def main():
# 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("!!!")
# Initialize input argument for each node in the eAE cluster
args = [
'--data_dir data/eeg_fpz_cz --output_dir results --n_folds 20 --fold_idx {} --pretrain_epochs 100 --finetune_epochs 200'
.format(fold_idx) for fold_idx in range(20)
]
# Submit a job
parameters_set = "\n".join(args)
cluster = "gpu"
computation_type = "GPU"
main_file = "train.py"
data_files = ['deepsleep', 'tensorlayer', 'data/eeg_fpz_cz']
host_ip = "host_ip_address" # IP address of the machine to run this script
ssh_port = "ssh_port" # Port for ssh
job = eae.submit_jobs(parameters_set, cluster, computation_type, main_file,
data_files, host_ip, ssh_port)
print(job)
def main():
db = TensorDB(ip='146.169.33.34',
port=27020,
db_name='TransferGan',
user_name='akara',
password='******',
studyID="MNIST")
create_mnist_dataset(db=db)
create_jobs(db=db,
job_name="cv_mnist",
models_dict={
"cnn": {
"lr": [0.01, 0.001, 0.001],
"n_cnn_layers": [1, 2, 2],
"n_filters": [64, 128, 256],
"n_epochs": [10, 10, 10],
},
"mlp": {
"lr": [0.05, 0.0001],
"n_layers": [1, 2],
"n_epochs": [10, 10],
}
})
# 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_all_jobs()
args = [str(j['_id']) for j in jobs]
# We submit a dummy job
parameters_set = "\n".join(args)
cluster = "gpu_dev"
computation_type = "GPU"
main_file = "/home/akara/Workspace/tl_paper/tutorial_tensordb_cv_mnist_worker.py"
data_files = ['/home/akara/Workspace/tl_paper/tensorlayer']
host_ip = "dsihuaweiroom.doc.ic.ac.uk"
ssh_port = "22"
job = eae.submit_jobs(parameters_set, cluster, computation_type, main_file,
data_files, host_ip, ssh_port)
print(job)
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)
def main():
# This is to initialize the connection to your MondonDB server
# Note: make sure your MongoDB is reachable before changing this line
db = TensorDB(ip='IP_ADDRESS_OR_YOUR_MONGODB', port=27017, db_name='DATABASE_NAME', user_name=None, password=None, studyID='ANY_ID (e.g., mnist)')
create_mnist_dataset(db=db)
create_jobs(db=db, job_name="cv_mnist", models_dict={
"cnn": {
"lr": [0.01, 0.001, 0.001],
"n_cnn_layers": [1, 2, 2],
"n_filters": [64, 128, 256],
"n_epochs": [10, 10, 10],
},
"mlp": {
"lr": [0.05, 0.0001],
"n_layers": [1, 2],
"n_epochs": [10, 10],
}
})
# Setting up the connection to interface
ip = "IP_ADDRESS_OF_EAE (e.g., 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_all_jobs()
args = [str(j['_id']) for j in jobs]
# We submit a dummy job
parameters_set = "\n".join(args)
cluster = "NAME_OF_CLUSTER (e.g., gpu_dev)"
computation_type = "COMPUTATION_TYPE (e.g., GPU)"
main_file = "ABSOLUTE_PATH_TO_MAIN_FILE"
data_files = ['ABSOLUTE_PATH_TO_DIRECTORY_OR_FILES_TO_BE_COPIED_TO_RUN_THE_MAIN_FILE']
host_ip = "IP_ADDRESS_OF_HOST_MACHINE_RUNNING_THIS_SCRIPT"
ssh_port = "SSH_PORT_OF_HOST_MACHINE"
job = eae.submit_jobs(parameters_set, cluster, computation_type, main_file, data_files, host_ip, ssh_port)
print(job)
# We import the eAE package
import time
from eAE import eAE
directory = ''
# We create the connection to the backend
eae = eAE.eAE("admin", "admin", "127.0.0.1")
# We list the jobs with their associated parameters
parameters = ["first_analysis_type 0 1"]
# We list the required files for the analysis to be sent to the back-end
data_files = ["job.py", "faust.txt"]
# We submit a job
answer = eae.submit_jobs("python2", "job.py", parameters, data_files)
# We check that the submission has been successful
print(answer)
"""
answer = { "status": "OK",
"jobID": "5c47530c6ad68800121c72be",
"jobPosition": 1,
"carriers": [
"carrier:3000"
]
}
"""