def do_until_convergence(params):
ps = sim.find_or_create_parameter_set(params)
ps.create_runs_upto(4)
Server.await_ps(ps)
e = err(ps)
while e > 0.2:
print(f"results for {params} is not converged")
ps.create_runs_upto(len(ps.runs()) + 4) # add four runs
Server.await_ps(ps)
e = err(ps)
print(f"converged results for {params}, error = {e}")
from caravan import Server, Task
with Server.start():
for i in range(20):
Task.create(f"sleep {1+i%3}")
from caravan import Server, Task
with Server.start():
tasks1 = [Task.create(f"sleep {1+i%3}") for i in range(5)]
Server.await_all_tasks(
tasks1) # this method blocks until all the tasks are finished
print("all running tasks are complete!")
for t in tasks1:
print(
f"task ID:{t.id()}, rc:{t.rc()}, rank:{t.rank()}, {t.start_at()}-{t.finish_at()}"
)
def err(ps):
runs = ps.runs()
r1 = [r.output() for r in runs]
n = len(runs)
avg = sum(r1) / n
err = math.sqrt(sum([(r - avg)**2 for r in r1]) / ((n - 1) * n))
return err
def do_until_convergence(params):
ps = sim.find_or_create_parameter_set(params)
ps.create_runs_upto(4)
Server.await_ps(ps)
e = err(ps)
while e > 0.2:
print(f"results for {params} is not converged")
ps.create_runs_upto(len(ps.runs()) + 4) # add four runs
Server.await_ps(ps)
e = err(ps)
print(f"converged results for {params}, error = {e}")
with Server.start():
for p1 in [1.0, 1.5, 2.0, 2.5]:
for p2 in [0.5, 1.0]:
Server.do_async(lambda param={
'mu': p1,
'sigma': p2
}: do_until_convergence(param))
from caravan import Server, Task
with Server.start():
for i in range(5):
task = Task.create(f"sleep {1+i%3}")
Server.await_task(
task) # this method blocks until the task is finished.
print(
f"step {i} finished. rc: {task.rc()}, rank: {task.rank()}, {task.start_at()}-{task.finish_at()}"
) # show info of completed task
from caravan import Server, Task
with Server.start():
t = Task.create("echo '[1.0,2.0,3.0]' > _output.json")
Server.await_task(t)
print(t.output())
def run_sequential_tasks(n):
for i in range(4):
task = Task.create(f"sleep {1+i%3}")
Server.await_task(
task) # this method blocks until the task is complete.
print(f"step {i} of {n} finished") # show the progress
import functools
from caravan import Server, Task
def run_sequential_tasks(n):
for i in range(4):
task = Task.create(f"sleep {1+i%3}")
Server.await_task(
task) # this method blocks until the task is complete.
print(f"step {i} of {n} finished") # show the progress
with Server.start():
for n in range(3):
Server.do_async(functools.partial(run_sequential_tasks, n))
import os, math
from caravan import Server, Simulator
this_dir = os.path.abspath(os.path.dirname(__file__))
sim = Simulator.create(f"python {this_dir}/mc_simulator.py > _output.json")
def err(ps):
runs = ps.runs()
r1 = [r.output() for r in runs]
n = len(runs)
avg = sum(r1) / n
err = math.sqrt(sum([(r - avg)**2 for r in r1]) / ((n - 1) * n))
return err
with Server.start():
ps = sim.find_or_create_parameter_set({'mu': 1.0, 'sigma': 2.0})
ps.create_runs_upto(4)
print("awaiting")
Server.await_ps(ps)
e = err(ps)
while e > 0.2:
print(f"error = {e}")
ps.create_runs_upto(len(ps.runs()) + 4) # add four runs
print("awaiting")
Server.await_ps(ps)
e = err(ps)
print(f"error = {e}")
from caravan import Server, Tables, Task
Tables.load("dump.pickle") # data are loaded
Task.reset_cancelled()
for t in Task.all():
print(t.to_dict()) # print Tasks
with Server.start(): # restart scheduler
pass
print("second execution done")
for t in Task.all():
print(t.to_dict()) # print Tasks
import os
from caravan import Server, Simulator
this_dir = os.path.abspath(os.path.dirname(__file__))
sim = Simulator.create(f"python {this_dir}/mc_simulator.py > _output.json")
with Server.start():
ps = sim.find_or_create_parameter_set({
'mu': 1.0,
'sigma': 2.0
}) # create a ParameterSet whose parameters are (mu=1.0,sigma=2.0).
ps.create_runs_upto(
10
) # create ten Runs. In the background, `make_cmd` is called to generate actual commands.
Server.await_ps(
ps) # wait until all the Runs of this ParameterSet finishes
avg = sum([r.output() for r in ps.runs()]) / len(
ps.runs()) # results are averaged over the Runs
print(f"average: {avg}")
for r in ps.runs():
print(f"id: {r.id()}, output: {r.output()}"
) # showing results of each Run