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System information

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

(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.

🐛 Bug

I stumbled upon excessive CPU usage for my training code running on GPU. After some investigations I found the culprit.
It basically was

x = torch.eye(256).to('cuda') 

To Reproduce

This is quick and loads single CPU core.

%%timeit
    torch.eye(181)
6.43 µs ± 218 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

This is 3 times slowe

It appears that the docs for Logistic Regression differ based on solvers and penalties. The "penalty" parameter states that "The ‘newton-cg’, ‘sag’ and ‘lbfgs’ solvers support only l2 penalties," while the "solver" parameter states that "‘newton-cg’, ‘lbfgs’, ‘sag’ and ‘saga’ handle L2 or no penalty" (attaching some screenshots). This was actually a little unclear to me, as I wasn't sure if the n

Julia 1.6.0-rc3

runtests.jl:

using Test

d = Dict("hello" => "world")
@test keys(d) == ["hello"]

Test output:

     Testing Running tests...
Test Failed at /Users/fons/Documents/Pluto.jl/test/runtests.jl:4
  Expression: keys(d) == ["hello"]
   Evaluated: ["hello"] == ["hello"]
ERROR: LoadError: There was an error during testing
in expression starting at /Users/

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.