def test_submission():
with tempfile.TemporaryDirectory() as directory:
with temporarily_change_directory(directory):
with DaskLocalCluster() as calculation_backend:
# Spin up a server instance.
server = EvaluatorServer(
calculation_backend=calculation_backend,
working_directory=directory,
)
with server:
# Connect a client.
client = EvaluatorClient()
# Submit an empty data set.
force_field_path = "smirnoff99Frosst-1.1.0.offxml"
force_field_source = SmirnoffForceFieldSource.from_path(
force_field_path
)
request, error = client.request_estimate(
PhysicalPropertyDataSet(), force_field_source
)
assert error is None
assert isinstance(request, Request)
result, error = request.results(polling_interval=0.01)
assert error is None
assert isinstance(result, RequestResult)
def main():
setup_timestamp_logging()
# Load in the force field
force_field_path = "openff-1.0.0-refit.offxml"
force_field_source = SmirnoffForceFieldSource.from_path(force_field_path)
# Load in the test set.
data_set = PhysicalPropertyDataSet.from_json("full_set.json")
# Set up a server object to run the calculations using.
working_directory = "working_directory"
# Set up a backend to run the calculations on. This assume running
# on a HPC resources with the LSF queue system installed.
queue_resources = QueueWorkerResources(
number_of_threads=1,
number_of_gpus=1,
preferred_gpu_toolkit=QueueWorkerResources.GPUToolkit.CUDA,
per_thread_memory_limit=5 * unit.gigabyte,
wallclock_time_limit="05:59",
)
worker_script_commands = [
"conda activate forcebalance", "module load cuda/10.1"
]
calculation_backend = DaskLSFBackend(
minimum_number_of_workers=1,
maximum_number_of_workers=50,
resources_per_worker=queue_resources,
queue_name="gpuqueue",
setup_script_commands=worker_script_commands,
adaptive_interval="1000ms",
)
with calculation_backend:
server = EvaluatorServer(
calculation_backend=calculation_backend,
working_directory=working_directory,
port=8002,
)
with server:
# Request the estimates.
client = EvaluatorClient(ConnectionOptions(server_port=8002))
request, _ = client.request_estimate(
property_set=data_set,
force_field_source=force_field_source,
)
# Wait for the results.
results, _ = request.results(True, 5)
results.json(f"results.json")
def main():
setup_timestamp_logging()
working_directory = "working_directory"
# Remove any existing data.
if path.isdir(working_directory):
shutil.rmtree(working_directory)
# Set up a backend to run the calculations on. This assume running
# on a HPC resources with the LSF queue system installed.
queue_resources = QueueWorkerResources(
number_of_threads=1,
number_of_gpus=1,
preferred_gpu_toolkit=QueueWorkerResources.GPUToolkit.CUDA,
per_thread_memory_limit=5 * unit.gigabyte,
wallclock_time_limit="05:59",
)
worker_script_commands = [
"conda activate forcebalance", "module load cuda/10.1"
]
calculation_backend = DaskLSFBackend(
minimum_number_of_workers=1,
maximum_number_of_workers=14,
resources_per_worker=queue_resources,
queue_name="gpuqueue",
setup_script_commands=worker_script_commands,
adaptive_interval="1000ms",
)
with calculation_backend:
server = EvaluatorServer(
calculation_backend=calculation_backend,
working_directory=working_directory,
port=8000,
)
# Tell the server to start listening for estimation requests.
server.start()