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add lbfgs walkthrough and small cleanups (#229)

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This commit is contained in:
Robert Nishihara
2016-07-08 12:32:48 -07:00
committed by Philipp Moritz
parent 82ab6d68f1
commit d8a621e5cb
5 changed files with 186 additions and 24 deletions
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examples/lbfgs/driver.py
+13 -22
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@@ -1,29 +1,24 @@
import numpy as np
import scipy.optimize
import os
import time
import ray
import ray.services as services
import ray.worker as worker
import ray.array.remote as ra
import ray.array.distributed as da
import functions
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
batch_size = 100
num_batches = mnist.train.num_examples / batch_size
batches = [mnist.train.next_batch(batch_size) for _ in range(num_batches)]
if __name__ == "__main__":
worker_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "worker.py")
services.start_ray_local(num_workers=16, worker_path=worker_path)
ray.services.start_ray_local(num_workers=16, worker_path=worker_path)
x_batches = [ray.put(batches[i][0]) for i in range(num_batches)]
y_batches = [ray.put(batches[i][1]) for i in range(num_batches)]
print "Downloading and loading MNIST data..."
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
batch_size = 100
num_batches = mnist.train.num_examples / batch_size
batches = [mnist.train.next_batch(batch_size) for _ in range(num_batches)]
batch_refs = [(ray.put(xs), ray.put(ys)) for (xs, ys) in batches]
# From the perspective of scipy.optimize.fmin_l_bfgs_b, full_loss is simply a
# function which takes some parameters theta, and computes a loss. Similarly,
@@ -36,17 +31,13 @@ if __name__ == "__main__":
# algorithm.
def full_loss(theta):
theta_ref = ray.put(theta)
val_ref = ra.sum_list(*[functions.loss(theta_ref, x_batches[i], y_batches[i]) for i in range(num_batches)])
return ray.get(val_ref)
loss_refs = [functions.loss(theta_ref, xs_ref, ys_ref) for (xs_ref, ys_ref) in batch_refs]
return sum([ray.get(loss_ref) for loss_ref in loss_refs])
def full_grad(theta):
theta_ref = ray.put(theta)
grad_ref = ra.sum_list(*[functions.grad(theta_ref, x_batches[i], y_batches[i]) for i in range(num_batches)])
return ray.get(grad_ref).astype("float64") # This conversion is necessary for use with fmin_l_bfgs_b.
grad_refs = [functions.grad(theta_ref, xs_ref, ys_ref) for (xs_ref, ys_ref) in batch_refs]
return sum([ray.get(grad_ref) for grad_ref in grad_refs]).astype("float64") # This conversion is necessary for use with fmin_l_bfgs_b.
theta_init = np.zeros(functions.dim)
start_time = time.time()
result = scipy.optimize.fmin_l_bfgs_b(full_loss, theta_init, maxiter=10, fprime=full_grad, disp=True)
end_time = time.time()
print "Elapsed time = {}".format(end_time - start_time)