def main():
# db = TensorDB(ip='146.169.33.34', port=27020, db_name='DRL', user_name='tensorlayer', password='******', studyID="20170524_1")
db = TensorDB(ip='146.169.15.140',
port=27017,
db_name='DRL',
user_name=None,
password=None,
studyID="1")
# Create jobs
n_jobs = 5
for j in range(n_jobs):
args = {
"id": j,
"name": "Deep Reinforcement Learning",
"file": "tutorial_tensordb_atari_pong_generator.py",
"args": "",
}
db.submit_job(args=args)
# Setting up the connection to interface
ip = "interfaceeae.doc.ic.ac.uk"
port = 443
eae = eAE(ip, port)
# Testing if the interface is Alive
is_alive = eae.is_eae_alive()
if is_alive != 200:
raise Exception("!!!")
# Get all jobs
jobs = db.get_jobs(status=JobStatus.WAITING)
for j in jobs:
# Start worker
parameters_set = "--job_id={}".format(str(j["_id"]))
cluster = "gpu"
computation_type = "GPU"
main_file = j["file"]
data_files = ['tensorlayer']
host_ip = "dsigpu2.ict-doc.ic.ac.uk"
ssh_port = "22222"
job = eae.submit_jobs(parameters_set, cluster, computation_type,
main_file, data_files, host_ip, ssh_port)
db.change_job_status(job_id=j["_id"], status=JobStatus.RUNNING)
print(job)
from tensorlayer.db import TensorDB
from tensorlayer.db import JobStatus
db = TensorDB(ip='146.169.15.140', port=27017, db_name='DRL', user_name=None, password=None, studyID="1")
# Terminate running jobs
jobs = db.get_jobs(status=JobStatus.RUNNING)
for j in jobs:
print db.change_job_status(job_id=j["_id"], status=JobStatus.TERMINATED)