NumPy

🚀 The feature, motivation and pitch

The current implementation of Zero Redundancy optimizer has its own implementation of object broadcasting.

We should replace it with c10d [broadcast_object_list](https://pytorch.org/docs/stable/distributed.html#torch.distributed.broadcast_object_lis

Adding a Dataset

• Name: Stanford dog dataset
• Description: The dataset is about 120 classes for a total of 20.580 images. You can find the dataset here http://vision.stanford.edu/aditya86/ImageNetDogs/
• Paper: http://vision.stanford.edu/aditya86/ImageNetDogs/
• Data: *[link to the Github repository or current dataset location](http://vision.stanford.edu/aditya86/Ima

In this docs https://github.com/dask/dask/blob/main/docs/source/dataframe-groupby.rst we are using df in some places for pandas dataframes and in other palces for dask dataframes. There is also mention to ddf and df_dask.

For example:

df (pandas):

>>> df = pd.DataFrame({
...   'a': ['a', 'b', 'a', 'a', 'b'],
...   'b': [0, 1, 0, 2, 5],
... })
>>> ddf = dd.from_pandas(d

• I have tried using the latest released version of Numba (most recent is
  visible in the change log (https://github.com/numba/numba/blob/main/CHANGE_LOG).
• I have included a self contained code sample to reproduce the problem.
  i.e. it's possible to run as 'python bug.py'.

I think I have discovered a very minor bug - or rather inconsistency with numpy - in Numba's implementation

Is there a way to train a bidirectional RNN (like LSTM or GRU) on trax nowadays?

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.

如何导入pytorch训练好的模型或者权重文件？

Wondering if this already exists? If not happy to create if valuable.

I'm looking for a mapping from the column names outputted, to the actual technical indicator it represents.

examples:
momentum_ao == "Momentum, Awesome Oscilator"
momentum_kama == "Momentum, Kaufman’s Adaptive Moving Average (KAMA)"

Can help quickly grasp what the features represent without having to refer back to do