What does this PR do?

PR #22722 introduced a common method for the validation of the parameters of an estimator. We now need to use it in all estimators.

Please open one PR per estimator or family of estimators (if one inherits from another). The title of the PR should mention which estimator it's dealing with and the description of the PR should begin with towards #23462.

Steps

• The estimator must define

What happened + What you expected to happen

When initializing a Ray Trainer, we provide a logdir argument, and the __init__ method of the Trainer stores it as a logdir class variable.

Then, when creating a Trainable with Trainer.to_tune_trainable(), it in-turn calls _create_tune_trainable(), which does not use self.logdir. So when tune_function is defined inside `_create_tu

Problem

See #3856 . Developer would like the ability to configure whether the developer menu or viewer menu is displayed while they are developing on cloud IDEs like Gitpod or Github Codespaces

Solution

Create a config option

showDeveloperMenu: true | false | auto

where

• true: always shows the developer menu locally and while deployed
• false: always sho

Hi, I see that is_last_batch trainer property isn't documented in https://pytorch-lightning.readthedocs.io/en/stable/common/trainer.html#properties.

I was lucky to find it here.

I feel it would be helpful to have all properties listed there.

Thanks.

cc @Borda @rohitgr7 @akihironitta

Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.

It can be clearly seen in chapter 6([CNN Lenet](ht

Adding a Dataset

• Name: Stanford dog dataset
• Description: The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/
• Paper: http://vision.stanford.edu/aditya86/ImageNetDogs/
• Data: *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/Ima

In gensim/models/fasttext.py:

    model = FastText(
        vector_size=m.dim,
        vector_size=m.dim,
        window=m.ws,
        window=m.ws,
        epochs=m.epoch,
        epochs=m.epoch,
        negative=m.neg,
        negative=m.neg,
        # FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
        # or model=3 supervi

We currently use pylint 2.11.1. This is a bit old so we should upgrade it to the latest version (2.14.4):

https://github.com/mlflow/mlflow/blob/d40780be361f4bd2741c2e8fcbd428c1d693edcf/requirements/lint-requirements.txt#L1

• https://pypi.org/project/pylint/2.11.1/
• https://pypi.org/project/pylint/2.14.4/

Currently, there are following warnings when running tests:

test/test_models.py::test_quantized_classification_model[googlenet]
  /root/project/torchvision/models/googlenet.py:47: FutureWarning: The default weight initialization of GoogleNet will be changed in future releases of torchvision. If you wish to keep the old behavior (which leads to long initialization times due to scipy/scipy#11