add pi_calc with Ray

Author: Thanh Chung

tutorial/pi_ray/pi_calc_with_ray.py

@@ -0,0 +1,36 @@
+import numpy as np
+import math, statistics, time
+import ray
+
+num_workers = 4
+trials = 20
+
+ray.init(ignore_reinit_error=True)
+
+def estimate_pi(num_samples):
+    xs = np.random.uniform(low=-1.0, high=1.0, size=num_samples)
+    ys = np.random.uniform(low=-1.0, high=1.0, size=num_samples)
+    xys = np.stack((xs, ys), axis=-1)
+    inside = xs*xs + ys*ys <= 1.0
+    xys_inside = xys[inside]
+    in_circle = xys_inside
+    approx_pi = 4.0 * in_circle / num_samples
+    return approx_pi
+
+Ns = [10000, 50000, 100000, 500000, 1000000] #, 5000000, 10000000]  # Larger values take a long time on small VMs and machines!
+maxN = Ns[-1]
+
+fmt = '{:10.5f} seconds: pi ~ {:7.6f}, stddev = {:5.4f}, error = {:5.4f}%'
+def try_it(n, trials):
+    print('trials = {:3d}, N = {:s}: '.format(trials, str_large_n(n, padding=12)), end='')   # str_large_n imported above.
+    start = time.time()
+    pis = [estimate_pi(n) for _ in range(trials)]
+    approx_pi = statistics.mean(pis)
+    stdev = statistics.stdev(pis)
+    duration = time.time() - start
+    error = (100.0*abs(approx_pi-np.pi)/np.pi)
+    print(fmt.format(duration, approx_pi, stdev, error))   # str_large_n imported above.
+    return trials, n, duration, approx_pi, stdev, error
+
+data_ns = [try_it(n, trials) for n in Ns]
+data_trials = [try_it(maxN, trials) for trials in range(5,20,2)]