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I've tensorflow installed with CUDA 7.5 and cuDNN 5.0. My graphics card is NVIDIA Geforce 820M with capability 2.1. However, I get this error.

Ignoring visible gpu device (device: 0, name: GeForce 820M, pci bus id: 0000:08:00.0) with Cuda compute capability 2.1. The minimum required Cuda capability is 3.0.
Device mapping: no known devices.

Is there any way to run GPU on a 2.1 capability? I scoured online to find that it is cuDNN that requires this capability, so will installing an earlier version of cuDNN enable me to use GPU?

talonmies
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Keerthana Gopalakrishnan
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    You are correct, Geforce 820M is a GPU with compute capability 2.1. I am reasonably sure that all versions of cuDNN require compute capability >= 3.0, so the answer to your question would appear to be "no". Based on the specification at [Wikipedia](https://en.wikipedia.org/wiki/GeForce_800M_series) this GPU is low-end and likely wouldn't provide much acceleration relative to a modern CPU if it were supported (I am surprised that NVIDIA launched a Fermi-based GPU as late as 2014). – njuffa Oct 25 '16 at 08:57
  • I have the same problem! How did you manage?? – MrRobot9 Feb 09 '17 at 22:45
  • Install tensorflow-cpu – Keerthana Gopalakrishnan Mar 21 '18 at 03:19

2 Answers2

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tensorflow-gpu requires GPUs of compute capability 3.0 or higher for GPU acceleration and this has been true since the very first release of tensorflow.

cuDNN has also required GPUs of compute capability 3.0 or higher since the very first release of cuDNN.

Robert Crovella
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With tensorflow (using Keras), you might be able to get it to run with PlaidML PlaidML. I have been able to run tensorflow with GPU on AMD and NVidia GPUs (some are old) with PlaidML. It's not as fast as CUDA, but much faster than your CPU.

For reference, I have run it on an old Macbook Pro (2012) with an NVidia 650 GPU (1.5 GB) as well as an AMD HD Radeon 750 3GB.

The caveat is that it needs to be Keras vs lower level TF. There are lots of articles on it, and now it has support from Intel.

Bryan Butler
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