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Installation#

(optional) Creating a conda environment#

It is common practice creating a separate conda environment to avoid dependencies mixing. You can create the new environment named ace with minimal amount of required packages with the following command: 

conda create -n ace python=3.9

Then, activate the environment with source activate ace or conda activate ace. To deactivate the environment, use deactivate command

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:

  1. Install Tensorflow (newer version should be also compatible)
pip install tensorflow==2.8.0
  1. Download the tensorpotential from this repository.
  2. Clone with
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
  1. 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:

  1. Download pyace from this repository.
  2. Clone with
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
  1. 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If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc 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.*