py-3.10.6-2024-q1

Upgrade nbresult to 0.1.0 cf lewagon/nbresult#12

py-3.10.6-2022-q4-v8

add missing unidecode module to allow assess DL challenge lewagon-assess/data-paintings-denoising-lyrics-detector-solution to run

py-3.8.12-2022-q2-v2-schlum-v1

Corrective runner for B2B Schlum batch from py-3.8.12-2022-q2-v2

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This branch introduces a corrective runner for the B2B Schlumberger batch based on the py-3.8.12-2022-q2-v2 branch

The students of the batch are supposed to have ran the Q2 2022 data-setup

But for some reason the numpy and pandas package versions of the students do not correspond to the setup contraints and the Kitt validation of the challenges fails

Setup constraints for base py-3.8.12-2022-q2-v2 branch:

numpy<1.20
pandas<1.4

Collected students versions:

pandas==1.4.4
numpy==1.23.3

py-3.10.6-2022-q4-v7

data setup 2002 Q4:

• remove rosetta for apple silicon
• python 3.10.6
• tensorflow 2.10.0 (tensorflow-macos 2.10.0)

updates:

• pandas 1.4.4
• add memoized-property

requirements from lewagon/data-setup#244

Data Engineering v0.0.2

According to the new Glovebox data runner implementation, the only requirements for this release is a good old bash

py-3.8.12-de-v0.0.1

add poetry

Data Engineering v0

Adds

• pytest
• dockerfile
• PyYAML
• poetry

DS Bootcamp 2022 MLOps v0

this adds:

• pytest-asyncio
• httpx

DS Bootcamp 2022 Q2 v2

bump nbresult to 0.0.8

DS Bootcamp 2022 Q2

• bump nbresult to 0.0.7