finoptim 0.0.21

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

• possibility to precise the period of the data in case it is not inferred correctly

• " call PyErr at suitable intervals inside your Rust function, and check the returned value. If it was -1, immediately return from your Rust function;"

• compute the better step size to avoid waiting too long

• coverage must folow the same inputs as cost

• allow for long DataFrame as input

• the cost function should return a gradient when evaluated (save some compute)

• add in documentation that for now optimisation only works if you have RI < SP < OD