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ec2_launcher_terabyte.py
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ec2_launcher_terabyte.py
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import sys
import boto3
import time
from pssh.clients import ParallelSSHClient
def kill_process(
client_trainers, client_emb_server, client_oracle_cacher, private_ip_trainers
):
run_args_kill_trainers = [
{"cmd": "pkill -9 python"} for i in range(len(private_ip_trainers))
]
run_args_kill_oracle = [{"cmd": "pkill -9 python"}]
run_args_kill_emb_server = [{"cmd": "pkill -9 python"}]
kill_trainers = client_trainers.run_command(
"%(cmd)s", host_args=run_args_kill_trainers
)
kill_emb_server = client_emb_server.run_command(
"%(cmd)s", host_args=run_args_kill_emb_server
)
kill_oracle_cacher = client_oracle_cacher.run_command(
"%(cmd)s", host_args=run_args_kill_oracle
)
print("Launched python kill command")
time.sleep(60)
def other_data_args_emb_server(
private_ip_trainers, private_ip_oracle_cacher, emb_info_file
):
run_args_emb_server = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_embedding_server_other_datasets.sh {} {} {} {}".format(
1,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
emb_info_file,
)
}
]
return run_args_emb_server
def other_data_args_oracle_server(
private_ip_trainers, private_ip_oracle_cacher, csv_location, emb_info_file
):
run_args_oracle_cacher = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_oracle_server_other_datasets.sh {} {} {} {} {} {}".format(
0,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
len(private_ip_trainers),
csv_location,
emb_info_file,
)
}
]
return run_args_oracle_cacher
def other_data_args_oracle_server_no_cache_no_prefetch(
private_ip_trainers, private_ip_oracle_cacher, csv_location, emb_info_file
):
run_args_oracle_cacher = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_oracle_server_no_cache_no_prefetch_other_datasets.sh {} {} {} {} {} {}".format(
0,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
len(private_ip_trainers),
csv_location,
emb_info_file,
)
}
]
return run_args_oracle_cacher
def data_download_avazu_single_machine_fae():
bucket_name = "recommendation-data-bagpipe"
run_download = [
{
"cmd": f"aws s3 cp s3://{bucket_name}/avazu_data ./ && aws s3 cp s3://{bucket_name}/avazu_emb_info ./ && aws s3 cp s3://{bucket_name}/avazu_normal.npz ./ && aws s3 cp s3://{bucket_name}/avazu_hot.npz ./ && aws s3 cp s3://{bucket_name}/avazu_hot_emb_dict.npz ./ "
}
]
return run_download
def data_download_avazu_trainer_machine_fae(private_ip_trainers):
bucket_name = "recommendation-data-bagpipe"
run_download = [
{
"cmd": f"aws s3 cp s3://{bucket_name}/avazu_data ./ && aws s3 cp s3://{bucket_name}/avazu_emb_info ./ && aws s3 cp s3://{bucket_name}/avazu_normal.npz ./ && aws s3 cp s3://{bucket_name}/avazu_hot.npz ./ && aws s3 cp s3://{bucket_name}/avazu_hot_emb_dict.npz ./ "
}
for i in range(len(private_ip_trainers))
]
return run_download
def data_download_avazu_single_machine():
run_download = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/avazu_data ./ && aws s3 cp s3://recommendation-data-bagpipe/avazu_emb_info ./"
}
]
return run_download
def data_download_avazu_trainer_machine(private_ip_trainers):
run_download = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/avazu_data ./ && aws s3 cp s3://recommendation-data-bagpipe/avazu_emb_info ./"
}
for i in range(len(private_ip_trainers))
]
return run_download
def data_download_movie_lens_single_machine():
run_download = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/movielen_data ./ && aws s3 cp s3://recommendation-data-bagpipe/movielen_emb_info ./"
}
]
return run_download
def data_download_movie_lens_trainer_machine(private_ip_trainers):
run_download = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/movielen_data ./ && aws s3 cp s3://recommendation-data-bagpipe/movielen_emb_info ./"
}
for i in range(len(private_ip_trainers))
]
return run_download
def other_data_args_trainer(
private_ip_trainers,
private_ip_oracle_cacher,
log_file_name,
num_iters,
emb_info_file,
):
run_args_trainers = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_trainers_other_datasets.sh {} {} {} {} {} {} {} {} {}".format(
i + 2,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
i,
len(private_ip_trainers),
private_ip_trainers[0],
log_file_name,
num_iters,
emb_info_file,
)
}
for i in range(len(private_ip_trainers))
]
return run_args_trainers
def other_data_args_trainer_no_cache_no_prefetch(
private_ip_trainers,
private_ip_oracle_cacher,
log_file_name,
num_iters,
emb_info_file,
):
run_args_trainers = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_trainers_no_cache_no_prefetch_other_datasets.sh {} {} {} {} {} {} {} {} {}".format(
i + 2,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
i,
len(private_ip_trainers),
private_ip_trainers[0],
log_file_name,
num_iters,
emb_info_file,
)
}
for i in range(len(private_ip_trainers))
]
return run_args_trainers
def return_args_trainers_bagpipe(
private_ip_trainers, private_ip_oracle_cacher, log_file_name, num_iters
):
"""
Arguments for trainers
"""
run_args_trainers = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_trainers.sh {} {} {} {} {} {} {} {}".format(
i + 2,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
i,
len(private_ip_trainers),
private_ip_trainers[0],
log_file_name,
num_iters,
)
}
for i in range(len(private_ip_trainers))
]
return run_args_trainers
def return_args_trainers_bagpipe_no_cache_no_prefetch(
private_ip_trainers, private_ip_oracle_cacher, log_file_name, num_iters
):
"""
Arguments for trainers
"""
run_args_trainers = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_trainers_no_cache_no_prefetch.sh {} {} {} {} {} {} {} {}".format(
i + 2,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
i,
len(private_ip_trainers),
private_ip_trainers[0],
log_file_name,
num_iters,
)
}
for i in range(len(private_ip_trainers))
]
return run_args_trainers
def return_args_trainers_bagpipe_fae(
private_ip_trainers, private_ip_oracle_cacher, log_file_name, num_iters
):
"""
Arguments for trainers
"""
run_args_trainers = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && git checkout fae && bash run_fae_trainer.sh {} {} {} {} {} {} {} {}".format(
i + 2,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
i,
len(private_ip_trainers),
private_ip_trainers[0],
log_file_name,
num_iters,
)
}
for i in range(len(private_ip_trainers))
]
return run_args_trainers
def return_args_emb_server(private_ip_trainers, private_ip_oracle_cacher):
"""
Return arguments for embedding server
"""
run_args_emb_server = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_embedding_server.sh {} {} {}".format(
1, (len(private_ip_trainers) + 2), private_ip_oracle_cacher
)
}
]
return run_args_emb_server
def return_args_emb_server_fae(private_ip_trainers, private_ip_oracle_cacher):
"""
Return arguments for embedding server
"""
run_args_emb_server = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && git checkout fae && bash run_embedding_server.sh {} {} {}".format(
1, (len(private_ip_trainers) + 2), private_ip_oracle_cacher
)
}
]
return run_args_emb_server
def return_args_oracle_server(
private_ip_trainers,
private_ip_oracle_cacher,
):
"""
Return arguments for oracle server
"""
run_args_oracle_cacher = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_oracle_server.sh {} {} {} {}".format(
0,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
len(private_ip_trainers),
)
}
]
return run_args_oracle_cacher
def return_args_oracle_server_no_cache_no_prefetch(
private_ip_trainers,
private_ip_oracle_cacher,
):
"""
Return arguments for oracle server
"""
run_args_oracle_cacher = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && bash run_oracle_server_no_cache_no_prefetch.sh {} {} {} {}".format(
0,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
len(private_ip_trainers),
)
}
]
return run_args_oracle_cacher
def return_args_oracle_server_fae(
private_ip_trainers,
private_ip_oracle_cacher,
):
"""
Return arguments for oracle server
"""
run_args_oracle_cacher = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && git checkout fae && bash run_fae_oracle_cacher.sh {} {} {} {}".format(
0,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
len(private_ip_trainers),
)
}
]
return run_args_oracle_cacher
def return_args_original_dlrm_training(private_ip_trainers, log_file_name, stop_iter):
"""
Returns arguments for original DLRM training
"""
run_args_distributed = [
{
"cmd": "rm -rf dlrm_original && git clone [email protected]:iidsample/dlrm_original.git && cd dlrm_original && bash run_dlrm.sh {} {} {} {} {}".format(
i,
len(private_ip_trainers),
private_ip_trainers[0],
log_file_name,
stop_iter,
)
}
for i in range(len(private_ip_trainers))
]
return run_args_distributed
def return_data_move_args_original(private_ip_trainers):
run_args_move_files = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/kaggle_criteo_info ./ && aws s3 cp s3://recommendation-data-bagpipe/kaggle_criteo_weekly.txt ./ && time wc -l /home/ubuntu/kaggle_criteo_weekly.txt"
}
for i in range(len(private_ip_trainers))
]
return run_args_move_files
def return_args_donwload_fae_kaggle_trainers(private_ip_trainers):
run_args_download_files = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/fae_hot_cold_bsize16384/hot_emb_dict.npz ./ && aws s3 cp s3://recommendation-data-bagpipe/fae_hot_cold_bsize16384/train_hot.npz ./ && aws s3 cp s3://recommendation-data-bagpipe/fae_hot_cold_bsize16384/train_normal.npz ./"
}
for i in range(len(private_ip_trainers))
]
return run_args_download_files
def return_args_donwload_fae_kaggle_oracle():
run_args_download_files = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/fae_hot_cold_bsize16384/hot_emb_dict.npz ./ && aws s3 cp s3://recommendation-data-bagpipe/fae_hot_cold_bsize16384/train_hot.npz ./ && aws s3 cp s3://recommendation-data-bagpipe/fae_hot_cold_bsize16384/train_normal.npz ./"
}
]
return run_args_download_files
def return_args_download_avazu(private_ip_trainers):
run_args_download_movielen = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/terabyte_sample.npz ./ && aws s3 cp s3://recommendation-data-bagpipe/criteo_emb_info ./"
}
for i in range(len(private_ip_trainers))
]
return run_args_download_movielen
def return_args_download_movielen(private_ip_trainers):
run_args_download_movielen = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/movielen_train.npz ./ && aws s3 cp s3://recommendation-data-bagpipe/movielen_emb_info ./"
}
for i in range(len(private_ip_trainers))
]
return run_args_download_movielen
def return_args_original_dlrm_training_movielens(
private_ip_trainers, log_file_name, stop_iter
):
"""
Returns arguments for original DLRM training
"""
run_args_distributed = [
{
"cmd": "rm -rf dlrm_original && git clone [email protected]:iidsample/dlrm_original.git && cd dlrm_original && bash run_dlrm_other_datasets.sh {} {} {} {} {}".format(
i,
len(private_ip_trainers),
private_ip_trainers[0],
log_file_name,
stop_iter,
)
}
for i in range(len(private_ip_trainers))
]
return run_args_distributed
def launch_instances_on_demand(launch_cfg):
client = boto3.client("ec2", region_name=launch_cfg["region"])
ec2 = boto3.resource("ec2", region_name=launch_cfg["region"])
instance_lifecycle = launch_cfg["method"]
instance_count = launch_cfg["instance_count"]
if instance_lifecycle == "onDemand":
print("in")
response = client.run_instances(
MaxCount=launch_cfg["instance_count"],
MinCount=launch_cfg["instance_count"],
ImageId=launch_cfg["ami_id"],
InstanceType=launch_cfg["instance_type"],
KeyName=launch_cfg["key_name"],
EbsOptimized=True,
IamInstanceProfile={"Name": launch_cfg["iam_role"]},
# Placement={"AvailabilityZone": launch_cfg["az"]},
# Placement={"GroupName": launch_cfg["GroupName"]},
SecurityGroups=launch_cfg["security_group"],
)
else:
print("Not a valid launch method")
sys.exit()
instance_ids = list()
for request in response["Instances"]:
instance_ids.append(request["InstanceId"])
time.sleep(5)
loop = True
while loop:
loop = False
print("Instance ids {}".format(instance_ids))
response = client.describe_instance_status(
InstanceIds=instance_ids, IncludeAllInstances=True
)
# print("Response {}".format(response))
for status in response["InstanceStatuses"]:
print("Status {}".format(status["InstanceState"]["Name"]))
if status["InstanceState"]["Name"] != "running":
loop = True
time.sleep(5)
print("All instances are running ...")
instance_collection = ec2.instances.filter(
Filters=[{"Name": "instance-id", "Values": instance_ids}]
)
print("Instance collection {}".format(instance_collection))
private_ip = []
public_ip = []
for instance in instance_collection:
print(instance.private_ip_address)
private_ip.append(instance.private_ip_address)
print(instance.public_ip_address)
public_ip.append(instance.public_ip_address)
return (private_ip, public_ip, instance_ids)
def launch_instances_spot(launch_cfg):
client = boto3.client("ec2", region_name=launch_cfg["region"])
ec2 = boto3.resource("ec2", region_name=launch_cfg["region"])
instance_lifecycle = launch_cfg["method"]
instance_count = launch_cfg["instance_count"]
launch_dict = {
"KeyName": launch_cfg["key_name"],
"ImageId": launch_cfg["ami_id"],
"InstanceType": launch_cfg["instance_type"],
"Placement": {"AvailabilityZone": launch_cfg["az"]},
# "Placement": {"GroupName": launch_cfg["GroupName"]},
"SecurityGroups": ["pytorch-distributed"],
"IamInstanceProfile": {"Name": launch_cfg["iam_role"]},
}
if instance_lifecycle == "spot":
response = client.request_spot_instances(
InstanceCount=launch_cfg["instance_count"],
LaunchSpecification=launch_dict,
SpotPrice=launch_cfg["spot_price"],
)
print(response)
else:
print("Spot is not being used")
sys.exit()
request_ids = list()
for request in response["SpotInstanceRequests"]:
request_ids.append(request["SpotInstanceRequestId"])
fulfilled_instances = list()
loop = True
print("Waiting for requests to fulfill")
time.sleep(5)
while loop:
request = client.describe_spot_instance_requests(
SpotInstanceRequestIds=request_ids
)
for req in request["SpotInstanceRequests"]:
print(req)
if req["State"] in ["closed", "cancelled", "failed"]:
print("{}:{}".format(req["SpotInstanceRequestId"], req["State"]))
loop = False
break
if "InstanceId" in req and req["InstanceId"]:
fulfilled_instances.append(req["InstanceId"])
print(req["InstanceId"] + "running...")
if len(fulfilled_instances) == launch_cfg["instance_count"]:
print("All requested instances are fulfilled")
break
time.sleep(5)
if loop == False:
print("Unable to fulfill all requested instance ..")
sys.exit()
while loop:
loop = False
response = client.describe_instance_status(InstanceIds=fulfilled_instances)
for status in response["InstanceStatuses"]:
if status["InstanceType"]["Name"] != "running":
loop = True
print("All instances are running ..")
# getting host keys
instance_collection = ec2.instances.filter(
Filters=[{"Name": "instance-id", "Values": fulfilled_instances}]
)
private_ip = []
public_ip = []
for instance in instance_collection:
print(instance.private_ip_address)
private_ip.append(instance.private_ip_address)
print(instance.public_ip_address)
public_ip.append(instance.public_ip_address)
return (private_ip, public_ip, fulfilled_instances)
def terminate_instances(instance_id, launch_cfg):
print("Terminating instances ....")
client = boto3.client("ec2", region_name=launch_cfg["region"])
ec2 = boto3.resource("ec2", region_name=launch_cfg["region"])
instance_collection = ec2.instances.filter(
Filters=[{"Name": "instance-id", "Values": instance_id}]
)
for instance in instance_collection:
instance.terminate()
print("Bye Bye instances ...")
def get_az(instance_id, launch_cfg):
client = boto3.client("ec2", region_name=launch_cfg["region"])
ec2 = boto3.resource("ec2", region_name=launch_cfg["region"])
response = client.describe_instance_status(
InstanceIds=[instance_id], IncludeAllInstances=True
)
for status in response["InstanceStatuses"]:
az_val = status["AvailabilityZone"]
return az_val
def other_dataset_args_trainers_bagpipe_fae(
private_ip_trainers,
private_ip_oracle_cacher,
log_file_name,
num_iters,
emb_info_file,
hot_emb_dict,
):
"""
Arguments for trainers
"""
run_args_trainers = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && git checkout fae && bash run_fae_trainer_other_datasets.sh {} {} {} {} {} {} {} {} {} {}".format(
i + 2,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
i,
len(private_ip_trainers),
private_ip_trainers[0],
log_file_name,
num_iters,
emb_info_file,
hot_emb_dict,
)
}
for i in range(len(private_ip_trainers))
]
return run_args_trainers
def other_dataset_args_emb_server_fae(
private_ip_trainers, private_ip_oracle_cacher, emb_info_file
):
"""
Return arguments for embedding server
"""
run_args_emb_server = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && git checkout fae && bash run_embedding_server.sh {} {} {}".format(
1, (len(private_ip_trainers) + 2), private_ip_oracle_cacher
)
}
]
return run_args_emb_server
def other_data_args_oracle_server_fae(
private_ip_trainers,
private_ip_oracle_cacher,
processed_csv,
train_normal_file,
train_hot_file,
train_hot_dict,
emb_info_file,
):
"""
Return arguments for oracle server
"""
run_args_oracle_cacher = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && git checkout fae && bash run_fae_oracle_cacher_other_datasets.sh {} {} {} {} {} {} {} {} {}".format(
0,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
len(private_ip_trainers),
processed_csv,
train_normal_file,
train_hot_file,
train_hot_dict,
emb_info_file,
)
}
]
return run_args_oracle_cacher
def other_data_args_emb_server_fae(
private_ip_trainers, private_ip_oracle_cacher, emb_info_file
):
"""
Return arguments for embedding server
"""
run_args_emb_server = [
{
"cmd": "rm -rf bagpipe && git clone [email protected]:iidsample/bagpipe.git && cd bagpipe && git checkout fae && bash run_embedding_server_other_datasets.sh {} {} {} {}".format(
1,
(len(private_ip_trainers) + 2),
private_ip_oracle_cacher,
emb_info_file,
)
}
]
return run_args_emb_server
run_args_ebs_warmnup = [
{
"cmd": "aws s3 cp s3://recommendation-data-bagpipe/kaggle_criteo_info ./ && aws s3 cp s3://recommendation-data-bagpipe/kaggle_criteo_weekly.txt ./ && time wc -l /home/ubuntu/kaggle_criteo_weekly.txt"
}
]
def run_large_scale():
launch_cfg = {
"name": "recommendation-setup",
"key_name": "saurabh_oregon_pc",
"key_path": "/home/saurabh/credentials/cs-shivaram/saurabh_oregon_pc.pem",
"region": "us-west-2",
"method": "onDemand", # onDemand
"az": "us-west-2c",
"GroupName": "distributed-training",
# "ami_id": "ami-0f07487e2b2761b0a", # nv old
# "ami_id": "ami-04e4121bc8f056792", # oregon old
"ami_id": "ami-00cfdc3a2d9df3424",
"ssh_username": "ubuntu",
"iam_role": "ec2-s3-final",
"instance_type": "p3.2xlarge",
# "instance_type": "t2.medium",
"instance_count": 2,
"spot_price": "4.5",
"security_group": ["pytorch-distributed"],
}
# launching trainers
launch_cfg["instance_type"] = "p3.2xlarge"
launch_cfg["method"] = "spot"
launch_cfg["instance_count"] = 8
(
private_ip_trainers,
public_ip_trainers,
instance_ids_trainers,
) = launch_instances_spot(launch_cfg)
# launching oracle cacher
p3_az = get_az(instance_ids_trainers[0], launch_cfg)
launch_cfg["instance_type"] = "c5.18xlarge"
launch_cfg["spot_price"] = "2.5"
launch_cfg["method"] = "spot"
launch_cfg["instance_count"] = 1
launch_cfg["az"] = p3_az
private_ips, public_ips, instance_ids = launch_instances_spot(launch_cfg)
private_ip_oracle_cacher = private_ips[0]
public_ip_oracle_cacher = public_ips[0]
instance_id_oracle_cacher = instance_ids[0]
# launch emb server
launch_cfg["instance_type"] = "c5.18xlarge"
launch_cfg["spot_price"] = "2.5"
launch_cfg["method"] = "spot"
launch_cfg["instance_count"] = 1
launch_cfg["az"] = p3_az
# launched emb server
private_ips, public_ips, instance_ids = launch_instances_spot(launch_cfg)
private_ip_emb_server = private_ips[0]
public_ip_emb_server = public_ips[0]
instance_id_emb_server = instance_ids[0]
# client oracle cache
client_oracle_cacher = ParallelSSHClient(
[public_ip_oracle_cacher], user="ubuntu", pkey=launch_cfg["key_path"]
)
# trainer client
client_trainers = ParallelSSHClient(
public_ip_trainers, user="ubuntu", pkey=launch_cfg["key_path"]
)
# client for emb server
client_emb_server = ParallelSSHClient(
[public_ip_emb_server], user="ubuntu", pkey=launch_cfg["key_path"]
)
if False:
# =================Run bagpipe avazu fae =====================
log_file_name = "avazu_bagpip_fae_prefetch_16384_{}_machines_1500_iter".format(
len(private_ip_trainers)
)
emb_info_file = "/home/ubuntu/avazu_emb_info"
avazu_processed_csv = "/home/ubuntu/avazu_data"
avazu_normal_csv = "/home/ubuntu/avazu_normal.npz"
avazu_hot_csv = "/home/ubuntu/avazu_hot.npz"
avazu_hot_dict = "/home/ubuntu/avazu_hot_emb_dict.npz"
run_args_trainers = other_dataset_args_trainers_bagpipe_fae(
private_ip_trainers,
private_ip_oracle_cacher,
log_file_name,
1500,
emb_info_file,
avazu_hot_dict,
)
run_args_oracle_cacher = other_data_args_oracle_server_fae(
private_ip_trainers,
private_ip_oracle_cacher,
avazu_processed_csv,
avazu_normal_csv,
avazu_hot_csv,
avazu_hot_dict,
emb_info_file,
)
run_args_emb_server = other_data_args_emb_server_fae(
private_ip_trainers, private_ip_oracle_cacher, emb_info_file
)
download_single = data_download_avazu_single_machine_fae()
download_trainers = data_download_avazu_trainer_machine_fae(private_ip_trainers)
print("Download single {}".format(download_single))
print("Download trainers {}".format(download_trainers))
output_download_tr = client_trainers.run_command(
"%(cmd)s", host_args=download_trainers
)
output_download_single_mach = client_emb_server.run_command(
"%(cmd)s", host_args=download_single
)
output_download_single_mach = client_oracle_cacher.run_command(
"%(cmd)s", host_args=download_single
)
for hosts_out in output_download_tr:
for line in hosts_out.stdout:
print(line)
print("Run args trainers {}".format(run_args_trainers))
print("Run args emb server {}".format(run_args_emb_server))
print("Runs args oracle {}".format(run_args_oracle_cacher))
trainer_output = client_trainers.run_command(
"%(cmd)s", host_args=run_args_trainers
)
emb_server_out = client_emb_server.run_command(
"%(cmd)s", host_args=run_args_emb_server
)
oracle_server_out = client_oracle_cacher.run_command(
"%(cmd)s", host_args=run_args_oracle_cacher
)
for hosts_out in oracle_server_out:
for line in hosts_out.stdout:
print(line)
if False:
# =========Run bagpipe avazu cache and prefetch ============
log_file_name = "avazu_bagpipe_prefetch_16384_{}_machines_2000_iter".format(
len(private_ip_trainers)
)
emb_info_file = "/home/ubuntu/avazu_emb_info"
run_args_emb_server = other_data_args_emb_server(
private_ip_trainers, private_ip_oracle_cacher, emb_info_file
)
csv_location = "/home/ubuntu/avazu_data"
run_args_oracle = other_data_args_oracle_server(
private_ip_trainers, private_ip_oracle_cacher, csv_location, emb_info_file
)
run_args_trainers = other_data_args_trainer(
private_ip_trainers,
private_ip_oracle_cacher,
log_file_name,
2000,
emb_info_file,
)
download_trainers = data_download_avazu_trainer_machine(private_ip_trainers)
download_single_machine = data_download_avazu_single_machine()
output_download_tr = client_trainers.run_command(
"%(cmd)s", host_args=download_trainers
)
output_download_single_mach = client_emb_server.run_command(
"%(cmd)s", host_args=download_single_machine
)
output_download_single_mach = client_oracle_cacher.run_command(
"%(cmd)s", host_args=download_single_machine
)
for hosts_out in output_download_tr:
for line in hosts_out.stdout:
print(line)
print("Run args trainers {}".format(run_args_trainers))
print("Run args emb server {}".format(run_args_emb_server))
print("Runs args oracle {}".format(run_args_oracle))
trainer_output = client_trainers.run_command(
"%(cmd)s", host_args=run_args_trainers
)
emb_server_out = client_emb_server.run_command(
"%(cmd)s", host_args=run_args_emb_server
)
oracle_server_out = client_oracle_cacher.run_command(
"%(cmd)s", host_args=run_args_oracle
)
for hosts_out in trainer_output:
for line in hosts_out.stdout:
print(line)
kill_process(
client_trainers,
client_emb_server,
client_oracle_cacher,
private_ip_trainers,
)
if False:
# ========Run bagpipe avazu no cache no prefetch ===========
log_file_name = (
"avazu_bagpipe_no_cache_no_prefetch_16384_{}_machines_2000_iter".format(
len(private_ip_trainers)
)
)
emb_info_file = "/home/ubuntu/avazu_emb_info"
run_args_emb_server = other_data_args_emb_server(
private_ip_trainers, private_ip_oracle_cacher, emb_info_file
)
csv_location = "/home/ubuntu/avazu_data"
run_args_oracle = other_data_args_oracle_server_no_cache_no_prefetch(
private_ip_trainers, private_ip_oracle_cacher, csv_location, emb_info_file
)
run_args_trainers = other_data_args_trainer_no_cache_no_prefetch(
private_ip_trainers,
private_ip_oracle_cacher,
log_file_name,
2000,
emb_info_file,
)
download_trainers = data_download_avazu_trainer_machine(private_ip_trainers)
download_single_machine = data_download_avazu_single_machine()
output_download_tr = client_trainers.run_command(
"%(cmd)s", host_args=download_trainers
)
output_download_single_mach = client_emb_server.run_command(
"%(cmd)s", host_args=download_single_machine
)
output_download_single_mach = client_oracle_cacher.run_command(
"%(cmd)s", host_args=download_single_machine