mirror of
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ea968f5cc3
Summary:
TensorBoard tests using SummaryWriter() may fail with a pandas import
complaint if TensorFlow packages are installed in the same python
environment as PyTorch:
Traceback (most recent call last):
File "test_tensorboard.py", line 212, in test_writer
with self.createSummaryWriter() as writer:
File "test_tensorboard.py", line 64, in createSummaryWriter
return SummaryWriter(temp_dir)
...
File "[...]/site-packages/pandas/core/arrays/categorical.py", line 52, in <module>
import pandas.core.algorithms as algorithms
AttributeError: module 'pandas' has no attribute 'core'
The exact failure may depend on the pandas version. We've also seen:
File "[...]/site-packages/pandas/core/arrays/categorical.py", line 9, in <module>
import pandas.compat as compat
AttributeError: module 'pandas' has no attribute 'compat'
The module import chain leading to the failure is tensorboard imports
tensorflow imports tensorflow_estimator imports pandas. pandas includes
a submodule named 'bottleneck', whose name collides with the PyTorch
'test/bottleneck/' subdirectory.
So IF tensorboard, tensorflow, tensorflow_estimator, and pandas are
installed in the python environment AND IF testing is run from within
PyTorch's 'test/' directory (or maybe just with 'test/' in PYTHONPATH,
etc.), then TensorBoard tests using SummaryWriter() will fail.
Rename the 'bottleneck/' directory slightly to avoid the name collision.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/29650
Differential Revision: D19698638
Pulled By: ezyang
fbshipit-source-id: cb59342ed407cb37aefc833d67f768a8809129ac
28 lines
574 B
Python
28 lines
574 B
Python
import torch
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import torch.nn as nn
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class Model(nn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.linear = nn.Linear(20, 20)
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def forward(self, input):
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out = self.linear(input[:, 10:30])
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return out.sum()
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def main():
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data = torch.randn(10, 50).cuda()
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model = Model().cuda()
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optimizer = torch.optim.SGD(model.parameters(), lr=0.0001)
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for i in range(10):
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optimizer.zero_grad()
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loss = model(data)
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loss.backward()
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optimizer.step()
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if __name__ == '__main__':
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main()
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