gpu

🚀 The feature, motivation and pitch

After the revert of pytorch/pytorch@7cf9b94 we've identified a need to add a lint that checks file names to ensure that they're compatible with Windows machines.

Observed error: (from example commit)

Error: error: invalid path 'test/test_ops_gradients.py '

A simple check on chang

There comes one special case which requires a reverse loop, so I used “ti.ndrange(8,0)” instead. However, the reverse loop just didn’t work with zero warnings or errors (I have no idea whether ti.ndrange can be used to loop in reverse order at that time)
Perhaps a warning message can be given when n1 > n2 in ti.ndrange((n1,n2))

import taichi as ti
ti.init(arch=ti.gpu, debug=True, de

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.

I am working on creating a WandbCallback for Weights and Biases. I am glad that CatBoost has a callback system in place but it would be great if we can extend the interface.

The current callback only supports after_iteration that takes info. Taking inspiration from XGBoost callback system it would be great if we can have before iteration that takes info, before_training, and `after

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:

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.

Description
When drawing meshes with a 0..0 instance range, ogpu crashes due to metal validation layers emitting an error. From my understanding on the WebGPU spec, this should be allowed and indeed works correctly on Vulkan and DX12.

Error

-[MTLDebugRenderCommandEncoder validateCommonDrawErrors:instanceCount:baseInstance:maxVertexID:]:5161: failed assertion `Draw Errors Valida