tarvaina / Tensorflow Tutorial
Basics of Tensorflow
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This is a short tutorial on Tensorflow I gave for Reaktor in March 2016. Currently there are probably more complete and more up-to-date tutorials available on the net.
Installation instructions
If you don't have Docker installed already:
- On Linux, install Docker
- On OS X or Windows:
- Install Docker Toolbox
- Run the Docker Quickstart Terminal application and use that as your shell for the rest of this tutorial
Then run the following commands:
# Clone this repository
git clone [email protected]:tarvaina/tensorflow-tutorial.git
cd tensorflow-tutorial
# Download the TensorFlow docker image
docker pull b.gcr.io/tensorflow/tensorflow
# Create container "jupyter" running Jupyter Notebook at port 8888
docker run \
--name jupyter \
-d \
-v "$(pwd)/notebooks:/root/notebooks" \
-v "$(pwd)/logs:/root/logs" \
-p 8888:8888 \
b.gcr.io/tensorflow/tensorflow \
/run_jupyter.sh /root/notebooks
# Create container "tensorboard" running TensorBoard at port 6006
docker run \
--name tensorboard \
-d \
-v "$(pwd)/logs:/root/logs" \
-p 6006:6006 \
b.gcr.io/tensorflow/tensorflow \
tensorboard --logdir /root/logs
Now you can open browser windows for Jupyter Notebook and TensorBoard:
- On Linux: open http://localhost:8888 and http://localhost:6006
- On OS X, run
open http://$(docker-machine ip default):8888 http://$(docker-machine ip default):6006
If everything went fine, you should see something like this:
Congratulations, you have installed the tutorial environment!
You can now open the notebook "01 Test installation" in Jupyter and follow its instructions to check that TensorFlow is indeed working.
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