Two different GPUs for Keras (Python)?
$begingroup$
One question guys, someone knows if it should be ok to get one more GPU of type Nvidia Geforce GTX 1070 (gaming version), given that now I have GTX 1070 Titanium? They don't have another Titanium card available here, so I have to get a different one, but closely similar, and I wonder if for using Keras (with TensorFlow backend), will it work fine? They are not exactly the same cards, but similar enough maybe. I want 2 GPUs for Keras.
python keras tensorflow gpu nvidia
asked 2 days ago
MachineLearningGodMachineLearningGod
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$begingroup$
One question guys, someone knows if it should be ok to get one more GPU of type Nvidia Geforce GTX 1070 (gaming version), given that now I have GTX 1070 Titanium? They don't have another Titanium card available here, so I have to get a different one, but closely similar, and I wonder if for using Keras (with TensorFlow backend), will it work fine? They are not exactly the same cards, but similar enough maybe. I want 2 GPUs for Keras.
python keras tensorflow gpu nvidia
asked 2 days ago
MachineLearningGodMachineLearningGod
82
New contributor
MachineLearningGod 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 |
$begingroup$
One question guys, someone knows if it should be ok to get one more GPU of type Nvidia Geforce GTX 1070 (gaming version), given that now I have GTX 1070 Titanium? They don't have another Titanium card available here, so I have to get a different one, but closely similar, and I wonder if for using Keras (with TensorFlow backend), will it work fine? They are not exactly the same cards, but similar enough maybe. I want 2 GPUs for Keras.
python keras tensorflow gpu nvidia
asked 2 days ago
MachineLearningGodMachineLearningGod
82
New contributor
MachineLearningGod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
One question guys, someone knows if it should be ok to get one more GPU of type Nvidia Geforce GTX 1070 (gaming version), given that now I have GTX 1070 Titanium? They don't have another Titanium card available here, so I have to get a different one, but closely similar, and I wonder if for using Keras (with TensorFlow backend), will it work fine? They are not exactly the same cards, but similar enough maybe. I want 2 GPUs for Keras.
python keras tensorflow gpu nvidia
python keras tensorflow gpu nvidia
asked 2 days ago
MachineLearningGodMachineLearningGod
82
New contributor
MachineLearningGod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
MachineLearningGodMachineLearningGod
82
New contributor
MachineLearningGod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
MachineLearningGodMachineLearningGod
82
New contributor
MachineLearningGod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 2 days ago
MachineLearningGodMachineLearningGod
82
asked 2 days ago
MachineLearningGodMachineLearningGod
82
82
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MachineLearningGod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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MachineLearningGod is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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1 Answer
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$begingroup$
I tried training with a 1080 and a 2080 ti and I found that I didn't get any speed up from multi-gpu training because the 1080 acted as a bottleneck. So I while I think this would work fine, you would be better served to run a different model on each GPU rather than trying to train across GPUs.
answered 2 days ago
LukeLuke
1183
$endgroup$
$begingroup$
Thank you. So even in worst case scenario that my two different GPUs don't contribute to speedup together, I can train models separately on each, and get speedup that way, but either way I get speedup, it should not be the case that getting the second GPU is a total loss, agree? Just to make sure I understand you correctly.
$endgroup$
– MachineLearningGod
2 days ago
$begingroup$
That is correct. I think the best approach for you would be to train model separately. You may need to think about how many PCIe lanes you have though. Because if you don't have enough, you may bottleneck on data loading. Then again, according to Tim Dettmers PCIe lanes don't matter when you have 3 GPUs or less.
$endgroup$
– Luke
2 days ago
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
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votes
$begingroup$
I tried training with a 1080 and a 2080 ti and I found that I didn't get any speed up from multi-gpu training because the 1080 acted as a bottleneck. So I while I think this would work fine, you would be better served to run a different model on each GPU rather than trying to train across GPUs.
answered 2 days ago
LukeLuke
1183
$endgroup$
$begingroup$
Thank you. So even in worst case scenario that my two different GPUs don't contribute to speedup together, I can train models separately on each, and get speedup that way, but either way I get speedup, it should not be the case that getting the second GPU is a total loss, agree? Just to make sure I understand you correctly.
$endgroup$
– MachineLearningGod
2 days ago
$begingroup$
That is correct. I think the best approach for you would be to train model separately. You may need to think about how many PCIe lanes you have though. Because if you don't have enough, you may bottleneck on data loading. Then again, according to Tim Dettmers PCIe lanes don't matter when you have 3 GPUs or less.
$endgroup$
– Luke
2 days ago
add a comment |
$begingroup$
I tried training with a 1080 and a 2080 ti and I found that I didn't get any speed up from multi-gpu training because the 1080 acted as a bottleneck. So I while I think this would work fine, you would be better served to run a different model on each GPU rather than trying to train across GPUs.
answered 2 days ago
LukeLuke
1183
$endgroup$
$begingroup$
Thank you. So even in worst case scenario that my two different GPUs don't contribute to speedup together, I can train models separately on each, and get speedup that way, but either way I get speedup, it should not be the case that getting the second GPU is a total loss, agree? Just to make sure I understand you correctly.
$endgroup$
– MachineLearningGod
2 days ago
$begingroup$
That is correct. I think the best approach for you would be to train model separately. You may need to think about how many PCIe lanes you have though. Because if you don't have enough, you may bottleneck on data loading. Then again, according to Tim Dettmers PCIe lanes don't matter when you have 3 GPUs or less.
$endgroup$
– Luke
2 days ago
add a comment |
$begingroup$
I tried training with a 1080 and a 2080 ti and I found that I didn't get any speed up from multi-gpu training because the 1080 acted as a bottleneck. So I while I think this would work fine, you would be better served to run a different model on each GPU rather than trying to train across GPUs.
answered 2 days ago
LukeLuke
1183
$endgroup$
I tried training with a 1080 and a 2080 ti and I found that I didn't get any speed up from multi-gpu training because the 1080 acted as a bottleneck. So I while I think this would work fine, you would be better served to run a different model on each GPU rather than trying to train across GPUs.
answered 2 days ago
LukeLuke
1183
answered 2 days ago
LukeLuke
1183
answered 2 days ago
LukeLuke
1183
answered 2 days ago
LukeLuke
1183
1183
$begingroup$
Thank you. So even in worst case scenario that my two different GPUs don't contribute to speedup together, I can train models separately on each, and get speedup that way, but either way I get speedup, it should not be the case that getting the second GPU is a total loss, agree? Just to make sure I understand you correctly.
$endgroup$
– MachineLearningGod
2 days ago
$begingroup$
That is correct. I think the best approach for you would be to train model separately. You may need to think about how many PCIe lanes you have though. Because if you don't have enough, you may bottleneck on data loading. Then again, according to Tim Dettmers PCIe lanes don't matter when you have 3 GPUs or less.
$endgroup$
– Luke
2 days ago
add a comment |
$begingroup$
Thank you. So even in worst case scenario that my two different GPUs don't contribute to speedup together, I can train models separately on each, and get speedup that way, but either way I get speedup, it should not be the case that getting the second GPU is a total loss, agree? Just to make sure I understand you correctly.
$endgroup$
– MachineLearningGod
2 days ago
$begingroup$
That is correct. I think the best approach for you would be to train model separately. You may need to think about how many PCIe lanes you have though. Because if you don't have enough, you may bottleneck on data loading. Then again, according to Tim Dettmers PCIe lanes don't matter when you have 3 GPUs or less.
$endgroup$
– Luke
2 days ago
$begingroup$
Thank you. So even in worst case scenario that my two different GPUs don't contribute to speedup together, I can train models separately on each, and get speedup that way, but either way I get speedup, it should not be the case that getting the second GPU is a total loss, agree? Just to make sure I understand you correctly.
$endgroup$
– MachineLearningGod
2 days ago
$begingroup$
Thank you. So even in worst case scenario that my two different GPUs don't contribute to speedup together, I can train models separately on each, and get speedup that way, but either way I get speedup, it should not be the case that getting the second GPU is a total loss, agree? Just to make sure I understand you correctly.
$endgroup$
– MachineLearningGod
2 days ago
$begingroup$
That is correct. I think the best approach for you would be to train model separately. You may need to think about how many PCIe lanes you have though. Because if you don't have enough, you may bottleneck on data loading. Then again, according to Tim Dettmers PCIe lanes don't matter when you have 3 GPUs or less.
$endgroup$
– Luke
2 days ago
$begingroup$
That is correct. I think the best approach for you would be to train model separately. You may need to think about how many PCIe lanes you have though. Because if you don't have enough, you may bottleneck on data loading. Then again, according to Tim Dettmers PCIe lanes don't matter when you have 3 GPUs or less.
$endgroup$
– Luke
2 days ago
add a comment |
MachineLearningGod is a new contributor. Be nice, and check out our Code of Conduct.
MachineLearningGod is a new contributor. Be nice, and check out our Code of Conduct.
MachineLearningGod is a new contributor. Be nice, and check out our Code of Conduct.
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