NumPy

Followup to pytorch/pytorch#74955 (comment).

It turns out that that cmake version was just bad and we can now unpin cmake once again.

cc @seemethere @malfet @pytorch/pytorch-dev-infra

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("

#9130 adds a sort_results argument to assert_eq, which allows us to assert DataFrame equality without sorting them (by default, assert_eq sorts the input DataFrames before checking equality). It'll be nice to identify+update the tests that would benefit from it.

A first step could be to run the dataframe tests with check_index=False and sort_results=False as defaults.

Numba ignores any indices that are out of bounds for a given array shape. This behavior is present in both basic and advanced indexing.

import numpy as np
import numba

@numba.njit
def something():
    arr = np.ones((3,3))
    arr[100] = 0
    arr[:, np.array([2,3,5,100])] = 0
    return arr

print(something()) # Does not raise error, ignores the out of bound indices comp

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