[Recommended Conda] Installation causes dependency conflicts

[walidabualafia]

Hi all,

I can't seem to install the GPU dependencies correctly for the latest version of eman2. I keep running into this dependency conflict:

Installing collected packages: jax-cuda12-pjrt, toolz, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-nvcc-cu12, nvidia-cuda-cupti-cu12, nvidia-cuda-cccl-cu12, nvidia-cublas-cu12, numpy, jax-cuda12-plugin, nvidia-nvshmem-cu12, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, jaxlib, jax, chex, optax
  Attempting uninstall: numpy
    Found existing installation: numpy 1.26.4
    Uninstalling numpy-1.26.4:
      Successfully uninstalled numpy-1.26.4
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
numba 0.63.1 requires numpy<2.4,>=1.22, but you have numpy 2.4.1 which is incompatible.
Successfully installed chex-0.1.91 jax-0.9.0 jax-cuda12-pjrt-0.9.0 jax-cuda12-plugin-0.9.0 jaxlib-0.9.0 numpy-2.4.1 nvidia-cublas-cu12-12.9.1.4 nvidia-cuda-cccl-cu12-12.9.27 nvidia-cuda-cupti-cu12-12.9.79 nvidia-cuda-nvcc-cu12-12.9.86 nvidia-cuda-nvrtc-cu12-12.9.86 nvidia-cuda-runtime-cu12-12.9.79 nvidia-cudnn-cu12-9.18.0.77
nvidia-cufft-cu12-11.4.1.4 nvidia-cusolver-cu12-11.7.5.82 nvidia-cusparse-cu12-12.5.10.65 nvidia-nccl-cu12-2.29.2 nvidia-nvjitlink-cu12-12.9.86 nvidia-nvshmem-cu12-3.5.19 optax-0.2.6 toolz-1.1.0

Is there a specific version of numba that I can install? Alternatively, does eman2 depend on a certain version of numpy that is not <2.4,>=1.22?

FWIW the conflicting numpy was installed with Conda during the eman2 installation (conda environment creation).

Thank you!

[sludtke42]

I think your problem may be trying to install too new a version of jax. There are often problems trying to get both tensorflow (used by a lot of our older programs) and jax (used by all new development) working at the same time. tensorflow does not yet support CUDA 13, but JAX does. I have two main machines I use for testing and data processing one, using a canonical eman-dev conda dependencies has these versions of the problem tools: jax 0.6.1 pypi_0 pypi jax-cuda12-pjrt 0.6.1 pypi_0 pypi jax-cuda12-plugin 0.6.1 pypi_0 pypi jaxlib 0.6.1 pypi_0 pypi numba 0.61.2 py312h2e6246c_0 conda-forge nvidia-cuda-cupti-cu12 12.9.79 pypi_0 pypi nvidia-cuda-nvcc-cu12 12.9.86 pypi_0 pypi nvidia-cuda-runtime-cu12 12.9.79 pypi_0 pypi tensorflow 2.18.0 cuda126py312h5379a72_200 conda-forge tensorflow-base 2.18.0 cuda126py312hfb0ba9c_200 conda-forge tensorflow-estimator 2.18.0 cuda126py312hd49ae37_200 conda-forge On another machine where I'm experimenting to see if we can update to numpy 2.x and other newer tools, I unpinned some of the eman-dev versions. Here is the complete list from that machine. While I'm using this machine successfully, I'm not confident yet that all problems have been fixed. This uses Numpy 2.3: _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge _python_abi3_support 1.0 hd8ed1ab_2 conda-forge _x86_64-microarch-level 3 3_zen2 conda-forge absl-py 2.3.1 pyhd8ed1ab_0 conda-forge alsa-lib 1.2.15.3 hb03c661_0 conda-forge anyio 4.12.1 pyhcf101f3_0 conda-forge argon2-cffi 25.1.0 pyhd8ed1ab_0 conda-forge argon2-cffi-bindings 25.1.0 py312h4c3975b_2 conda-forge arrow 1.4.0 pyhcf101f3_0 conda-forge asttokens 3.0.1 pyhd8ed1ab_0 conda-forge astunparse 1.6.3 pyhd8ed1ab_3 conda-forge async-lru 2.0.5 pyh29332c3_0 conda-forge attr 2.5.2 h39aace5_0 conda-forge attrs 25.4.0 pyhcf101f3_1 conda-forge babel 2.17.0 pyhd8ed1ab_0 conda-forge backports.zstd 1.3.0 py312h90b7ffd_0 conda-forge beautifulsoup4 4.14.3 pyha770c72_0 conda-forge biopython 1.86 py312h4c3975b_0 conda-forge bleach 6.3.0 pyhcf101f3_0 conda-forge bleach-with-css 6.3.0 h5f6438b_0 conda-forge boost 1.85.0 h9cebb41_4 conda-forge brotli 1.2.0 hed03a55_1 conda-forge brotli-bin 1.2.0 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cuda-cudart-static_linux-64 12.9.79 h3f2d84a_0 conda-forge cuda-cudart_linux-64 12.9.79 h3f2d84a_0 conda-forge cuda-cupti 12.9.79 h676940d_1 conda-forge cuda-cupti-dev 12.9.79 h676940d_1 conda-forge cuda-nvcc-tools 12.9.86 he02047a_2 conda-forge cuda-nvrtc 12.9.86 hecca717_1 conda-forge cuda-nvtx 12.9.79 hecca717_1 conda-forge cuda-nvvm-tools 12.9.86 h4bc722e_2 conda-forge cuda-version 12.9 h4f385c5_3 conda-forge cudnn 9.10.2.21 hbcb9cd8_0 conda-forge cycler 0.12.1 pyhcf101f3_2 conda-forge cyrus-sasl 2.1.28 hd9c7081_0 conda-forge dbus 1.16.2 h24cb091_1 conda-forge debugpy 1.8.19 py312h372c3de_0 conda-forge decorator 5.2.1 pyhd8ed1ab_0 conda-forge defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge double-conversion 3.4.0 hecca717_0 conda-forge etils 1.12.2 pyhd8ed1ab_0 conda-forge exceptiongroup 1.3.1 pyhd8ed1ab_0 conda-forge executing 2.2.1 pyhd8ed1ab_0 conda-forge fftw 3.3.10 nompi_h3b011a4_111 conda-forge flatbuffers 25.2.10 hb7832b1_0 conda-forge font-ttf-dejavu-sans-mono 2.37 hab24e00_0 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pyhcf101f3_0 conda-forge requests 2.32.5 pyhcf101f3_1 conda-forge rfc3339-validator 0.1.4 pyhd8ed1ab_1 conda-forge rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge rfc3987-syntax 1.1.0 pyhe01879c_1 conda-forge rhash 1.4.6 hb9d3cd8_1 conda-forge rich 14.2.0 pyhcf101f3_0 conda-forge rpds-py 0.30.0 py312h868fb18_0 conda-forge scikit-learn 1.8.0 np2py312h3226591_1 conda-forge scipy 1.17.0 py312h54fa4ab_0 conda-forge send2trash 2.1.0 pyha191276_0 conda-forge setuptools 80.9.0 pyhff2d567_0 conda-forge sip 6.10.0 py312h1289d80_1 conda-forge six 1.17.0 pyhe01879c_1 conda-forge snappy 1.2.2 h03e3b7b_1 conda-forge sniffio 1.3.1 pyhd8ed1ab_2 conda-forge soupsieve 2.8.1 pyhd8ed1ab_0 conda-forge stack_data 0.6.3 pyhd8ed1ab_1 conda-forge tbb 2022.3.0 hb700be7_2 conda-forge tensorboard 2.19.0 pyhd8ed1ab_0 conda-forge tensorboard-data-server 0.7.0 py312h4eba8b5_4 conda-forge tensorflow 2.19.1 cuda129py312ha3fd0c4_252 conda-forge tensorflow-base 2.19.1 cuda129py312h59614e2_252 conda-forge tensorflow-estimator 2.19.1 cuda129py312h813c257_252 conda-forge termcolor 3.3.0 pyhd8ed1ab_0 conda-forge terminado 0.18.1 pyhc90fa1f_1 conda-forge tf-keras 2.15.0 pyhd8ed1ab_0 conda-forge threadpoolctl 3.6.0 pyhecae5ae_0 conda-forge tinycss2 1.5.1 pyhcf101f3_0 conda-forge tk 8.6.13 noxft_ha0e22de_103 conda-forge toml 0.10.2 pyhcf101f3_3 conda-forge tomli 2.4.0 pyhcf101f3_0 conda-forge toolz 1.1.0 pyhd8ed1ab_1 conda-forge tornado 6.5.4 py312h961da02_0 conda-forge tqdm 4.67.1 pyhd8ed1ab_1 conda-forge traitlets 5.14.3 pyhd8ed1ab_1 conda-forge typing-extensions 4.15.0 h396c80c_0 conda-forge typing_extensions 4.15.0 pyhcf101f3_0 conda-forge typing_utils 0.1.0 pyhd8ed1ab_1 conda-forge tzdata 2025c hc9c84f9_1 conda-forge umap-learn 0.5.11 py312h7900ff3_0 conda-forge unicodedata2 17.0.0 py312h4c3975b_1 conda-forge uri-template 1.3.0 pyhd8ed1ab_1 conda-forge urllib3 2.6.3 pyhd8ed1ab_0 conda-forge wayland 1.24.0 hd6090a7_1 conda-forge wcwidth 0.2.14 pyhd8ed1ab_0 conda-forge webcolors 25.10.0 pyhd8ed1ab_0 conda-forge webencodings 0.5.1 pyhd8ed1ab_3 conda-forge websocket-client 1.9.0 pyhd8ed1ab_0 conda-forge werkzeug 3.1.5 pyhcf101f3_0 conda-forge wheel 0.45.1 pyhd8ed1ab_1 conda-forge widgetsnbextension 4.0.15 pyhd8ed1ab_0 conda-forge wrapt 2.0.1 py312h4c3975b_1 conda-forge xcb-util 0.4.1 h4f16b4b_2 conda-forge xcb-util-cursor 0.1.6 hb03c661_0 conda-forge xcb-util-image 0.4.0 hb711507_2 conda-forge xcb-util-keysyms 0.4.1 hb711507_0 conda-forge xcb-util-renderutil 0.3.10 hb711507_0 conda-forge xcb-util-wm 0.4.2 hb711507_0 conda-forge xkeyboard-config 2.46 hb03c661_0 conda-forge xorg-libice 1.1.2 hb9d3cd8_0 conda-forge xorg-libsm 1.2.6 he73a12e_0 conda-forge xorg-libx11 1.8.12 h4f16b4b_0 conda-forge xorg-libxau 1.0.12 hb03c661_1 conda-forge xorg-libxcomposite 0.4.6 hb9d3cd8_2 conda-forge xorg-libxcursor 1.2.3 hb9d3cd8_0 conda-forge xorg-libxdamage 1.1.6 hb9d3cd8_0 conda-forge xorg-libxdmcp 1.1.5 hb03c661_1 conda-forge xorg-libxext 1.3.6 hb9d3cd8_0 conda-forge xorg-libxfixes 6.0.2 hb03c661_0 conda-forge xorg-libxi 1.8.2 hb9d3cd8_0 conda-forge xorg-libxrandr 1.5.4 hb9d3cd8_0 conda-forge xorg-libxrender 0.9.12 hb9d3cd8_0 conda-forge xorg-libxshmfence 1.3.3 hb9d3cd8_0 conda-forge xorg-libxtst 1.2.5 hb9d3cd8_3 conda-forge xorg-libxxf86vm 1.1.6 hb9d3cd8_0 conda-forge xz 5.8.1 hbcc6ac9_2 conda-forge xz-gpl-tools 5.8.1 hbcc6ac9_2 conda-forge xz-tools 5.8.1 hb9d3cd8_2 conda-forge yaml 0.2.5 h280c20c_3 conda-forge zeromq 4.3.5 h387f397_9 conda-forge zipp 3.23.0 pyhcf101f3_1 conda-forge zlib 1.3.1 hb9d3cd8_2 conda-forge zlib-ng 2.3.2 hceb46e0_1 conda-forge zstd 1.5.7 hb78ec9c_6 conda-forge

…

On Jan 23, 2026, at 1:40 PM, walid ***@***.***> wrote: walidabualafia created an issue (cryoem/eman2#582) <#582> Hi all, I can't seem to install the GPU dependencies correctly for the latest version of eman2. I keep running into this dependency conflict: Installing collected packages: jax-cuda12-pjrt, toolz, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-nvcc-cu12, nvidia-cuda-cupti-cu12, nvidia-cuda-cccl-cu12, nvidia-cublas-cu12, numpy, jax-cuda12-plugin, nvidia-nvshmem-cu12, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, jaxlib, jax, chex, optax Attempting uninstall: numpy Found existing installation: numpy 1.26.4 Uninstalling numpy-1.26.4: Successfully uninstalled numpy-1.26.4 ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. numba 0.63.1 requires numpy<2.4,>=1.22, but you have numpy 2.4.1 which is incompatible. Successfully installed chex-0.1.91 jax-0.9.0 jax-cuda12-pjrt-0.9.0 jax-cuda12-plugin-0.9.0 jaxlib-0.9.0 numpy-2.4.1 nvidia-cublas-cu12-12.9.1.4 nvidia-cuda-cccl-cu12-12.9.27 nvidia-cuda-cupti-cu12-12.9.79 nvidia-cuda-nvcc-cu12-12.9.86 nvidia-cuda-nvrtc-cu12-12.9.86 nvidia-cuda-runtime-cu12-12.9.79 nvidia-cudnn-cu12-9.18.0.77 nvidia-cufft-cu12-11.4.1.4 nvidia-cusolver-cu12-11.7.5.82 nvidia-cusparse-cu12-12.5.10.65 nvidia-nccl-cu12-2.29.2 nvidia-nvjitlink-cu12-12.9.86 nvidia-nvshmem-cu12-3.5.19 optax-0.2.6 toolz-1.1.0 Is there a specific version of numba that I can install? Alternatively, does eman2 depend on a certain version of numpy that is not <2.4,>=1.22? Thank you! — Reply to this email directly, view it on GitHub <#582>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACPKF2XLLLSGEVICFSXQ3X34IJ2L3AVCNFSM6AAAAACSWQAO62VHI2DSMVQWIX3LMV43ASLTON2WKOZTHA2DQOBZG4YTGNI>. You are receiving this because you are subscribed to this thread.

[walidabualafia]

@sludtke42, thanks for this.

This is not working for me either. See error below. Any chance you can conda env export > env.yaml and share that spec file with me?

It would also be extremely helpful to include this file (one with GPU packages, one without) in the main repo.

Error:

ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow 2.18.0 requires ml-dtypes<0.5.0,>=0.4.0, but you have ml-dtypes 0.5.4 which is incompatible.

Thanks again,
Walid

[inDane]

I am facing a similar problem:

pip install jax[cuda12] optax

Installing collected packages: jax-cuda12-pjrt, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-nvcc-cu12, nvidia-cuda-cupti-cu12, nvidia-cuda-cccl-cu12, nvidia-cublas-cu12, numpy, jax-cuda12-plugin, nvidia-nvshmem-cu12, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, jaxlib, jax, optax
  Attempting uninstall: numpy
    Found existing installation: numpy 1.26.4
    Uninstalling numpy-1.26.4:
      Successfully uninstalled numpy-1.26.4
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
numba 0.63.1 requires numpy<2.4,>=1.22, but you have numpy 2.4.2 which is incompatible.
Successfully installed jax-0.9.0.1 jax-cuda12-pjrt-0.9.0.1 jax-cuda12-plugin-0.9.0.1 jaxlib-0.9.0.1 numpy-2.4.2 nvidia-cublas-cu12-12.9.1.4 nvidia-cuda-cccl-cu12-12.9.27 nvidia-cuda-cupti-cu12-12.9.79 nvidia-cuda-nvcc-cu12-12.9.86 nvidia-cuda-nvrtc-cu12-12.9.86 nvidia-cuda-runtime-cu12-12.9.79 nvidia-cudnn-cu12-9.19.0.56 nvidia-cufft-cu12-11.4.1.4 nvidia-cusolver-cu12-11.7.5.82 nvidia-cusparse-cu12-12.5.10.65 nvidia-nccl-cu12-2.29.3 nvidia-nvjitlink-cu12-12.9.86 nvidia-nvshmem-cu12-3.5.21 optax-0.2.7

e2version.py

EMAN 2.99.72 ( GITHUB: 2026-02-27 16:16 - commit: 1bb4904d8 )
Your EMAN2 is running on: Linux-6.17.0-14-generic-x86_64-with-glibc2.39 6.17.0-14-generic
Your Python version is: 3.12.12

e2speedtest.py

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.4.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/home/USER/miniforge3/envs/eman2/bin/e2speedtest.py", line 43, in <module>
    from EMAN2 import *
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2.py", line 55, in <module>
    from EMAN2_cppwrap import *
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2_cppwrap.py", line 47, in <module>
    from libpyTypeConverter2 import *
Traceback (most recent call last):
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/numpy/core/_multiarray_umath.py", line 46, in __getattr__
    raise ImportError(msg)
ImportError: 
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.4.2 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.


ImportError: numpy.core.multiarray failed to import

The above exception was the direct cause of the following exception:

SystemError: <built-in function __import__> returned a result with an exception set
ImportError: numpy.core._multiarray_umath failed to import
ImportError: numpy.core.umath failed to import

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/USER/miniforge3/envs/eman2/bin/e2speedtest.py", line 43, in <module>
    from EMAN2 import *
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2.py", line 55, in <module>
    from EMAN2_cppwrap import *
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2_cppwrap.py", line 47, in <module>
    from libpyTypeConverter2 import *
SystemError: initialization of libpyTypeConverter2 raised unreported exception

This is Ubuntu 24.04 with miniforge and following the instructions from here.

EDIT: Update: Similar error with Numba 0.63.1 and numpy 2.3.5:

e2speedtest.py

A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.3.5 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.

Traceback (most recent call last):  File "/home/USER/miniforge3/envs/eman2/bin/e2speedtest.py", line 43, in <module>
    from EMAN2 import *
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2.py", line 55, in <module>
    from EMAN2_cppwrap import *
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2_cppwrap.py", line 47, in <module>
    from libpyTypeConverter2 import *
Traceback (most recent call last):
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/numpy/core/_multiarray_umath.py", line 46, in __getattr__
    raise ImportError(msg)
ImportError: 
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.3.5 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.

If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.


ImportError: numpy.core.multiarray failed to import

The above exception was the direct cause of the following exception:

SystemError: <built-in function __import__> returned a result with an exception set
ImportError: numpy.core._multiarray_umath failed to import
ImportError: numpy.core.umath failed to import

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "/home/USER/miniforge3/envs/eman2/bin/e2speedtest.py", line 43, in <module>
    from EMAN2 import *
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2.py", line 55, in <module>
    from EMAN2_cppwrap import *
  File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2_cppwrap.py", line 47, in <module>
    from libpyTypeConverter2 import *
SystemError: initialization of libpyTypeConverter2 raised unreported exception

[sludtke42]

It is tricky to get and environment set up with numpy 2+ right now because eman-dep is still pinned to an earlier version. I'm attaching the current environment I've been using to fix numpy2 problems. I think I have most of the issues fixed at this point... conda env create -f eman2_np2.yml -n eman2 should work?

On Feb 27, 2026, at 9:25 AM, inDane ***@***.***> wrote: inDane left a comment (cryoem/eman2#582) <#582 (comment)> I am facing a similar problem: pip install jax[cuda12] optax Installing collected packages: jax-cuda12-pjrt, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-nvcc-cu12, nvidia-cuda-cupti-cu12, nvidia-cuda-cccl-cu12, nvidia-cublas-cu12, numpy, jax-cuda12-plugin, nvidia-nvshmem-cu12, nvidia-cusparse-cu12, nvidia-cufft-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, jaxlib, jax, optax Attempting uninstall: numpy Found existing installation: numpy 1.26.4 Uninstalling numpy-1.26.4: Successfully uninstalled numpy-1.26.4 ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. numba 0.63.1 requires numpy<2.4,>=1.22, but you have numpy 2.4.2 which is incompatible. Successfully installed jax-0.9.0.1 jax-cuda12-pjrt-0.9.0.1 jax-cuda12-plugin-0.9.0.1 jaxlib-0.9.0.1 numpy-2.4.2 nvidia-cublas-cu12-12.9.1.4 nvidia-cuda-cccl-cu12-12.9.27 nvidia-cuda-cupti-cu12-12.9.79 nvidia-cuda-nvcc-cu12-12.9.86 nvidia-cuda-nvrtc-cu12-12.9.86 nvidia-cuda-runtime-cu12-12.9.79 nvidia-cudnn-cu12-9.19.0.56 nvidia-cufft-cu12-11.4.1.4 nvidia-cusolver-cu12-11.7.5.82 nvidia-cusparse-cu12-12.5.10.65 nvidia-nccl-cu12-2.29.3 nvidia-nvjitlink-cu12-12.9.86 nvidia-nvshmem-cu12-3.5.21 optax-0.2.7 e2version.py EMAN 2.99.72 ( GITHUB: 2026-02-27 16:16 - commit: 1bb4904 ) Your EMAN2 is running on: Linux-6.17.0-14-generic-x86_64-with-glibc2.39 6.17.0-14-generic Your Python version is: 3.12.12 e2speedtest.py A module that was compiled using NumPy 1.x cannot be run in NumPy 2.4.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. Traceback (most recent call last): File "/home/USER/miniforge3/envs/eman2/bin/e2speedtest.py", line 43, in <module> from EMAN2 import * File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2.py", line 55, in <module> from EMAN2_cppwrap import * File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2_cppwrap.py", line 47, in <module> from libpyTypeConverter2 import * Traceback (most recent call last): File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/numpy/core/_multiarray_umath.py", line 46, in __getattr__ raise ImportError(msg) ImportError: A module that was compiled using NumPy 1.x cannot be run in NumPy 2.4.2 as it may crash. To support both 1.x and 2.x versions of NumPy, modules must be compiled with NumPy 2.0. Some module may need to rebuild instead e.g. with 'pybind11>=2.12'. If you are a user of the module, the easiest solution will be to downgrade to 'numpy<2' or try to upgrade the affected module. We expect that some modules will need time to support NumPy 2. ImportError: numpy.core.multiarray failed to import The above exception was the direct cause of the following exception: SystemError: <built-in function __import__> returned a result with an exception set ImportError: numpy.core._multiarray_umath failed to import ImportError: numpy.core.umath failed to import The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/USER/miniforge3/envs/eman2/bin/e2speedtest.py", line 43, in <module> from EMAN2 import * File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2.py", line 55, in <module> from EMAN2_cppwrap import * File "/home/USER/miniforge3/envs/eman2/lib/python3.12/site-packages/EMAN2_cppwrap.py", line 47, in <module> from libpyTypeConverter2 import * SystemError: initialization of libpyTypeConverter2 raised unreported exception This is Ubuntu 24.04 with miniforge and following the instructions from here <https://blake.bcm.edu/emanwiki/doku.php?id=eman2:install:sourceinstall>. — Reply to this email directly, view it on GitHub <#582 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACPKF2TEC6UQ7KFC5UQKZGT4OBOWPAVCNFSM6AAAAACSWQAO62VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTSNZTGUZTKOJWHA>. You are receiving this because you were mentioned.



[inDane]

thanks for your fast response, unfortunately the attachment seems to got lost on transit :D

[sludtke42]

Hmm... Maybe Github strips them? I'll paste it below. It's just text: eman2_np2.yml -------------------- name: eman2 channels: - conda-forge dependencies: - _libgcc_mutex==0.1=conda_forge - _openmp_mutex==4.5=2_gnu - _python_abi3_support==1.0=hd8ed1ab_2 - absl-py==2.3.1=pyhd8ed1ab_0 - alsa-lib==1.2.15.3=hb03c661_0 - anyio==4.12.1=pyhcf101f3_0 - argon2-cffi-bindings==25.1.0=py312h4c3975b_2 - argon2-cffi==25.1.0=pyhd8ed1ab_0 - arrow==1.4.0=pyhcf101f3_0 - asttokens==3.0.1=pyhd8ed1ab_0 - astunparse==1.6.3=pyhd8ed1ab_3 - async-lru==2.0.5=pyh29332c3_0 - attr==2.5.2=h39aace5_0 - attrs==25.4.0=pyhcf101f3_1 - babel==2.17.0=pyhd8ed1ab_0 - backports.zstd==1.3.0=py312h90b7ffd_0 - beautifulsoup4==4.14.3=pyha770c72_0 - biopython==1.86=py312h4c3975b_0 - bleach-with-css==6.3.0=h5f6438b_0 - bleach==6.3.0=pyhcf101f3_0 - boost==1.85.0=h9cebb41_4 - brotli-bin==1.2.0=hb03c661_1 - brotli-python==1.2.0=py312hdb49522_1 - brotli==1.2.0=hed03a55_1 - bzip2==1.0.8=hda65f42_8 - c-ares==1.34.6=hb03c661_0 - ca-certificates==2026.1.4=hbd8a1cb_0 - cached-property==1.5.2=hd8ed1ab_1 - cached_property==1.5.2=pyha770c72_1 - cairo==1.18.4=h3394656_0 - certifi==2026.1.4=pyhd8ed1ab_0 - cffi==2.0.0=py312h460c074_1 - charset-normalizer==3.4.4=pyhd8ed1ab_0 - chex==0.1.91=pyhd8ed1ab_0 - cmake==4.2.1=hc85cc9f_0 - colorama==0.4.6=pyhd8ed1ab_1 - comm==0.2.3=pyhe01879c_0 - configparser==7.2.0=pyhd8ed1ab_0 - contourpy==1.3.3=py312hd9148b4_3 - cpython==3.12.12=py312hd8ed1ab_1 - cuda-cccl_linux-64==12.9.27=ha770c72_0 - cuda-crt-dev_linux-64==12.9.86=ha770c72_2 - cuda-crt-tools==12.9.86=ha770c72_2 - cuda-cudart-dev_linux-64==12.9.79=h3f2d84a_0 - cuda-cudart-static_linux-64==12.9.79=h3f2d84a_0 - cuda-cudart==12.9.79=h5888daf_0 - cuda-cudart_linux-64==12.9.79=h3f2d84a_0 - cuda-cupti-dev==12.9.79=h676940d_1 - cuda-cupti==12.9.79=h676940d_1 - cuda-nvcc-tools==12.9.86=he02047a_2 - cuda-nvrtc==12.9.86=hecca717_1 - cuda-nvtx==12.9.79=hecca717_1 - cuda-nvvm-tools==12.9.86=h4bc722e_2 - cuda-version==12.9=h4f385c5_3 - cudnn==9.10.2.21=hbcb9cd8_0 - cycler==0.12.1=pyhcf101f3_2 - cyrus-sasl==2.1.28=hd9c7081_0 - dbus==1.16.2=h24cb091_1 - debugpy==1.8.19=py312h372c3de_0 - decorator==5.2.1=pyhd8ed1ab_0 - defusedxml==0.7.1=pyhd8ed1ab_0 - double-conversion==3.4.0=hecca717_0 - etils==1.12.2=pyhd8ed1ab_0 - exceptiongroup==1.3.1=pyhd8ed1ab_0 - executing==2.2.1=pyhd8ed1ab_0 - fftw==3.3.10=nompi_h3b011a4_111 - flatbuffers==25.2.10=hb7832b1_0 - font-ttf-dejavu-sans-mono==2.37=hab24e00_0 - font-ttf-inconsolata==3.000=h77eed37_0 - font-ttf-source-code-pro==2.038=h77eed37_0 - font-ttf-ubuntu==0.83=h77eed37_3 - fontconfig==2.15.0=h7e30c49_1 - fonts-conda-ecosystem==1=0 - fonts-conda-forge==1=hc364b38_1 - fonttools==4.61.1=py312h8a5da7c_0 - fqdn==1.5.1=pyhd8ed1ab_1 - freetype==2.14.1=ha770c72_0 - ftgl==2.4.0=h6cc6478_2 - future==1.0.0=pyhd8ed1ab_2 - gast==0.7.0=pyhd8ed1ab_0 - giflib==5.2.2=hd590300_0 - glib-tools==2.86.3=hf516916_0 - glib==2.86.3=h5192d8d_0 - google-pasta==0.2.0=pyhd8ed1ab_2 - graphite2==1.3.14=hecca717_2 - grpcio==1.73.1=py312h6f3464c_1 - gsl==2.8=hbf7d49c_1 - gst-plugins-base==1.24.11=h651a532_0 - gstreamer==1.24.11=hc37bda9_0 - h11==0.16.0=pyhcf101f3_1 - h2==4.3.0=pyhcf101f3_0 - h5py==3.15.1=nompi_py312ha4f8f14_101 - harfbuzz==12.2.0=h15599e2_0 - hdf5==1.14.6=nompi_h1b119a7_104 - hpack==4.1.0=pyhd8ed1ab_0 - httpcore==1.0.9=pyh29332c3_0 - httpx==0.28.1=pyhd8ed1ab_0 - hyperframe==6.1.0=pyhd8ed1ab_0 - icu==75.1=he02047a_0 - idna==3.11=pyhd8ed1ab_0 - importlib-metadata==8.7.0=pyhe01879c_1 - ipykernel==7.1.0=pyha191276_0 - ipython==9.9.0=pyh53cf698_0 - ipython_pygments_lexers==1.1.1=pyhd8ed1ab_0 - ipywidgets==8.1.8=pyhd8ed1ab_0 - isoduration==20.11.0=pyhd8ed1ab_1 - jax==0.7.2=pyhd8ed1ab_0 - jaxlib==0.7.2=cuda129_py312h3ee6d78_202 - jedi==0.19.2=pyhd8ed1ab_1 - jinja2==3.1.6=pyhcf101f3_1 - joblib==1.5.3=pyhd8ed1ab_0 - json5==0.13.0=pyhd8ed1ab_0 - jsonpointer==3.0.0=pyhcf101f3_3 - jsonschema-specifications==2025.9.1=pyhcf101f3_0 - jsonschema-with-format-nongpl==4.26.0=hcf101f3_0 - jsonschema==4.26.0=pyhcf101f3_0 - jupyter-lsp==2.3.0=pyhcf101f3_0 - jupyter_client==8.8.0=pyhcf101f3_0 - jupyter_core==5.9.1=pyhc90fa1f_0 - jupyter_events==0.12.0=pyh29332c3_0 - jupyter_server==2.17.0=pyhcf101f3_0 - jupyter_server_terminals==0.5.4=pyhcf101f3_0 - jupyterlab==4.5.2=pyhd8ed1ab_0 - jupyterlab_pygments==0.3.0=pyhd8ed1ab_2 - jupyterlab_server==2.28.0=pyhcf101f3_0 - jupyterlab_widgets==3.0.16=pyhcf101f3_1 - keras==3.12.0=pyh753f3f9_0 - keyutils==1.6.3=hb9d3cd8_0 - kiwisolver==1.4.9=py312h0a2e395_2 - krb5==1.21.3=h659f571_0 - lame==3.100=h166bdaf_1003 - lark==1.3.1=pyhd8ed1ab_0 - lcms2==2.18=h0c24ade_0 - ld_impl_linux-64==2.45=default_hbd61a6d_105 - lerc==4.0.0=h0aef613_1 - libabseil==20250512.1=cxx17_hba17884_0 - libaec==1.1.4=h3f801dc_0 - libblas==3.11.0=5_h4a7cf45_openblas - libboost-devel==1.85.0=h00ab1b0_4 - libboost-headers==1.85.0=ha770c72_4 - libboost-python-devel==1.85.0=py312h9cebb41_4 - libboost-python==1.85.0=py312hf74af30_4 - libboost==1.85.0=h0ccab89_4 - libbrotlicommon==1.2.0=hb03c661_1 - libbrotlidec==1.2.0=hb03c661_1 - libbrotlienc==1.2.0=hb03c661_1 - libcap==2.77=h3ff7636_0 - libcblas==3.11.0=5_h0358290_openblas - libclang-cpp21.1==21.1.8=default_h99862b1_1 - libclang13==21.1.8=default_h746c552_1 - libcublas-dev==12.9.1.4=h676940d_1 - libcublas==12.9.1.4=h676940d_1 - libcudnn-dev==9.10.2.21=h58dd1b1_0 - libcudnn==9.10.2.21=hf7e9902_0 - libcufft-dev==11.4.1.4=hecca717_1 - libcufft==11.4.1.4=hecca717_1 - libcups==2.3.3=hb8b1518_5 - libcurand-dev==10.3.10.19=h676940d_1 - libcurand==10.3.10.19=h676940d_1 - libcurl==8.18.0=h4e3cde8_0 - libcusolver-dev==11.7.5.82=h676940d_2 - libcusolver==11.7.5.82=h676940d_2 - libcusparse-dev==12.5.10.65=hecca717_2 - libcusparse==12.5.10.65=hecca717_2 - libdeflate==1.25=h17f619e_0 - libdrm==2.4.125=hb03c661_1 - libedit==3.1.20250104=pl5321h7949ede_0 - libegl==1.7.0=ha4b6fd6_2 - libev==4.33=hd590300_2 - libevent==2.1.12=hf998b51_1 - libexpat==2.7.3=hecca717_0 - libffi==3.5.2=h9ec8514_0 - libflac==1.5.0=he200343_1 - libfreetype6==2.14.1=h73754d4_0 - libfreetype==2.14.1=ha770c72_0 - libgcc-ng==15.2.0=h69a702a_16 - libgcc==15.2.0=he0feb66_16 - libgfortran5==15.2.0=h68bc16d_16 - libgfortran==15.2.0=h69a702a_16 - libgl==1.7.0=ha4b6fd6_2 - libglib==2.86.3=h6548e54_0 - libglu==9.0.3=h5888daf_1 - libglvnd==1.7.0=ha4b6fd6_2 - libglx==1.7.0=ha4b6fd6_2 - libgomp==15.2.0=he0feb66_16 - libgrpc==1.73.1=h3288cfb_1 - libhwloc==2.12.2=default_hafda6a7_1000 - libiconv==1.18=h3b78370_2 - libjpeg-turbo==3.1.2=hb03c661_0 - liblapack==3.11.0=5_h47877c9_openblas - libllvm21==21.1.8=hf7376ad_0 - liblzma-devel==5.8.1=hb9d3cd8_2 - liblzma==5.8.1=hb9d3cd8_2 - libml_dtypes-headers==0.5.4=h707e725_0 - libnghttp2==1.67.0=had1ee68_0 - libnsl==2.0.1=hb9d3cd8_1 - libntlm==1.8=hb9d3cd8_0 - libnvjitlink==12.9.86=hecca717_2 - libogg==1.3.5=hd0c01bc_1 - libopenblas==0.3.30=pthreads_h94d23a6_4 - libopengl==1.7.0=ha4b6fd6_2 - libopus==1.6.1=h280c20c_0 - libpciaccess==0.18=hb9d3cd8_0 - libpng==1.6.54=h421ea60_0 - libpq==18.1=h5c52fec_2 - libprotobuf==6.31.1=h49aed37_4 - libre2-11==2025.11.05=h7b12aa8_0 - libsndfile==1.2.2=hc7d488a_2 - libsodium==1.0.20=h4ab18f5_0 - libsqlite==3.51.2=h0c1763c_0 - libssh2==1.11.1=hcf80075_0 - libstdcxx-ng==15.2.0=hdf11a46_16 - libstdcxx==15.2.0=h934c35e_16 - libsystemd0==258.3=h6569c3e_0 - libtensorflow_cc==2.19.1=cuda129h19f333e_252 - libtensorflow_framework==2.19.1=cuda129h775b7a8_252 - libtiff==4.7.1=h9d88235_1 - libuuid==2.41.3=h5347b49_0 - libuv==1.51.0=hb03c661_1 - libvorbis==1.3.7=h54a6638_2 - libvulkan-loader==1.4.328.1=h5279c79_0 - libwebp-base==1.6.0=hd42ef1d_0 - libxcb==1.17.0=h8a09558_0 - libxcrypt==4.4.36=hd590300_1 - libxkbcommon==1.13.1=hca5e8e5_0 - libxml2-16==2.15.1=ha9997c6_0 - libxml2==2.15.1=h26afc86_0 - libxslt==1.1.43=h711ed8c_1 - libzlib==1.3.1=hb9d3cd8_2 - llvmlite==0.46.0=py312h7424e68_0 - markdown-it-py==4.0.0=pyhd8ed1ab_0 - markdown==3.10=pyhcf101f3_1 - markupsafe==3.0.3=py312h8a5da7c_0 - matplotlib-base==3.10.8=py312he3d6523_0 - matplotlib-inline==0.2.1=pyhd8ed1ab_0 - matplotlib==3.10.8=py312h7900ff3_0 - mdurl==0.1.2=pyhd8ed1ab_1 - mistune==3.2.0=pyhcf101f3_0 - ml_dtypes==0.5.4=np2py312h0f77346_0 - mpg123==1.32.9=hc50e24c_0 - munkres==1.1.4=pyhd8ed1ab_1 - namex==0.1.0=pyhd8ed1ab_0 - nbclient==0.10.4=pyhd8ed1ab_0 - nbconvert-core==7.16.6=pyhcf101f3_1 - nbformat==5.10.4=pyhd8ed1ab_1 - nccl==2.29.2.1=h4d09622_1 - ncurses==6.5=h2d0b736_3 - nest-asyncio==1.6.0=pyhd8ed1ab_1 - nodejs==24.12.0=h36edbcc_0 - notebook-shim==0.2.4=pyhd8ed1ab_1 - nspr==4.38=h29cc59b_0 - nss==3.118=h445c969_0 - numba==0.63.1=py312hd1dde6f_0 - numpy==2.3.5=py312h33ff503_1 - openjpeg==2.5.4=h55fea9a_0 - openldap==2.6.10=he970967_0 - openssl==3.6.1 - opt_einsum==3.4.0=pyhd8ed1ab_1 - optax==0.2.6=pyhd8ed1ab_0 - optree==0.18.0=py312hd9148b4_0 - overrides==7.7.0=pyhd8ed1ab_1 - packaging==25.0=pyh29332c3_1 - pandas==2.3.3=py312hf79963d_1 - pandocfilters==1.5.0=pyhd8ed1ab_0 - parso==0.8.5=pyhcf101f3_0 - pcre2==10.47=haa7fec5_0 - pexpect==4.9.0=pyhd8ed1ab_1 - pillow==12.1.0=py312h50c33e8_0 - pip==25.3=pyh8b19718_0 - pixman==0.46.4=h54a6638_1 - platformdirs==4.5.1=pyhcf101f3_0 - ply==3.11=pyhd8ed1ab_3 - prometheus_client==0.24.1=pyhd8ed1ab_0 - prompt-toolkit==3.0.52=pyha770c72_0 - protobuf==6.31.1=py312hb8af0ac_2 - psutil==7.2.1=py312h5253ce2_0 - pthread-stubs==0.4=hb9d3cd8_1002 - ptyprocess==0.7.0=pyhd8ed1ab_1 - pulseaudio-client==17.0=h9a6aba3_3 - pure_eval==0.2.3=pyhd8ed1ab_1 - pycparser==2.22=pyh29332c3_1 - pygments==2.19.2=pyhd8ed1ab_0 - pynndescent==0.5.13=pyhd8ed1ab_1 - pyopengl==3.1.6=pyhfc05efe_1 - pyparsing==3.3.1=pyhcf101f3_0 - pyqt5-sip==12.17.0=py312h1289d80_2 - pyqt==5.15.11=py312h82c0db2_2 - pyside6==6.10.1=py312h9da60e5_0 - pysocks==1.7.1=pyha55dd90_7 - python-dateutil==2.9.0.post0=pyhe01879c_2 - python-fastjsonschema==2.21.2=pyhe01879c_0 - python-flatbuffers==25.9.23=pyh1e1bc0e_0 - python-gil==3.12.12=hd8ed1ab_1 - python-json-logger==2.0.7=pyhd8ed1ab_0 - python-tzdata==2025.3=pyhd8ed1ab_0 - python==3.12.12=hd63d673_1_cpython - python_abi==3.12=8_cp312 - pytz==2025.2=pyhd8ed1ab_0 - pyyaml==6.0.3=py312h8a5da7c_0 - pyzmq==27.1.0=py312hfb55c3c_0 - qhull==2020.2=h434a139_5 - qt-main==5.15.15=h3c3fd16_6 - qt6-main==6.10.1=h6f76662_3 - re2==2025.11.05=h5301d42_0 - readline==8.3=h853b02a_0 - referencing==0.37.0=pyhcf101f3_0 - requests==2.32.5=pyhcf101f3_1 - rfc3339-validator==0.1.4=pyhd8ed1ab_1 - rfc3986-validator==0.1.1=pyh9f0ad1d_0 - rfc3987-syntax==1.1.0=pyhe01879c_1 - rhash==1.4.6=hb9d3cd8_1 - rich==14.2.0=pyhcf101f3_0 - rpds-py==0.30.0=py312h868fb18_0 - scikit-learn==1.8.0=np2py312h3226591_1 - scipy==1.17.0=py312h54fa4ab_0 - send2trash==2.1.0=pyha191276_0 - setuptools==80.9.0=pyhff2d567_0 - sip==6.10.0=py312h1289d80_1 - six==1.17.0=pyhe01879c_1 - snappy==1.2.2=h03e3b7b_1 - sniffio==1.3.1=pyhd8ed1ab_2 - soupsieve==2.8.1=pyhd8ed1ab_0 - stack_data==0.6.3=pyhd8ed1ab_1 - tbb==2022.3.0=hb700be7_2 - tensorboard-data-server==0.7.0=py312h4eba8b5_4 - tensorboard==2.19.0=pyhd8ed1ab_0 - tensorflow-base==2.19.1=cuda129py312h59614e2_252 - tensorflow-estimator==2.19.1=cuda129py312h813c257_252 - tensorflow==2.19.1=cuda129py312ha3fd0c4_252 - termcolor==3.3.0=pyhd8ed1ab_0 - terminado==0.18.1=pyhc90fa1f_1 - tf-keras==2.15.0=pyhd8ed1ab_0 - threadpoolctl==3.6.0=pyhecae5ae_0 - tinycss2==1.5.1=pyhcf101f3_0 - tk==8.6.13=noxft_ha0e22de_103 - toml==0.10.2=pyhcf101f3_3 - tomli==2.4.0=pyhcf101f3_0 - toolz==1.1.0=pyhd8ed1ab_1 - tornado==6.5.4=py312h961da02_0 - tqdm==4.67.1=pyhd8ed1ab_1 - traitlets==5.14.3=pyhd8ed1ab_1 - typing-extensions==4.15.0=h396c80c_0 - typing_extensions==4.15.0=pyhcf101f3_0 - typing_utils==0.1.0=pyhd8ed1ab_1 - tzdata==2025c=hc9c84f9_1 - umap-learn==0.5.11=py312h7900ff3_0 - unicodedata2==17.0.0=py312h4c3975b_1 - uri-template==1.3.0=pyhd8ed1ab_1 - urllib3==2.6.3=pyhd8ed1ab_0 - wayland==1.24.0=hd6090a7_1 - wcwidth==0.2.14=pyhd8ed1ab_0 - webcolors==25.10.0=pyhd8ed1ab_0 - webencodings==0.5.1=pyhd8ed1ab_3 - websocket-client==1.9.0=pyhd8ed1ab_0 - werkzeug==3.1.5=pyhcf101f3_0 - wheel==0.45.1=pyhd8ed1ab_1 - widgetsnbextension==4.0.15=pyhd8ed1ab_0 - wrapt==2.0.1=py312h4c3975b_1 - xcb-util-cursor==0.1.6=hb03c661_0 - xcb-util-image==0.4.0=hb711507_2 - xcb-util-keysyms==0.4.1=hb711507_0 - xcb-util-renderutil==0.3.10=hb711507_0 - xcb-util-wm==0.4.2=hb711507_0 - xcb-util==0.4.1=h4f16b4b_2 - xkeyboard-config==2.46=hb03c661_0 - xorg-libice==1.1.2=hb9d3cd8_0 - xorg-libsm==1.2.6=he73a12e_0 - xorg-libx11==1.8.12=h4f16b4b_0 - xorg-libxau==1.0.12=hb03c661_1 - xorg-libxcomposite==0.4.6=hb9d3cd8_2 - xorg-libxcursor==1.2.3=hb9d3cd8_0 - xorg-libxdamage==1.1.6=hb9d3cd8_0 - xorg-libxdmcp==1.1.5=hb03c661_1 - xorg-libxext==1.3.6=hb9d3cd8_0 - xorg-libxfixes==6.0.2=hb03c661_0 - xorg-libxi==1.8.2=hb9d3cd8_0 - xorg-libxrandr==1.5.4=hb9d3cd8_0 - xorg-libxrender==0.9.12=hb9d3cd8_0 - xorg-libxshmfence==1.3.3=hb9d3cd8_0 - xorg-libxtst==1.2.5=hb9d3cd8_3 - xorg-libxxf86vm==1.1.6=hb9d3cd8_0 - xz-gpl-tools==5.8.1=hbcc6ac9_2 - xz-tools==5.8.1=hb9d3cd8_2 - xz==5.8.1=hbcc6ac9_2 - yaml==0.2.5=h280c20c_3 - zeromq==4.3.5=h387f397_9 - zipp==3.23.0=pyhcf101f3_1 - zlib-ng==2.3.2=hceb46e0_1 - zlib==1.3.1=hb9d3cd8_2 - zstd==1.5.7=hb78ec9c_6 - gemmi prefix: /home2/stevel/miniforge3/envs/e3

[inDane]

Looks good thank you!

( i had to add cryoem to the channels)

channels:
  - conda-forge
  - cryoem

But then it installed and built:

(eman2-np)USER@PC:~/eman2/eman2-build$ e2speedtest.py 
This could take a few minutes. Please be patient.
Initializing
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Your machines speed factor = 1.9719 +- 0.0254 (0.0117 +- 0.00015 sec)