I see that you have tensorflow-gpu 2.4.1 from your Python_Installed_Packages.pdf. My suggestion will be to first get your TF detect your GPU. Once it does, Nengo-DL shouldn’t have any issues in using it. I don’t know much about whether TF 2.4.1 is supported by your current CUDA version 11.0 (as can be seen in the screenshot you attached) or not. So if you are not bound to TF-2.4.1, then may be you can install TF 2.4.0 and check. Here’s one link to help you set up GPU for TF-2.4.0.
You can find more info about Nengo-DL and TF installation here. My environment’s info is mentioned below.
>>> import tensorflow as tf
>>> tf.__version__
'2.2.0'
>>> print(tf.config.list_physical_devices('GPU'))
2021-04-30 15:51:06.032334: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2021-04-30 15:51:06.075846: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1561] Found device 0 with properties:
pciBusID: 0000:04:00.0 name: Tesla P100-PCIE-12GB computeCapability: 6.0
coreClock: 1.3285GHz coreCount: 56 deviceMemorySize: 11.91GiB deviceMemoryBandwidth: 511.41GiB/s
2021-04-30 15:51:06.085741: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1
2021-04-30 15:51:06.189458: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
2021-04-30 15:51:06.266299: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10
2021-04-30 15:51:06.373993: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10
2021-04-30 15:51:06.440953: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10
2021-04-30 15:51:06.491673: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10
2021-04-30 15:51:06.601603: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2021-04-30 15:51:06.603795: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1703] Adding visible gpu devices: 0
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
>>> import nengo_dl
>>> nengo_dl.__version__
'3.4.0'
You may want to use conda to create separate environments and install desired versions of TF, Nengo-DL, etc. libraries. Honestly, setting up TF and GPU is just a Google Search away.
EDIT: Found another related issue on this forum. Might be of some help. You may find some more resolved issues similar to this forum. You can use search bar on the top right corner (besides the burger menu) to search for similar issues.