For up-to-date binary installations you must use the instructions on this page. The old binary installation images were retired in late 2023. They are still available for historical purposes, but are no longer updated.

While the installation method on this page is fine, if you install from source, then you can rapidly update when we make small patches to the code. Code is being developed continuously, and binaries are often built only every few months.

1. Install Anaconda or MiniForge (for your platform)

• On that page, scroll down to the Latest Release, then download the appropriate file, for example: Miniforge3-Linux-x86_64.sh
• Run the installer, for example: bash Miniforge3-Linux-x86_64.sh
• On M1/M2 Macs: make sure you have the ARM64 !MiniForge, not Intel.
• On Windows 11: the native Windows !MiniForge will not work, you must use the Linux !MiniForge within a WSL2 environment!
• We suggest !MiniForge to avoid licensing issues with Anaconda, but if your institution subscribes to Anaconda, that's fine too. If you use Anaconda, you may need to install Mamba manually below.

2. Install mamba (only if necessary):

• if you just installed MiniForge above, you will already have mamba. If you have your own Anaconda installation, you may need this step

conda install mamba -c conda-forge

3. Update (optional): consider updating Anaconda/!MiniForge before installing. It's a good idea to do this periodically:

mamba update --all

but if you have many other (non-EMAN) environments, you may consider if this will impact them.

4. Install EMAN2: We strongly recommend installing EMAN2 within its own !MiniForge environment like this:

conda create -n eman2 eman-dev -c cryoem -c conda-forge

||Current version: || || || ||||

5. Activate to Use: Any time you want to use EMAN2 you will need to first activate the correct environment. You can deactivate when done.

conda activate eman2

6. Linux: see the GPU section below.

7. Run these programs to see if the install worked:

#!highlight bash
# This will display the exact version you have installed. If you ask for help, provide the full output of this command.
e2version.py
# Relative speed factor of a single core on your computer, 2-3 typ.
e2speedtest.py
# Opens a browser window you can use to view any EMAN2 compatible file in various ways. Close the main window when done.
e2display.py
# Creates a small image with a circle in the middle of it
e2proc2d.py :64:64:1 test.hdf --process mask.sharp:outer_radius=24
# Displays the file you just created
e2display.py test.hdf

«Anchor(GPU)»

Older EMAN2 programs make use of Tensorflow for Deep Learning/GPU support, but we have been gradually migrating to the new JAX library, so having both installed is strongly suggested.

• Tensorflow
  • Should be installed automatically as part of the EMAN2 environment setup above
  • You can check to see what version of Tensorflow and whether it is GPU accelerated with: “conda list|grep tensorflow”. On a machine with an NVIDIA GPU and properly configured drivers, you should see a tensorflow version like “2.17.0 cuda120py312h02ad488_203”. This says that you are using the CUDA 12.0, Python 3.12 version of tensorflow 2.17.0. If you see “cpu” instead of “cuda” then you do not have GPU acceleration, and you will need to sort that out.
  • You can test tensorflow with: $CONDA_PREFIX/examples/test_tensorflow_basic.py

• JAX
  • While it is possible to install JAX using conda/mamba, it can be difficult to find a version of GPU JAX and a version of GPU Tensorflow which will install properly together this way.
  • Instead, we strongly suggest just installing JAX using pip:
    • conda activate eman2
    • Linux (NVIDIA): pip install jax[cuda12] optax
    • Other (cpu): pip install jax optax
  • see https://pypi.org/project/jax/ for up to date JAX instructions, this may change more frequently than this page is updated
  • You can test after installation with: $CONDA_PREFIX/examples/test_jax_basic.py

If you have problems you cannot figure out, feel free to post questions to the EMAN2 Google Group.