Part 2 - Building the command line application

Now that you've built and trained a deep neural network on the flower data set, it's time to convert it into an application that others can use. Your application should be a pair of Python scripts that run from the command line. For testing, you should use the checkpoint you saved in the first part.

Specifications

The project submission must include at least two files train.py and predict.py. The first file, train.py, will train a new network on a dataset and save the model as a checkpoint. The second file, predict.py, uses a trained network to predict the class for an input image. Feel free to create as many other files as you need. Our suggestion is to create a file just for functions and classes relating to the model and another one for utility functions like loading data and preprocessing images. Make sure to include all files necessary to run train.py and predict.py in your submission.

1. Train

Train a new network on a data set with train.py

• Basic usage: python train.py data_directory
• Prints out training loss, validation loss, and validation accuracy as the network trains
• Options:
  • Set directory to save checkpoints: python train.py data_dir --save_dir save_directory
  • Choose architecture: python train.py data_dir --arch "vgg13"
  • Set hyperparameters: python train.py data_dir --learning_rate 0.01 --hidden_units 512 --epochs 20
  • Use GPU for training: python train.py data_dir --gpu

2. Predict

Predict flower name from an image with predict.py along with the probability of that name. That is, you'll pass in a single image /path/to/image and return the flower name and class probability.

• Basic usage: python predict.py /path/to/image checkpoint
• Options:
  • Return top K most likely classes: python predict.py input checkpoint --top_k 3
  • Use a mapping of categories to real names: python predict.py input checkpoint --category_names cat_to_name.json
  • Use GPU for inference: python predict.py input checkpoint --gpu

The best way to get the command line input into the scripts is with the argparse module in the standard library. You can also find a nice tutorial for argparse here.

Caution - Compute and Storage Capacity of Workspaces

Each workspace is provided with adequate compute capacity and storage space (~2.5Gb) to accommodate the ML models. Sometimes, when you return to your workspace, you might face difficulty restoring the workspace because of the presence of bulky interim files generated during training ML models. So, avoid filling up your workspace by doing a simple step mentioned below.

a. How to prevent filling up your workspace?

As you go on to train your models, the models will be saved in your /home/workspace/saved_models directory along with the path configuration (*.pth) files created in the project directory. Both these "saved_models" directory and ".pth" files are bulky interim files, meaning they will be created every time you attempt to train your ML model. These files with a total size > 2.5Gb can fill up your workspace to the brim, thus causing the workspace-restoration problems when you'd return to the workspace next time.

Therefore, we strongly encourage you to delete these large interim files and directories before navigating to another page or closing the browser tab.

b. Where do I save the "heavy" files having total size > 2.5Gb, if required?

You can move your heavy files, such as *.pth file, into the ~/opt directory, where you will have tons of space. Your files will be available there as long as you are working in a given session. Sessions are terminated after 30 minutes of inactivity. All your files in the ~/opt are temporary, and will not restore in the next session.

Note: For the current exercise, you don't need Gigabytes of data, and in general, students should never submit projects with that much data either.

c. What if you neither delete these heavy files nor move them to the ~/opt directory before closing your workspace session?

In such a case, you might face a workspace-restoration problem, and the only way out is to contact Udacity support.