# Tensorflow 1.13 and Theano 1.0.4 Not Using GPU

**Date:** Jun 15, 2019  
**User:** Pablo Vega Behar

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**Update**: I uninstalled and reinstalled the lambda stack. It did install with CUDA 10.1 and tensorflow 1.13. `tensorflow.test.is_gpu_available` still returns ‘False’ and the tensorflow GPU requirements page mentions support only for CUDA 10.0. Anyone else run into this problem? Why is the lambda stack using ve…

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So apparently TF 1.13 does not work with CUDA 10.1. I’m guessing 10.1 is not the current lambda stack version. Does this mean I have another Ubuntu auto update somewhere updating CUDA? If so, how can I find this?

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I created a virtual environment using the lambdastack. Using tensorflow 1.13 and running:

```python
tf.test.is_gpu_available()
```

returns a stack that includes:

“failed call to cuInit: CUDA_ERROR_UNKNOWN: unknown error” which is not terribly helpful. A similar check for Theano shows it’s not using the GPU. Any tips?

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## Related Thread: Why Lambda Stack Not Install Tensorflow-GPU

Did you solve this issue? My models are also training on CPU, not GPU. Tensorflow does not recognize the GPU from Jupyter or console. I created an environment from lambda stack following the instructions in this forum. `pip list` does show tensorflow-gpu in the virtual environment.