Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch
Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.
Cloning Github Repo to Google Colab
It is easy to clone a Github repo with Git.
Step 1: Find the Github Repo and Get “Git” Link
Find any Github repo to use. For instance: https://github.com/wxs/keras-mnist-tutorial
Clone or download > Copy the link!
2. Git Clone
Simply run:
3. Open the Folder in Google Drive
Folder has a same the with Github repo of course :)
4. Open The Notebook
Right Click > Open With > Colaboratory
5. Run
Now you are able to run Github repo in Google Colab.
Some Useful Tips
1. How to Install Libraries?
Keras
!pip install -q keras import keras
PyTorch
!pip install -q http://download.pytorch.org/whl/cu75/torch-0.2.0.post3-cp27-cp27mu-manylinux1_x86_64.whl torchvision import torch
MxNet
!apt install libnvrtc8.0 !pip install mxnet-cu80 import mxnet as mx
OpenCV
!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python import cv2
XGBoost
!pip install -q xgboost==0.4a30 import xgboost
GraphViz
!apt-get -qq install -y graphviz && pip install -q pydot import pydot
7zip Reader
!apt-get -qq install -y libarchive-dev && pip install -q -U libarchive import libarchive
Other Libraries
!pip install or !apt-get install
to install other libraries.
2. Is GPU Working?
To see if you are currently using the GPU in Colab, you can run the following code in order to cross-check:
import tensorflow as tf tf.test.gpu_device_name()
3. Which GPU Am I Using?
from tensorflow.python.client import device_lib device_lib.list_local_devices()
Currently, Colab only provides Tesla K80.
4. What about RAM?
!cat /proc/meminfo
5. What about CPU?
!cat /proc/cpuinfo
6. Changing Working Directory
Normally when you run this code:
!ls
You probably see datalab and drive folders.
Therefore you must add drive/app before defining each filename.
To get rid of this problem, you can simply change the working directory. (In this tutorial I changed to app folder) with this simple code:
import os os.chdir("drive/app")
After running code above, if you run again
!ls
You would see app folder content and don’t need to add drive/app all the time anymore.
7. “No backend with GPU available
“ Error Solution
If you encounter this error:
Failed to assign a backend No backend with GPU available. Would you like to use a runtime with no accelerator?
Try again a bit later. A lot of people are kicking the tires on GPUs right now, and this message arises when all GPUs are in use.
8. How to Clear Outputs of All Cells
Follow Tools>>Command Palette>>Clear All Outputs
9. “apt-key output should not be parsed (stdout is not a terminal)” Warning
If you encounter this warning:
Warning: apt-key output should not be parsed (stdout is not a terminal)
That means authentication has already done. You only need to mount Google Drive:
!mkdir -p drive !google-drive-ocamlfuse drive
10. How to Use Tensorboard with Google Colab?
I recommend this repo:
https://github.com/mixuala/colab_utils
Conclusion
I think Colab will bring a new breath to Deep Learning and AI studies all over the world.
If you found this article helpful, it would mean a lot if you gave it some applause👏 and shared to help others find it! And feel free to leave a comment below.
You can find me on LinkedIn.
Bio: Fuat Beşer is a Deep Learning Researcher, and the founder of Deep Learning Turkey, the largest AI community in Turkey.
Original. Reposted with permission.
Related:
- 3 Essential Google Colaboratory Tips & Tricks
- Fast.ai Lesson 1 on Google Colab (Free GPU)
- A Simple Starter Guide to Build a Neural Network