docker-tensorflow-keras-gpu

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docker-tensorflow-keras-gpu

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Run Tensorflow and Keras with GPU support on Kubernetes

docker-tensorflow-keras-gpu 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# docker-tensorflow-keras-gpu
Base image with Tensorflow and Keras with GPU support. The purpose of this project is to run a single Pod on Kubernetes on a GPU backed node.

# Run it on Kubernetes

First, we need to label the gpu instance (if not already done). If you choose to use a different labeling, you need to adjust the `nodeSelector` part in the `example-pod.yaml` file.
```
kubectl label node <NODE_NAME> gpu="true"
```

__Tricky part ahead:__

In order to use the GPU inside your docker container, you need to pass the location of the NVidia driver on the host into your container. Since the GPU kernel driver on the host has to match the nvidia-driver inside the contianer, we want to decouple that. Adjust the `path` in the `example-pod.yaml` file in case your nvidia-driver location is different.

```
volumes:
  - name: nvidia-driver
    hostPath:
      path: /var/lib/nvidia-docker/volumes/nvidia_driver/latest
```

Now run a simple deployment
```
kubectl create -f example-pod.yaml
```

Attach to the running container
```
kubectl exec -it tensorflow-keras-gpu -- /bin/bash
```

# Lets do some training

Inside the docker container I placed the Keras examples from github. To run a simple training example on the IMDB dataset
```
cd /keras/example
python imdb_cnn.py
```
If your configuration and driver mapping was done correctly, you should see something like that before the training starts.
```
Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX TITAN X, pci bus id: 0000:09:00.0)
```

# References
* https://www.tensorflow.org/
* https://keras.io/
* https://kubernetes.io/docs/user-guide/kubectl/kubectl_label/
* https://kubernetes.io/docs/user-guide/kubectl/kubectl_create/
* https://kubernetes.io/docs/user-guide/kubectl/kubectl_exec/

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