The short answer is No. The latest Tensorflow, which is currently Tensorflow 2.9, is not yet ready to work with CUDA 11.6. If you check the Tensorflow compatibility matrix below you will find that as of today the recommended CUDA version you should install is 11.2:
| Version | Python version | Compiler | Build tools | cuDNN | CUDA |
|---|---|---|---|---|---|
| tensorflow-2.9.0 | 3.7-3.10 | GCC 9.3.1 | Bazel 5.0.0 | 8.1 | 11.2 |
| tensorflow-2.8.0 | 3.7-3.10 | GCC 7.3.1 | Bazel 4.2.1 | 8.1 | 11.2 |
| tensorflow-2.7.0 | 3.7-3.9 | GCC 7.3.1 | Bazel 3.7.2 | 8.1 | 11.2 |
| tensorflow-2.6.0 | 3.6-3.9 | GCC 7.3.1 | Bazel 3.7.2 | 8.1 | 11.2 |
| tensorflow-2.5.0 | 3.6-3.9 | GCC 7.3.1 | Bazel 3.7.2 | 8.1 | 11.2 |
| tensorflow-2.4.0 | 3.6-3.8 | GCC 7.3.1 | Bazel 3.1.0 | 8.0 | 11.0 |
| tensorflow-2.3.0 | 3.5-3.8 | GCC 7.3.1 | Bazel 3.1.0 | 7.6 | 10.1 |
| tensorflow-2.2.0 | 3.5-3.8 | GCC 7.3.1 | Bazel 2.0.0 | 7.6 | 10.1 |
| tensorflow-2.1.0 | 2.7, 3.5-3.7 | GCC 7.3.1 | Bazel 0.27.1 | 7.6 | 10.1 |
| tensorflow-2.0.0 | 2.7, 3.3-3.7 | GCC 7.3.1 | Bazel 0.26.1 | 7.4 | 10.0 |
| tensorflow_gpu-1.15.0 | 2.7, 3.3-3.7 | GCC 7.3.1 | Bazel 0.26.1 | 7.4 | 10.0 |
If you are looking to install the latest Tensorflow version (currently 2.9) with GPU support, then, you will want to check the following article:
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