def run(self, **kwargs):
"""
Runs the experiment. If the experiment is run on the cloud, blocks until all jobs complete.
"""
if not self.configured:
raise BaseException("Must configure experiment first")
logger.debug('Running experiment')
cloud_job_ids = []
self.run_params = kwargs.copy()
run_mode = kwargs.get('run_mode', 'local')
self.run_mode = run_mode
self.jobs = []
for job in self.iter_jobs():
result = job.run(**kwargs)
if run_mode == 'cloud':
cloud_job_ids.append(result)
self.jobs.append(job)
if run_mode == 'cloud':
logger.info("Waiting for cloud jobs to finish")
cloud.join(cloud_job_ids)
logger.info("Cloud jobs finished")
def __contains__(self, item):
print "checking cache"
print item
print self.hash_key(item)
logger.debug("bucket list is %r" % cloud.bucket.list())
return self.hash_key(item) in cloud.bucket.list()
def f():
if run_mode == "cloud":
store = storage.CloudStore()
elif run_mode == "local":
store = storage.LocalStore()
else:
raise BaseException("Run mode %r not recognized" % run_mode)
store.auto_hash = False
if use_cache and (self.key in store):
logger.debug("Cache hit")
return
logger.debug("Cache miss")
logger.debug("Running job")
data._load_data()
chain.data = data.train_data
chain.data_source = data
ioff()
states = chain._run()
ion()
logger.debug('Chain completed')
history = History()
history.states = states
history.job = self
logger.debug("Summarizing chain")
history.summary = chain.summarize(history)
logger.debug("Chain summarized")
logger.debug("Job params: %r" % (self.params, ))
store[self.key] = history
store.close()
def __contains__(self, item):
print "checking cache"
print item
print self.hash_key(item)
logger.debug("bucket list is %r" % cloud.bucket.list())
return self.hash_key(item) in cloud.bucket.list()
