installation instructions

Author: pdobbelaere

.github/workflows/run_pytest.yaml

@@ -36,7 +36,7 @@ jobs:
           pip install --no-cache-dir git+https://github.com/i-pi/i-pi.git@v3.0.0-beta4
           pip install torch==2.5.1
           pip install git+https://github.com/acesuit/MACE.git@v0.3.5
-          apptainer exec oras://ghcr.io/molmod/cp2k:2024.1 ls
+          apptainer exec docker://cp2k/cp2k:2025.2_mpich_x86_64_psmp ls
           apptainer exec oras://ghcr.io/molmod/gpaw:24.1 ls
       - name: Checkout specific commit
         uses: actions/checkout@v4

install_local.sh

@@ -0,0 +1,32 @@
+# Create a psiflow installation including ModelTraining and ModelEvaluation dependencies
+
+ENV_NAME="psiflow-dev"
+micromamba env create -n $ENV_NAME python=3.14 -c conda-forge
+micromamba activate $ENV_NAME
+
+# install workqueue
+micromamba install ndcctools -c conda-forge
+
+# install psiflow
+pip install -e psiflow[dev]
+
+# install PyTorch and MACE
+pip install torch --index-url https://download.pytorch.org/whl/cu128
+pip install mace-torch
+pip install cuequivariance cuequivariance-torch cuequivariance-ops-torch-cu12
+
+# install basic PLUMED and python API
+micromamba install plumed -c conda-forge
+pip install plumed
+
+# make PLUMED visible to the py-plumed interface
+PLUMED_KERNEL="$CONDA_PREFIX/lib/libplumedKernel.so"
+echo "{\"env_vars\": {\"PLUMED_KERNEL\": \"$PLUMED_KERNEL\"}}" >> "$CONDA_PREFIX/conda-meta/state"
+
+# install simple-dftd3
+micromamba install simple-dftd3 dftd3-python -c conda-forge
+
+# install cp2k-input-tools 
+# (its dependencies are a right mess, so we fix some and pip will complain)
+pip install cp2k-input-tools
+pip install --force-reinstall Pint

psiflow/execution.py

@@ -546,10 +546,6 @@ def multi_gpu(self) -> bool:
         # only for WQ
         return (self.spec or {}).get("gpus", 0) > 1
 
-    def train_command(self, initialize: bool = False):
-        command = "psiflow-mace-train"
-        return self.wrap_in_timeout(command)
-
     def wq_resources(self, *args, **kwargs) -> dict:
         if self.spec is None:
             return {}  # threadpool

psiflow/functions.py

@@ -177,6 +177,7 @@ def __call__(
 
 @dataclass(frozen=True)
 class MACEFunction(Function):
+    # TODO: why are some arguments separated and others 'calc_kwargs'?
     model_path: str | Path
     ncores: int
     device: str

psiflow/models/mace.py

@@ -3,17 +3,16 @@
 import logging
 from pathlib import Path
 from enum import StrEnum
-from typing import Optional, Callable, Any
+from typing import Optional, Any
 from collections.abc import Sequence
-from functools import partial
 
 import ase
 import ase.io
 import yaml
 import parsl
 from ase.data import atomic_numbers
 from parsl import bash_app, python_app, File, join_app
-from parsl.dataflow.futures import AppFuture, DataFuture, Future
+from parsl.dataflow.futures import AppFuture, Future
 
 import psiflow
 from psiflow.data import Dataset

psiflow/reference/gpaw_.py

@@ -49,7 +49,7 @@ def parse_output(stdout: str, properties: Sequence[str]) -> dict:
 @psiflow.register_serializable
 class GPAW(Reference):
     executor: str = "GPAW"
-    _execute_label = "gpaw_singlepoint"
+    _execute_label: str = "gpaw_singlepoint"
     parameters: dict
     script: str
 
@@ -59,7 +59,6 @@ def __init__(self, parameters: dict, script: str | Path = FILEPATH, **kwargs):
         assert (script := Path(script)).is_file()
         self.script = str(script.resolve())  # absolute path
         self.bash_template = make_bash_template(self.executor, self.script)
-        print(self.bash_template)
 
     def compute_atomic_energy(self, element, box_size=None) -> AppFuture:
         return copy_app_future(0.0)  # GPAW computes formation energy by default

psiflow/sampling/sampling.py

@@ -9,7 +9,6 @@
 from parsl.app.app import bash_app
 from parsl.data_provider.files import File
 from parsl.dataflow.futures import AppFuture, DataFuture
-from sympy import print_glsl
 
 import psiflow
 from psiflow.data import Dataset