![]() ![]() The TensorFlow Docker images are already configured to run TensorFlow. How to install Docker CE on Ubuntu / Debian / Fedora / Arch / CentOS If you don’t have a Docker Engine installed on Ubuntu/Debian Linux, our guide should come in handy. You can also run TensorFlow in a docker container. When TensorBoard is fully configured, access the URL on The Dashboard looks like this: Running Tensorflow (CPU Only) in Docker Container Not that by default Tensorflow outputs are stored under the /tmp directory. You can kill TensorBoard process by Pressing CTRL+C TensorBoard 1.12.1 at (Press CTRL+C to quit) On running the tensorboardcommand, the output like below will be printed in your screen. Start TensorBoard by running: mkdir ~/tensor_logs Use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. TensorBoard is a group of visualization tools that make it easier to understand, debug, and optimize TensorFlow programs. Running a test Model: mkdir ~/tensorflow_projectsĮxport PYTHONPATH="$PYTHONPATH:$(pwd)/models" Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA Python3 -c "import tensorflow as tf tf.enable_eager_execution() print(tf.reduce_sum(tf.random_normal()))" Python -c "import tensorflow as tf tf.enable_eager_execution() print(tf.reduce_sum(tf.random_normal()))" Verify that your Tensorflow is working fine. ![]() Verify Tensorflow (CPU Only) Installation on Ubuntu 20.04|18.04 / Debian 10|9 #Python2īut don’t forget that GPU packages require a CUDA®-enabled GPU card. If you have CUDA-enabled GPU cards, then you can install the GPU package. Sudo pip3 install -upgrade tensorflow requests Sudo pip install -upgrade tensorflow requests ![]()
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