TF Stage

A fast and canonical project setup for TensorFlow models. The most difficult part of getting started with TensorFlow isn't deep learning, it's putting together hundreds of API calls into a cohesive model.

$ tfstage --help
usage: tfstage [-h] name

TensorFlow project scaffolding

positional arguments:
  name                  Project name
  install_dependencies  Install pip dependencies

optional arguments:
  -h, --help            show this help message and exit

Usage

Install tfstage:
```
pip install tfstage
```
Create a new empty project directory
```
$ mkdir my_project/
$ cd my_project/
```

Run tfstage my_project:

$ tfstage my_project
Project created: ./my_project

This stubs out an entire TensorFlow project, completely runnable using a simple XOR dataset and model. For example:

$ python -m my_project.main --job-dir logs/

...

INFO:tensorflow:Saving checkpoints for 1 into logs/model.ckpt.
INFO:tensorflow:loss = 1.20236, step = 1
INFO:tensorflow:Starting evaluation at 2017-07-13-18:22:20
INFO:tensorflow:Restoring parameters from logs/model.ckpt-1

...

Workflow

When starting a new project we run tfstage, run the code to verify everything works, then search and replace the TODO comments in the code which mark important changes.

Environment

High-level description of a new project:

main.py: defines command-line arguments and sets up learn_runner
experiment.py: defines a tf.contrib.learn.Experiment for training
inputs.py: defines the input pipeline for training and evaluation
model.py: defines the model, loss, and training optimization
augment.py: defines any data augmentation or feature engineering
serve.py: defines placeholders for TensorFlow Serving and Google Cloud ML Engine predictions.

In addition, several common files are created including:

README.md
requirements.txt for local development
setup.py for local and GCE deployment
.gitignore

Local Deployment

PROJECT_NAME=my_project
MODULE_NAME="${PROJECT_NAME}.main"
PACKAGE_PATH="${PROJECT_NAME}/"
JOB_DIR=logs/

gcloud ml-engine local train \
  --module-name $MODULE_NAME \
  --package-path $PACKAGE_PATH \
  --job-dir $JOB_DIR \
  -- \
  [args]

Cloud Deployment