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("

We're trying to introduce Parquet into our team, and the largest blocker that we've seen is the dreaded "schemas are inconsistent" error message:

RuntimeError: Schemas are inconsistent, try using to_parquet(..., schema="infer"), or pass an explicit pyarrow schema. Such as to_parquet(..., schema={"column1": pa.string()})

This error message is super unhelpful: surely Dask knows what th

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