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Installation#

(Optional) Creating a Separate Environment#

It is a common practice to create a separate Python environment to avoid dependency conflicts. You can create a new environment named ace with the minimal required packages using the following command:

Mamba is a fast, robust, and cross-platform package manager. Check the Mamba installation guide for details.

Installation steps: * Download the Miniforge3 distribution from here.

  • Run the downloaded file: bash sh Miniforge3-Linux-x86_64.sh and follow the installation instructions.

  • Create the mamba environment: bash mamba create -n ace python=3.9

  • Activate the environment with either: source activate ace or mamba activate ace. To deactivate the environment, use the command mamba deactivate

Using conda#

Note: Due to recent changes in Anaconda's licensing, please ensure you are permitted to use it. The mamba package manager might be a preferable alternative.

To create the environment with conda:

conda create -n ace python=3.9

Installation of tensorpotential#

tensorpotential allows for the GPU accelerated optimization of the ACE potential using TensorFlow. However, it is recommended to use it even if you don't have a GPU available.

Install it using the following commands:

  • Install Tensorflow (newer version should be also compatible)
pip install tensorflow==2.8.0

or to have CUDA support in latest versions of TensorFlow

pip install tensorflow[and-cuda] 

git clone https://github.com/ICAMS/TensorPotential.git
cd TensorPotential

or download

wget https://github.com/ICAMS/TensorPotential/archive/refs/heads/main.zip
unzip main.zip
cd TensorPotential-main
  • Run installation script
pip install --upgrade .

or (for more installation details)

python setup.py install

Installation of pacemaker and pyace#

The pyace (aka python-ace) package is located in this repository. It contains the pacemaker tools and other Python wrappers and utilities.

To install pyace:

git clone https://github.com/ICAMS/python-ace.git
cd python-ace

or download 

wget https://github.com/ICAMS/python-ace/archive/refs/heads/master.zip
cd python-ace-master
  • Run installation script
pip install --upgrade .

or (for more installation details)

python setup.py install

Now, pacemaker and other tools (pace_yaml2yace, pace_info, pace_activeset) should be available from the terminal, if corresponding conda environment is loaded.

Known installation issues#

Segmentation fault#

If you see Segmentation fault error message, then check that you are using correct version of Python from corresponding conda environment, i.e. check that which python points to right location inside conda environment.

TypeError: Descriptors cannot not be created directly#

If you see this error message

TypeError: Descriptors cannot not be created directly.
If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0.
If you cannot immediately regenerate your protos, some other possible workarounds are:
 1. Downgrade the protobuf package to 3.20.x or lower.
 2. Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower).```

then try to downgrade protobuf package, i.e.

pip install protobuf==3.20.*