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)