milady — Python launcher for the MiLaDy solver

milady_run(...) stages a working directory and runs mpirun -np N milady_main.exe in it, so a MiLaDy run becomes one Python call.

from milady import milady_run

res = milady_run(
    input="/path/to/vacancy.ml",          # the .ml namelist
    db_model="/path/to/db_model.in",       # the db_model table
    DB="/path/to/MPtrj_2022.9_full.json",  # database: .json / .xyz file, or a classic DB dir
    nprocs=4,
)

print(res.returncode, res.duration_s, res.workdir)
for f in res.output_files:                 # files the run created / changed
    print(f)

Equivalent on the command line:

$ milady-run -i vacancy.ml -m db_model.in -d MPtrj_2022.9_full.json -n 4

What it does

The solver always runs inside a directory and reads fixed names from it. milady_run builds that directory for you:

staged file source name used
name.in derived one line: the run stem (e.g. vacancy)
<stem>.ml input= stem from input's basename, or name=
<db_file> db_model= db_file= in the .ml (default db_model.in)
<db_path> DB= db_path= in the .ml; else DB for a directory (classic DB/), else the file's basename

The database is symlinked by default (it can be tens of GB); pass copy_db=True to copy it. Small text inputs are copied. Extra auxiliary inputs (*.gin, train_files.milady, …) go through extra_files=[...].

Finding the solver

Resolution order (first hit wins):

  1. exe= / mpirun= arguments
  2. $MILADY_EXE / $MILADY_MPIRUN
  3. milady_main.exe / mpirun on PATH (the case after conda install milady)
  4. $CONDA_PREFIX/bin, then $MLD_BUIDIR/bin (and _gnu / _intel / _mix variants)

Runtime shared libraries: $MILADY_LIB (a :-list) if set, otherwise the conda prefix the solver lives in plus whatever ldd currently resolves — these are prepended to LD_LIBRARY_PATH for the child process only.

Key options

argument default meaning
nprocs 1 MPI ranks (mpirun -np)
workdir fresh temp dir where to run; not deleted afterwards
name from input run stem
mpi_args () extra launcher tokens, e.g. ["--oversubscribe"]
env None extra environment for the run
use_mpirun None None = auto (launcher if found or nprocs>1)
stream_output False also echo solver output live
check True raise MiladyError on non-zero exit
timeout None seconds before the run is killed
log_file "milady.out" combined stdout+stderr log written in workdir
output None extra combined-log path (abs, or relative to CWD), e.g. run.out

Install

The PyPI distribution name is milady-mlip; the import name stays milady.

want command contents
solver + launcher, no toolchain pip install milady-mlip self-contained wheel (Linux x86-64); pulls mkl
solver + launcher, via conda conda install -c <ch> milady milady + milady-bin (see ../conda/)
launcher only (you supply milady_main.exe) pip install ./python pure Python, any OS

The self-contained wheel is built by wheel/build_wheel.sh (desktop) or the wheels GitHub Actions workflow (manylinux, PyPI-ready) — see wheel/README.md.

Project links

Key dates

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Credits

Author: Cosmin Marinica

License

LicenseRef-MILADY

Requires

Python >=3.8

Provides Extra

test

Tags

milady machine-learning interatomic-potential MLIP materials

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