CUDA

I see comments suggesting adding this to understand how loops are being handled by numba, and in the their own FAQ (https://numba.pydata.org/numba-doc/latest/user/faq.html)

from llvmlite import binding as llvm
llvm.set_option('','--debug-only=loop-vectorize')

You would then create your njit function and run it, and I believe the idea is that it prints debug information about whether

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

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.

Is your feature request related to a problem? Please describe.
Hi,

While porting some code from Pandas to cuDF, I have noticed that cuDF series do not support unstack method.
As an additional request, It would be great if fill_values could be supported in both cudf.DataFrame.unstack and cudf.Series.unstack methods. Thanks!

Describe the solution you'd like
To have that meth

请问可以直接training tmfile出来吗? 因为tengine-convert-tool covert 会有error

tengine-lite library version: 1.4-dev
Get input tensor failed

或是有例子能training出下面tmfile 呢?
![Screenshot from 2021-05-27 07-01-46](https://user-images.githubusercontent.com/40915044/11

Current implementation of join can be improved by performing the operation in a single call to the backend kernel instead of multiple calls.

This is a fairly easy kernel and may be a good issue for someone getting to know CUDA/ArrayFire internals. Ping me if you want additional info.

First of all, great library!

I am having some confusion in understanding the role of values_first2 in set_difference_by_key as mentioned here.

In general terms, the result of set difference D = A - B will not contain values from B therefore values_first2 can never be part of D. So `val

在oneflow里，交叉熵损失有以下几种：

• binary_cross_entropy_loss
• binary_cross_entropy_with_logits_loss
• sparse_cross_entropy
• distributed_sparse_cross_entropy
• cross_entropy
• sparse_softmax_cross_entropy
• softmax_cross_entropy

在pytorch里，交叉熵损失有以下几种：

• binary_cross_entropy
• binary_cross_entropy_with_logits
• cross_entropy

由此可见，oneflow中交叉熵损失存在API冗余，重复，容易让用户疑惑，因此，这里应该精简一下。除此之外，label smooth

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While the estimator guide offers a great breakdown of how to use many of the tools in api_context_managers.py, it would be helpful to have information right in the docstring during development to more easily understand what is actually going on in each of the provided functions/classes/methods. This is particularly important for

In order to test manually altered IR, it would be nice to have a --skip-compilation flag for futhark test, just like we do for futhark bench.