implemented wandb to classifier

wandb/run-20200126_144058-y7pjuj9j/config.yaml

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

wandb/run-20200126_144058-y7pjuj9j/diff.patch

@@ -0,0 +1,64 @@
+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
+ 

wandb/run-20200126_144058-y7pjuj9j/requirements.txt

@@ -0,0 +1,109 @@
+apturl==0.5.2
+argh==0.26.2
+asn1crypto==0.24.0
+bcrypt==3.1.6
+binwalk==2.1.2
+blinker==1.4
+brlapi==0.6.7
+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
+keyrings.alt==3.1.1
+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
+pyjwt==1.7.0
+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

wandb/run-20200126_144058-y7pjuj9j/wandb-events.jsonl

@@ -0,0 +1,2 @@
+{"system.cpu": 60.79, "system.memory": 41.35, "system.disk": 8.1, "system.proc.memory.availableMB": 4517.53, "system.proc.memory.rssMB": 174.98, "system.proc.memory.percent": 2.27, "system.proc.cpu.threads": 5.67, "system.network.sent": 316198, "system.network.recv": 232223, "_wandb": true, "_timestamp": 1580049687, "_runtime": 29}
+{"system.cpu": 38.45, "system.memory": 41.2, "system.disk": 8.1, "system.proc.memory.availableMB": 4528.38, "system.proc.memory.rssMB": 178.53, "system.proc.memory.percent": 2.32, "system.proc.cpu.threads": 6.0, "system.network.sent": 389159, "system.network.recv": 245293, "_wandb": true, "_timestamp": 1580049691, "_runtime": 33}

wandb/run-20200126_144058-y7pjuj9j/wandb-metadata.json

@@ -0,0 +1,23 @@
+{
+    "root": "/home/clemens/repositorys/pytorch-ai",
+    "program": "mnist_classifier.py",
+    "git": {
+        "remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
+        "commit": "8aec5e4d07a210e412aaea245303788228d5cf72"
+    },
+    "email": "clemens.dautermann@gmail.com",
+    "startedAt": "2020-01-26T14:40:58.218384",
+    "host": "ubuntu-laptop",
+    "username": "clemens",
+    "executable": "/usr/bin/python3",
+    "os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
+    "python": "3.7.5",
+    "cpu_count": 2,
+    "args": [],
+    "state": "killed",
+    "jobType": null,
+    "mode": "run",
+    "project": "pytorch_ai",
+    "heartbeatAt": "2020-01-26T14:41:31.740178",
+    "exitcode": 255
+}