adam-dziedzic

group: database schema
description[[ the data for exercises 6.10 - 6.14]]

course = { course_id, title, dept_name, credits
  BIO-101 Intro_to_Biology Biology, 4
  BIO-301 Genetics  Biology 4
  BIO-399 Computional_Biology Biology 3
  CS-101 Intro_to_computer_science Comp.Sci. 4
  HIS-100 Ancient_Greece History 1
  HIS-102 Ancient_Egypt History 2

adam-dziedzic

group: university schema
description[[ the data for homework 1, exercise 6.10

* the relation _course_ courses at the university
* the relation _takes_ which coures a student takes
* the relation _stuent_ students
* the relation _instructor_ instructors

]]

adam-dziedzic

group: employee schema
description[[ the data for problems: 6.11, 6.13 ]]

employee = {person_name street city
adam kenwood chicago
bob kimbark chicago
joe michigan chicago
kate woodlawn boston
noelle woodlawn boston
veronique kimbark warsaw

adam-dziedzic

group: library schema
description[[ the data for problem: 6.14 ]]

member = {memb_no name dob
1 adam 123
2 mike 456
3 jack 789
4 luke 100
5 tom 111
6 veronique 101

adam-dziedzic

#!/bin/bash -e
#
#  Copyright (c) 2014, Facebook, Inc.
#  All rights reserved.
#
#  This source code is licensed under the BSD-style license found in the
#  LICENSE file in the root directory of this source tree. An additional grant
#  of patent rights can be found in the PATENTS file in the same directory.
#
init_dir=`pwd`

adam-dziedzic

Usually, located at /usr/local/cuda/bin

Non-Visual Profiler

$ nvprof python train_mnist.py

I prefer to use --print-gpu-trace.

adam-dziedzic

adam-dziedzic

-- Remove the history from 
rm -rf .git

-- recreate the repos from the current content only
git init
git add .
git commit -m "Initial commit"

-- push to the github remote repos ensuring you overwrite history
git remote add origin [email protected]:<YOUR ACCOUNT>/<YOUR REPOS>.git

adam-dziedzic

import pprint as pp


fine_labels = [
    'apple',  # id 0
    'aquarium_fish',
    'baby',
    'bear',
    'beaver',
    'bed',

adam-dziedzic

import torch
from torch.utils.data import Dataset
from typing import Tuple
import numpy as np
import requests
import pandas as pd

#### LOADING THE MODEL

from torchvision.models import resnet18