Why is PyTorch's DataLoader not deterministic?
$begingroup$
I've set the seeds like this (hoping to cover all bases):
random.seed(666)
np.random.seed(666)
torch.manual_seed(666)
torch.cuda.manual_seed_all(666)
torch.backends.cudnn.deterministic = True
But the below code will still output DIFFERENT batches for both namesTrainLoader1 and namesTrainLoader2, when they should be really the same. Hoe come that creating the model is affecting the deterministic values?
namesDataset = NamesDataset()
namesTrainLoader1 = DataLoader(namesDataset, batch_size=5, shuffle=True)
for each in namesTrainLoader1:
print(each)
model = TorchRNN(inputSize, hiddenSize, outputSize)
namesTrainLoader2 = DataLoader(namesDataset, batch_size=5, shuffle=True)
for each in namesTrainLoader2:
print(each)
Output for namesTrainLoader1:
('saiki', 'close', 'sloan', 'horos', 'roman')
...
Output for namesTrainLoader2:
('david', 'abeln', 'hatit', 'holan', 'protz')
...
I also tried using worker_init_fn (e.g. with lambda x: 0) in the DataLoader, but that made no difference.
Why is this not deterministic? How can I make it deterministic? i.e. reset the internal seed of the DataLoader?
pytorch
asked 2 hours ago
MuppetMuppet
1011
New contributor
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$endgroup$
add a comment |
$begingroup$
I've set the seeds like this (hoping to cover all bases):
random.seed(666)
np.random.seed(666)
torch.manual_seed(666)
torch.cuda.manual_seed_all(666)
torch.backends.cudnn.deterministic = True
But the below code will still output DIFFERENT batches for both namesTrainLoader1 and namesTrainLoader2, when they should be really the same. Hoe come that creating the model is affecting the deterministic values?
namesDataset = NamesDataset()
namesTrainLoader1 = DataLoader(namesDataset, batch_size=5, shuffle=True)
for each in namesTrainLoader1:
print(each)
model = TorchRNN(inputSize, hiddenSize, outputSize)
namesTrainLoader2 = DataLoader(namesDataset, batch_size=5, shuffle=True)
for each in namesTrainLoader2:
print(each)
Output for namesTrainLoader1:
('saiki', 'close', 'sloan', 'horos', 'roman')
...
Output for namesTrainLoader2:
('david', 'abeln', 'hatit', 'holan', 'protz')
...
I also tried using worker_init_fn (e.g. with lambda x: 0) in the DataLoader, but that made no difference.
Why is this not deterministic? How can I make it deterministic? i.e. reset the internal seed of the DataLoader?
pytorch
asked 2 hours ago
MuppetMuppet
1011
New contributor
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I've set the seeds like this (hoping to cover all bases):
random.seed(666)
np.random.seed(666)
torch.manual_seed(666)
torch.cuda.manual_seed_all(666)
torch.backends.cudnn.deterministic = True
But the below code will still output DIFFERENT batches for both namesTrainLoader1 and namesTrainLoader2, when they should be really the same. Hoe come that creating the model is affecting the deterministic values?
namesDataset = NamesDataset()
namesTrainLoader1 = DataLoader(namesDataset, batch_size=5, shuffle=True)
for each in namesTrainLoader1:
print(each)
model = TorchRNN(inputSize, hiddenSize, outputSize)
namesTrainLoader2 = DataLoader(namesDataset, batch_size=5, shuffle=True)
for each in namesTrainLoader2:
print(each)
Output for namesTrainLoader1:
('saiki', 'close', 'sloan', 'horos', 'roman')
...
Output for namesTrainLoader2:
('david', 'abeln', 'hatit', 'holan', 'protz')
...
I also tried using worker_init_fn (e.g. with lambda x: 0) in the DataLoader, but that made no difference.
Why is this not deterministic? How can I make it deterministic? i.e. reset the internal seed of the DataLoader?
pytorch
asked 2 hours ago
MuppetMuppet
1011
New contributor
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I've set the seeds like this (hoping to cover all bases):
random.seed(666)
np.random.seed(666)
torch.manual_seed(666)
torch.cuda.manual_seed_all(666)
torch.backends.cudnn.deterministic = True
But the below code will still output DIFFERENT batches for both namesTrainLoader1 and namesTrainLoader2, when they should be really the same. Hoe come that creating the model is affecting the deterministic values?
namesDataset = NamesDataset()
namesTrainLoader1 = DataLoader(namesDataset, batch_size=5, shuffle=True)
for each in namesTrainLoader1:
print(each)
model = TorchRNN(inputSize, hiddenSize, outputSize)
namesTrainLoader2 = DataLoader(namesDataset, batch_size=5, shuffle=True)
for each in namesTrainLoader2:
print(each)
Output for namesTrainLoader1:
('saiki', 'close', 'sloan', 'horos', 'roman')
...
Output for namesTrainLoader2:
('david', 'abeln', 'hatit', 'holan', 'protz')
...
I also tried using worker_init_fn (e.g. with lambda x: 0) in the DataLoader, but that made no difference.
Why is this not deterministic? How can I make it deterministic? i.e. reset the internal seed of the DataLoader?
pytorch
pytorch
asked 2 hours ago
MuppetMuppet
1011
New contributor
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
MuppetMuppet
1011
New contributor
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
MuppetMuppet
1011
New contributor
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 hours ago
MuppetMuppet
1011
asked 2 hours ago
MuppetMuppet
1011
1011
New contributor
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Muppet is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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