Bug Report: Understand and fix TensorFlow scaling behavior about pyhf HOT 10 CLOSED

import time
import tensorflow as tf
import pyhf.tensor
import pyhf.simplemodels


for i in range(10):
  tfb = pyhf.tensor.tensorflow_backend.tensorflow_backend()
  tfb.session = tf.Session()
  pyhf.set_backend(tfb)

  now = time.time()
  p = pyhf.simplemodels.hepdata_like([7.],[50.],[7.])
  pyhf.runOnePoint(1.0, [50.] + p.config.auxdata, p, p.config.suggested_init(), p.config.suggested_bounds())
  delta = time.time() - now
  print(delta)
  tf.reset_default_graph()

  tf.reset_default_graph()
  pyhf.tensorlib.session = tf.Session()

def benchmark_backend(backend, n_bins, n_runs=5):
    default_backend = pyhf.tensorlib
    pyhf.set_backend(backend)

    times = [[time_runOnePoint(bins) for bins in n_bins]
             for _ in range(n_runs)]

    # Reset backend
    pyhf.set_backend(default_backend)

    return summary_stats(times)

def benchmark_backend(n_bins, n_runs=5):
    times = [[time_runOnePoint(bins) for bins in n_bins]
             for _ in range(n_runs)]
    return summary_stats(times)

default_backend = pyhf.tensorlib
pyhf.set_backend(backend)
benchmark_backend(.., ...)
pyhf.set_backend(default_backend)

import time
import tensorflow as tf
import pyhf.tensor
import pyhf.simplemodels

tfb = pyhf.tensor.tensorflow_backend.tensorflow_backend()
tfb.session = tf.Session()

p = pyhf.simplemodels.hepdata_like([7.],[50.],[7.])

pyhf.set_backend(tfb)

for i in range(10):
  now = time.time()
  pyhf.runOnePoint(1.0, [50.] + p.config.auxdata, p, p.config.suggested_init(), p.config.suggested_bounds())
  delta = time.time() - now
  print(delta)