Adam Dziedzic adam-dziedzic

## mnist_juelich.py
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.optim.lr_scheduler import StepLR


class Net(nn.Module):

## example.py
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

## cifar100_fine_coarse_labels.py
import pprint as pp


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

## git-clearHistory
-- 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 git@github.com:<YOUR ACCOUNT>/<YOUR REPOS>.git

## beamer-slides-with-notes.pdf

      
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                adam-dziedzic
                / beamer-slides-with-notes.pdf
            
            
              Created
              October 18, 2020 20:10
                — forked from andrejbauer/beamer-slides-with-notes.pdf
            
          
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## nvvp.md

      
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                adam-dziedzic
                / nvvp.md
            
            
              Created
              June 6, 2020 04:08
                — forked from sonots/nvvp.md
            
              
                How to use NVIDIA profiler
              
          
    Usually, located at /usr/local/cuda/bin
Non-Visual Profiler

$ nvprof python train_mnist.py

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

  
## install_folly_fbthrift_thpp_gblualib.sh
#!/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`

## LibrarySchema
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

## EmployeeSchema
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

## UniversitySchemaDBUchicago
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

]]
	import argparse
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	import torch.optim as optim
	from torchvision import datasets, transforms
	from torch.optim.lr_scheduler import StepLR


	class Net(nn.Module):
	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
	import pprint as pp


	fine_labels = [
	'apple', # id 0
	'aquarium_fish',
	'baby',
	'bear',
	'beaver',
	'bed',
	-- 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 git@github.com:<YOUR ACCOUNT>/<YOUR REPOS>.git
	#!/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`
	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
	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
	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

	]]