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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"from ase import units\n",
"from dask.distributed import Client\n",
"from dask_jobqueue import SLURMCluster\n",
"from dotenv import load_dotenv\n",
"from prefect import flow, task\n",
"from prefect_dask import DaskTaskRunner\n",
"\n",
"from mlip_arena.models import REGISTRY, MLIPEnum\n",
"from mlip_arena.tasks.md import run as MD\n",
"from mlip_arena.tasks.stability.input import get_atoms_from_db\n",
"\n",
"load_dotenv()\n",
"\n",
"HF_TOKEN = os.environ.get(\"HF_TOKEN\", None)\n",
"MP_API_KEY = os.environ.get(\"MP_API_KEY\", None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"nodes_per_alloc = 1\n",
"gpus_per_alloc = 4\n",
"ntasks = 1\n",
"\n",
"cluster_kwargs = dict(\n",
" cores=1,\n",
" memory=\"64 GB\",\n",
" processes=1,\n",
" shebang=\"#!/bin/bash\",\n",
" account=\"matgen\",\n",
" walltime=\"04:00:00\",\n",
" # job_cpu=128,\n",
" job_mem=\"0\",\n",
" job_script_prologue=[\n",
" \"source ~/.bashrc\",\n",
" \"module load python\",\n",
" \"source activate /pscratch/sd/c/cyrusyc/.conda/mlip-arena\",\n",
" ],\n",
" job_directives_skip=[\"-n\", \"--cpus-per-task\", \"-J\"],\n",
" job_extra_directives=[\n",
" \"-J stability-nvt\",\n",
" \"-q preempt\",\n",
" \"--time-min=00:30:00\",\n",
" \"--comment=12:00:00\",\n",
" f\"-N {nodes_per_alloc}\",\n",
" \"-C gpu\",\n",
" f\"-G {gpus_per_alloc}\",\n",
" ],\n",
")\n",
"\n",
"cluster = SLURMCluster(**cluster_kwargs)\n",
"print(cluster.job_script())\n",
"cluster.adapt(minimum_jobs=10, maximum_jobs=50)\n",
"client = Client(cluster)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from prefect.cache_policies import INPUTS, TASK_SOURCE\n",
"from prefect.futures import wait\n",
"\n",
"from mlip_arena.tasks.utils import get_calculator\n",
"\n",
"selected_models = [\n",
" \"MACE-MP(M)\",\n",
" \"CHGNet\",\n",
" \"M3GNet\",\n",
" \"MatterSim\",\n",
" \"eqV2(OMat)\",\n",
" \"MACE-MPA\",\n",
" \"ORBv2\",\n",
" \"SevenNet\",\n",
" \"ALIGNN\",\n",
"]\n",
"\n",
"\n",
"@task(cache_policy=TASK_SOURCE + INPUTS)\n",
"def run_one(\n",
" atoms,\n",
" model,\n",
"):\n",
" try:\n",
" result = MD.with_options(\n",
" # timeout_seconds=600,\n",
" # retries=1,\n",
" refresh_cache=True\n",
" )(\n",
" atoms=atoms,\n",
" calculator=get_calculator(\n",
" model.name,\n",
" calculator_kwargs=None,\n",
" ),\n",
" ensemble=\"nvt\",\n",
" dynamics=\"nose-hoover\",\n",
" time_step=None,\n",
" dynamics_kwargs=dict(\n",
" ttime=25 * units.fs,\n",
" # pfactor=((75 * units.fs) ** 2) * 1e2 * units.GPa\n",
" ),\n",
" total_time=1e4, # 5e4, # fs\n",
" temperature=[300, 3000],\n",
" pressure=None,\n",
" traj_file=f\"{REGISTRY[model.name]['family']}/{model.name}_{atoms.info.get('material_id', 'random')}_{atoms.get_chemical_formula()}_nvt.traj\",\n",
" traj_interval=10,\n",
" )\n",
" except Exception as e:\n",
" print(e)\n",
" return e\n",
"\n",
" return result\n",
"\n",
"\n",
"@flow\n",
"def heat():\n",
" futures = []\n",
" # To download the database automatically, `huggingface_hub login` or provide HF_TOKEN\n",
" for atoms in get_atoms_from_db(\"random-mixture.db\", force_download=False):\n",
" for model in MLIPEnum:\n",
" if model.name not in selected_models:\n",
" continue\n",
"\n",
" future = run_one.with_options(\n",
" timeout_seconds=600, retries=2, refresh_cache=False\n",
" ).submit(atoms.copy(), model)\n",
" futures.append(future)\n",
"\n",
" wait(futures)\n",
"\n",
" return [\n",
" f.result(timeout=None, raise_on_failure=False)\n",
" for f in futures\n",
" if f.state.is_completed()\n",
" ]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"heat.with_options(\n",
" task_runner=DaskTaskRunner(address=client.scheduler.address), log_prints=True\n",
")()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "mlip-arena",
"language": "python",
"name": "mlip-arena"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {},
"version_major": 2,
"version_minor": 0
}
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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