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docs/source/getting_started/installation.rst
@@ -85,13 +85,3 @@ You can also build and install vLLM from source:
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$ nvcc --version # verify that nvcc is in your PATH
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$ ${CUDA_HOME}/bin/nvcc --version # verify that nvcc is in your CUDA_HOME
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-
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-.. note::
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- If you are developing the C++ backend of vLLM, consider building vLLM with
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- .. code-block:: console
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- $ python setup.py develop
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- since it will give you incremental builds. The downside is that this method
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- is `deprecated by setuptools <https://github.com/pypa/setuptools/issues/917>`_.
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