DLND-image-classification

This is the git repository for my solution to the Udacity Deep Learning Nanodegree Foundation assignment 2. It implements a convolutional neural network for classifying the CIFAR-10 dataset

Running (Linux/Mac + git + conda)

If you want to run the notebook on your own (using anaconda)

    git clone https://github.com/RomelTorres/DLND-Image-Classification.git
    cd DLND-Image-Classification
    # Create a conda environment from the environment.yml file
    conda env create -f environment.yml
    source activate iclassification
    jupyter notebook dlnd_image_classification.ipynb

The notebook is also rendered by github, so you can simply click on the file dlnd_image_classification.ipynb here and you will see my results.

Running the project on floydhub.com

1. Create an account on floydhub.com (don't forget to confirm your email). You will automatically receive 100 free GPU hours.

2. Install the floyd command on your computer:

    pip install -U floyd-cli
   

   Do this even if you already installed floyd-cli before, just to make sure you have the most recent version (Its pace of development is fast!).

3. Associate the command with your Floyd account:

    floyd login
   

   (a page with authentication token will open; you will need to copy the token into your terminal)

4. Clone this repository:

    git clone https://github.com/ludwiktrammer/deep-learning.git
   

   Note: There are couple minor differences between this repository and the original Udacity repository. You can read about them in README. To follow this instructions you need to use this repository.

5. Enter the folder for the image classification project:

    cd image-classification
   

6. Initiate a Floyd project:

    floyd init dlnd_image_classification
   

7. Run the project:

    floyd run --gpu --env tensorflow --mode jupyter --data diSgciLH4WA7HpcHNasP9j
   

   It will be run on a machine with GPU (--gpu), using a Tenserflow environment (--env tensorflow), as a Jupyter notebook (--mode jupyter), with Floyd's built-in cifar-10 dataset available (--data diSgciLH4WA7HpcHNasP9j).

8. Wait for the Jupyter notebook to become available and then access the URL displayed in the terminal (described as "path to jupyter notebook"). You will see the notebook.

9. Remember to explicitly stop the experiment when you are not using the notebook. As long as it runs (even in the background) it will cost GPU hours. You can stop an experiment in the "Experiments" section on floyd.com or using the floyd stop command:

    floyd stop ID
   

   (where ID is the "RUN ID" displayed in the terminal when you run the project; if you lost it you can also find it in the "Experiments" section on floyd.com)

Important: When you run a project it will always start from scratch (i.e. from the state present locally on your computer). If you made changes in the remote jupiter notebook during a previous run, the changes will not be present in subsequent runs. To make them permanent you need to add the changes to your local project folder. When running the notebook you can download them directly from Jupyter - File / Download / Notebook. After downloading it, just replace your local dlnd_image_classification.ipynb file with the newly downloaded one.

Alternatively, If you already stoped the experiment, you can still download the file using the floyd output command:

floyd output ID

(where ID is the "RUN ID" displayed in the terminal when you run the project; if you lost it you can also find it in the "Experiments" section on floyd.com)

Just run the command above, download dlnd_image_classification.ipynb and replace your local version with the newly downloaded one.