finoptim 0.0.22

A small package to do some finoptim optimisations

FinOps package

The best python package to help you optimise your cloud spendings

Usage example

import finoptim as fp
import pandas as pd


past_usage = pd.DataFrame(...)
guid_to_price = fp.cloud.load_aws_prices(as_dict=True)
prices = np.array(past_usqge.index.map(guid_to_price))

usage = fp.normalize(past_usage)

res = fp.optimise_past(usage, prices)

predictions = pd.DataFrame(...) # some SQL query
current_reservations = pd.DataFrame(...) # some SQL query

normalize_reservations = fp.normalize(current_reservations)

res = fp.optimise_past(predictions, prices)

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

• possibility to precise the period of the data in case it is not inferred correctly
• coverage must follow the same inputs as cost
• allow for long DataFrame as input
• the cost function should return a gradient when evaluated (save some compute)
• need to listen to keyboard interupt from Rust (harder than expected with multi threading)

actual problems

• add in documentation that for now optimisation only works if you have RI < SP < OD
• compute the better step size to avoid waiting too long (more or less done, but not even necessary with the inertial optimiser)
• 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

if the problem is $f(w) = \frac{1}{2} w^T A w :-: b^T w$ then the optimal parameters for the inertial optimiser are :

$$ \alpha = \left(\frac{2}{\sqrt{\lambda_1} + \sqrt{\lambda_n}} \right) ^2 $$

$$ \beta = \left( \frac{\sqrt{\lambda_n} - \sqrt{\lambda_1}}{\sqrt{\lambda_1} + \sqrt{\lambda_n}} \right) ^2 $$

with $\lambda_1$ and $\lambda_n$ respectively the smallest and largest eigenvalues of $A$

lets admit constant usage for all the instances. Then $f(w) = $