A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 15, 09:45 UTC
[Chore] astral's ty (#18116)
Signed-off-by: Aaron Pham <contact@aarnphm.xyz>
Aaron Pham committed
May 15, 2025 at 01:00 UTC
afe3236e90d6eb2f3aa608b3453d82fc42a02c38
2 files changed
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-2
docs/source/getting_started/quickstart.md
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@@ -19,8 +19,8 @@ If you are using NVIDIA GPUs, you can install vLLM using [pip](https://pypi.org/
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It's recommended to use [uv](https://docs.astral.sh/uv/), a very fast Python environment manager, to create and manage Python environments. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment and install vLLM using the following commands:
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```console
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-uv venv myenv --python 3.12 --seed
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-source myenv/bin/activate
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+uv venv --python 3.12 --seed
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+source .venv/bin/activate
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uv pip install vllm
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```
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pyproject.toml
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@@ -170,3 +170,9 @@ plugins.md013.enabled = false # line-length
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plugins.md041.enabled = false # first-line-h1
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plugins.md033.enabled = false # inline-html
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plugins.md024.allow_different_nesting = true # no-duplicate-headers
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+
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+[tool.ty]
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+respect-ignore-files = true
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+
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+[tool.ty.environment]
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+python = "./.venv"