def run()

in tcav/tcav.py [0:0]

• 23 lines of code
• 7 McCabe index (conditional complexity)

  def run(self, num_workers=10, run_parallel=False, overwrite=False, return_proto=False):
    """Run TCAV for all parameters (concept and random), write results to html.

    Args:
      num_workers: number of workers to parallelize
      run_parallel: run this parallel.
      overwrite: if True, overwrite any saved CAV files.
      return_proto: if True, returns results as a tcav.Results object; else,
        return as a list of dicts.

    Returns:
      results: an object (either a Results proto object or a list of
        dictionaries) containing metrics for TCAV results.
    """
    # for random exp,  a machine with cpu = 30, ram = 300G, disk = 10G and
    # pool worker 50 seems to work.
    tf.compat.v1.logging.info('running %s params' % len(self.params))
    results = []
    now = time.time()
    if run_parallel:
      pool = multiprocessing.Pool(num_workers)
      for i, res in enumerate(pool.imap(
          lambda p: self._run_single_set(
            p, overwrite=overwrite, run_parallel=run_parallel),
          self.params), 1):
        tf.compat.v1.logging.info('Finished running param %s of %s' % (i, len(self.params)))
        results.append(res)
      pool.close()
    else:
      for i, param in enumerate(self.params):
        tf.compat.v1.logging.info('Running param %s of %s' % (i, len(self.params)))
        results.append(self._run_single_set(param, overwrite=overwrite, run_parallel=run_parallel))
    tf.compat.v1.logging.info('Done running %s params. Took %s seconds...' % (len(
        self.params), time.time() - now))
    if return_proto:
      return utils.results_to_proto(results)
    else:
      return results