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System information

• TensorFlow version: 1.13.1
• Doc Link: https://www.tensorflow.org/api_docs/python/tf/cus

As discussed in #3926, the following files do not have .py extensions because TensorFlow does not yet support the current Python.

• dynamic_programming/k_means_clustering_tensorflow.py_tf
• machine_learning/lstm/lstm_prediction.py_tf
• neural_network/gan.py_tf
• neural_network/input_data.py_tf

TODO: Restore the .py file extensions

(e.g. for links and images), because some of these examples are now being rendered in the docs.

Added by @fchollet in requests for contributions.

🚀 Feature

Add support for torch.max with:

• CUDA bfloat16
• CPU float16 and bfloat16

Motivation

Currently, torch.max has support for CUDA float16:

>>> torch.rand(10, dtype=torch.float16, device='cuda').max()
tensor(0.8530, device='cuda:0', dtype=torch.float16)

But all three other combinations of CPU/CUDA and float16/bfloat16 are not supported:

>>> torch.ra

Most functions in scipy.linalg functions (e.g. svd, qr, eig, eigh, pinv, pinv2 ...) have a default kwarg check_finite=True that we typically leave to the default value in scikit-learn.

As we already validate the input data for most estimators in scikit-learn, this check is redundant and can cause significant overhead, especially at predict / transform time. We should probably a

Description

Usage of scrapy.utils.response.response_httprepr inside DownloaderStats middleware causing application to make unnecessary memory allocation.
response_httprepr used only one time - to calculate response sizes fo

Currently, after user editing a metric in Edit dataset modal in Explore, the edited metric jump to the bottom of the metric list.
it create a few issues:

1. when the metric list is long, by dropping to the bottom, user might take a while to find it or think the most recent edited metic is lost.
2. every time users edit a single metric, they see a different order of metric list showing in the

Hello everyone, I will explain the current status of manim in this issue.

Now there are three main manim versions, and their differences:

1. The master branch of 3b1b/manim: Rendering on GPU using OpenGL and moderngl. Support interaction and have higher efficiency.
2. ManimCommunity/manim: (@ManimCommunity

Issue here is:

1. in .pre-commit-config.yaml, change the ref of pyupgrade from v2.7.4 to v2.8.0
2. run pre-commit run pyupgrade --all-files
3. run pre-commit run flake8 --all-files - there will be lots of errors from unused imports, so you should remove them (or you could look into using autoflake to do it for you)
4. if both `pre-commit run fl

In all of our current uses of os.umask or filesystem.umask, we always temporarily set it to a different value and then restore it using a try/finally block. I expect this pattern to continue.

Because of that, why don't we create a simple function in certbot.util that can be used as a context manager to set umask to a value and restore it to its previous value when exiting the with blo