def main():
model = Net()
if args.cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
loss_function = F.nll_loss
missinglink_project = missinglink.PyTorchProject(owner_id=OWNER_ID, project_token=PROJECT_TOKEN)
with missinglink_project.create_experiment(
model,
metrics={'loss': loss_function},
display_name='PyTorch convolutional neural network',
description='Two dimensional convolutional neural network') as experiment:
loss_function = experiment.metrics['loss']
for epoch in experiment.epoch_loop(epochs=args.epochs):
train(experiment, model, loss_function, optimizer, epoch)
test(experiment, model, loss_function)
x = self.fc2(x)
return F.log_softmax(x)
model = Net()
if args.cuda:
model.cuda()
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
loss_function = F.nll_loss
# Override credential values if provided as arguments
OWNER_ID = args.owner_id or OWNER_ID
PROJECT_TOKEN = args.project_token or PROJECT_TOKEN
missinglink_project = missinglink.PyTorchProject(owner_id=OWNER_ID,
project_token=PROJECT_TOKEN)
def train(epoch):
model.train()
for batch_idx, (data,
target) in experiment.batch_loop(iterable=train_loader):
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
output = model(data)
loss = loss_function(output, target)
loss.backward()
optimizer.step()
if batch_idx % args.log_interval == 0:
################################
from __future__ import print_function
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.autograd import Variable
import missinglink
# Change the `project_token` to your own when trying to run this.
missinglink_project = missinglink.PyTorchProject(project_token='KzcbCxZWjewiqxCi')
################################
## Constants
################################
class dotdict(dict):
"""dot.notation access to dictionary attributes"""
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
args = dotdict()
args.seed = 321
args.batch_size = 200
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--fp16-allreduce', action='store_true', default=False,
help='use fp16 compression during allreduce')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
# Horovod: initialize library.
hvd.init()
torch.manual_seed(args.seed)
missinglink_project = missinglink.PyTorchProject(project='5740395606573056')
is_hvd_leader = (hvd.rank() == 0)
class NullContextManager(object):
def __init__(self, dummy_resource=None):
self.dummy_resource = dummy_resource
def __enter__(self):
return self.dummy_resource
def __exit__(self, *args):
pass
null_with = NullContextManager()
if args.cuda:
# Horovod: pin GPU to local rank.
torch.cuda.set_device(hvd.local_rank())
## Imports
################################
from __future__ import print_function
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.autograd import Variable
import missinglink
# Change the `project_token` to your own when trying to run this.
missinglink_project = missinglink.PyTorchProject(project=6385640660795392)
################################
## Constants
################################
class dotdict(dict):
"""dot.notation access to dictionary attributes"""
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
args = dotdict()
from __future__ import print_function
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.autograd import Variable
import missinglink
missinglink_project = missinglink.PyTorchProject()
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument(
'--batch-size',
type=int,
default=64,
metavar='N',
help='input batch size for training (default: 64)')
parser.add_argument(
'--test-batch-size',
type=int,
default=1000,
metavar='N',
help='input batch size for testing (default: 1000)')
parser.add_argument(
'--epochs',
type=int,
default=2,
