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Flopt performance

Bump jinja2 from 3.0.3 to 3.1.3 (#112) #111

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Bump jinja2 from 3.0.3 to 3.1.3 (#112)

Bump jinja2 from 3.0.3 to 3.1.3 (#112) #111

Workflow file for this run

.github/workflows/flopt-performance.yml at eca9de3
# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
# For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions
name: Flopt performance
on:
push:
branches: [ "develop", "main", "master" ]
pull_request:
branches: [ "develop", "main", "master" ]
permissions:
contents: read
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python 3.9
uses: actions/setup-python@v3
with:
python-version: "3.9"
- name: Preprocess
run: |
python -m pip install --upgrade pip
python -m pip install wheel
python -m pip install -r requirements.txt
cp performance/compute_performance.py compute_performance.py
cp performance/fit_models.py fit_models.py
- name: Compute performance of func dataset
run: |
python compute_performance.py Random Random --datasets func --timelimit 2 --log_level 10
python compute_performance.py OptunaTPE OptunaTPE --datasets func --timelimit 2 --log_level 10
python compute_performance.py Hyperopt Hyperopt --datasets func --timelimit 2 --log_level 10
python compute_performance.py OptunaCmaEs OptunaCmaEs --datasets func --timelimit 2 --log_level 10
python compute_performance.py SFLA SFLA --datasets func --timelimit 2 --log_level 10
python compute_performance.py Scipy Scipy --datasets func --timelimit 2 --log_level 10
python compute_performance.py auto auto --datasets func --timelimit 2 --log_level 10
- name: Compute performance of tsp dataset
run: |
python compute_performance.py Random Random --datasets tsp --timelimit 2 --log_level 10
python compute_performance.py 2-Opt 2-Opt --datasets tsp --timelimit 2 --log_level 10
python compute_performance.py auto auto --datasets func --timelimit 2 --log_level 10
- name: Compute performance of mip dataset
run: |
python compute_performance.py ScipyMilp ScipyMilp --datasets mip --timelimit 2 --log_level 10
python compute_performance.py Pulp Pulp --datasets mip --timelimit 2 --log_level 10
python compute_performance.py auto auto --datasets mip --timelimit 2 --log_level 10
- name: Collect trainting data of Auto Solver
run: |
python fit_models.py nonlinear --collect --samples 3 --debug
python fit_models.py nonlinear_mip --collect --samples 3 --debug
python fit_models.py ising --collect --samples 3 --debug