mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
10947c83b3
Uses a direct `pip install` instead of creating a conda env to make pip installs incremental to the cluster environment. Separates the handling of `pip` and `conda` dependencies. The new `pip` approach still works if only the base Ray is installed on the cluster and the user specifies libraries like "ray[serve]" in the `pip` field. The mechanism is as follows: - We don't actually want to reinstall ray via pip, since this could lead to version mismatch issues. Instead, we want to use the Ray that's already installed in the cluster. - So if "ray" was included by the user in the pip list, remove it - If a library "ray[serve]" or "ray[tune, rllib]" was included in the pip list, remove it and replace it by its dependencies (e.g. "uvicorn", "requests", ..) Co-authored-by: architkulkarni <arkulkar@gmail.com> Co-authored-by: architkulkarni <architkulkarni@users.noreply.github.com> |
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| .. | ||
| client | ||
| modules | ||
| tests | ||
| __init__.py | ||
| agent.py | ||
| BUILD | ||
| consts.py | ||
| dashboard.py | ||
| datacenter.py | ||
| head.py | ||
| k8s_utils.py | ||
| memory_utils.py | ||
| optional_deps.py | ||
| utils.py | ||