some special requirements are needed to run on a GPU machine (paperspace P5000). for that we use "requirements_paperspace.txt" file instead of "requirements.txt":
pip3 install -r requirements_paperspace.txt
Also, we need to revert from Cuda 9.1 to Cuda 9.0. Please refer to "setting_up_paperspace_environment.txt" for step by step explanation.
Install virtualenv via pip (make sure to use Python3 pip):
Install virtualenv with pip if you don't have it:
pip3 install virtualenv
With virtualenv, create a new environment in the ~/venv/deeplearn
directory, or where you want to store the virtualenvironment for python.
virtualenv ~/venv/deeplearn
Enter the new environment (you might want to make an alias for this - alias=):
workon deeplearn
Use the requirements.txt file to install the required libraries
pip3 install -r requirements.txt
Check the default backend in use for keras: open ~/.keras/keras.json, for this project we are using tensorflow as backend, so the Json should look like : { "image_data_format": "channels_last", "epsilon": 1e-07, "floatx": "float32", "backend": "tensorflow" }
If you are using Virtual Studio Code you can configure the Editor to use the Virtualenv Python binary. To do this you first need to configure your Virtualenv Root folder with this user setting:
"python.venvPath": "~/venv",
After a restart you can select your Python Interpreter (Shift+Cmd+P -> "Python: Select Interpreter" -> Select the interpreter in the deeplearning venv, e.g. ~/venv/deeplearn/bin/python). This allows VSCode to access the packages installed in the virtualenv and the editor will use the virtualenv for installing new packages.
At first you will need to verify your kaggle account and accept the competition terms and conditions. Follow this link to do so (https://www.kaggle.com/c/dogs-vs-cats/rules).
Then you can use resources/prepare_data.sh script to configure the directories and download the training and validation data
./resources/prepare_data.sh
If you have tensorboard installed, you can view the visualisations metrics
tensorboard --logdir=./tensorboard
After starting tensorboard you can open your browser on localhost:6006
to view the tensorboard visualization.