FinOptim package

The best python package to help you optimise your cloud spendings

Instalation

The package is available on PyPI, it is possible to install it directly with pip.

pip install finoptim

It requires at least Python 3.10. The documentation is available on readthedoc.

Usage example

import finoptim as fp
import pandas as pd


past_usage = pd.DataFrame(...) # some query of yours
prices = fp.prices.aws()
print(prices_df.iloc[:, :4].to_markdown(
    tablefmt='rounded_outline',
    numalign='center'))
>>>
╭──────┬────────────┬──────────────┬─────────────────┬──────────────╮
│      │  Arlequin  │  Moratorium  │  Anthropophage  │  Apophtegme  │
├──────┼────────────┼──────────────┼─────────────────┼──────────────┤
│ OD   │   0.167    │    0.122     │     0.0056      │    0.0058    │
│ RI3Y │ 0.0637352  │  0.0476682   │    0.0022035    │  0.00234155  │
│ RI1Y │ 0.0981063  │  0.0716404   │   0.00330824    │  0.00330824  │
│ SP1Y │   0.115    │     0.09     │     0.0038      │    0.0039    │
│ SP3Y │   0.073    │    0.054     │     0.0025      │    0.0026    │
╰──────┴────────────┴──────────────┴─────────────────┴──────────────╯

All the prices are per hours.

Proceeding to the optimisation is made with the optimise function

res = fp.optimise(past_usage, prices)

The optimise function can take as input lots of different predictions, and also current commitments. The optimisation is made with all the pricing models found in the prices object

predictions = [pd.DataFrame(...), ...] # some query of yours

res = fp.optimise(
    predictions,
    prices,
    current_commitments={"type" : "RI3Y", "level" : 10 * 32, 'guid' : 'Moule à gaufres', "end_date" : date(2024, 12, 21), 'price_key' : .0123},
    convergence_details=True
    )

Now the res object hold the best levels of commitment on the time period.

guid_to_instance_name = {"K7YHHNFGTNN2DP28" : 'i3.large', 'SAHHHV5TXVX4DCTS' : 'r5.large'}
res.format(instance_type=guid_to_instance_name)
print(res)
>>>
╭─────────────────┬──────────────────────────┬───────────────╮
│ instance_type   │  three_year_commitments  │ price_per_day │
├─────────────────┼──────────────────────────┼───────────────┤
│ i3.large        │           1338           │     2,886     │
│ r5.large        │           1570           │     2,564     │
│ savings plans   │           1937           │     1,937     │
╰─────────────────┴──────────────────────────┴───────────────╯

TODO

lib convenience

• allow for long DataFrame as input
• the cost function should return a gradient when evaluated (save some compute)(the function is a nightmare : abort the mission)
• listening to keyboard interupt from Rust is harder than expected with multi threading
• logging instead of printing, both in the Python and Rust sides

actual problems

• find a real stop condition for the inertial optimiser
• can we guess the "eigenvectors" of the problem ? if we have estimations, we can set great parameters for the inertial optimiser
• problem is highly non linear and this will require more thinking

Project size

wc -l src/finoptim/*.py rust/src/*.rs src/finoptim/prices/*.py tests/*.py

is around 3k lines of code