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. 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cwd=/home/clemens/Dokumente/repos/pytorch-ai, universal_newlines=False, shell=None, istream=None) diff --git a/wandb/run-20200127_141031-92wkr1uh/output.log b/wandb/run-20200127_141031-92wkr1uh/output.log new file mode 100644 index 0000000..77aa1c8 --- /dev/null +++ b/wandb/run-20200127_141031-92wkr1uh/output.log @@ -0,0 +1,16 @@ +runnning on cuda:0 + 0%| | 0/4000 [00:00 + 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 diff --git a/wandb/run-20200127_141031-92wkr1uh/requirements.txt b/wandb/run-20200127_141031-92wkr1uh/requirements.txt new file mode 100644 index 0000000..4d1e5f0 --- /dev/null +++ b/wandb/run-20200127_141031-92wkr1uh/requirements.txt @@ -0,0 +1,322 @@ +absl-py==0.7.1 +adal==1.2.1 +advancedhtmlparser==8.1.6 +aenum==2.1.2 +altgraph==0.16.1 +amqp==1.4.9 +anyjson==0.3.3 +apturl==0.5.2 +asn1crypto==0.24.0 +astor==0.8.0 +atomicwrites==1.3.0 +attrs==19.1.0 +autopep8==1.3.3 +azure-applicationinsights==0.1.0 +azure-batch==4.1.3 +azure-common==1.1.18 +azure-cosmosdb-nspkg==2.0.2 +azure-cosmosdb-table==1.0.5 +azure-datalake-store==0.0.41 +azure-eventgrid==1.2.0 +azure-graphrbac==0.40.0 +azure-keyvault==1.1.0 +azure-loganalytics==0.1.0 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b/wandb/run-20200127_141329-lscctqxi/diff.patch @@ -0,0 +1,221 @@ +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. 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100644 index 0000000..e631fad --- /dev/null +++ b/wandb/run-20200127_141329-lscctqxi/output.log @@ -0,0 +1,16 @@ +runnning on cuda:0 + 0%| | 0/4000 [00:00 + 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 diff --git a/wandb/run-20200127_141329-lscctqxi/requirements.txt b/wandb/run-20200127_141329-lscctqxi/requirements.txt new file mode 100644 index 0000000..4d1e5f0 --- /dev/null +++ b/wandb/run-20200127_141329-lscctqxi/requirements.txt @@ -0,0 +1,322 @@ +absl-py==0.7.1 +adal==1.2.1 +advancedhtmlparser==8.1.6 +aenum==2.1.2 +altgraph==0.16.1 +amqp==1.4.9 +anyjson==0.3.3 +apturl==0.5.2 +asn1crypto==0.24.0 +astor==0.8.0 +atomicwrites==1.3.0 +attrs==19.1.0 +autopep8==1.3.3 +azure-applicationinsights==0.1.0 +azure-batch==4.1.3 +azure-common==1.1.18 +azure-cosmosdb-nspkg==2.0.2 +azure-cosmosdb-table==1.0.5 +azure-datalake-store==0.0.41 +azure-eventgrid==1.2.0 +azure-graphrbac==0.40.0 +azure-keyvault==1.1.0 +azure-loganalytics==0.1.0 +azure-mgmt-advisor==1.0.1 +azure-mgmt-applicationinsights==0.1.1 +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 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"_timestamp": 1580134414, "_runtime": 5} diff --git a/wandb/run-20200127_141329-lscctqxi/wandb-history.jsonl b/wandb/run-20200127_141329-lscctqxi/wandb-history.jsonl new file mode 100644 index 0000000..e69de29 diff --git a/wandb/run-20200127_141329-lscctqxi/wandb-metadata.json b/wandb/run-20200127_141329-lscctqxi/wandb-metadata.json new file mode 100644 index 0000000..3f08438 --- /dev/null +++ b/wandb/run-20200127_141329-lscctqxi/wandb-metadata.json @@ -0,0 +1,25 @@ +{ + "root": "/home/clemens/Dokumente/repos/pytorch-ai", + "program": "mnist_classifier.py", + "git": { + "remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git", + "commit": "1fe643d464bc16b62628d9cc0149a97a96b1f1a6" + }, + "email": "clemens.dautermann@t-online.de", + "startedAt": "2020-01-27T14:13:29.262082", + "host": "clemens-ubuntu", + "username": "clemens", + "executable": "/usr/bin/python3", + "os": "Linux-4.15.0-58-generic-x86_64-with-Ubuntu-18.04-bionic", + "python": "3.6.9", + "gpu": "GeForce GTX 960", + "gpu_count": 1, + "cpu_count": 4, + "args": [], + "state": "failed", + "jobType": null, + "mode": "run", + "project": "pytorch_ai", + "heartbeatAt": "2020-01-27T14:13:35.088003", + "exitcode": 1 +} diff --git a/wandb/run-20200127_141628-v2eude0z/config.yaml b/wandb/run-20200127_141628-v2eude0z/config.yaml new file mode 100644 index 0000000..fdfce98 --- /dev/null +++ b/wandb/run-20200127_141628-v2eude0z/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: 3.6.9 diff --git a/wandb/run-20200127_141628-v2eude0z/diff.patch b/wandb/run-20200127_141628-v2eude0z/diff.patch new file mode 100644 index 0000000..e4fbd68 --- /dev/null +++ b/wandb/run-20200127_141628-v2eude0z/diff.patch @@ -0,0 +1,222 @@ +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 @@ +-2020-01-26 15:40:59,370 DEBUG MainThread:14805 [wandb_config.py:_load_defaults():111] no defaults not found in config-defaults.yaml +-2020-01-26 15:40:59,388 DEBUG MainThread:14805 [cmd.py:execute():728] Popen(['git', 'cat-file', '--batch-check'], cwd=/home/clemens/repositorys/pytorch-ai, universal_newlines=False, shell=None, istream=) +-2020-01-26 15:40:59,411 DEBUG MainThread:14805 [cmd.py:execute():728] Popen(['git', 'rev-parse', '--show-toplevel'], 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100644 index 0000000..7ba4c4c --- /dev/null +++ b/wandb/run-20200127_142807-st277t0v/output.log @@ -0,0 +1,16 @@ +runnning on cuda:0 + 0%| | 0/4000 [00:00 + output = net(X.view(-1, 784)) + 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 diff --git a/wandb/run-20200127_142807-st277t0v/requirements.txt b/wandb/run-20200127_142807-st277t0v/requirements.txt new file mode 100644 index 0000000..4d1e5f0 --- /dev/null +++ b/wandb/run-20200127_142807-st277t0v/requirements.txt @@ -0,0 +1,322 @@ +absl-py==0.7.1 +adal==1.2.1 +advancedhtmlparser==8.1.6 +aenum==2.1.2 +altgraph==0.16.1 +amqp==1.4.9 +anyjson==0.3.3 +apturl==0.5.2 +asn1crypto==0.24.0 +astor==0.8.0 +atomicwrites==1.3.0 +attrs==19.1.0 +autopep8==1.3.3 +azure-applicationinsights==0.1.0 +azure-batch==4.1.3 +azure-common==1.1.18 +azure-cosmosdb-nspkg==2.0.2 +azure-cosmosdb-table==1.0.5 +azure-datalake-store==0.0.41 +azure-eventgrid==1.2.0 +azure-graphrbac==0.40.0 +azure-keyvault==1.1.0 +azure-loganalytics==0.1.0 +azure-mgmt-advisor==1.0.1 +azure-mgmt-applicationinsights==0.1.1 +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 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0000000..803923d --- /dev/null +++ b/wandb/run-20200127_143851-1cqtetvf/diff.patch @@ -0,0 +1,239 @@ +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 +index 0c37b2a..a961583 100644 +--- a/wandb/debug.log ++++ b/wandb/debug.log +@@ -1,173 +1,18 @@ +-2020-01-26 15:40:59,370 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