl-config#main-wsl-settings>`_, so using ``export MAX_JOBS=1`` can avoid compiling multiple files simultaneously and running out of memory.
A side effect is a much slower build process.
Additionally, if you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.
@@ -209,7 +209,7 @@ Here is a sanity check to verify that the CUDA Toolkit is correctly installed:
Unsupported OS build
--------------------
vLLM can fully run only on Linux but for development purposes, you can still build it on other systems (for example, macOS), allowing for imports and a more convenient development environment. The binaries will not be compiled and won't work on non-Linux systems.
vLLM can fully run only on Linux but for development purposes, you can still build it on other systems (for example, macOS), allowing for imports and a more convenient development environment. The binaries will not be compiled and won't work on non-Linux systems.
Simply disable the ``VLLM_TARGET_DEVICE`` environment variable before installing: