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import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim

bhavika

Setup Instructions for Pyleargist

https://pypi.org/project/pyleargist/

Installing FFTW

Unix Instructions: http://www.fftw.org/fftw3_doc/Installation-on-Unix.html

Pyleargist setup instructions

bhavika

# Sudoku Solver - Peter A. Norvig

digits = '123456789'
rows = 'ABCDEFGHI'
cols = digits

def cross(A, B):
    return [a+b for a in A for b in B]

squares = cross(rows, cols)

bhavika

From MySQL's docs:

• Switching Masters During Failover
• mysqlfailover — Automatic replication health monitoring and failover
• Replication with Global Transaction Identifiers
• MySQL Multi-Source Replication
• Improving Replication Performance
• Using Replication for Backups

From Facebook:

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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

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git_current_branch () {
        local ref
        ref=$(command git symbolic-ref --quiet HEAD 2> /dev/null) 
        local ret=$? 
        if [[ $ret != 0 ]]
        then
                [[ $ret == 128 ]] && return
                ref=$(command git rev-parse --short HEAD 2> /dev/null)  || return
        fi
        echo ${ref#refs/heads/}

bhavika

import numpy as np
from transformers import AutoTokenizer, AutoModel
from pathlib import Path
from optimum.exporters import TasksManager
from optimum.exporters.onnx import export
import onnx
from optimum.exporters.onnx import validate_model_outputs
from optimum.onnxruntime import ORTModelForFeatureExtraction
import torch