From f3823ae0d1486ef4c728a26e1144bbc09a4813cf Mon Sep 17 00:00:00 2001 From: Rachel Clune Date: Thu, 22 Oct 2026 12:52:55 -0700 Subject: [PATCH] Require pip 26.2.2+ and refresh CA cert packages Older pip releases still trust the withdrawn emSign root CA certificate, so require a newer pip and updated certificate packages. - Upgrade pip to >=26.2.2 in the documentation workflow before installing Sphinx. - Document the pip requirement and add an explicit pip upgrade step in installation.md. - Unpin ca-certificates and set certifi>=2026.10.19 and pip>=26.2.2 in se3_flow_matching/fm.yml. --- .github/workflows/documentation_workflow.yml | 1 + doc/source/installation.md | 9 +++++++++ se3_flow_matching/fm.yml | 6 +++--- 3 files changed, 13 insertions(+), 3 deletions(-) diff --git a/.github/workflows/documentation_workflow.yml b/.github/workflows/documentation_workflow.yml index 9ceef53..998e69d 100644 --- a/.github/workflows/documentation_workflow.yml +++ b/.github/workflows/documentation_workflow.yml @@ -13,6 +13,7 @@ jobs: - uses: actions/setup-python@v5 - name: Install dependencies run: | + python -m pip install --upgrade "pip>=26.2.2" pip install sphinx sphinx_mdinclude furo sphinx-copybutton #sphinx-new-tab-link - name: Sphinx build run: | diff --git a/doc/source/installation.md b/doc/source/installation.md index e2f3ad8..bf0efa6 100644 --- a/doc/source/installation.md +++ b/doc/source/installation.md @@ -47,6 +47,10 @@ You can find the prepared environment files in the `envs` directory - `cuda124_env.yml` - This is appropriate for systems able to run CUDA 12.4 and PyTorch 2.4.0 - This uses requirements_cuda124.txt to install dependencies via `pip` +> Note: Use pip 26.2.2 or newer. Older pip releases still trust the withdrawn emSign root CA certificate. +> The environment files do not pin `pip`, so if conda installs an older version, create the environment +> and install the pip dependencies separately as described in step 1 below. + If you have trouble with these files but they *should* work based on your system specifications here are a few things to try: 1. Separate the creation of the environment and the installation of dependencies via pip: 1. Remove the last two lines from the above `.yml` files @@ -54,6 +58,7 @@ If you have trouble with these files but they *should* work based on your system ``` conda env create -f cuda121_env.yml conda activate rfd2_env + python -m pip install --upgrade pip # pip 26.2.2 or newer pip install -r requirements_cuda121.txt ``` This will force the dependencies you want installed by CUDA to be installed before pip is used. @@ -89,6 +94,10 @@ For some useful troubleshooting tips, see the [Troubleshooting](#install_trouble > **REMEMBER:** You will need to change your CUDA version based on what is available on your system. This will need to be changed in the > NVIDIA channel, the dglteam channel, the pytorch version, and the pytorch-cuda version. +1. Upgrade pip to version 26.2.2 or newer: + ``` + python -m pip install --upgrade pip + ``` 1. Use pip to install several Python libraries: ``` pip install \ diff --git a/se3_flow_matching/fm.yml b/se3_flow_matching/fm.yml index 9b8c4fa..d10cdb3 100644 --- a/se3_flow_matching/fm.yml +++ b/se3_flow_matching/fm.yml @@ -30,10 +30,10 @@ dependencies: - bzip2=1.0.8=h7f98852_4 - c-ares=1.19.1=hd590300_0 - c-blosc2=2.10.2=hb4ffafa_0 - - ca-certificates=2023.7.22=hbcca054_0 + - ca-certificates - cached-property=1.5.2=hd8ed1ab_1 - cached_property=1.5.2=pyha770c72_1 - - certifi=2023.7.22=pyhd8ed1ab_0 + - certifi>=2026.10.19 - cffi=1.15.1=py310h255011f_3 - charset-normalizer=3.2.0=pyhd8ed1ab_0 - comm=0.1.4=pyhd8ed1ab_0 @@ -188,7 +188,7 @@ dependencies: - pexpect=4.8.0=pyh1a96a4e_2 - pickleshare=0.7.5=py_1003 - pillow=10.0.0=py310h582fbeb_0 - - pip=23.2.1=pyhd8ed1ab_0 + - pip>=26.2.2 - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_0 - platformdirs=3.10.0=pyhd8ed1ab_0 - pooch=1.7.0=pyha770c72_3