NumPy

🚀 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

Describe the bug

Streaming Datasets can't be pickled, so any interaction between them and multiprocessing results in a crash.

Steps to reproduce the bug

import transformers
from transformers import Trainer, AutoModelForCausalLM, TrainingArguments
import datasets

ds = datasets.load_dataset('oscar', "unshuffled_deduplicated_en", split='train', streaming=True).with_format("

Auto-chunking involves reasoning about dtype size, but for flexible dtypes like unicode strings that is not necessarily known. From the numpy docs:

For 18 of the 21 types this number is fixed by the data-type. For the flexible data-types, this number can be anything.

In particular, it seems t

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

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.

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.

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

pydata/xarray#5865 (reply in thread)

I wonder if it's possible to implement a built-in function like:
da.str.format("%.2f") or xr.string_format(da, "%.2f)

To wrap:

import xarray as xr

da = xr.DataArray([5., 6., 7.])
das = xr.DataArray("%.2f")
das.str % da

<xarray.DataArray (dim_0: 3)>
array(['5.00', '6.00', '7.00'], dtype='<U4')
Dim