MIT intro to deep learning how to run exercises on 4GB or less GPU memory locally

Hello everybody,

that’s my fist post here, so pleas be nice 🙂 I’m totaly new to tensorflow, so this is a beginners guide and no deep dive.

Like you may now the new free MIT intro to Deep Learning Course is online. some of the there given Models are kinda Memory hungry so here the solution:

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CAUTION: think while coping form online Tutorials!

First of all it is a bless to work with the tensorflow/tensorflow:latest-gpu Docker Container so Yea, just do it.

first some dependencys, the notebooks do need python3-opencv and the lab 1 needs abcmidi and timidity

apt install python3-opencv abcmidi timidity 

to edit the code in a personal directory and not in the container you need a non root user

adduser nonroot 

login to the user

su - nonroot 

install your editor, it’s jupyter lab for me

pip install jupyterlab 

start jupyter lab on 0.0.0.0 in the bound directory

jupyter lab --ip 0.0.0.0 

add those lines on the top before importing tensorflow

import os os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' 

and those after importing tensorflow as tf

physical_devices = tf.config.list_physical_devices('GPU') try: tf.config.experimental.set_memory_growth(physical_devices[0], True) except: # Invalid device or cannot modify virtual devices once initialized. pass 

Tip: add

%config Completer.use_jedi = False 

if you have problems with autocomplete.

I hope that helps somebody!