def ipython_current(parallel, config):
has_mincores = any(x.startswith("mincores=") for x in parallel["resources"])
cores = min(ip._get_common_cores(config["resources"]), parallel["system_cores"])
if cores > 1 and not has_mincores:
adj_cores = max(1, int(math.floor(cores * float(parallel.get("mem_pct", 1.0)))))
# if we have less scheduled cores than per machine, use the scheduled count
if cores > parallel["cores"]:
cores = parallel["cores"]
# if we have less total cores required for the entire process, use that
elif adj_cores > parallel["num_jobs"] * parallel["cores_per_job"]:
cores = parallel["num_jobs"] * parallel["cores_per_job"]
else:
cores = adj_cores
cores = ip.per_machine_target_cores(cores, parallel["num_jobs"] // cores)
parallel["resources"].append("mincores=%s" % cores)
return parallel
def ipython_fn(parallel, config):
has_mincores = any(x.startswith("mincores=") for x in parallel["resources"])
common_cores = min(ip._get_common_cores(config["resources"]), parallel["system_cores"])
# if has_mincores, check memory usage use min(adj_cores, mincores)
# min(common_cores, parallel[cores])
if common_cores > 1 and not has_mincores:
adj_cores = max(1, int(math.floor(common_cores * float(parallel.get("mem_pct", 1.0)))))
num_engines = adj_cores
# if we have less scheduled cores than per machine, use the scheduled count
if num_engines > parallel["cores"]:
num_engines = parallel["cores"]
print "common %s cores_per_job %s cores %s" % (common_cores, parallel['cores_per_job'], num_engines)
if (parallel['cores_per_job'] * num_engines) > common_cores:
num_engines = int(math.floor(float(common_cores) / parallel['cores_per_job']))
parallel["resources"].append("mincores=%s" % num_engines)
return parallel
