def benchmark_logreg(ctx, timer):
print "#worker:", ctx.num_workers
#N_EXAMPLES = 40000000 * ctx.num_workers
N_EXAMPLES = 5000000 * 64
x = expr.eager(expr.rand(N_EXAMPLES, N_DIM, tile_hint=(N_EXAMPLES / ctx.num_workers, N_DIM)))
y = expr.eager(expr.rand(N_EXAMPLES, 1, tile_hint=(N_EXAMPLES / ctx.num_workers, 1)))
start = time.time()
logistic_regression.logistic_regression(x, y, ITERATION)
total = time.time() - start
util.log_warn("time cost : %s s" % (total*1.0/ITERATION,))
def benchmark_logreg(ctx, timer):
print "#worker:", ctx.num_workers
FLAGS.opt_parakeet_gen = 0
N_EXAMPLES = 1000 * ctx.num_workers
N_DIM = 512
#N_EXAMPLES = 5000000 * 64
x = expr.rand(N_EXAMPLES, N_DIM)
y = expr.rand(N_EXAMPLES, 1)
start = time.time()
logistic_regression.logistic_regression(x, y, ITERATION)
total = time.time() - start
util.log_warn("time cost : %s s" % (total*1.0/ITERATION,))
def benchmark_logreg(ctx, timer):
print "#worker:", ctx.num_workers
FLAGS.opt_parakeet_gen = 0
N_EXAMPLES = 1000 * ctx.num_workers
N_DIM = 512
#N_EXAMPLES = 5000000 * 64
x = expr.rand(N_EXAMPLES, N_DIM)
y = expr.rand(N_EXAMPLES, 1)
start = time.time()
logistic_regression.logistic_regression(x, y, ITERATION)
total = time.time() - start
util.log_warn("time cost : %s s" % (total * 1.0 / ITERATION, ))