diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4050ea0..797cf99 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -10,17 +10,17 @@ repos: exclude: '\.(json)$' - id: debug-statements - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.17.2 + rev: v0.17.5 hooks: - id: ruff-check args: ["--fix"] - id: ruff-format - repo: https://github.com/tox-dev/pyproject-fmt - rev: v2.30.3 + rev: v2.30.6 hooks: - id: pyproject-fmt - repo: https://github.com/tombi-toml/tombi-pre-commit - rev: v1.9.6 + rev: v1.11.2 hooks: - id: tombi-format args: ["--offline"] diff --git a/examples/mvp/run.py b/examples/mvp/run.py index a402b58..c10a478 100644 --- a/examples/mvp/run.py +++ b/examples/mvp/run.py @@ -19,7 +19,13 @@ N_MOLECULES = 200 INITIAL_FORCE_FIELD = "openff-2.3.0.offxml" -MERGE_DATA = False +# these weights should be exposed to the user in some sort of training config +# probably a good phase II task +COEFFICIENTS = { + "energy": 0.1, + "force": 0.01, + "thermo": 100_000.0, +} def load_datasets(root: Path) -> tuple[Dataset, Dataset]: @@ -58,9 +64,7 @@ def main(root: Path) -> None: smiles_set={ *thermo.extract_smiles(thermo_data), *energy.extract_smiles(energy_data), - } - if MERGE_DATA - else {*thermo.extract_smiles(thermo_data)}, + }, smirnoff_force_field=ForceField(INITIAL_FORCE_FIELD), ) @@ -75,36 +79,23 @@ def main(root: Path) -> None: }, attributes={}, ) + theta = trainable.to_values().requires_grad_(True) + + # TODO: Possible drop in L-M optimizer optimizer = Adam([theta], lr=0.01) # TODO: inner/outer loop, or any way of stepping through QM optimization # without running MD jobs each time for epoch in range(N_EPOCHS): - # maybe do this outside of loop for iteration 0, then - # at end of loop for [1, ..., N_EPOCHS]? + # this is linked under the hood to theta - needed to gradients together current_ff = trainable.to_force_field(theta) # 287 - # import json - # from tyff._serialization import load_tensor_force_field - - # sum(t.numel() for t in _pack_force_field(ref)[0] if t is not None) - # is probably 208 - - # current_ff = load_tensor_force_field( - # json.loads( - # open( - # 'examples/mvp/EPOCH_0/simulations/reference_force_fields/reference.json' - # ).read() - # ) - # ) - - # no return value? simulate( force_field=current_ff, epoch_dir=root / f"EPOCH_{epoch}", thermo_data=thermo_data, - smirnoff_force_field=ForceField(INITIAL_FORCE_FIELD), + smirnoff_force_field=INITIAL_FORCE_FIELD, n_replicates=N_REPLICATES, n_molecules=N_MOLECULES, ) @@ -115,27 +106,26 @@ def main(root: Path) -> None: epoch_dir=root / f"EPOCH_{epoch}", ) - if MERGE_DATA: - energy_ref, energy_pred, forces_ref, forces_pred = energy.predict( - energy_data, - current_ff, - topologies, - normalize=False, - ) + energy_ref, energy_pred, forces_ref, forces_pred = energy.predict( + energy_data, + current_ff, + topologies, + normalize=False, + ) - # the weights in front of each category of loss (energy, forces, and thermo) - # should be exposed to the user in some sort of training config - # probably a good phase II task - loss = ( - (energy_pred - energy_ref).square().mean() - + 0.1 * (forces_pred - forces_ref).square().mean() - + (y_pred - y_ref).square().mean() - ) - else: - loss = (y_pred - y_ref).square().mean() + # can construct loss function in many different ways, including modifying coefficients + loss = ( + COEFFICIENTS["energy"] * (energy_pred - energy_ref).square().mean() + + COEFFICIENTS["force"] * (forces_pred - forces_ref).square().mean() + + COEFFICIENTS["thermo"] * (y_pred - y_ref).square().mean() + ) logger.info(f"EPOCH {epoch}: loss={loss.item():.6f}") print(f"EPOCH {epoch}: loss={loss.item():.6f}") + print(f"\tloss component energy (after applying {COEFFICIENTS=})") + print(f"\tenergy {COEFFICIENTS['energy'] * (energy_pred - energy_ref).square().mean()}") + print(f"\tforce {COEFFICIENTS['force'] * (forces_pred - forces_ref).square().mean()}") + print(f"\tthermo {COEFFICIENTS['thermo'] * (y_pred - y_ref).square().mean()}") optimizer.zero_grad() @@ -156,7 +146,7 @@ def main(root: Path) -> None: indent=4, ) - # write out FF as SMIRNOFF force field + # update SMIRNOFF force field with new values from tensor FF convert_tensor_force_field( original_force_field=ForceField(INITIAL_FORCE_FIELD), tensor_force_field=updated_tensor_force_field, diff --git a/pixi.lock b/pixi.lock index a371af5..7e958ff 100644 --- a/pixi.lock +++ b/pixi.lock @@ -20,7 +20,7 @@ environments: packages: linux-64: - 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name: types-pyyaml - version: 6.0.12.20261103 - sha256: 444654c61f635dab82c092a511aa96a0ce31164810784b9a37314e3e56532ac8 - requires_python: '>=3.10' - pypi: https://files.pythonhosted.org/packages/e8/ea/47fbdc8f37410956fd445720e927589b1e2a47772c5f28109f0ce2fa26d9/pytest_mock-3.17.0-py3-none-any.whl name: pytest-mock version: 3.17.0 @@ -21606,6 +21411,11 @@ packages: - typing-extensions>=4 ; extra == 'optional' - google-re2>=1.1 ; extra == 're2' requires_python: '>=3.9' +- pypi: https://files.pythonhosted.org/packages/f2/8a/1e29c0c3785ae7e5cd0d54ee8717a08867ef71545796340be4c653c57e24/ruff-0.17.5-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl + name: ruff + version: 0.17.5 + sha256: 40ca8afaac309b41a5b2e09e4e7beb1f824826b93c25697c4d74f573324fba4e + requires_python: '>=3.7' - pypi: https://files.pythonhosted.org/packages/f9/33/bd5b9137445ea4b680023eb0469b2bb969d61303dedb2aac6560ff3d14a1/notebook_shim-0.2.4-py3-none-any.whl name: notebook-shim version: 0.2.4 @@ -21634,3 +21444,8 @@ packages: version: 0.16.0 sha256: 5bcc2c4726ced915b00de0c9856a4eeabfb3fddb93e10e0b8f735b7709358b6d requires_python: '>=3.9' +- pypi: https://files.pythonhosted.org/packages/fd/2e/36fc0941a40ef066dbc8abac41bbbefb41389f547c779aef832aea6b873d/types_pyyaml-6.0.12.20261113-py3-none-any.whl + name: types-pyyaml + version: 6.0.12.20261113 + sha256: 3600749e613748afdc4327f0b17c3296cf880cdbc7335f320007abd9dd815f67 + requires_python: '>=3.10' diff --git a/pixi.toml b/pixi.toml index 0d1e312..4304b57 100644 --- a/pixi.toml +++ b/pixi.toml @@ -7,7 +7,7 @@ platforms = ["linux-64", "osx-arm64"] run_mvp = "python examples/mvp/run.py" run_mypy = "python -m mypy -p 'tyff'" run_tests = { cmd = "python -m pytest -n logical tyff/_tests", env = { OPENMM_DEFAULT_PLATFORM = "CPU" } } -dev_tests = { cmd = "python -m pytest -s -x --pdb --lf tyff/_tests", env = { OPENMM_DEFAULT_PLATFORM = "CPU" } } +dev_tests = "python -m pytest -s -x --pdb --lf tyff/_tests" [dependencies] # conda-only dependencies diff --git a/tyff/_tests/compute/__init__.py b/tyff/_tests/compute/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tyff/_tests/compute/test_pack.py b/tyff/_tests/compute/test_pack.py index 6ccf38a..e4045af 100644 --- a/tyff/_tests/compute/test_pack.py +++ b/tyff/_tests/compute/test_pack.py @@ -12,13 +12,13 @@ @pytest.fixture def bulk_liquid() -> BulkLiquid: - return BulkLiquid( + return BulkLiquid( # type: ignore tag="liquid", # TODO: might make tests quicker to have a slimmed-down Sage with fewer parameters # (but still use AshGC charges) force_field="openff-2.3.0.offxml", n_molecules=100, - smiles=["CC", "O"], + smiles=["[H:3][C:1]([H:4])([H:5])[C:2]([H:6])([H:7])[H:8]", "[H:2][O:1][H:3]"], x=[0.6, 0.4], temperature=301.15, pressure=101.325, diff --git a/tyff/_tests/integration_tests/__init__.py b/tyff/_tests/integration_tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tyff/compute/_equilibrate.py b/tyff/compute/_equilibrate.py index fed9387..642bb10 100644 --- a/tyff/compute/_equilibrate.py +++ b/tyff/compute/_equilibrate.py @@ -10,6 +10,7 @@ EquilibrationFiles, MinimizationFiles, ) +from tyff.compute._openmm import _get_platform from tyff.configs.liquid import BulkLiquid from tyff.exceptions import PressureNotDefinedError @@ -71,6 +72,7 @@ def _run_equilibration( topology, system, integrator, + platform=_get_platform(logger), ) try: @@ -142,7 +144,7 @@ def _run_equilibration( output_file=files["msgpack_trajectory"].filepath, report_interval=1000, beta=1.0 / (openmm.unit.MOLAR_GAS_CONSTANT_R * compute_config["temperature"] * openmm.unit.kelvin), - pressure=pressure if pressure is None else openmm.unit.kilopascal, + pressure=pressure if pressure is None else pressure * openmm.unit.kilopascal, ) ) @@ -152,7 +154,10 @@ def _run_equilibration( logger.info(f"Using OpenMM platform: {detected_platform}") - simulation.step(10_000) + try: + simulation.step(10_000) + except openmm.OpenMMException as error: + raise RuntimeError(f"OpenMM crashed during equilibration in {job_dir=}") from error with open(files["topology"].filepath, "w") as f: openmm.app.PDBFile.writeFile( diff --git a/tyff/compute/_jacobian.py b/tyff/compute/_jacobian.py index 2745a06..fff6954 100644 --- a/tyff/compute/_jacobian.py +++ b/tyff/compute/_jacobian.py @@ -78,7 +78,7 @@ def _get_ensemble_average_and_jacobian( compute_config = BulkLiquid(**json.load(f)) # type: ignore[typeddict-item] temperature = compute_config["temperature"] # kelvin, float - pressure = compute_config.get("pressure") # atmosphere, float | None + pressure = compute_config.get("pressure") # kPa, float | None interchanges = [] @@ -141,7 +141,7 @@ def _get_ensemble_average_and_jacobian( worker_force_field, frames_path, temperature * openmm.unit.kelvin, - None if pressure is None else pressure * openmm.unit.atmosphere, + None if pressure is None else pressure * openmm.unit.kilopascal, ) # originally this was sorted(means), @@ -154,7 +154,11 @@ def _get_ensemble_average_and_jacobian( # save ensemble averages and jacobian in each job dir, consider revisiting this decision in the future with open(f"{job_dir}/ensemble_averages.json", "w") as f: - json.dump({name: value.detach().tolist() for name, value in means.items()}, f) + json.dump( + {name: value.detach().tolist() for name, value in means.items()}, + f, + indent=4, + ) with open(f"{job_dir}/jacobian.pt", "wb") as f: torch.save(jacobian.detach(), f) diff --git a/tyff/compute/_minimize.py b/tyff/compute/_minimize.py index 201f2db..9f24710 100644 --- a/tyff/compute/_minimize.py +++ b/tyff/compute/_minimize.py @@ -6,6 +6,7 @@ from parsl import File from tyff.compute._files import MinimizationFiles, PreparingFiles +from tyff.compute._openmm import _get_platform from tyff.configs.liquid import BulkLiquid @@ -49,21 +50,13 @@ def _minimize_energy( # this should be in Kelvin temperature = compute_config["temperature"] - molecules = [Molecule.from_smiles(smiles) for smiles in compute_config["smiles"]] + molecules = [Molecule.from_mapped_smiles(smiles) for smiles in compute_config["smiles"]] topology = Topology.from_pdb( system_future["prepared_files"]["packed_topology"].filepath, unique_molecules=molecules, ) - try: - platform = openmm.Platform.getPlatformByName("CUDA") - logger.info("Trying to use CUDA platform ...") - except (openmm.OpenMMException, Exception) as error: - platform = openmm.Platform.getPlatformByName("CPU") - logger.info(f"Could not use CUDA platform because {error}") - logger.info("Trying to use CPU platform ...") - simulation = openmm.app.Simulation( topology=topology.to_openmm(), system=system, @@ -72,7 +65,7 @@ def _minimize_energy( 1.0 / openmm.unit.picosecond, 1.0 * openmm.unit.femtoseconds, # TODO: This should be user input ), - platform=platform, + platform=_get_platform(logger), ) simulation.context.setPositions(topology.get_positions().to_openmm()) @@ -83,7 +76,10 @@ def _minimize_energy( original_state = simulation.context.getState(energy=True) - simulation.minimizeEnergy() + try: + simulation.minimizeEnergy() + except openmm.OpenMMException as error: + raise RuntimeError(f"OpenMM crashed during minimization in {job_dir=}") from error final_state = simulation.context.getState(energy=True, positions=True) diff --git a/tyff/compute/_openmm.py b/tyff/compute/_openmm.py new file mode 100644 index 0000000..dfef95f --- /dev/null +++ b/tyff/compute/_openmm.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +import logging +import os + +import openmm + + +def _get_platform(logger: logging.Logger) -> openmm.Platform: + """ + Get the OpenMM platform to use for minimization. + + Returns + ------- + openmm.Platform + The OpenMM platform to use for minimization. + """ + if "OPENMM_DEFAULT_PLATFORM" in os.environ: + logger.info("Using platform set in $OPENMM_DEFAULT_PLATFORM") + logger.info(f"\t({os.environ['OPENMM_DEFAULT_PLATFORM']=})") + return openmm.Platform.getPlatformByName(openmm.os.environ["OPENMM_DEFAULT_PLATFORM"]) + + available_platforms = [openmm.Platform.getPlatform(i).getName() for i in range(openmm.Platform.getNumPlatforms())] + for platform_name in ["CUDA", "OpenCL", "CPU"]: + if platform_name not in available_platforms: + logger.info(f"Could not use {platform_name} platform because it is not detected as available") + continue + + try: + logger.info(f"Trying to use {platform_name} platform ...") + platform = openmm.Platform.getPlatformByName(platform_name) + except openmm.OpenMMException as error: + logger.info(f"Could not use {platform_name} platform because {error}") + continue + + logger.info(f"Successfully requested platform {platform.getName()}") + + return platform + + raise ValueError(f"Could not find a usable OpenMM platform. Available platforms: {available_platforms}") diff --git a/tyff/compute/_pack.py b/tyff/compute/_pack.py index fddd166..976c55a 100644 --- a/tyff/compute/_pack.py +++ b/tyff/compute/_pack.py @@ -41,7 +41,7 @@ def _prepare_packed_topology( time.sleep(n_molecules * 0.001) packed_topology_file = f"{job_dir}/packed_topology.pdb" - molecules = [Molecule.from_smiles(smiles) for smiles in compute_config["smiles"]] + molecules = [Molecule.from_mapped_smiles(smiles) for smiles in compute_config["smiles"]] n_copies = [int(n_molecules * x) for x in compute_config["x"]] diff --git a/tyff/compute/_produce.py b/tyff/compute/_produce.py index e5903df..9da48f9 100644 --- a/tyff/compute/_produce.py +++ b/tyff/compute/_produce.py @@ -12,6 +12,7 @@ EquilibrationFiles, ProductionFiles, ) +from tyff.compute._openmm import _get_platform from tyff.configs.liquid import BulkLiquid from tyff.exceptions import PressureNotDefinedError @@ -67,6 +68,7 @@ def _run_production( topology, system, integrator, + platform=_get_platform(logger), ) try: @@ -138,21 +140,22 @@ def _run_production( output_file=files["msgpack_trajectory"].filepath, report_interval=1000, beta=1.0 / (openmm.unit.MOLAR_GAS_CONSTANT_R * compute_config["temperature"] * openmm.unit.kelvin), - pressure=pressure if pressure is None else openmm.unit.kilopascal, + pressure=pressure if pressure is None else pressure * openmm.unit.kilopascal, ) ) logger.info("Running 100,000 steps of MD") - platform = simulation.context.getPlatform() - platform_name = platform.getName() + detected_platform = simulation.context.getPlatform().getName() - # Log or print the result - logger.info(f"Using OpenMM platform: {platform_name}") + logger.info(f"Using OpenMM platform: {detected_platform}") for index in range(10): logger.info(f"Running from step {index * 10_000} to step {(index + 1) * 10_000}") - simulation.step(10_000) + try: + simulation.step(10_000) + except openmm.OpenMMException as error: + raise RuntimeError(f"OpenMM crashed during production run in {job_dir=}") from error with open(files["topology"].filepath, "w") as f: openmm.app.PDBFile.writeFile( diff --git a/tyff/compute/configs.py b/tyff/compute/configs.py index 303d1df..54e5ea4 100644 --- a/tyff/compute/configs.py +++ b/tyff/compute/configs.py @@ -1,20 +1,17 @@ +import platform + from parsl.config import Config -from parsl.executors import HighThroughputExecutor, ThreadPoolExecutor +from parsl.executors import HighThroughputExecutor from parsl.monitoring.monitoring import MonitoringHub from parsl.providers import LocalProvider, SlurmProvider -from parsl.utils import get_all_checkpoints def local_config(max_workers: int = 10): - """For development and testing.""" - if False: - # If openff-packmol/temporary_cd was threadsafe, better to use - # ThreadPoolExecutor and local config - return Config( - executors=[ThreadPoolExecutor(label="local")], - checkpoint_mode="task_exit", - checkpoint_files=get_all_checkpoints(), - ) + """For development and testing - CPU, but force OpenCL on macOS.""" + worker_init = "export OMP_NUM_THREADS=1; " + + if platform.system() == "Darwin": + worker_init += "export OPENMM_DEFAULT_PLATFORM=OpenCL; " return Config( executors=[ @@ -24,7 +21,7 @@ def local_config(max_workers: int = 10): init_blocks=1, min_blocks=1, max_blocks=1, - worker_init=("export OPENMM_DEFAULT_PLATFORM=CPU; export OMP_NUM_THREADS=1; "), + worker_init=worker_init, ), max_workers_per_node=max_workers, cores_per_worker=1, diff --git a/tyff/compute/workflow.py b/tyff/compute/workflow.py index a8990be..26c58e7 100644 --- a/tyff/compute/workflow.py +++ b/tyff/compute/workflow.py @@ -73,7 +73,7 @@ def __init__(self, base_dir: str, parsl_config: parsl.Config): logger.info(f"Initialized SimulationWorkflow with base_dir={base_dir}") - parsl.load(parsl_config) + self._config = parsl_config logger.info("Parsl config loaded") @@ -162,11 +162,32 @@ def submit_target( def submit_dataset( self, dataset: datasets.Dataset, # should be something that unpacks to DataEntry on iteration + tensor_force_field: tyff.TensorForceField, force_field: str, n_molecules: int, n_replicates: int = 3, ): - import openff.toolkit + """ + Submit a dataset of targets for computation. + + Parameters + ---------- + dataset : datasets.Dataset + The dataset containing targets to be submitted. + tensor_force_field : tyff.TensorForceField + The tensor representation of the force field to be used. + force_field : str + The path to the reference force field. + n_molecules : int + The number of molecules to consider for each target. + n_replicates : int, optional + The number of replicates for each target, by default 3. + + Returns + ------- + list + A list of results from submitting each target. + """ from tyff._serialization import dump_tensor_force_field @@ -185,52 +206,16 @@ def submit_dataset( with open(pathlib.Path(self.base_dir) / "thermo_targets" / "targets.json", "w") as f: json.dump(targets, f, indent=4) - unique_smiles = set() - - for target in dataset: - target: DataEntry - for smiles_ in target["smiles"]: - unique_smiles.add(smiles_) - - # TODO: Probably drop this - (pathlib.Path(self.base_dir) / "interchanges").mkdir(exist_ok=True) - - interchanges = list() - - force_field_ = openff.toolkit.ForceField(force_field) - for index, unique_smiles_ in enumerate(sorted(unique_smiles)): - interchange = force_field_.create_interchange( - openff.toolkit.Molecule.from_mapped_smiles(unique_smiles_).to_topology() - ) - interchanges.append(interchange) - - with open(pathlib.Path(self.base_dir) / "interchanges" / f"interchange_{index}.json", "w") as f: - f.write(interchange.model_dump_json(indent=4)) - - self._reference_force_fields = pathlib.Path(self.base_dir) / "reference_force_fields" - self._reference_force_fields.mkdir(exist_ok=True) - - assert self._reference_force_fields.exists() - assert self._reference_force_fields.is_dir() - - logger.info(f"Made a path to store reference force fields at {self._reference_force_fields}") - # this is a "global"/reference force field composed from all unique molecules, - # don't think we're going to use the topologies here - reference_force_field, _tensor_topologies = tyff.converters.convert_interchange(interchanges) - - # make sure this is not empty - assert len(reference_force_field.potentials) > 0 - logger.info( "Writing reference force field from path (which is probably outside of " f"this workflow's root): {force_field=}" - f"to path {self._reference_force_fields / 'reference.json'}" + f"to path {pathlib.Path(self.base_dir) / 'reference_force_field.json'}" ) # dumps a JSON representation of the tensor representation of the SMIRNOFF force field - with open(self._reference_force_fields / "reference.json", "w") as f: + with open(pathlib.Path(self.base_dir) / "reference_force_field.json", "w") as f: json.dump( - dump_tensor_force_field(reference_force_field), + dump_tensor_force_field(tensor_force_field), f, indent=4, ) @@ -369,7 +354,9 @@ def _common_run(self, compute_config: BaseComputeConfig) -> tuple[dict[str, torc indent=4, ) - if pathlib.Path(job_dir, "ensemble_averages.json").exists(): + if (pathlib.Path(job_dir, "ensemble_averages.json").exists()) and ( + pathlib.Path(job_dir, "jacobian.pt").exists() + ): logger.info(f"short-circuiting {job_id}!") # already done, skip @@ -413,17 +400,20 @@ def _common_run(self, compute_config: BaseComputeConfig) -> tuple[dict[str, torc jacobian_future = create_jacobian( production_future=production_future, job_dir=job_dir, - # pathing here is weird, but there should only be one global reference - # force field for this workflow - reference_force_field_path=self._reference_force_fields / "reference.json", + reference_force_field_path=pathlib.Path(self.base_dir) / "reference_force_field.json", ) return {"job_id": job_id, "future": jacobian_future} def shutdown(self): + self._dfk.cleanup() parsl.clear() def __enter__(self): + parsl.clear() + + self._dfk = parsl.load(self._config) + return self def __exit__(self, *args): diff --git a/tyff/targets/new_thermo.py b/tyff/targets/new_thermo.py index 853c7bb..6326859 100644 --- a/tyff/targets/new_thermo.py +++ b/tyff/targets/new_thermo.py @@ -19,7 +19,7 @@ def simulate( force_field: tyff.TensorForceField, epoch_dir: Path, thermo_data: Dataset, - smirnoff_force_field: ForceField, + smirnoff_force_field: str, n_molecules: int, n_replicates: int, ) -> None: @@ -36,8 +36,8 @@ def simulate( The directory for this epoch where simulation results will be stored. thermo_data : Dataset The dataset containing thermodynamic data for the simulation. - smirnoff_force_field : ForceField - The original SMIRNOFF force field corresponding to the tensor force field. + smirnoff_force_field : str + The path to the original SMIRNOFF force field corresponding to the tensor force field. n_molecules : int The number of molecules to simulate. n_replicates : int @@ -50,14 +50,14 @@ def simulate( # write out this epoch's (tensor) force field as a SMIRNOFF force field current_smirnoff_force_field = convert_tensor_force_field( - original_force_field=smirnoff_force_field, + original_force_field=ForceField(smirnoff_force_field), tensor_force_field=force_field, ) epoch_dir.mkdir(exist_ok=True) smirnoff_force_field_file = str(epoch_dir / "current_force_field.offxml") - current_smirnoff_force_field.to_file(smirnoff_force_field_file) + current_smirnoff_force_field.to_file(filename=smirnoff_force_field_file) with SimulationWorkflow( base_dir=str(epoch_dir / "simulations"), @@ -65,13 +65,17 @@ def simulate( ) as workflow: workflow.submit_dataset( dataset=thermo_data, - force_field=smirnoff_force_field_file, + tensor_force_field=force_field, + force_field=smirnoff_force_field_file, # possibly smoother to pass through openff.toolkit.ForceField n_molecules=n_molecules, n_replicates=n_replicates, ) workflow.wait() + # here may be a place to free up memory, i.e. `del workflow` or something more involved with Parsl + # but this probably should be handled automatically by SimulationWorkflow? + def load_remote_observables( force_field_per_epoch: tyff.TensorForceField,