alex0dd

import torch

from pytorch_memory_profiling import memory_logging
from torchvision.models import resnet18

def training_loop(model, criterion, optimizer, dataloader):
    model.train()
    for (batch_x, batch_y) in dataloader:
        pred_y = model(batch_x)
        loss = criterion(pred_y, batch_y)

ejmejm

arestifo

import tensorflow as tf
from tensorflow.keras import layers, Model, optimizers, initializers, constraints
from tensorflow.keras import backend as k
from tensorflow.keras.layers import Dense, Conv2D, Input, LeakyReLU, Reshape, Flatten
from tensorflow.keras.layers import AveragePooling2D, UpSampling2D
from tensorflow.keras.constraints import max_norm
import matplotlib.pyplot as plt
import numpy as np
import glob
import os

danmou

from typing import Any, Mapping, Optional, Sequence, TypeVar, Union

import tensorflow as tf
from tensorflow.keras import layers


T = TypeVar('T')
Nested = Union[T, Sequence[T], Mapping[Any, T]]

class AutoShapeMixin:

BarclayII

import pandas as pd
import dgl
import os
import torch

class MovieLens(object):
    def __init__(self, directory):
        '''
        directory: path to movielens directory which should have the three
                   files:

thomwolf

model.zero_grad()                                   # Reset gradients tensors
for i, (inputs, labels) in enumerate(training_set):
    predictions = model(inputs)                     # Forward pass
    loss = loss_function(predictions, labels)       # Compute loss function
    loss = loss / accumulation_steps                # Normalize our loss (if averaged)
    loss.backward()                                 # Backward pass
    if (i+1) % accumulation_steps == 0:             # Wait for several backward steps
        optimizer.step()                            # Now we can do an optimizer step
        model.zero_grad()                           # Reset gradients tensors
        if (i+1) % evaluation_steps == 0:           # Evaluate the model when we...

MarcoWorms

function createStore (reducers) {
  var state = reducers()
  const store = {
    dispatch: (action) => {
      state = reducers(state, action) 
    },
    getState: () => {
      return state
    } 
  }

emiller

#!/bin/bash
#
# git-mv-with-history -- move/rename file or folder, with history.
#
# Moving a file in git doesn't track history, so the purpose of this
# utility is best explained from the kernel wiki:
#
#   Git has a rename command git mv, but that is just for convenience.
#   The effect is indistinguishable from removing the file and adding another
#   with different name and the same content.

jterrace

XVFB=/usr/bin/Xvfb
XVFBARGS=":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset"
PIDFILE=/var/run/xvfb.pid
case "$1" in
  start)
    echo -n "Starting virtual X frame buffer: Xvfb"
    start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS
    echo "."
    ;;
  stop)

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs