mrdvince

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

pytorch torchvision cudatoolkit=10.1 -c pytorch-nightly

mrdvince

!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

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

  full_db_result = query(db)

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

mrdvince

mrdvince

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

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

# 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

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])