gpu

https://github.com/pytorch/pytorch/blob/9270bccaf67022042f42b57b97c7b630b1e05750/torch/utils/data/sampler.py#L86

The optional argument 'num_samples' to the RandomSampler class is listed as type Optional[int], but it is not optional, as an exception is raised if an int is not passed in:

https://github.com/pytorch/pytorch/blob/9270bccaf67022042f42b57b97c7b630b1e05750/torch/utils/data/sampler.p

The taichi.lang.util.warning function just prints the warning without consulting the current state of pythons standard library warnings module.

For example:

import warnings
import taichi as ti

with warnings.catch_warnings():
    warnings.simplefilter("ignore

At this moment relu_layer op doesn't allow threshold configuration, and legacy RELU op allows that.
We should add configuration option to relu_layer.

I want to preemptively start this thread to survey for suggestions. A cursory search lead me to this promising repository https://github.com/enigo-rs/enigo

Since closing the window is a common point of failure, that will be the focus for the first pass of testing as I learn how to use the library.

Components for testing:

• bridge
• editor
• renderer
• settings
• wind

Hi ,

I have tried out both loss.backward() and model_engine.backward(loss) for my code. There are several subtle differences that I have observed , for one retain_graph = True does not work for model_engine.backward(loss) . This is creating a problem since buffers are not being retained every time I run the code for some reason.

Please look into this if you could.

Problem:

_catboost.pyx in _catboost._set_features_order_data_pd_data_frame()

_catboost.pyx in _catboost.get_cat_factor_bytes_representation()

CatBoostError: Invalid type for cat_feature[non-default value idx=1,feature_idx=336]=2.0 : cat_features must be integer or string, real number values and NaN values should be converted to string.

Could you also print a feature name, not o

Our users are often confused by the output from programs such as zip2john sometimes being very large (multi-gigabyte). Maybe we should identify and enhance these programs to output a message to stderr to explain to users that it's normal for the output to be very large - maybe always or maybe only when the output size is above a threshold (e.g., 1 million bytes?)

Description

https://numpy.org/doc/stable/reference/generated/numpy.corrcoef.html

https://docs.cupy.dev/en/stable/reference/generated/cupy.corrcoef.html

Seems args are different

Additional Information

dtype argument added in NumPy version 1.20.

https://github.com/brendangregg/FlameGraph

Is your feature request related to a problem? Please describe.
The current value of alpha value is hardcoded in many places to 0.05.

Describe the solution you'd like
Take this as a setup argument and use it everywhere for consistency. The default value can be 0.05.

Is your feature request related to a problem? Please describe.
While reviewing PR #9817 to introduce DataFrame.diff, I noticed that it is restricted to acting on numeric types.

A time-series diff is probably a very common user need, if provided a series of timestamps and seeking the durations between observations.

Pandas supports diffs on non-numeric types like timestamps:

Description
I'm trying to port an existing application using GLSL to wgpu, so I have existing complex shaders I want to modify to be compatible. While trying to get them working, I have found that if the shader has (something which naga considers) a syntax error, wgpu will panic via .unwrap():

https://github.com/gfx-rs/wgpu/blob/326af60df8623e93b47a0de090e6cb449c8507f5/wgpu/src/bac

环境

1.系统环境：

• Ubuntu 20.04.2 LTS x86_64
• GLIBCXX: 3.4.28 (libstdc++.so.6.0.28)
• GPU:
  

2.MegEngine版本：1.6.0rc1
3.python版本：Python 3.8.10

The program stuck at net.load when I was trying to use the MegFlow. I wait for more than 10min and there is no sign of finishing it.