SDLabs Wrapper

This wrapper is used for quick usage of SDLabs for optimization purposes.

Initialize

1. Install the sdlabs_sdk (follow tutorial on SDLabs platform)
2. pip install . in the root directory
3. Fetch your API key from the Account page on SDLabs platform and export it as an env variable: export SDLABS_API_KEY=<paste it here>

Quickstart

Create optimization specs inside of config/optimization_config.json (see JSON schema #/components/schemas/OptimizationConfig)

design_json = {
    "optimization_name": "Example",
    "description": "Optimize example",
    "sdlabs_group_id": "Atinary",
    "parameters": [
        {
            "name": "param_a",
            "low_value": 0,
            "high_value": 10,
            "type": "continuous"
        },
        {
            "name": "param_b",
            "low_value": 0,
            "high_value": 10,
            "type": "continuous"
        }
    ],
    "multi_objective_function": "chimera",
    "objectives": [
        {
            "name": "conductivity",
            "goal": "max",
            "multi_objective_configuration": {
                "hierarchy": 0,
                "relative": 10
            }
        },
        {
            "name": "toxicity",
            "goal": "min",
            "multi_objective_configuration": {
                "hierarchy": 1,
                "relative": 10
            }
        }
    ],
    "batch_size": 1,
    "algorithm": "dragonfly",
    "budget": 20
}

Initialize optimization (with api key). Either pass it as an argument or export it as an env variable SDLABS_API_KEY

import json
import random

file_path = "config/optimization_config.json"
# load config as dict
with open(file_path, "rb") as f:
    config_dict = json.load(f)
wrapper = initialize_optimization(
    spec_file_content=config_dict,
)
for iteration in range(wrapper.config.budget):
    LOGGER.info(f"Iteration {iteration+1}: Fetching new suggestions")
    suggestions = wrapper.get_new_suggestions(max_retries=6, sleep_time_s=30)
    LOGGER.info(f"Iteration {iteration+1} New Suggestions: {suggestions}")
    for suggestion in suggestions:
        for obj in wrapper.config.objectives:
            suggestion.measurements[obj.name] = random.random()
    if suggestions:
        wrapper.send_measurements(suggestions)
        LOGGER.info(f"Iteration {iteration+1} Measurements sent")

Examples

• Optimize Battmo simulation

Constraints (Only supported with enterprise account and premium algorithms)

You can see the format of constraints inside of atinary_wrapper/models.py::Constraint.

Linear Equality Constraints

Here is an example of a LinearEquality constraint where we want the sum of Manganese, Iron, Chromium and Aluminum to add up to 1.0.

"constraints": [
        {
            "type": "linear_eq",
            "definitions": [
                {
                    "parameter": "Manganese",
                    "weight": 1
                },
                {
                    "parameter": "Iron",
                    "weight": 1
                },
                {
                    "parameter": "Chromium",
                    "weight": 1
                },
                {
                    "parameter": "Aluminum",
                    "weight": 1
                }
            ],
            "targets": [
                1
            ]
        }
]

Exclusion constraint

This one only excludes a specific range of a single parameter (Manganese cannot be between 0 and 1).

"constraints": [

        {
            "type":"exclusion",
            "definitions":[
                {"parameter":"Manganese","bounds":[[0,1]]}
            ]
        }
    ]

Conditional Exclusion constraint

This one only excludes a conditional range of 2 or more parameters (Manganese cannot have a value between 0 and 1 if Iron is between 0 and 2 and viceversa)

"constraints": [

        {
            "type":"conditional_exclusion",
            "definitions":[
                {"parameter":"Manganese","bounds":[[0,1]]},
                {"parameter":"Iron","bounds":[[0,2]]}
            ]
        }
    ]

More info

You can inspect the atinary_wrapper/models.py for more information about the data models.

Funding

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957189. The project is part of BATTERY 2030+, the large-scale European research initiative for inventing the sustainable batteries of the future.