Rename zoo_keeper -> zookeeper (#16)

* Rename zoo_keeper -> zookeeper

* Update readme

* Rename folder

* Add readme badges

README.md

@@ -1,22 +1,24 @@
-# Zoo Keeper
+# Zookeeper
+
+[![Azure DevOps builds](https://img.shields.io/azure-devops/build/plumerai/zookeeper/11.svg?logo=azure-devops)](https://plumerai.visualstudio.com/zookeeper/_build/latest?definitionId=11&branchName=master) [![Azure DevOps coverage](https://img.shields.io/azure-devops/coverage/plumerai/zookeeper/11.svg?logo=azure-devops)](https://plumerai.visualstudio.com/zookeeper/_build/latest?definitionId=11&branchName=master) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/zookeeper.svg)](https://pypi.org/project/zookeeper/) [![PyPI](https://img.shields.io/pypi/v/zookeeper.svg)](https://pypi.org/project/zookeeper/) [![PyPI - License](https://img.shields.io/pypi/l/zookeeper.svg)](https://github.com/plumerai/zookeeper/blob/master/LICENSE) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black) [![Join the community on Spectrum](https://withspectrum.github.io/badge/badge.svg)](https://spectrum.chat/larq)
 
 A small library for managing deep learning models, hyper parameters and datasets designed to make training deep learning models easy and reproducible.
 
 ## Getting Started
 
-Zoo Keeper allows you to build command line interfaces for training deep learning models with very little boiler plate using [click](https://click.palletsprojects.com/) and [TensorFlow Datasets](https://www.tensorflow.org/datasets/). It helps you structure your machine learning projects in a framework agnostic and effective way.
-Zoo Keeper is heavily inspired by [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) and [Fairseq](https://github.com/pytorch/fairseq/) but is designed to be used as a library making it lightweight and very flexible. Currently Zoo Keeper is limited to image classification tasks but we are working on making it useful for other tasks as well.
+Zookeeper allows you to build command line interfaces for training deep learning models with very little boiler plate using [click](https://click.palletsprojects.com/) and [TensorFlow Datasets](https://www.tensorflow.org/datasets/). It helps you structure your machine learning projects in a framework agnostic and effective way.
+Zookeeper is heavily inspired by [Tensor2Tensor](https://github.com/tensorflow/tensor2tensor) and [Fairseq](https://github.com/pytorch/fairseq/) but is designed to be used as a library making it lightweight and very flexible. Currently zookeeper is limited to image classification tasks but we are working on making it useful for other tasks as well.
 
 ### Installation
 
 ```console
-pip install zoo-keeper
+pip install zookeeper
 pip install colorama  # optional for colored console output
 ```
 
 ### Registry
 
-Zoo Keeper uses registries to keep track of you data preprocessing, models and hyperparameters.
+Zookeeper keeps track of data preprocessing, models and hyperparameters to allow you to reference them by name from the commandline.
 
 #### Datasets and Preprocessing
 
@@ -26,7 +28,7 @@ In the following we will use [MNIST](http://yann.lecun.com/exdb/mnist) and defin
 ```python
 import tensorflow as tf
 
-from zoo_keeper import cli, build_train, HParams, registry
+from zookeeper import cli, build_train, HParams, registry
 
 @registry.register_preprocess("mnist")
 def default(image, training=False):
@@ -78,7 +80,7 @@ class basic(HParams):
 
 ### Training loop
 
-To train the models registered above we will need to write a custom training loop. Zoo Keeper will then tie everything together:
+To train the models registered above we will need to write a custom training loop. Zookeeper will then tie everything together:
 
 ```python
 @cli.command()
@@ -111,11 +113,11 @@ if __name__ == "__main__":
     cli()
 ```
 
-If you want to register your models in separate files, make sure to import them before calling `cli` to allow Zoo Keeper to properly register them. To install your CLI as a executable command checkout the [`setuptools` integration](http://click.palletsprojects.com/en/7.x/setuptools/) of Click.
+If you want to register your models in separate files, make sure to import them before calling `cli` to allow zookeeper to properly register them. To install your CLI as a executable command checkout the [`setuptools` integration](http://click.palletsprojects.com/en/7.x/setuptools/) of Click.
 
 #### Usage
 
-Zoo Keeper already ships with `prepare`, `plot`, and `tensorboard` commands, but now also includes the `train` command we created above:
+Zookeeper already ships with `prepare`, `plot`, and `tensorboard` commands, but now also includes the `train` command we created above:
 
 ```console
 python examples/train.py --help

azure-pipelines.yml

@@ -33,7 +33,7 @@ jobs:
         displayName: "pytest"
         condition: ne(variables['coverage'], 'true')
 
-      - script: pytest . --junitxml=junit/test-results.xml --cov=zoo_keeper --cov-report=xml --cov-report=html --cov-config=.coveragerc
+      - script: pytest . --junitxml=junit/test-results.xml --cov=zookeeper --cov-report=xml --cov-report=html --cov-config=.coveragerc
         displayName: "pytest coverage"
         condition: eq(variables['coverage'], 'true')
 

examples/train.py

@@ -1,4 +1,4 @@
-from zoo_keeper import cli, build_train, registry, HParams
+from zookeeper import cli, build_train, registry, HParams
 import tensorflow as tf
 
 

setup.py

@@ -7,14 +7,14 @@ def readme():
 
 
 setup(
-    name="zoo-keeper",
+    name="zookeeper",
     version="0.1.0",
     author="Plumerai",
     author_email="[email protected]",
     description="A small library for managing deep learning models, hyper parameters and datasets",
     long_description=readme(),
     long_description_content_type="text/markdown",
-    url="https://github.com/plumerai/zoo-keeper",
+    url="https://github.com/plumerai/zookeeper",
     packages=find_packages(),
     license="Apache 2.0",
     install_requires=["click>=7.0", "matplotlib>=3.0.3", "tensorflow-datasets>=1.0.2"],

zookeeper/__init__.py

@@ -0,0 +1,4 @@
+from zookeeper.hparam import HParams
+from zookeeper.cli import build_train, cli
+
+__all__ = ["build_train", "cli", "HParams"]

zoo_keeper/cli.py → zookeeper/cli.py

@@ -47,7 +47,7 @@ def build_train(function):
     )
     @click.option(
         "--output-prefix",
-        default=os.path.expanduser("~/zoo-keeper-logs"),
+        default=os.path.expanduser("~/zookeeper-logs"),
         help="Directory prefix used to save model checkpoints and logs.",
     )
     @click.option(
@@ -76,7 +76,7 @@ def train(
         validationset,
         **kwargs,
     ):
-        from zoo_keeper import registry
+        from zookeeper import registry
 
         dataset = registry.get_dataset(
             dataset_name,
@@ -118,7 +118,7 @@ def prepare(datasets, data_dir):
 @click.option("--dataset", help="Tensorflow dataset name.")
 @click.option(
     "--output-prefix",
-    default=os.path.expanduser("~/zoo-keeper-logs"),
+    default=os.path.expanduser("~/zookeeper-logs"),
     help="Directory prefix used to save model checkpoints and logs.",
 )
 @click.option("--output-dir", "--logdir", help="Directory containing checkpoints.")
@@ -141,14 +141,14 @@ def tensorboard(model, dataset, output_prefix, output_dir):
 @click.option("--data-dir", type=str, help="Directory with training data.")
 @click.option(
     "--output-prefix",
-    default=os.path.join(os.path.expanduser("~/zoo-keeper-logs"), "plots"),
+    default=os.path.join(os.path.expanduser("~/zookeeper-logs"), "plots"),
     help="Directory prefix used to save plots",
 )
 @click.option(
     "--format", default="pdf", type=click.Choice(["png", "pdf", "ps", "eps", "svg"])
 )
 def plot(dataset, preprocess_fn, data_dir, output_prefix, format):
-    from zoo_keeper import registry, data_vis
+    from zookeeper import registry, data_vis
 
     output_dir = Path(output_prefix).joinpath(dataset, preprocess_fn)
     output_dir.mkdir(parents=True, exist_ok=True)

zoo_keeper/cli_test.py → zookeeper/cli_test.py

@@ -1,4 +1,4 @@
-from zoo_keeper import registry, cli, build_train, HParams, data
+from zookeeper import registry, cli, build_train, HParams, data
 from click.testing import CliRunner
 import click
 from unittest import mock
@@ -155,7 +155,7 @@ def test_cli_wrong_hparams():
 def test_tensorboard(os_system):
     result = runner.invoke(cli, ["tensorboard", "foo", "--dataset", "bar"])
     assert result.exit_code == 0
-    logdir = path.expanduser("~/zoo-keeper-logs/bar/foo")
+    logdir = path.expanduser("~/zookeeper-logs/bar/foo")
     os_system.assert_called_once_with(f"tensorboard --logdir={logdir}")
 
 

zoo_keeper/data_test.py → zookeeper/data_test.py

@@ -1,6 +1,6 @@
 import pytest
 from unittest import mock
-from zoo_keeper import data
+from zookeeper import data
 
 
 @mock.patch("os.makedirs")

zoo_keeper/hparam_test.py → zookeeper/hparam_test.py

@@ -2,7 +2,7 @@
 """Tests for hparam."""
 import pytest
 import click
-from zoo_keeper.hparam import HParams
+from zookeeper.hparam import HParams
 
 
 @pytest.fixture

zoo_keeper/registry.py → zookeeper/registry.py

@@ -1,5 +1,5 @@
 import tensorflow_datasets as tfds
-from zoo_keeper.data import Dataset
+from zookeeper.data import Dataset
 
 MODEL_REGISTRY = {}
 HPARAMS_REGISTRY = {}