Lawrencium77

""" 
SmoothQuant implementation. See: https://arxiv.org/pdf/2211.10438.pdf
Some details are model-specific, so the code may need tweaking.
"""
import functools
import torch
from torch import nn, Tensor
from typing import Dict, Iterable, Tuple

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

laszukdawid

def convert_tb_data(root_dir, sort_by=None):
    """Convert local TensorBoard data into Pandas DataFrame.
    
    Function takes the root directory path and recursively parses
    all events data.    
    If the `sort_by` value is provided then it will use that column
    to sort values; typically `wall_time` or `step`.
    
    *Note* that the whole data is converted into a DataFrame.
    Depending on the data size this might take a while. If it takes

nathanielatom

# encoding: utf-8
import bokeh.models as bkm
import bokeh.core as bkc

from bokeh.util.compiler import JavaScript


class AudioPlayerModel(bkm.layouts.Column):
    """
    Audio player using https://howlerjs.com/.

Zearin

NOTE: This is a question I found on StackOverflow which I’ve archived here, because the answer is so effing phenomenal.

---

Q: How can I make a chain of function decorators in Python?

---

If you are not into long explanations, see [Paolo Bergantino’s answer][2].

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD