Source code for tvb.adapters.simulator.hpc_simulator_adapter

# -*- coding: utf-8 -*-
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"""
.. moduleauthor:: Paula Popa <paula.popa@codemart.ro>
"""

import os
import uuid
import typing

from tvb.adapters.analyzers.metrics_group_timeseries import TimeseriesMetricsAdapterModel, \
    TimeseriesMetricsAdapter
from tvb.adapters.datatypes.db.mapped_value import DatatypeMeasureIndex
from tvb.adapters.datatypes.db.simulation_history import SimulationHistoryIndex
from tvb.adapters.datatypes.db.time_series import TimeSeriesIndex
from tvb.adapters.simulator.simulator_adapter import SimulatorAdapter, SimulatorAdapterModel
from tvb.basic.neotraits.api import HasTraits
from tvb.config import ALGORITHMS
from tvb.core.entities.generic_attributes import GenericAttributes
from tvb.core.neocom import h5
from tvb.core.neotraits.h5 import H5File, ViewModelH5
from tvb.core.services.backend_clients.hpc_scheduler_client import HPCSchedulerClient


[docs] class HPCSimulatorAdapter(SimulatorAdapter): def __init__(self, storage_path, is_group_launch): super(HPCSimulatorAdapter, self).__init__() self.storage_path = storage_path self.is_group_launch = is_group_launch def _prelaunch(self, operation, view_model, available_disk_space=0): self.available_disk_space = available_disk_space super(HPCSimulatorAdapter, self)._prelaunch(operation, view_model, available_disk_space)
[docs] def get_output(self): return [TimeSeriesIndex, SimulationHistoryIndex, DatatypeMeasureIndex]
[docs] def load_traited_by_gid(self, data_gid): # type: (uuid.UUID) -> HasTraits """ Load a generic HasTraits instance, specified by GID. """ trait, _ = h5.load_with_links_from_dir(self.storage_path, data_gid) return trait
[docs] def load_with_references(self, dt_gid): # type: (typing.Union[uuid.UUID, str]) -> HasTraits dt, _ = h5.load_with_references_from_dir(self.storage_path, dt_gid) return dt
def _try_load_region_mapping(self): return None, None def _is_group_launch(self): """ Return true if this adapter is launched from a group of operations """ return self.is_group_launch def _get_output_path(self): output_path = os.path.join(self.storage_path, HPCSchedulerClient.OUTPUT_FOLDER) if not os.path.isdir(output_path): os.mkdir(output_path) return output_path
[docs] def extract_operation_data(self, operation=None): """ Do nothing for HPC run. :param operation: None """
def _update_operation_entity(self, operation, required_disk_space): """ """ def _capture_operation_results(self, result): """ Update h5 files with generic attributes """ storage_path = self._get_output_path() for file in os.listdir(storage_path): path = os.path.join(storage_path, file) if issubclass(H5File.h5_class_from_file(path), ViewModelH5): continue with H5File.from_file(path) as f: f.store_generic_attributes(self.generic_attributes) return "", 2 def _ensure_enough_resources(self, available_disk_space, view_model): return 0
[docs] def launch(self, view_model): # type: (SimulatorAdapterModel) -> [TimeSeriesIndex, SimulationHistoryIndex] simulation_results = super(HPCSimulatorAdapter, self).launch(view_model) if not self.is_group_launch: return simulation_results for dt in simulation_results: if issubclass(type(dt), TimeSeriesIndex): self._compute_metrics_for_pse_launch(dt) return simulation_results
def _compute_metrics_for_pse_launch(self, time_series_index): # type: (TimeSeriesIndex) -> [DatatypeMeasureIndex] metric_vm = TimeseriesMetricsAdapterModel() metric_vm.time_series = time_series_index.gid metric_vm.algorithms = tuple(ALGORITHMS.keys()) h5.store_view_model(metric_vm, self._get_output_path()) metric_adapter = HPCTimeseriesMetricsAdapter(self._get_output_path(), time_series_index) metric_adapter._prelaunch(None, metric_vm, self.available_disk_space)
[docs] def path_for(self, h5_file_class, gid, dt_class=None): return h5.path_by_dir(self.storage_path, h5_file_class, gid, dt_class)
[docs] def store_complete(self, datatype, generic_attributes=GenericAttributes()): return h5.store_complete_to_dir(datatype, self.storage_path, generic_attributes)
[docs] def get_storage_path(self): """ """
[docs] class HPCTimeseriesMetricsAdapter(TimeseriesMetricsAdapter): def __init__(self, storage_path, input_time_series_index): super(HPCTimeseriesMetricsAdapter, self).__init__() self.storage_path = storage_path self.input_time_series_index = input_time_series_index
[docs] def configure(self, view_model): # type: (TimeseriesMetricsAdapterModel) -> None """ Store the input shape to be later used to estimate memory usage. """ self.input_shape = (self.input_time_series_index.data_length_1d, self.input_time_series_index.data_length_2d, self.input_time_series_index.data_length_3d, self.input_time_series_index.data_length_4d)
[docs] def load_traited_by_gid(self, data_gid): # type: (typing.Union[uuid.UUID, str]) -> HasTraits """ Load a generic HasTraits instance, specified by GID. """ trait, _ = h5.load_with_links_from_dir(self.storage_path, data_gid) return trait
def _get_output_path(self): return self.storage_path
[docs] def extract_operation_data(self, operation=None): """ Do nothing for HPC run. :param operation: None """
def _update_operation_entity(self, operation, required_disk_space): """ """ def _capture_operation_results(self, result): """ """ return "", 1 def _ensure_enough_resources(self, available_disk_space, view_model): return 0
[docs] def path_for(self, h5_file_class, gid, dt_class=None): return h5.path_by_dir(self.storage_path, h5_file_class, gid, dt_class)
[docs] def store_complete(self, datatype, generic_attributes=GenericAttributes()): return h5.store_complete_to_dir(datatype, self.storage_path, generic_attributes)