keep_dims is deprecated, use keepdims instead Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWhy TensorFlow can't fit simple linear model if I am minimizing absolute mean error instead of the mean squared error?CUDA_ERROR_OUT_OF_MEMORYDropout on inputs instead on outputs = DropConnect?Which convolution should I use? Conv2d or Conv1dHow to use Keras Linear Regression for Multiple input-output?DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20How to use a NN architecture that is too big for GPU?Keras y-labels range between 0 and 1 instead of binary?When to use Dense, Conv1/2D, Dropout, Flatten, and all the other layers?Can I use the Softmax function with a binary classification in deep learning?
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keep_dims is deprecated, use keepdims instead
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 23:30 UTC (7:30pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhy TensorFlow can't fit simple linear model if I am minimizing absolute mean error instead of the mean squared error?CUDA_ERROR_OUT_OF_MEMORYDropout on inputs instead on outputs = DropConnect?Which convolution should I use? Conv2d or Conv1dHow to use Keras Linear Regression for Multiple input-output?DeprecationWarning: The 'categorical_features' keyword is deprecated in version 0.20How to use a NN architecture that is too big for GPU?Keras y-labels range between 0 and 1 instead of binary?When to use Dense, Conv1/2D, Dropout, Flatten, and all the other layers?Can I use the Softmax function with a binary classification in deep learning?
1
$begingroup$
I downloaded:
!git clone https://www.github.com/matterport/Mask_RCNN.git
os.chdir('Mask_RCNN')
And I've got an error:
which version I should have of Keras?
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1154: calling reduce_max (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1188: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1290: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
Futhermore:
totalMemory: 5.94GiB freeMemory: 5.44GiB
2019-04-03 22:37:38.374934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0, 1
2019-04-03 22:37:40.343417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-04-03 22:37:40.344366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0 1
2019-04-03 22:37:40.344373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N N
2019-04-03 22:37:40.344377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 1: N N
2019-04-03 22:37:40.345556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11435 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:02:00.0, compute capability: 5.2)
2019-04-03 22:37:40.450785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 5220 MB memory) -> physical GPU (device: 1, name: GeForce GTX TITAN Black, pci bus id: 0000:01:00.0, compute capability: 3.5)
2019-04-03 22:37:42.518519: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:42.601229: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.648032: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.678817: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.706928: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
[I 22:37:55.611 NotebookApp] Starting buffering for fa2cd5ca-20f3-4472-b6ca-6821e2f56118:02508f46d629494ab46babe6d7611656
python neural-network deep-learning keras tensorflow
edited Apr 3 at 21:06
Vaalizaadeh
7,64062265
asked Apr 3 at 19:53
BadumBadum
537
$endgroup$
add a comment |
1
$begingroup$
I downloaded:
!git clone https://www.github.com/matterport/Mask_RCNN.git
os.chdir('Mask_RCNN')
And I've got an error:
which version I should have of Keras?
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1154: calling reduce_max (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1188: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1290: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
Futhermore:
totalMemory: 5.94GiB freeMemory: 5.44GiB
2019-04-03 22:37:38.374934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0, 1
2019-04-03 22:37:40.343417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-04-03 22:37:40.344366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0 1
2019-04-03 22:37:40.344373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N N
2019-04-03 22:37:40.344377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 1: N N
2019-04-03 22:37:40.345556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11435 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:02:00.0, compute capability: 5.2)
2019-04-03 22:37:40.450785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 5220 MB memory) -> physical GPU (device: 1, name: GeForce GTX TITAN Black, pci bus id: 0000:01:00.0, compute capability: 3.5)
2019-04-03 22:37:42.518519: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:42.601229: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.648032: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.678817: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.706928: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
[I 22:37:55.611 NotebookApp] Starting buffering for fa2cd5ca-20f3-4472-b6ca-6821e2f56118:02508f46d629494ab46babe6d7611656
python neural-network deep-learning keras tensorflow
edited Apr 3 at 21:06
Vaalizaadeh
7,64062265
asked Apr 3 at 19:53
BadumBadum
537
$endgroup$
add a comment |
1
1
1
$begingroup$
I downloaded:
!git clone https://www.github.com/matterport/Mask_RCNN.git
os.chdir('Mask_RCNN')
And I've got an error:
which version I should have of Keras?
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1154: calling reduce_max (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1188: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1290: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
Futhermore:
totalMemory: 5.94GiB freeMemory: 5.44GiB
2019-04-03 22:37:38.374934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0, 1
2019-04-03 22:37:40.343417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-04-03 22:37:40.344366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0 1
2019-04-03 22:37:40.344373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N N
2019-04-03 22:37:40.344377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 1: N N
2019-04-03 22:37:40.345556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11435 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:02:00.0, compute capability: 5.2)
2019-04-03 22:37:40.450785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 5220 MB memory) -> physical GPU (device: 1, name: GeForce GTX TITAN Black, pci bus id: 0000:01:00.0, compute capability: 3.5)
2019-04-03 22:37:42.518519: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:42.601229: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.648032: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.678817: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.706928: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
[I 22:37:55.611 NotebookApp] Starting buffering for fa2cd5ca-20f3-4472-b6ca-6821e2f56118:02508f46d629494ab46babe6d7611656
python neural-network deep-learning keras tensorflow
edited Apr 3 at 21:06
Vaalizaadeh
7,64062265
asked Apr 3 at 19:53
BadumBadum
537
$endgroup$
I downloaded:
!git clone https://www.github.com/matterport/Mask_RCNN.git
os.chdir('Mask_RCNN')
And I've got an error:
which version I should have of Keras?
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1154: calling reduce_max (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1188: calling reduce_sum (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py:1290: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
Futhermore:
totalMemory: 5.94GiB freeMemory: 5.44GiB
2019-04-03 22:37:38.374934: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0, 1
2019-04-03 22:37:40.343417: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-04-03 22:37:40.344366: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0 1
2019-04-03 22:37:40.344373: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N N
2019-04-03 22:37:40.344377: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 1: N N
2019-04-03 22:37:40.345556: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 11435 MB memory) -> physical GPU (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:02:00.0, compute capability: 5.2)
2019-04-03 22:37:40.450785: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 5220 MB memory) -> physical GPU (device: 1, name: GeForce GTX TITAN Black, pci bus id: 0000:01:00.0, compute capability: 3.5)
2019-04-03 22:37:42.518519: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:42.601229: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.648032: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.678817: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
2019-04-03 22:37:51.706928: W tensorflow/core/framework/allocator.cc:108] Allocation of 51380224 exceeds 10% of system memory.
[I 22:37:55.611 NotebookApp] Starting buffering for fa2cd5ca-20f3-4472-b6ca-6821e2f56118:02508f46d629494ab46babe6d7611656
python neural-network deep-learning keras tensorflow
python neural-network deep-learning keras tensorflow
edited Apr 3 at 21:06
Vaalizaadeh
7,64062265
asked Apr 3 at 19:53
BadumBadum
537
edited Apr 3 at 21:06
Vaalizaadeh
7,64062265
asked Apr 3 at 19:53
BadumBadum
537
edited Apr 3 at 21:06
Vaalizaadeh
7,64062265
edited Apr 3 at 21:06
Vaalizaadeh
7,64062265
edited Apr 3 at 21:06
Vaalizaadeh
7,64062265
7,64062265
asked Apr 3 at 19:53
BadumBadum
537
asked Apr 3 at 19:53
BadumBadum
537
asked Apr 3 at 19:53
BadumBadum
537
537
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
0
$begingroup$
It's not an error, it's simply notifying you that the code is written using past versions of Tensorlfow and some of the arguments of special methods were going to be deprecated in the future releases of the library. It's ok to be used, but you can also check what version is used and make a virtual environment and install the specified version of the library you want.
You can run your cell twice in order not to see the warning again.
answered Apr 3 at 20:56
VaalizaadehVaalizaadeh
7,64062265
$endgroup$
$begingroup$
Ok. Futhermore my GPU is very slow. I used to use GTX 1050 and training one epoch last about 1-1.5h.
$endgroup$
– Badum
Apr 3 at 21:25
$begingroup$
Now. I use two GPU (TITAN X, TITAN BLACK = 18GB) but time is similiar to GTX 1050(4GB).
$endgroup$
– Badum
Apr 3 at 21:26
$begingroup$
You have to find your bottle neck. It can be somewhere between disk and memory or memory and gpu memory.
$endgroup$
– Vaalizaadeh
Apr 3 at 21:33
$begingroup$
How should I do it?
$endgroup$
– Badum
Apr 4 at 9:30
$begingroup$
It is customary for DL tasks to load data to memory as much as possible and feed a subset of those to your gpu because gpu has a limited amount of memory. The first part can be implemented simply using generators.
$endgroup$
– Vaalizaadeh
Apr 5 at 17:22
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
$begingroup$
It's not an error, it's simply notifying you that the code is written using past versions of Tensorlfow and some of the arguments of special methods were going to be deprecated in the future releases of the library. It's ok to be used, but you can also check what version is used and make a virtual environment and install the specified version of the library you want.
You can run your cell twice in order not to see the warning again.
answered Apr 3 at 20:56
VaalizaadehVaalizaadeh
7,64062265
$endgroup$
$begingroup$
Ok. Futhermore my GPU is very slow. I used to use GTX 1050 and training one epoch last about 1-1.5h.
$endgroup$
– Badum
Apr 3 at 21:25
$begingroup$
Now. I use two GPU (TITAN X, TITAN BLACK = 18GB) but time is similiar to GTX 1050(4GB).
$endgroup$
– Badum
Apr 3 at 21:26
$begingroup$
You have to find your bottle neck. It can be somewhere between disk and memory or memory and gpu memory.
$endgroup$
– Vaalizaadeh
Apr 3 at 21:33
$begingroup$
How should I do it?
$endgroup$
– Badum
Apr 4 at 9:30
$begingroup$
It is customary for DL tasks to load data to memory as much as possible and feed a subset of those to your gpu because gpu has a limited amount of memory. The first part can be implemented simply using generators.
$endgroup$
– Vaalizaadeh
Apr 5 at 17:22
add a comment |
0
$begingroup$
It's not an error, it's simply notifying you that the code is written using past versions of Tensorlfow and some of the arguments of special methods were going to be deprecated in the future releases of the library. It's ok to be used, but you can also check what version is used and make a virtual environment and install the specified version of the library you want.
You can run your cell twice in order not to see the warning again.
answered Apr 3 at 20:56
VaalizaadehVaalizaadeh
7,64062265
$endgroup$
$begingroup$
Ok. Futhermore my GPU is very slow. I used to use GTX 1050 and training one epoch last about 1-1.5h.
$endgroup$
– Badum
Apr 3 at 21:25
$begingroup$
Now. I use two GPU (TITAN X, TITAN BLACK = 18GB) but time is similiar to GTX 1050(4GB).
$endgroup$
– Badum
Apr 3 at 21:26
$begingroup$
You have to find your bottle neck. It can be somewhere between disk and memory or memory and gpu memory.
$endgroup$
– Vaalizaadeh
Apr 3 at 21:33
$begingroup$
How should I do it?
$endgroup$
– Badum
Apr 4 at 9:30
$begingroup$
It is customary for DL tasks to load data to memory as much as possible and feed a subset of those to your gpu because gpu has a limited amount of memory. The first part can be implemented simply using generators.
$endgroup$
– Vaalizaadeh
Apr 5 at 17:22
add a comment |
0
0
0
$begingroup$
It's not an error, it's simply notifying you that the code is written using past versions of Tensorlfow and some of the arguments of special methods were going to be deprecated in the future releases of the library. It's ok to be used, but you can also check what version is used and make a virtual environment and install the specified version of the library you want.
You can run your cell twice in order not to see the warning again.
answered Apr 3 at 20:56
VaalizaadehVaalizaadeh
7,64062265
$endgroup$
It's not an error, it's simply notifying you that the code is written using past versions of Tensorlfow and some of the arguments of special methods were going to be deprecated in the future releases of the library. It's ok to be used, but you can also check what version is used and make a virtual environment and install the specified version of the library you want.
You can run your cell twice in order not to see the warning again.
answered Apr 3 at 20:56
VaalizaadehVaalizaadeh
7,64062265
edited Apr 3 at 21:06
answered Apr 3 at 20:56
VaalizaadehVaalizaadeh
7,64062265
answered Apr 3 at 20:56
VaalizaadehVaalizaadeh
7,64062265
answered Apr 3 at 20:56
VaalizaadehVaalizaadeh
7,64062265
7,64062265
$begingroup$
Ok. Futhermore my GPU is very slow. I used to use GTX 1050 and training one epoch last about 1-1.5h.
$endgroup$
– Badum
Apr 3 at 21:25
$begingroup$
Now. I use two GPU (TITAN X, TITAN BLACK = 18GB) but time is similiar to GTX 1050(4GB).
$endgroup$
– Badum
Apr 3 at 21:26
$begingroup$
You have to find your bottle neck. It can be somewhere between disk and memory or memory and gpu memory.
$endgroup$
– Vaalizaadeh
Apr 3 at 21:33
$begingroup$
How should I do it?
$endgroup$
– Badum
Apr 4 at 9:30
$begingroup$
It is customary for DL tasks to load data to memory as much as possible and feed a subset of those to your gpu because gpu has a limited amount of memory. The first part can be implemented simply using generators.
$endgroup$
– Vaalizaadeh
Apr 5 at 17:22
add a comment |
$begingroup$
Ok. Futhermore my GPU is very slow. I used to use GTX 1050 and training one epoch last about 1-1.5h.
$endgroup$
– Badum
Apr 3 at 21:25
$begingroup$
Now. I use two GPU (TITAN X, TITAN BLACK = 18GB) but time is similiar to GTX 1050(4GB).
$endgroup$
– Badum
Apr 3 at 21:26
$begingroup$
You have to find your bottle neck. It can be somewhere between disk and memory or memory and gpu memory.
$endgroup$
– Vaalizaadeh
Apr 3 at 21:33
$begingroup$
How should I do it?
$endgroup$
– Badum
Apr 4 at 9:30
$begingroup$
It is customary for DL tasks to load data to memory as much as possible and feed a subset of those to your gpu because gpu has a limited amount of memory. The first part can be implemented simply using generators.
$endgroup$
– Vaalizaadeh
Apr 5 at 17:22
$begingroup$
Ok. Futhermore my GPU is very slow. I used to use GTX 1050 and training one epoch last about 1-1.5h.
$endgroup$
– Badum
Apr 3 at 21:25
$begingroup$
Ok. Futhermore my GPU is very slow. I used to use GTX 1050 and training one epoch last about 1-1.5h.
$endgroup$
– Badum
Apr 3 at 21:25
$begingroup$
Now. I use two GPU (TITAN X, TITAN BLACK = 18GB) but time is similiar to GTX 1050(4GB).
$endgroup$
– Badum
Apr 3 at 21:26
$begingroup$
Now. I use two GPU (TITAN X, TITAN BLACK = 18GB) but time is similiar to GTX 1050(4GB).
$endgroup$
– Badum
Apr 3 at 21:26
$begingroup$
You have to find your bottle neck. It can be somewhere between disk and memory or memory and gpu memory.
$endgroup$
– Vaalizaadeh
Apr 3 at 21:33
$begingroup$
You have to find your bottle neck. It can be somewhere between disk and memory or memory and gpu memory.
$endgroup$
– Vaalizaadeh
Apr 3 at 21:33
$begingroup$
How should I do it?
$endgroup$
– Badum
Apr 4 at 9:30
$begingroup$
How should I do it?
$endgroup$
– Badum
Apr 4 at 9:30
$begingroup$
It is customary for DL tasks to load data to memory as much as possible and feed a subset of those to your gpu because gpu has a limited amount of memory. The first part can be implemented simply using generators.
$endgroup$
– Vaalizaadeh
Apr 5 at 17:22
$begingroup$
It is customary for DL tasks to load data to memory as much as possible and feed a subset of those to your gpu because gpu has a limited amount of memory. The first part can be implemented simply using generators.
$endgroup$
– Vaalizaadeh
Apr 5 at 17:22
add a comment |
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