tfstage 0.1.7

TensorFlow project scaffolding

# 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

1. Install `tfstage`:

```
pip install tfstage
```

2. Create a new empty project directory

```
$ mkdir my_project/
$ cd my_project/
```

3. Run `tfstage my_project`:

```
$ tfstage my_project
Project created: ./my_project
```

4. 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`](https://goo.gl/I6TwxA)
- experiment.py: defines a [`tf.contrib.learn.Experiment`](https://goo.gl/nMvwLx) 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](https://goo.gl/bM3jpA) and [Google Cloud ML Engine predictions](https://goo.gl/yTBv2e).

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

```
MODULE_NAME="${PROJECT_NAME}.main"
PACKAGE_PATH="${PROJECT_NAME}/"
JOB_NAME="${PROJECT_NAME}_1"
JOB_DIR="gs://${PROJECT_NAME}/${JOB_NAME}"
REGION=us-east1

gcloud ml-engine jobs submit training $JOB_NAME \
--job-dir $JOB_DIR \
--module-name $MODULE_NAME \
--package-path $PACKAGE_PATH \
--region $REGION \
-- \
[args]
```