Hi @toddswri,
I’m not entirely sure what’s going on, but I think there may be a mismatch between the TensorFlow version and CUDA toolkit version you have installed in your environment. Fixing an existing Conda environment is hit or miss, so I recommend creating a new environment from scratch. Here are the instructions to get just TensorFlow and GPU support installed in your environment (you can install everything else once you test that the GPU is detected):
Create a new Conda environment
Create a new Conda environment (replace <env_name> with a name you like). Since you were using Python 3.7, I’ve replicated it here:
conda create -n <env_name> python=3.7
conda activate <env_name>
Install CUDA toolkit
conda install cudatoolkit
You should see it install v11.0. If it doesn’t, let me know!
The following NEW packages will be INSTALLED:
cudatoolkit pkgs/main/win-64::cudatoolkit-11.0.221-h74a9793_0
Install cuDNN
conda install -c conda-forge cudnn
You should see it install v8.1 (and some other stuff).
The following NEW packages will be INSTALLED:
cudnn conda-forge/win-64::cudnn-8.1.0.77-h3e0f4f4_0
Install TensorFlow
Note the use of pip instead of conda here!
pip install tensorflow
You should see it install v2.4.1, and a whole bunch of other packages:
Collecting tensorflow
Using cached tensorflow-2.4.1-cp37-cp37m-win_amd64.whl (370.7 MB)
Test TensorFlow with GPU
Now comes time to test that TensorFlow can pick up your GPU. Start Python from your Conda terminal:
python
Then do the list_physical_devices test:
import tensorflow as tf
print(tf.config.list_physical_devices("GPU"))
When I did these two steps in a fresh Conda environment on my Windows machine, I see:
>>> import tensorflow as tf
2021-04-30 16:09:19.465296: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
>>> print(tf.config.list_physical_devices("GPU"))
2021-04-30 16:09:43.351770: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-30 16:09:43.352497: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-04-30 16:09:43.374485: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:2d:00.0 name: NVIDIA GeForce RTX 3090 computeCapability: 8.6
coreClock: 1.74GHz coreCount: 82 deviceMemorySize: 24.00GiB deviceMemoryBandwidth: 871.81GiB/s
2021-04-30 16:09:43.374629: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-04-30 16:09:43.402428: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-04-30 16:09:43.402503: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-04-30 16:09:43.421391: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-04-30 16:09:43.423192: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-04-30 16:09:43.673219: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-04-30 16:09:43.682491: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-04-30 16:09:43.683142: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-04-30 16:09:43.683281: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
If the test successfully passes, you can then continue to install the other packages (i.e., Nengo, NengoDL, Jupyter, etc.)
Let me know if these instructions work for you!
Note: These instructions work with the latest version of NengoDL. If you want to use NengoGUI, it doesn’t support the latest version of NengoDL, so the instructions will change slightly (see the link that @zerone posted)