mrdvince’s gists

mrdvince / train.py

Created June 25, 2020 23:22

	def train(dataloader, model, optimizer, device, scheduler=None):
	# scaler -> an instance of the GradScaler object
	scaler = amp.GradScaler()
	model.train()
	for batch_idx, d in enumerate(dataloader):
	ids = d['ids']
	token_type_ids = d['token_type_ids']
	mask = d['mask']
	targets = d['targets']

mrdvince / con1.sh

Last active June 25, 2020 22:52

pytorch torchvision cudatoolkit=10.1 -c pytorch-nightly

mrdvince / torch16.py

Created June 25, 2020 22:37

	!wget -c https://repo.anaconda.com/miniconda/Miniconda3-4.5.4-Linux-x86_64.sh
	!chmod +x Miniconda3-4.5.4-Linux-x86_64.sh
	!bash ./Miniconda3-4.5.4-Linux-x86_64.sh -b -f -p /usr/local
	# update 1
	!conda install -q -y --prefix /usr/local python=3.6 pytorch torchvision cudatoolkit=10.1 -c pytorch-nightly
	# update 2
	import sys
	sys.path.append('/usr/local/lib/python3.6/site-packages')

mrdvince / diceroller.kt

Last active September 2, 2019 18:17

	override fun onCreate(savedInstanceState: Bundle?) {
	super.onCreate(savedInstanceState)
	setContentView(R.layout.activity_main)

	val rollButton: Button = findViewById(R.id.roll_button)
	diceImage = findViewById(R.id.dice_image)
	rollButton.setOnClickListener {
	rollDice()
	}
	}

mrdvince / query.py

Created July 23, 2019 23:45

	# Query function
	def query(db):
	return db.sum()

	full_db_result = query(db)

	sensitivity = 0
	for pdb in pdbs:
	pdb_result = query(pdb)

mrdvince / db.ipynb

Created July 23, 2019 23:30

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mrdvince / cat_name.csv

Created July 13, 2019 12:12

We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 102 columns, instead of 53 in line 1.

1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102

pink primrose,hard-leaved pocket orchid,canterbury bells,sweet pea,english marigold,tiger lily,moon orchid,bird of paradise,monkshood,globe thistle,snapdragon,colt's foot,king protea,spear thistle,yellow iris,globe-flower,purple coneflower,peruvian lily,balloon flower,giant white arum lily,fire lily,pincushion flower,fritillary,red ginger,grape hyacinth,corn poppy,prince of wales feathers,stemless gentian,artichoke,sweet william,carnation,garden phlox,love in the mist,mexican aster,alpine sea holly,ruby-lipped cattleya,cape flower,great masterwort,siam tulip,lenten rose,barbeton daisy,daffodil,sword lily,poinsettia,bolero deep blue,wallflower,marigold,buttercup,oxeye daisy,common dandelion,petunia,wild pansy,primul

mrdvince / cat_name.csv

Created July 13, 2019 12:12

We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 102 columns, instead of 53 in line 1.

1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102

pink primrose,hard-leaved pocket orchid,canterbury bells,sweet pea,english marigold,tiger lily,moon orchid,bird of paradise,monkshood,globe thistle,snapdragon,colt's foot,king protea,spear thistle,yellow iris,globe-flower,purple coneflower,peruvian lily,balloon flower,giant white arum lily,fire lily,pincushion flower,fritillary,red ginger,grape hyacinth,corn poppy,prince of wales feathers,stemless gentian,artichoke,sweet william,carnation,garden phlox,love in the mist,mexican aster,alpine sea holly,ruby-lipped cattleya,cape flower,great masterwort,siam tulip,lenten rose,barbeton daisy,daffodil,sword lily,poinsettia,bolero deep blue,wallflower,marigold,buttercup,oxeye daisy,common dandelion,petunia,wild pansy,primul

mrdvince / training loop.py

Created July 12, 2019 11:28

	# model:
	model = YourModelHere()

	# params you need to specify:
	epochs = 5
	train_loader, val_loader = # put your data loader here
	loss_function = nn.CrossEntropyLoss() # your loss function, cross entropy works well for multi-class problems

	# optimizer, I've used Adadelta, as it wokrs well without any magic numbers
	optimizer = optim.Adadelta(model.parameters())

mrdvince / owner.py

Created July 1, 2019 18:21

	ds.owner
	<VirtualWorker id:me #tensors:0>

	# get the objects from remote machine
	remote_ds.get()

	tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])

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