GPU not detected / tf.test.is_built_with_cuda returns False

[carlmwilson]

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System information

• Have I written custom code (as opposed to using a stock example script
  provided in TensorFlow): N/A
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Windows 10
• Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue
  happens on a mobile device: N/A
• TensorFlow installed from (source or binary): conda
• TensorFlow version (use command below): 2.3.0
• Python version: 3.8.5 [Anaconda 2020.11]
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version: 10.1 / 7.6.4 [graphics driver 461.40]
• GPU model and memory: GeForce RTX 3080 / 10GB [EVGA Black]
• Exact command to reproduce: import tensorflow as tf, tf.test.is_built_with_cuda()

You can collect some of this information using our environment capture script:

https://github.com/tensorflow/tensorflow/tree/master/tools/tf_env_collect.sh

You can obtain the TensorFlow version with:

python -c "import tensorflow as tf; print(tf.version.GIT_VERSION, tf.version.VERSION)"

Describe the problem

-Installed MSVS 2019 Community Edition
-Installed Cuda Toolkit 10.1 [in preparation for TensorFlow 2.3.0 from conda]. NOTE: Did custom installation, not installing the graphics or physics drivers as I already have the most up to date drivers.
-Installed / copied drivers across from cuDNN 7.6.4
-Installed Anaconda 2020.11
-Created environment and installed TensorFlow-GPU [conda create -n tf-gpu tensorflow-gpu]
-Activated tf-gpu environment [conda activate tf-gpu]
-started python [python]
-imported tensorflow [import tensorflow as tf]
-checked installation [tf.test.is_built_with_cuda()]
returns False
-checked for GPU [tf.config.list_physical_devices('GPU')
returns []

I'm not sure what I'm missing or doing wrong during the installation. I don't have the CPU only tensorflow installed. Is it because I am not installing the graphics drivers with CUDA Toolkit? Any help is much appreciated.

Source code / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached. Try to provide a reproducible test case that is the bare minimum necessary to generate the problem.

[carlmwilson]

As an update I ran the deviceQuery program and CUDA seems to be able to see by GPU; but tensorflow doesn't?

C:\Users\XXXX>cd C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\demo_suite

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\extras\demo_suite>deviceQuery.exe
deviceQuery.exe Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce RTX 3080"
CUDA Driver Version / Runtime Version 11.2 / 10.1
CUDA Capability Major/Minor version number: 8.6
Total amount of global memory: 10240 MBytes (10737418240 bytes)
MapSMtoCores for SM 8.6 is undefined. Default to use 64 Cores/SM
MapSMtoCores for SM 8.6 is undefined. Default to use 64 Cores/SM
(68) Multiprocessors, ( 64) CUDA Cores/MP: 4352 CUDA Cores
GPU Max Clock rate: 1710 MHz (1.71 GHz)
Memory Clock rate: 9501 Mhz
Memory Bus Width: 320-bit
L2 Cache Size: 5242880 bytes
Maximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)
Maximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers
Maximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers
Total amount of constant memory: zu bytes
Total amount of shared memory per block: zu bytes
Total number of registers available per block: 65536
Warp size: 32
Maximum number of threads per multiprocessor: 1536
Maximum number of threads per block: 1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch: zu bytes
Texture alignment: zu bytes
Concurrent copy and kernel execution: Yes with 5 copy engine(s)
Run time limit on kernels: Yes
Integrated GPU sharing Host Memory: No
Support host page-locked memory mapping: Yes
Alignment requirement for Surfaces: Yes
Device has ECC support: Disabled
CUDA Device Driver Mode (TCC or WDDM): WDDM (Windows Display Driver Model)
Device supports Unified Addressing (UVA): Yes
Device supports Compute Preemption: Yes
Supports Cooperative Kernel Launch: Yes
Supports MultiDevice Co-op Kernel Launch: No
Device PCI Domain ID / Bus ID / location ID: 0 / 1 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.2, CUDA Runtime Version = 10.1, NumDevs = 1, Device0 = GeForce RTX 3080
Result = PASS

[amahendrakar]

@deepLearner84,
Please try installing TensorFlow v2.4 with CUDA 11.0 and cuDNN 8 as mentioned in the tested build configurations and check if you are facing the same issue.

Version	Python version	Compiler	Build tools	cuDNN	CUDA
tensorflow_gpu-2.4.0	3.6-3.8	MSVC 2019	Bazel 3.1.0	8.0	11.0
tensorflow_gpu-2.3.0	3.5-3.8	MSVC 2019	Bazel 3.1.0	7.6	10.1
tensorflow_gpu-2.2.0	3.5-3.8	MSVC 2019	Bazel 2.0.0	7.6	10.1

Thanks!

[carlmwilson]

@amahendrakar Thank you for your quick response, this has resolved the issue. What had I done wrong? My understanding was that cuDNN 7.6 / CUDA 10.1 was the tested build configuration for tensorflow 2.3.0? Cheers

[google-ml-butler]

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