Ray provides a simple, universal API for building distributed applications.
Ray is packaged with the following libraries for accelerating machine learning workloads:
There are also many community integrations with Ray, including Dask, MARS, Modin, Horovod, Hugging Face, Scikit-learn, and others. Check out the full list of Ray distributed libraries here.
Install Ray with: pip install ray
. For nightly wheels, see the
Installation page.
Execute Python functions in parallel.
import ray
ray.init()
@ray.remote
def f(x):
return x * x
futures = [f.remote(i) for i in range(4)]
print(ray.get(futures))
To use Ray's actor model:
import ray
ray.init()
@ray.remote
class Counter(object):
def __init__(self):
self.n = 0
def increment(self):
self.n += 1
def read(self):
return self.n
counters = [Counter.remote() for i in range(4)]
[c.increment.remote() for c in counters]
futures = [c.read.remote() for c in counters]
print(ray.get(futures))
Ray programs can run on a single machine, and can also seamlessly scale to large clusters. To execute the above Ray script in the cloud, just download this configuration file, and run:
ray submit [CLUSTER.YAML] example.py --start
Read more about launching clusters.
Tune is a library for hyperparameter tuning at any scale.
To run this example, you will need to install the following:
$ pip install "ray[tune]"
This example runs a parallel grid search to optimize an example objective function.
from ray import tune
def objective(step, alpha, beta):
return (0.1 + alpha * step / 100)**(-1) + beta * 0.1
def training_function(config):
# Hyperparameters
alpha, beta = config["alpha"], config["beta"]
for step in range(10):
# Iterative training function - can be any arbitrary training procedure.
intermediate_score = objective(step, alpha, beta)
# Feed the score back back to Tune.
tune.report(mean_loss=intermediate_score)
analysis = tune.run(
training_function,
config={
"alpha": tune.grid_search([0.001, 0.01, 0.1]),
"beta": tune.choice([1, 2, 3])
})
print("Best config: ", analysis.get_best_config(metric="mean_loss", mode="min"))
# Get a dataframe for analyzing trial results.
df = analysis.results_df
If TensorBoard is installed, automatically visualize all trial results:
tensorboard --logdir ~/ray_results
RLlib is an open-source library for reinforcement learning built on top of Ray that offers both high scalability and a unified API for a variety of applications.
pip install tensorflow # or tensorflow-gpu
pip install "ray[rllib]"
import gym
from gym.spaces import Discrete, Box
from ray import tune
class SimpleCorridor(gym.Env):
def __init__(self, config):
self.end_pos = config["corridor_length"]
self.cur_pos = 0
self.action_space = Discrete(2)
self.observation_space = Box(0.0, self.end_pos, shape=(1, ))
def reset(self):
self.cur_pos = 0
return [self.cur_pos]
def step(self, action):
if action == 0 and self.cur_pos > 0:
self.cur_pos -= 1
elif action == 1:
self.cur_pos += 1
done = self.cur_pos >= self.end_pos
return [self.cur_pos], 1 if done else 0, done, {}
tune.run(
"PPO",
config={
"env": SimpleCorridor,
"num_workers": 4,
"env_config": {"corridor_length": 5}})
Ray Serve is a scalable model-serving library built on Ray. It is:
To run this example, you will need to install the following:
$ pip install scikit-learn
$ pip install "ray[serve]"
This example runs serves a scikit-learn gradient boosting classifier.
from ray import serve
import pickle
import requests
from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier
# Train model
iris_dataset = load_iris()
model = GradientBoostingClassifier()
model.fit(iris_dataset["data"], iris_dataset["target"])
# Define Ray Serve model,
class BoostingModel:
def __init__(self):
self.model = model
self.label_list = iris_dataset["target_names"].tolist()
def __call__(self, flask_request):
payload = flask_request.json["vector"]
print("Worker: received flask request with data", payload)
prediction = self.model.predict([payload])[0]
human_name = self.label_list[prediction]
return {"result": human_name}
# Deploy model
client = serve.start()
client.create_backend("iris:v1", BoostingModel)
client.create_endpoint("iris_classifier", backend="iris:v1", route="/iris")
# Query it!
sample_request_input = {"vector": [1.2, 1.0, 1.1, 0.9]}
response = requests.get("http://localhost:8000/iris", json=sample_request_input)
print(response.text)
# Result:
# {
# "result": "versicolor"
# }
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