Add Python 3.12 to the CI

[nzw0301]

Motivation

Sub-task of optuna/optuna#5000.

Description

see the title of this issue.

Alternatives (optional)

Additional context (optional)

Here is a list of the target yaml files under .github/workflows.

• aim.yml
• allennlp.yml
• base.yml
• catboost.yml
• chainer.yml
• checks.yml
• dashboard.yml
• dask.yml
• dask_ml.yml
• fastai.yml
• haiku.yml
• hiplot.yml
• hydra.yml
• keras.yml
• lightgbm.yml
• mlflow.yml
• multi_objective.yml
• mxnet.yml
• pytorch.yml
• ray.yml
• rl.yml
• samplers.yml
• skimage.yml
• sklearn.yml
• stale.yml
• tensorboard.yml
• tensorflow.yml
• terminator.yml
• tfkeras.yml
• visualization.yml
• wandb.yml
• xgboost.yml

I've checked listings addressed by #214 and have not removed the deprecated library's examples yet.

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[Alnusjaponica]

Following libraries have been already archived will not support python 3.12.

• allennlp
• chainer
• mxnet

Following libraries does not (officially) support python 3.12 for now.

• aim Support Python 3.12 aimhubio/aim#3111
• fastai, haiku, lightgbm, mlflow (the CI does not run on python 3.11 and 3.12)
• keras, tensorboard, tensorflow, tfkeras Support Python 3.12 tensorflow/tensorflow#62003
• ray [Feature] Core: Python 3.12 wheel ray-project/ray#40211

Following libraries have newly supported python 3.12 recently.

• catboost Support python 3.12 catboost/catboost#2510
• dask Support for Python 3.12 dask/dask#10544
• pytorch Pytorch for Python 3.12 not available pytorch/pytorch#110436
• wandb [Q] pip3.12 install wandb error wandb/wandb#6468

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[nzw0301]

I investigated the current status of the unaddressed items above partially with my local env (mac):

tfkeras

It works w/ python 3.12 but an example shows a warning message every trial as follows:

python tfkeras/tfkeras_integration.py

...
2024-08-03 14:53:51.516660: W tensorflow/core/kernels/data/cache_dataset_ops.cc:913] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

Similarly, tfkeras/tfkeras_simple.py shows warning message at the first model construction:

python tfkeras/tfkeras_simple.py
[I 2024-08-03 14:59:38,046] A new study created in memory with name: no-name-b1748d29-9666-4ad1-96d8-2d95c2ea4b00
/opt/homebrew/Caskroom/miniconda/base/envs/optuna-312/lib/python3.12/site-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
  super().__init__(activity_regularizer=activity_regularizer, **kwargs)

ray

Still ongoing task according to ray-project/ray#40211.

lightgbm

It supports Python 3.12. But similar to thetfkeras case, an example shows a warning message as follows:

python lightgbm/lightgbm_tuner_cv.py

[I 2024-08-03 15:04:28,351] A new study created in memory with name: no-name-a3244a73-6d43-41e9-954e-1f3494e21ac4
feature_fraction, val_score: inf:   0%|                                                  | 0/7 [00:00<?, ?it/s]
/optuna-312/lib/python3.12/site-packages/sklearn/model_selection/_split.py:91: UserWarning: The groups parameter is ignored by KFold
  warnings.warn(
Training until validation scores don't improve for 100 rounds

and terminator example stopped at the trial 19 locally.

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