rkaplan

import functools

def my_collate(batch, use_shared_memory=False):
    r"""Puts each data field into a tensor with outer dimension batch size"""

    error_msg = "batch must contain tensors, numbers, dicts or lists; found {}"
    elem_type = type(batch[0])
    if isinstance(batch[0], torch.Tensor):
        out = None
        if use_shared_memory:

rkaplan

#!/bin/bash
#set -x 

# Shows you the largest objects in your repo's pack file.
# Written for osx.
#
# @see https://stubbisms.wordpress.com/2009/07/10/git-script-to-show-largest-pack-objects-and-trim-your-waist-line/
# @author Antony Stubbs
#
# Found on https://stackoverflow.com/questions/10622179/how-to-find-identify-large-files-commits-in-git-history

rkaplan

import random
import torch

class DynamicNet(torch.nn.Module):
  def __init__(self, D_in, H, D_out):
    super(DynamicNet, self).__init__()
    self.backbone = torch.nn.Linear(D_in, H)
    self.head1 = torch.nn.Linear(H, D_out)
    self.head2 = torch.nn.Linear(H, D_out)

rkaplan

# Generates aliases for .bashrc that allow easy toggling of CUDA_VISIBLE_DEVICES

# The printf is to name the tmux pane with which GPUs are available
ALIAS_STR = 'alias {name}=\'printf "\\033]2;%s\\033\\\\" "GPU: {gpu_list}"; export CUDA_VISIBLE_DEVICES="{gpu_list}"\''

ngpu = 8
for i in range(ngpu):
    print(ALIAS_STR.format(name="g" + str(i), gpu_list=str(i)))

for i in range(ngpu-1):

rkaplan

public void postData() {
    // Create a new HttpClient and Post Header
    HttpClient httpclient = new DefaultHttpClient();
    HttpPost httppost = new HttpPost("https://www.metamind.io/language/classify")
    httppost.setHeader("Authorization: Basic", "YOUR API KEY");

    try {
        // Add your data
        List<NameValuePair> nameValuePairs = new ArrayList<NameValuePair>(2);
        nameValuePairs.add(new BasicNameValuePair("classifier_id", 155));