removed wandb runs to avoid unneccesary dependabot comments

This commit is contained in:
Clemens-Dautermann 2020-06-09 12:15:29 +02:00
parent c0043b8997
commit 0405cf0971
90 changed files with 0 additions and 90306 deletions

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@ -1,9 +0,0 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

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@ -1,64 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 8eac991..4578ce3 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -3,7 +3,10 @@ import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import transforms, datasets
+from tqdm import tqdm
+import wandb
+wandb.init(project='pytorch_ai')
train = datasets.MNIST('./datasets', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor()
@@ -37,31 +40,32 @@ class Net(nn.Module):
net = Net()
+wandb.watch(net)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
-for epoch in range(10): # 3 full passes over the data
- for data in trainset: # `data` is a batch of data
+for epoch in range(10): # 10 full passes over the data
+ for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
- loss = F.nll_loss(output, y) # calc and grab the loss value
+ loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
optimizer.step() # attempt to optimize weights to account for loss/gradients
+ wandb.log({'loss': loss})
- print(loss) # print loss. We hope loss (a measure of wrong-ness) declines!
- torch.save(net, './nets/net_' + str(epoch) + ".pt")
+ # torch.save(net, './nets/net_' + str(epoch) + ".pt")
correct = 0
total = 0
with torch.no_grad():
for data in testset:
X, y = data
output = net(X.view(-1, 784))
- # print(output)
for idx, i in enumerate(output):
- # print(torch.argmax(i), y[idx])
if torch.argmax(i) == y[idx]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
+ wandb.log({'epoch': epoch})
diff --git a/wandb/settings b/wandb/settings
index 8dae664..73c329e 100644
--- a/wandb/settings
+++ b/wandb/settings
@@ -1,2 +1,4 @@
[default]
+project = pytorch_ai
+entity = cdautermann

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@ -1,109 +0,0 @@
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blinker==1.4
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certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
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kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
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pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

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@ -1,23 +0,0 @@
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@ -1,9 +0,0 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.6.9

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diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..b2dd674 100644
--- a/wandb/debug.log
+++ b/wandb/debug.log
@@ -1,173 +1,20 @@
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pip==19.0.1
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protobuf==3.6.1
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@ -1,220 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..fba8bb0 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,6 +54,7 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..c89ef1d 100644
--- a/wandb/debug.log
+++ b/wandb/debug.log
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@ -1,9 +0,0 @@
wandb_version: 1
_wandb:
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@ -1,221 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..fba8bb0 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,6 +54,7 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..eb1de04 100644
--- a/wandb/debug.log
+++ b/wandb/debug.log
@@ -1,173 +1,19 @@
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@ -1,16 +0,0 @@
runnning on cuda:0
0%| | 0/4000 [00:00<?, ?it/s]
Traceback (most recent call last):
File "mnist_classifier.py", line 58, in <module>
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "mnist_classifier.py", line 34, in forward
x = F.relu(self.fc1(x))
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py", line 1370, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: Expected object of device type cuda but got device type cpu for argument #2 'mat1' in call to _th_addmm

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@ -1,322 +0,0 @@
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pip==19.0.1
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protobuf==3.6.1
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@ -1,221 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..909c2c0 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,6 +54,8 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X.to(device)
+ y.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..6d047d9 100644
--- a/wandb/debug.log
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@ -1,16 +0,0 @@
runnning on cuda:0
0%| | 0/4000 [00:00<?, ?it/s]
Traceback (most recent call last):
File "mnist_classifier.py", line 59, in <module>
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "mnist_classifier.py", line 34, in forward
x = F.relu(self.fc1(x))
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 532, in __call__
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/linear.py", line 87, in forward
return F.linear(input, self.weight, self.bias)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py", line 1370, in linear
ret = torch.addmm(bias, input, weight.t())
RuntimeError: Expected object of device type cuda but got device type cpu for argument #2 'mat1' in call to _th_addmm

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@ -1,322 +0,0 @@
absl-py==0.7.1
adal==1.2.1
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azure-mgmt-devspaces==0.1.0
azure-mgmt-devtestlabs==2.2.0
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future==0.16.0
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markdown==3.1.1
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nbconvert==5.5.0
nbformat==4.4.0
netifaces==0.10.4
networkx==2.3
notebook==5.7.8
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nvidia-ml-py3==7.352.0
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paramiko==2.4.1
parso==0.4.0
pathtools==0.1.2
pbkdf2==1.3
pefile==2018.8.8
pexpect==4.2.1
pickleshare==0.7.5
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pip==19.0.1
plotly==3.9.0
pluggy==0.12.0
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promise==2.3
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protobuf==3.6.1
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py==1.8.0
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python-dateutil==2.7.3
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@ -1,25 +0,0 @@
{
"root": "/home/clemens/Dokumente/repos/pytorch-ai",
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"git": {
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"commit": "1fe643d464bc16b62628d9cc0149a97a96b1f1a6"
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"email": "clemens.dautermann@t-online.de",
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@ -1,9 +0,0 @@
wandb_version: 1
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value:
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framework: torch
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@ -1,222 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..394a799 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,7 +54,9 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X = X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
+ output = output.cpu()
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
optimizer.step() # attempt to optimize weights to account for loss/gradients
diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..c1a264f 100644
--- a/wandb/debug.log
+++ b/wandb/debug.log
@@ -1,173 +1,18 @@
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defer==1.0.6
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django==2.1
djongo==1.2.29
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pip==19.0.1
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promise==2.3
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protobuf==3.6.1
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py==1.8.0
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vine==1.1.4
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@ -1,229 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..28f9aa9 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,13 +54,15 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X = X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
+ output = output.cpu()
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
optimizer.step() # attempt to optimize weights to account for loss/gradients
wandb.log({'loss': loss})
- # torch.save(net, './nets/net_' + str(epoch) + ".pt")
+ torch.save(net, './nets/net_gpu_' + str(epoch) + ".pt")
correct = 0
total = 0
with torch.no_grad():
diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..82d4d6e 100644
--- a/wandb/debug.log
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diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..ea2d4d8 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,19 +54,24 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X = X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
+ output = output.cpu()
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
optimizer.step() # attempt to optimize weights to account for loss/gradients
wandb.log({'loss': loss})
-
- # torch.save(net, './nets/net_' + str(epoch) + ".pt")
+ net = net.cpu()
+ torch.save(net, './nets/net_gpu_' + str(epoch) + ".pt")
+ net = net.to(device)
correct = 0
total = 0
with torch.no_grad():
for data in testset:
X, y = data
+ X = X.to(device)
output = net(X.view(-1, 784))
+ output = output.cpu()
for idx, i in enumerate(output):
if torch.argmax(i) == y[idx]:
correct += 1
diff --git a/wandb/debug.log b/wandb/debug.log
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@ -1,9 +0,0 @@
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@ -1,248 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..c71a5c8 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -17,7 +17,7 @@ test = datasets.MNIST('./datasets', train=False, download=True,
transforms.ToTensor()
]))
-trainset = torch.utils.data.DataLoader(train, batch_size=15, shuffle=True)
+trainset = torch.utils.data.DataLoader(train, batch_size=64, shuffle=True)
testset = torch.utils.data.DataLoader(test, batch_size=10, shuffle=False)
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,19 +54,24 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X = X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
+ output = output.cpu()
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
optimizer.step() # attempt to optimize weights to account for loss/gradients
wandb.log({'loss': loss})
-
- # torch.save(net, './nets/net_' + str(epoch) + ".pt")
+ net = net.cpu()
+ torch.save(net, './nets/net_gpu_large_batch_' + str(epoch) + ".pt")
+ net = net.to(device)
correct = 0
total = 0
with torch.no_grad():
for data in testset:
X, y = data
+ X = X.to(device)
output = net(X.view(-1, 784))
+ output = output.cpu()
for idx, i in enumerate(output):
if torch.argmax(i) == y[idx]:
correct += 1
diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..0be4d95 100644
--- a/wandb/debug.log
+++ b/wandb/debug.log
@@ -1,173 +1,18 @@
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@ -1,248 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..99dd863 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -17,7 +17,7 @@ test = datasets.MNIST('./datasets', train=False, download=True,
transforms.ToTensor()
]))
-trainset = torch.utils.data.DataLoader(train, batch_size=15, shuffle=True)
+trainset = torch.utils.data.DataLoader(train, batch_size=200, shuffle=True)
testset = torch.utils.data.DataLoader(test, batch_size=10, shuffle=False)
@@ -42,6 +42,11 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -49,19 +54,24 @@ for epoch in range(10): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X = X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
+ output = output.cpu()
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
optimizer.step() # attempt to optimize weights to account for loss/gradients
wandb.log({'loss': loss})
-
- # torch.save(net, './nets/net_' + str(epoch) + ".pt")
+ net = net.cpu()
+ torch.save(net, './nets/net_gpu_large_batch_' + str(epoch) + ".pt")
+ net = net.to(device)
correct = 0
total = 0
with torch.no_grad():
for data in testset:
X, y = data
+ X = X.to(device)
output = net(X.view(-1, 784))
+ output = output.cpu()
for idx, i in enumerate(output):
if torch.argmax(i) == y[idx]:
correct += 1
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protobuf==3.6.1
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@ -1,9 +0,0 @@
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@ -1,249 +0,0 @@
diff --git a/mnist_classifier.py b/mnist_classifier.py
index 4578ce3..989207b 100644
--- a/mnist_classifier.py
+++ b/mnist_classifier.py
@@ -17,7 +17,7 @@ test = datasets.MNIST('./datasets', train=False, download=True,
transforms.ToTensor()
]))
-trainset = torch.utils.data.DataLoader(train, batch_size=15, shuffle=True)
+trainset = torch.utils.data.DataLoader(train, batch_size=200, shuffle=True)
testset = torch.utils.data.DataLoader(test, batch_size=10, shuffle=False)
@@ -42,26 +42,36 @@ class Net(nn.Module):
net = Net()
wandb.watch(net)
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('runnning on %s' % device)
+
+net = net.to(device)
+
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
-for epoch in range(10): # 10 full passes over the data
+for epoch in range(200): # 10 full passes over the data
for data in tqdm(trainset): # `data` is a batch of data
X, y = data # X is the batch of features, y is the batch of targets.
net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step.
+ X = X.to(device)
output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm)
+ output = output.cpu()
loss = loss_function(output, y) # calc and grab the loss value
loss.backward() # apply this loss backwards thru the network's parameters
optimizer.step() # attempt to optimize weights to account for loss/gradients
wandb.log({'loss': loss})
-
- # torch.save(net, './nets/net_' + str(epoch) + ".pt")
+ net = net.cpu()
+ torch.save(net, './nets/net_gpu_large_batch_' + str(epoch) + ".pt")
+ net = net.to(device)
correct = 0
total = 0
with torch.no_grad():
for data in testset:
X, y = data
+ X = X.to(device)
output = net(X.view(-1, 784))
+ output = output.cpu()
for idx, i in enumerate(output):
if torch.argmax(i) == y[idx]:
correct += 1
diff --git a/wandb/debug.log b/wandb/debug.log
index 0c37b2a..ed5b516 100644
--- a/wandb/debug.log
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azure-mgmt-authorization==0.50.0
azure-mgmt-batch==5.0.1
azure-mgmt-batchai==2.0.0
azure-mgmt-billing==0.2.0
azure-mgmt-cdn==3.0.0
azure-mgmt-cognitiveservices==3.0.0
azure-mgmt-commerce==1.0.1
azure-mgmt-compute==4.4.0
azure-mgmt-consumption==2.0.0
azure-mgmt-containerinstance==1.4.0
azure-mgmt-containerregistry==2.7.0
azure-mgmt-containerservice==4.4.0
azure-mgmt-cosmosdb==0.4.1
azure-mgmt-datafactory==0.6.0
azure-mgmt-datalake-analytics==0.6.0
azure-mgmt-datalake-nspkg==3.0.1
azure-mgmt-datalake-store==0.5.0
azure-mgmt-datamigration==1.0.0
azure-mgmt-devspaces==0.1.0
azure-mgmt-devtestlabs==2.2.0
azure-mgmt-dns==2.1.0
azure-mgmt-eventgrid==1.0.0
azure-mgmt-eventhub==2.3.0
azure-mgmt-hanaonazure==0.1.1
azure-mgmt-iotcentral==0.1.0
azure-mgmt-iothub==0.5.0
azure-mgmt-iothubprovisioningservices==0.2.0
azure-mgmt-keyvault==1.1.0
azure-mgmt-loganalytics==0.2.0
azure-mgmt-logic==3.0.0
azure-mgmt-machinelearningcompute==0.4.1
azure-mgmt-managementgroups==0.1.0
azure-mgmt-managementpartner==0.1.0
azure-mgmt-maps==0.1.0
azure-mgmt-marketplaceordering==0.1.0
azure-mgmt-media==1.0.0
azure-mgmt-monitor==0.5.2
azure-mgmt-msi==0.2.0
azure-mgmt-network==2.5.1
azure-mgmt-notificationhubs==2.0.0
azure-mgmt-nspkg==3.0.2
azure-mgmt-policyinsights==0.1.0
azure-mgmt-powerbiembedded==2.0.0
azure-mgmt-rdbms==1.5.0
azure-mgmt-recoveryservices==0.3.0
azure-mgmt-recoveryservicesbackup==0.3.0
azure-mgmt-redis==5.0.0
azure-mgmt-relay==0.1.0
azure-mgmt-reservations==0.2.1
azure-mgmt-resource==2.1.0
azure-mgmt-scheduler==2.0.0
azure-mgmt-search==2.0.0
azure-mgmt-servicebus==0.5.3
azure-mgmt-servicefabric==0.2.0
azure-mgmt-signalr==0.1.1
azure-mgmt-sql==0.9.1
azure-mgmt-storage==2.0.0
azure-mgmt-subscription==0.2.0
azure-mgmt-trafficmanager==0.50.0
azure-mgmt-web==0.35.0
azure-mgmt==4.0.0
azure-nspkg==3.0.2
azure-servicebus==0.21.1
azure-servicefabric==6.3.0.0
azure-servicemanagement-legacy==0.20.6
azure-storage-blob==1.5.0
azure-storage-common==1.4.0
azure-storage-file==1.4.0
azure-storage-queue==1.4.0
azure==4.0.0
backcall==0.1.0
bcrypt==3.1.4
beautifulsoup4==4.6.0
billiard==3.3.0.23
binwalk==2.1.1
bleach==1.5.0
blinker==1.4
brlapi==0.6.6
browser-cookie3==0.6.4
celery==3.1.26.post2
certifi==2017.11.5
cffi==1.11.5
chardet==3.0.4
click==7.0
cloudpickle==1.2.1
command-not-found==0.3
configparser==4.0.2
cryptography==2.1.4
cupshelpers==1.0
cycler==0.10.0
dataclasses==0.6
dbf==0.97.11
dbfread==2.0.7
decorator==4.4.0
defer==1.0.6
defusedxml==0.5.0
distro-info==0.18ubuntu0.18.04.1
django-celery==3.2.2
django==2.1
djongo==1.2.29
docker-pycreds==0.4.0
docopt==0.6.2
docx==0.2.4
entrypoints==0.3
enum34==1.1.6
f.lux-indicator-applet==1.1.11-pre
feedparser==5.2.1
flask==1.0.2
future==0.16.0
gast==0.2.2
gephistreamer==2.0.3
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
grpcio==1.11.1
html2markdown==0.1.7
html5lib==0.9999999
httplib2==0.9.2
idna==2.6
ifaddr==0.1.4
imageio==2.5.0
importlib-metadata==0.19
ipaddress==1.0.22
ipykernel==5.1.1
ipython-genutils==0.2.0
ipython==7.5.0
ipywidgets==7.4.2
isodate==0.6.0
itsdangerous==1.1.0
jedi==0.13.3
jinja2==2.10.1
jsonschema==3.0.1
jupyter-client==5.2.4
jupyter-console==6.0.0
jupyter-core==4.4.0
jupyter==1.0.0
keyring==10.6.0
keyrings.alt==3.0
kiwisolver==1.0.1
kombu==3.0.37
language-selector==0.1
launchpadlib==1.10.6
lazr.restfulclient==0.13.5
lazr.uri==1.0.3
llvmlite==0.29.0
louis==3.5.0
lxml==4.2.1
macaroonbakery==1.1.3
macholib==1.11
mako==1.0.7
markdown==3.1.1
markupsafe==1.0
matplotlib==2.2.0
mechanize==0.2.5
mistune==0.8.4
mlagents-envs==0.9.1
mlagents==0.9.1
more-itertools==7.2.0
msrest==0.6.4
msrestazure==0.6.0
nbconvert==5.5.0
nbformat==4.4.0
netifaces==0.10.4
networkx==2.3
notebook==5.7.8
numba==0.44.1
numpy==1.14.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==3.0.1
olefile==0.45.1
pandocfilters==1.4.2
paramiko==2.4.1
parso==0.4.0
pathtools==0.1.2
pbkdf2==1.3
pefile==2018.8.8
pexpect==4.2.1
pickleshare==0.7.5
pillow==5.2.0
pip==19.0.1
plotly==3.9.0
pluggy==0.12.0
pocketsphinx==0.1.3
prometheus-client==0.6.0
promise==2.3
prompt-toolkit==2.0.9
protobuf==3.6.1
psutil==5.6.7
psycopg2==2.7.5
ptyprocess==0.6.0
py==1.8.0
pyaes==1.6.1
pyasn1==0.4.2
pyaudio==0.2.11
pycairo==1.16.2
pycodestyle==2.3.1
pycparser==2.18
pycrypto==2.6.1
pycups==1.9.73
pydeepl==0.9
pyglet==1.4.1
pygments==2.3.1
pygobject==3.26.1
pyinstaller==3.4
pyjwt==1.5.3
pymacaroons==0.13.0
pymongo==3.7.1
pynacl==1.1.2
pyopengl==3.1.0
pyparsing==2.2.0
pypdf2==1.26.0
pyqtgraph==0.10.0
pyrfc3339==1.0
pyrsistent==0.15.2
pytest==3.10.1
python-apt==1.6.5+ubuntu0.2
python-dateutil==2.7.3
python-debian==0.1.32
python-docx==0.8.6
python-gitlab==1.3.0
pytz==2018.4
pywavelets==1.0.3
pyxdg==0.25
pyyaml==3.12
pyzmq==18.0.1
qtconsole==4.4.4
queryablelist==3.1.0
reportlab==3.4.0
requests-oauthlib==1.2.0
requests-unixsocket==0.1.5
requests==2.21.0
retrying==1.3.3
scikit-image==0.15.0
scipy==1.3.0
scour==0.36
screen-resolution-extra==0.0.0
secretstorage==2.3.1
selenium==3.7.0
send2trash==1.5.0
sentry-sdk==0.14.1
setuptools==38.4.0
shortuuid==0.5.0
simplejson==3.13.2
six==1.11.0
smmap2==2.0.5
speechrecognition==3.8.1
sqlparse==0.2.4
ssh-import-id==5.7
subprocess32==3.5.4
system-service==0.3
systemd-python==234
tensorboard==1.7.0
tensorflow==1.7.1
termcolor==1.1.0
terminado==0.8.2
testpath==0.4.2
torch==1.4.0
torchfile==0.1.0
torchvision==0.5.0
tornado==6.0.1
tqdm==4.23.4
traitlets==4.3.2
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
unity-scope-calculator==0.1
unity-scope-chromiumbookmarks==0.1
unity-scope-colourlovers==0.1
unity-scope-devhelp==0.1
unity-scope-firefoxbookmarks==0.1
unity-scope-manpages==0.1
unity-scope-openclipart==0.1
unity-scope-texdoc==0.1
unity-scope-tomboy==0.1
unity-scope-virtualbox==0.1
unity-scope-yelp==0.1
unity-scope-zotero==0.1
urllib3==1.22
usb-creator==0.3.3
vine==1.1.4
visdom==0.1.8.8
wadllib==1.3.2
wakeonlan==1.1.6
wandb==0.8.22
watchdog==0.10.0
wcwidth==0.1.7
webencodings==0.5.1
websocket-client==0.55.0
werkzeug==0.15.2
wheel==0.30.0
widgetsnbextension==3.4.2
ws4py==0.5.1
xkit==0.0.0
zeroconf==0.21.3
zipp==0.5.2
zope.interface==4.3.2

View file

@ -1,269 +0,0 @@
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