Difference between revisions of "Running TensorFlow App on AWS"
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rsync -azv -e "ssh -i$HOME/.ssh/mykeyDFT.pem" * ubuntu@ec2-54-159-224-197.compute-1.amazonaws.com:ML | rsync -azv -e "ssh -i$HOME/.ssh/mykeyDFT.pem" * ubuntu@ec2-54-159-224-197.compute-1.amazonaws.com:ML | ||
| + | * back in other Terminal window, launch app: | ||
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| + | python theApp.py parameter1=value parameter2=value | ||
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Latest revision as of 17:21, 8 October 2017
--D. Thiebaut (talk) 18:18, 8 October 2017 (EDT)
- open aws console
- EC2
- Pick 64-bit Deep Learning AMI Ubuntu Version 2.3_Sep2017 - ami-06e80e7c
- Pick instance (e.g. c3.4xlarge)
- Launch
- Connect
- Open Terminal
ssh -i "$HOME/.ssh/mykeyDFT.pem" ubuntu@ec2-nnn-nnn-nnn-nnn.compute-1.amazonaws.com
- mkdir a directory to hold the data + Tensorflow code
mkdir ML
- In other Terminal, cd to directory with Tensorflow program & data
rsync -azv -e "ssh -i$HOME/.ssh/mykeyDFT.pem" * ubuntu@ec2-54-159-224-197.compute-1.amazonaws.com:ML
- back in other Terminal window, launch app:
python theApp.py parameter1=value parameter2=value
