airavata-api/airavata-client-sdks/airavata-python-sdk/airavata_experiments/base.py (77 lines of code) (raw):

# Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import annotations import abc from itertools import product from typing import Any, Generic, TypeVar import uuid import random from .plan import Plan from .runtime import Runtime from .task import Task class GUIApp: app_id: str def __init__(self, app_id: str) -> None: self.app_id = app_id def open(self, runtime: Runtime, location: str) -> None: """ Open the GUI application """ raise NotImplementedError() @classmethod @abc.abstractmethod def initialize(cls, **kwargs) -> GUIApp: ... class ExperimentApp: app_id: str def __init__(self, app_id: str) -> None: self.app_id = app_id @classmethod @abc.abstractmethod def initialize(cls, **kwargs) -> Experiment: ... T = TypeVar("T", ExperimentApp, GUIApp) class Experiment(Generic[T], abc.ABC): name: str application: T inputs: dict[str, Any] input_mapping: dict[str, tuple[Any, str]] resource: Runtime = Runtime.default() tasks: list[Task] = [] def __init__(self, name: str, application: T): self.name = name self.application = application self.input_mapping = {} def with_inputs(self, **inputs: Any) -> Experiment[T]: """ Add shared inputs to the experiment """ self.inputs = inputs return self def with_resource(self, resource: Runtime) -> Experiment[T]: self.resource = resource return self def create_task(self, *allowed_runtimes: Runtime, name: str | None = None) -> None: """ Create a task to run the experiment on a given runtime. """ runtime = random.choice(allowed_runtimes) if len(allowed_runtimes) > 0 else self.resource uuid_str = str(uuid.uuid4())[:4].upper() self.tasks.append( Task( name=name or f"{self.name}_{uuid_str}", app_id=self.application.app_id, inputs={**self.inputs}, runtime=runtime, ) ) print(f"Task created. ({len(self.tasks)} tasks in total)") def add_sweep(self, *allowed_runtimes: Runtime, **space: list) -> None: """ Add a sweep to the experiment. """ for values in product(space.values()): runtime = random.choice(allowed_runtimes) if len(allowed_runtimes) > 0 else self.resource uuid_str = str(uuid.uuid4())[:4].upper() task_specific_params = dict(zip(space.keys(), values)) agg_inputs = {**self.inputs, **task_specific_params} task_inputs = {k: {"value": agg_inputs[v[0]], "type": v[1]} for k, v in self.input_mapping.items()} self.tasks.append(Task( name=f"{self.name}_{uuid_str}", app_id=self.application.app_id, inputs=task_inputs, runtime=runtime or self.resource, )) def plan(self, **kwargs) -> Plan: if len(self.tasks) == 0: self.create_task(self.resource) tasks = [] for t in self.tasks: agg_inputs = {**self.inputs, **t.inputs} task_inputs = {k: {"value": agg_inputs[v[0]], "type": v[1]} for k, v in self.input_mapping.items()} tasks.append(Task(name=t.name, app_id=self.application.app_id, inputs=task_inputs, runtime=t.runtime)) return Plan(tasks=tasks)