How does “conda install” handle the installation of CUDA and CudNN for [Tensorflow GPU]?

Multi tool use
I’m running:
Python 3.6.7
- Anaconda 4.5.12
- GPU Zotac RTX2080Ti
- Windows10
And trying to test if my system actually is using CUDA 9 and CudNN which I have installed everything by
conda install -c anaconda tensorflow-gpu
This seem to have CUDA 9 and CudNN included in itself therefore I assumed it was all working fine as I also could train some GANs and see that my GPU is rising to approx 70 degrees and also can hear it working :-)
This is what I get as result for running the code below to check if Tensorflow can communicate with the GPU.
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
2018-12-30 09:38:19.922499: I
tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports
instructions that this TensorFlow binary was not compiled to use: AVX
AVX2 2018-12-30 09:38:20.282040: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0
with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5
memoryClockRate(GHz): 1.665 pciBusID: 0000:07:00.0 totalMemory:
11.00GiB freeMemory: 8.99GiB 2018-12-30 09:38:20.287430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible
gpu devices: 0 2018-12-30 09:38:21.762777: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device
interconnect StreamExecutor with strength 1 edge matrix: 2018-12-30
09:38:21.766780: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2018-12-30 09:38:21.768893: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2018-12-30 09:38:21.770801: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created
TensorFlow device (/device:GPU:0 with 8665 MB memory) -> physical GPU
(device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:07:00.0,
compute capability: 7.5) [name: "/device:CPU:0" device_type: "CPU"
memory_limit: 268435456 locality { } incarnation: 5100127316371337047
, name: "/device:GPU:0" device_type: "GPU" memory_limit: 9086468751
locality { bus_id: 1 links { } } incarnation:
17768141356003925426 physical_device_desc: "device: 0, name: GeForce
RTX 2080 Ti, pci bus id: 0000:07:00.0, compute capability: 7.5"
So, my question is that would it work at all [for example train a GAN as I stated above] even though it is not using CudNN? If so, how can I check? The reason I’m worried is that I saw this error yesterday on a different code, which included something like ‘You might not be using CudNN for this’ - sadly I couldn’t reproduce the error but it got me worried cause I don't want to train anything slower than I potentially can.
I've also tried this but still wasn't sure of the result...
import ctypes
ctypes.WinDLL("cudnn64_7.dll")
WinDLL 'cudnn64_7.dll', handle 7fff66600000 at 0x1674cc82128>
PS:I know these similar topics have been extensively discussed but it seemed RTX2080 made it special due to a few common driver incompatibility issues around it
Thanks very much.
python tensorflow anaconda cudnn rtx
add a comment |
I’m running:
Python 3.6.7
- Anaconda 4.5.12
- GPU Zotac RTX2080Ti
- Windows10
And trying to test if my system actually is using CUDA 9 and CudNN which I have installed everything by
conda install -c anaconda tensorflow-gpu
This seem to have CUDA 9 and CudNN included in itself therefore I assumed it was all working fine as I also could train some GANs and see that my GPU is rising to approx 70 degrees and also can hear it working :-)
This is what I get as result for running the code below to check if Tensorflow can communicate with the GPU.
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
2018-12-30 09:38:19.922499: I
tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports
instructions that this TensorFlow binary was not compiled to use: AVX
AVX2 2018-12-30 09:38:20.282040: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0
with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5
memoryClockRate(GHz): 1.665 pciBusID: 0000:07:00.0 totalMemory:
11.00GiB freeMemory: 8.99GiB 2018-12-30 09:38:20.287430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible
gpu devices: 0 2018-12-30 09:38:21.762777: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device
interconnect StreamExecutor with strength 1 edge matrix: 2018-12-30
09:38:21.766780: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2018-12-30 09:38:21.768893: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2018-12-30 09:38:21.770801: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created
TensorFlow device (/device:GPU:0 with 8665 MB memory) -> physical GPU
(device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:07:00.0,
compute capability: 7.5) [name: "/device:CPU:0" device_type: "CPU"
memory_limit: 268435456 locality { } incarnation: 5100127316371337047
, name: "/device:GPU:0" device_type: "GPU" memory_limit: 9086468751
locality { bus_id: 1 links { } } incarnation:
17768141356003925426 physical_device_desc: "device: 0, name: GeForce
RTX 2080 Ti, pci bus id: 0000:07:00.0, compute capability: 7.5"
So, my question is that would it work at all [for example train a GAN as I stated above] even though it is not using CudNN? If so, how can I check? The reason I’m worried is that I saw this error yesterday on a different code, which included something like ‘You might not be using CudNN for this’ - sadly I couldn’t reproduce the error but it got me worried cause I don't want to train anything slower than I potentially can.
I've also tried this but still wasn't sure of the result...
import ctypes
ctypes.WinDLL("cudnn64_7.dll")
WinDLL 'cudnn64_7.dll', handle 7fff66600000 at 0x1674cc82128>
PS:I know these similar topics have been extensively discussed but it seemed RTX2080 made it special due to a few common driver incompatibility issues around it
Thanks very much.
python tensorflow anaconda cudnn rtx
add a comment |
I’m running:
Python 3.6.7
- Anaconda 4.5.12
- GPU Zotac RTX2080Ti
- Windows10
And trying to test if my system actually is using CUDA 9 and CudNN which I have installed everything by
conda install -c anaconda tensorflow-gpu
This seem to have CUDA 9 and CudNN included in itself therefore I assumed it was all working fine as I also could train some GANs and see that my GPU is rising to approx 70 degrees and also can hear it working :-)
This is what I get as result for running the code below to check if Tensorflow can communicate with the GPU.
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
2018-12-30 09:38:19.922499: I
tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports
instructions that this TensorFlow binary was not compiled to use: AVX
AVX2 2018-12-30 09:38:20.282040: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0
with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5
memoryClockRate(GHz): 1.665 pciBusID: 0000:07:00.0 totalMemory:
11.00GiB freeMemory: 8.99GiB 2018-12-30 09:38:20.287430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible
gpu devices: 0 2018-12-30 09:38:21.762777: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device
interconnect StreamExecutor with strength 1 edge matrix: 2018-12-30
09:38:21.766780: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2018-12-30 09:38:21.768893: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2018-12-30 09:38:21.770801: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created
TensorFlow device (/device:GPU:0 with 8665 MB memory) -> physical GPU
(device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:07:00.0,
compute capability: 7.5) [name: "/device:CPU:0" device_type: "CPU"
memory_limit: 268435456 locality { } incarnation: 5100127316371337047
, name: "/device:GPU:0" device_type: "GPU" memory_limit: 9086468751
locality { bus_id: 1 links { } } incarnation:
17768141356003925426 physical_device_desc: "device: 0, name: GeForce
RTX 2080 Ti, pci bus id: 0000:07:00.0, compute capability: 7.5"
So, my question is that would it work at all [for example train a GAN as I stated above] even though it is not using CudNN? If so, how can I check? The reason I’m worried is that I saw this error yesterday on a different code, which included something like ‘You might not be using CudNN for this’ - sadly I couldn’t reproduce the error but it got me worried cause I don't want to train anything slower than I potentially can.
I've also tried this but still wasn't sure of the result...
import ctypes
ctypes.WinDLL("cudnn64_7.dll")
WinDLL 'cudnn64_7.dll', handle 7fff66600000 at 0x1674cc82128>
PS:I know these similar topics have been extensively discussed but it seemed RTX2080 made it special due to a few common driver incompatibility issues around it
Thanks very much.
python tensorflow anaconda cudnn rtx
I’m running:
Python 3.6.7
- Anaconda 4.5.12
- GPU Zotac RTX2080Ti
- Windows10
And trying to test if my system actually is using CUDA 9 and CudNN which I have installed everything by
conda install -c anaconda tensorflow-gpu
This seem to have CUDA 9 and CudNN included in itself therefore I assumed it was all working fine as I also could train some GANs and see that my GPU is rising to approx 70 degrees and also can hear it working :-)
This is what I get as result for running the code below to check if Tensorflow can communicate with the GPU.
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
2018-12-30 09:38:19.922499: I
tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports
instructions that this TensorFlow binary was not compiled to use: AVX
AVX2 2018-12-30 09:38:20.282040: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0
with properties: name: GeForce RTX 2080 Ti major: 7 minor: 5
memoryClockRate(GHz): 1.665 pciBusID: 0000:07:00.0 totalMemory:
11.00GiB freeMemory: 8.99GiB 2018-12-30 09:38:20.287430: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible
gpu devices: 0 2018-12-30 09:38:21.762777: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device
interconnect StreamExecutor with strength 1 edge matrix: 2018-12-30
09:38:21.766780: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2018-12-30 09:38:21.768893: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2018-12-30 09:38:21.770801: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created
TensorFlow device (/device:GPU:0 with 8665 MB memory) -> physical GPU
(device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:07:00.0,
compute capability: 7.5) [name: "/device:CPU:0" device_type: "CPU"
memory_limit: 268435456 locality { } incarnation: 5100127316371337047
, name: "/device:GPU:0" device_type: "GPU" memory_limit: 9086468751
locality { bus_id: 1 links { } } incarnation:
17768141356003925426 physical_device_desc: "device: 0, name: GeForce
RTX 2080 Ti, pci bus id: 0000:07:00.0, compute capability: 7.5"
So, my question is that would it work at all [for example train a GAN as I stated above] even though it is not using CudNN? If so, how can I check? The reason I’m worried is that I saw this error yesterday on a different code, which included something like ‘You might not be using CudNN for this’ - sadly I couldn’t reproduce the error but it got me worried cause I don't want to train anything slower than I potentially can.
I've also tried this but still wasn't sure of the result...
import ctypes
ctypes.WinDLL("cudnn64_7.dll")
WinDLL 'cudnn64_7.dll', handle 7fff66600000 at 0x1674cc82128>
PS:I know these similar topics have been extensively discussed but it seemed RTX2080 made it special due to a few common driver incompatibility issues around it
Thanks very much.
python tensorflow anaconda cudnn rtx
python tensorflow anaconda cudnn rtx
edited Dec 30 '18 at 16:28
LaSul
560217
560217
asked Dec 30 '18 at 12:33
unknownplayerunknownplayer
13
13
add a comment |
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ifUsing("editor", function () {
StackExchange.using("externalEditor", function () {
StackExchange.using("snippets", function () {
StackExchange.snippets.init();
});
});
}, "code-snippets");
StackExchange.ready(function() {
var channelOptions = {
tags: "".split(" "),
id: "1"
};
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function() {
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled) {
StackExchange.using("snippets", function() {
createEditor();
});
}
else {
createEditor();
}
});
function createEditor() {
StackExchange.prepareEditor({
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader: {
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
},
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
});
}
});
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53977615%2fhow-does-conda-install-handle-the-installation-of-cuda-and-cudnn-for-tensorfl%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function () {
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53977615%2fhow-does-conda-install-handle-the-installation-of-cuda-and-cudnn-for-tensorfl%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function () {
StackExchange.helpers.onClickDraftSave('#login-link');
});
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
JpZChYwaygbxKitqU4a1dA176wgdrB368