Speaker: Bennett Wineholt (Cornell University)
Presentation courtesy of Bennett Wineholt.
Scaling up systems for resource-intensive machine learning tasks demands convenient methods to manage computations distributed across multiple servers. Come and learn about both the processes underlying new Deep Learning techniques that have been applied to piloting drones and driving autonomous vehicles as well as the Docker containerization tools used to train these systems at scale.
You may need to run the Docker application, or the command
$ sudo service docker start
A reboot may be required since Docker ties tightly into system functions.
$ docker pull tensorflow/tensorflow
Now that we have downloaded a docker container, the next step is to run it:
$ docker run -it -p 8888:8888 tensorflow/tensorflow
Go to your browser on
command printed on container start). Consider reading the MNIST from Scratch
jupyter notebook comments to get a good idea of the steps involved.
Use the MNIST from Scratch jupyter notebook to execute the code, running
Cell -> Run All.
All credit to the authors: https://www.tensorflow.org/get_started/mnist/beginners
In a terminal list running containers:
$ docker ps
container_id(s) you want to shut down and use it to stop the containers, then remove them permanently
$ docker stop container_id $ docker rm container_id
Then shut down Docker; based on your OS there may be a service icon, or try
the Linux command (your Linux OS may or may not be using
service, but it is
more likely it is than not):
$ sudo service docker stop