Polyaxon-Lib
Deep Learning and Reinforcement learning library for TensorFlow for building end to end models and experiments.
Design Goals
Polyaxon-Lib was built with the following goals:
-
Modularity: The creation of a computation graph based on modular and understandable modules, with the possibility to reuse and share the module in subsequent usage.
-
Usability: Training a model should be easy enough, and should enable quick experimentations.
-
Configurable: Models and experiments could be created using a YAML/Json file, but also in python files.
-
Extensibility: The modularity and the extensive documentation of the code makes it easy to build and extend the set of provided modules.
-
Performance: Polyaxon is based on internal
tensorflowcode base and leverage the builtin distributed learning. -
Data Preprocessing: Polyaxon provides many pipelines and data processor to support different data inputs.
Quick start
A simple linear regression
from polyaxon_schemas.losses import MeanSquaredErrorConfig
from polyaxon_schemas.optimizers import SGDConfig
import polyaxon_lib as plx
X = np.linspace(-1, 1, 100)
y = 2 * X + np.random.randn(*X.shape) * 0.33
# Test a data set
X_val = np.linspace(1, 1.5, 10)
y_val = 2 * X_val + np.random.randn(*X_val.shape) * 0.33
def graph_fn(mode, inputs):
return plx.layers.Dense(units=1,)(inputs['X'])
def model_fn(features, labels, mode):
model = plx.models.Regressor(
mode,
graph_fn=graph_fn,
loss=MeanSquaredErrorConfig(),
optimizer=SGDConfig(learning_rate=0.009),
summaries='all',
name='regressor')
return model(features, labels)
estimator = plx.estimators.Estimator(model_fn=model_fn, model_dir="/tmp/polyaxon_logs/linear")
estimator.train(input_fn=numpy_input_fn(
{'X': X}, y, shuffle=False, num_epochs=10000, batch_size=len(X)))A reinforcement learning problem
from polyaxon_schemas.losses import HuberLossConfig
from polyaxon_schemas.optimizers import SGDConfig
from polyaxon_schemas.rl.explorations import DecayExplorationConfig
import polyaxon_lib as plx
env = plx.envs.GymEnvironment('CartPole-v0')
def graph_fn(mode, features):
return plx.layers.Dense(units=512)(features['state'])
def model_fn(features, labels, mode):
model = plx.models.DDQNModel(
mode,
graph_fn=graph_fn,
loss=HuberLossConfig(),
num_states=env.num_states,
num_actions=env.num_actions,
optimizer=SGDConfig(learning_rate=0.01),
exploration_config=DecayExplorationConfig(),
target_update_frequency=10,
summaries='all')
return model(features, labels)
memory = plx.rl.memories.Memory()
agent = plx.estimators.Agent(
model_fn=model_fn, memory=memory, model_dir="/tmp/polyaxon_logs/ddqn_cartpole")
agent.train(env)A classification problem
import tensorflow as tf
import polyaxon_lib as plx
from polyaxon_schemas.optimizers import AdamConfig
from polyaxon_schemas.losses import SigmoidCrossEntropyConfig
from polyaxon_schemas.metrics import AccuracyConfig
def graph_fn(mode, features):
x = plx.layers.Conv2D(filters=32, kernel_size=5)(features['image'])
x = plx.layers.MaxPooling2D(pool_size=2)(x)
x = plx.layers.Conv2D(filters=64, kernel_size=5)(x)
x = plx.layers.MaxPooling2D(pool_size=2)(x)
x = plx.layers.Flatten()(x)
x = plx.layers.Dense(units=10)(x)
return x
def model_fn(features, labels, params, mode, config):
model = plx.models.Classifier(
mode=mode,
graph_fn=graph_fn,
loss=SigmoidCrossEntropyConfig(),
optimizer=AdamConfig(
learning_rate=0.007, decay_type='exponential_decay', decay_rate=0.1),
metrics=[AccuracyConfig()],
summaries='all',
one_hot_encode=True,
n_classes=10)
return model(features=features, labels=labels, params=params, config=config)
def experiment_fn(output_dir):
"""Creates an experiment using Lenet network.
Links:
* http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
"""
dataset_dir = '../data/mnist'
plx.datasets.mnist.prepare(dataset_dir)
train_input_fn, eval_input_fn = plx.datasets.mnist.create_input_fn(dataset_dir)
experiment = plx.experiments.Experiment(
estimator=plx.estimators.Estimator(model_fn=model_fn, model_dir=output_dir),
train_input_fn=train_input_fn,
eval_input_fn=eval_input_fn,
train_steps=10000,
eval_steps=10)
return experiment
def main(*args):
plx.experiments.run_experiment(experiment_fn=experiment_fn,
output_dir="/tmp/polyaxon_logs/lenet",
schedule='continuous_train_and_eval')
if __name__ == "__main__":
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()A regression problem
from polyaxon_schemas.losses import MeanSquaredErrorConfig
from polyaxon_schemas.metrics import (
RootMeanSquaredErrorConfig,
MeanAbsoluteErrorConfig,
)
from polyaxon_schemas.optimizers import AdagradConfig
import polyaxon_lib as plx
NUM_RNN_LAYERS = 2
NUM_RNN_UNITS = 2
def graph_fn(mode, features):
x = features['x']
for i in range(NUM_LAYERS):
x = plx.layers.LSTM(units=NUM_RNN_UNITS)(x)
return plx.layers.Dense(units=1)(x)
def model_fn(features, labels, mode):
return plx.models.Regressor(
mode=mode,
graph_fn=graph_fn,
loss=MeanSquaredErrorConfig(),
optimizer=AdagradConfig(learning_rate=0.1),
metrics=[
RootMeanSquaredErrorConfig(),
MeanAbsoluteErrorConfig()
]
)(features=features, labels=labels)
xp = plx.experiments.Experiment(
estimator=plx.estimators.Estimator(model_fn=model_fn, model_dir=output_dir),
train_input_fn=plx.processing.numpy_input_fn(
x={'x': x['train']}, y=y['train'], batch_size=64, num_epochs=None, shuffle=False),
eval_input_fn=plx.processing.numpy_input_fn(
x={'x': x['train']}, y=y['train'], batch_size=32, num_epochs=None, shuffle=False),
train_steps=train_steps,
eval_steps=10)
xp.continuous_train_and_evaluate()Creating a distributed experiment
import numpy as np
import tensorflow as tf
from polyaxon_schemas.settings import RunConfig, ClusterConfig
import polyaxon_lib as plx
from polyaxon_schemas.losses import AbsoluteDifferenceConfig
from polyaxon_schemas.optimizers import SGDConfig
tf.logging.set_verbosity(tf.logging.INFO)
def create_experiment(task_type, task_id=0):
def graph_fn(mode, features):
x = plx.layers.Dense(units=32, activation='tanh')(features['X'])
return plx.layers.Dense(units=1, activation='sigmoid')(x)
def model_fn(features, labels, mode):
model = plx.models.Regressor(
mode, graph_fn=graph_fn,
loss=AbsoluteDifferenceConfig(),
optimizer=SGDConfig(learning_rate=0.5,
decay_type='exponential_decay',
decay_steps=10),
summaries='all', name='xor')
return model(features, labels)
config = RunConfig(cluster=ClusterConfig(master=['127.0.0.1:9000'],
worker=['127.0.0.1:9002'],
ps=['127.0.0.1:9001']))
config = plx.estimators.RunConfig.from_config(config)
config = config.replace(task_type=task_type, task_id=task_id)
est = plx.estimators.Estimator(model_fn=model_fn, model_dir="/tmp/polyaxon_logs/xor",
config=config)
# Data
x = np.asarray([[0., 0.], [0., 1.], [1., 0.], [1., 1.]], dtype=np.float32)
y = np.asarray([[0], [1], [1], [0]], dtype=np.float32)
def input_fn(num_epochs=1):
return plx.processing.numpy_input_fn({'X': x}, y,
shuffle=False,
num_epochs=num_epochs,
batch_size=len(x))
return plx.experiments.Experiment(est, input_fn(10000), input_fn(100))
# >> create_experiment('master').train_and_evaluate()
# >> create_experiment('worker').train()
# >> create_experiment('ps').run_std_server()Creating concurrent experiments in kubernetes clusters based on a yaml file
---
version: 1
project:
name: conv_mnsit
matrix:
lr:
logspace: 0.01:0.1:2
settings:
logging:
level: INFO
run_type: kubernetes
environment:
delay_workers_by_global_step: true
n_workers: 5
n_ps: 3
run_config:
save_summary_steps: 100
save_checkpoints_steps: 100
model:
classifier:
loss:
SigmoidCrossEntropy:
optimizer:
Adam:
learning_rate: "{{ lr }}"
metrics:
- Accuracy
- Precision
one_hot_encode: true
n_classes: 10
graph:
input_layers: image
layers:
- Conv2D:
filters: 32
kernel_size: 3
strides: 1
activation: elu
regularizer:
L2:
l: 0.02
- MaxPooling2D:
pool_size: 2
- Conv2D:
filters: 64
kernel_size: 3
activation: relu
regularizer:
L2:
l: 0.02
- MaxPooling2D:
pool_size: 2
- Flatten:
- Dense:
units: 128
activation: tanh
- Dropout:
rate: 0.8
- Dense:
units: 256
activation: tanh
- Dropout:
rate: 0.8
- Dense:
units: 10
train:
train_steps: 100
data_pipeline:
TFRecordImagePipeline:
batch_size: 64
num_epochs: 5
shuffle: true
data_files: ["../data/mnist/mnist_train.tfrecord"]
meta_data_file: "../data/mnist/meta_data.json"
feature_processors:
image:
input_layers: [image]
layers:
- Cast:
dtype: float32
eval:
data_pipeline:
TFRecordImagePipeline:
batch_size: 32
num_epochs: 1
shuffle: False
data_files: ["../data/mnist/mnist_eval.tfrecord"]
meta_data_file: "../data/mnist/meta_data.json"
feature_processors:
image:
input_layers: [image]
layers:
- Cast:
dtype: float32
Installation
To install the latest version of Polyaxon: pip install polyaxon-lib
Alternatively, you can also install from source by running (from source folder): python setup.py install
Or you can just clone the repo git clone https://github.com/polyaxon/polyaxon-lib.git, and use the commands to do everything in docker:
-
cmd/rebuildto build the docker containers. -
cmd/pyto start a python3 shell with all requirements installed. -
cmd/jupyterto start a jupyter notebook server. -
cmd/tensorboardto start a tensorboard server. -
cmd/testto run the tests.
Examples
Some examples are provided here, more examples and use cases will pushed, a contribution with an example is also appreciated.
Project status
Polyaxon is in a pre-release "alpha" state. All interfaces, programming interfaces, and data structures may be changed without prior notice. We'll do our best to communicate potentially disruptive changes.
Contributions
Please follow the contribution guide line: Contribute to Polyaxon.
License
Credit
This work is based and was inspired from different projects, tensorflow.contrib.learn, keras, sonnet, seq2seq and many other great open source projects, see ACKNOWLEDGEMENTS.
The idea behind creating this library is to provide a tool that allow engineers and researchers to develop and experiment with end to end solutions.
The choice of creating a new library was very important to have a complete control over the apis and future design decisions.
