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import argparse
import time
import os
import subprocess
from datetime import datetime
from pathlib import Path
import boto3
import sagemaker
from sagemaker.pytorch import PyTorch
NAME = "openlm-main"
INSTANCE_MAPPER = {
"p4": "ml.p4d.24xlarge",
"p4de": "ml.p4de.24xlarge",
"p5": "ml.p5.48xlarge",
}
def run_command(command):
print(f"=> {command}")
subprocess.run(command, shell=True, check=True)
def get_image(user, instance_type, build_type=None, profile="default", region="us-east-1"):
os.environ["AWS_PROFILE"] = f"{profile}"
account = subprocess.getoutput(
f"aws --region {region} --profile {profile} sts get-caller-identity --query Account --output text"
)
docker_dir = Path(__file__).parent
if instance_type in ("p4", "p4de"):
algorithm_name = f"{user}-{NAME}-p4"
dockerfile_base = docker_dir / "Dockerfile"
dockerfile_update = docker_dir / "Dockerfile_update"
elif instance_type == "p5":
algorithm_name = f"{user}-{NAME}-p5"
dockerfile_base = docker_dir / "Dockerfile"
dockerfile_update = docker_dir / "Dockerfile_update"
else:
raise ValueError(f"Unknown instance_type: {instance_type}")
fullname = f"{account}.dkr.ecr.{region}.amazonaws.com/{algorithm_name}:latest"
if build_type is None:
return fullname
login_cmd = f"aws ecr get-login-password --region {region} --profile {profile} | docker login --username AWS --password-stdin"
if build_type == "full":
print("Building container")
commands = [
# Log in to Sagemaker account to get image.
f"{login_cmd} 763104351884.dkr.ecr.{region}.amazonaws.com",
f"docker build --progress=plain -f {dockerfile_base} --build-arg AWS_REGION={region} -t {algorithm_name} .",
f"docker tag {algorithm_name} {fullname}",
f"{login_cmd} {fullname}",
(
f"aws --region {region} ecr describe-repositories --repository-names {algorithm_name} || "
f"aws --region {region} ecr create-repository --repository-name {algorithm_name}"
),
]
elif build_type == "update":
print("Updating container")
commands = [
f"docker build --progress=plain -f {dockerfile_update} --build-arg BASE_DOCKER={algorithm_name} -t {algorithm_name} .",
f"docker tag {algorithm_name} {fullname}",
f"{login_cmd} {fullname}",
]
else:
raise ValueError(f"Unknown build_type: {build_type}")
# Create command, making sure to exit if any part breaks.
command = "\n".join([f"{x} || exit 1" for x in commands])
run_command(command)
run_command(f"docker push {fullname}")
print("Sleeping for 5 seconds to ensure push succeeded")
time.sleep(5)
return f"{account}.dkr.ecr.{region}.amazonaws.com/{algorithm_name}:latest"
def main():
# Use first line of file docstring as description if it exists.
parser = argparse.ArgumentParser()
parser.add_argument("--build-type", choices=["full", "update"], help="Build image from scratch")
parser.add_argument("--local", action="store_true")
parser.add_argument("--user", required=True, help="User name")
parser.add_argument("--cfg-path", required=True, help="Launch config")
# AWS profile args
parser.add_argument("--region", default="us-east-1", help="AWS region")
parser.add_argument("--profile", default="default", help="AWS profile to use")
parser.add_argument("--arn", default=None, help="If None, reads from SAGEMAKER_ARN env var")
parser.add_argument(
"--s3-remote-sync", default=None, help="S3 path to sync to. If none, reads from S3_REMOTE_SYNC env var"
)
# Instance args
parser.add_argument("--instance-count", default=1, type=int, help="Number of instances")
parser.add_argument("--instance-type", default="p4de", choices=list(INSTANCE_MAPPER.keys()))
parser.add_argument("--spot-instance", action="store_true")
args = parser.parse_args()
main_after_setup_move(args)
def main_after_setup_move(args):
if args.arn is None:
assert "SAGEMAKER_ARN" in os.environ, "Please specify --arn or set the SAGEMAKER_ARN environment variable"
args.arn = os.environ["SAGEMAKER_ARN"]
if args.s3_remote_sync is None:
assert (
"S3_REMOTE_SYNC" in os.environ
), "Please specify --s3-remote-sync or set the S3_REMOTE_SYNC environment variable"
args.s3_remote_sync = os.environ["S3_REMOTE_SYNC"]
image = get_image(
args.user,
args.instance_type,
region=args.region,
build_type=args.build_type,
profile=args.profile,
)
##########
# Create session and make sure of account and region
##########
sagemaker_session = sagemaker.Session(boto_session=boto3.session.Session(region_name=args.region))
if args.local:
from sagemaker.local import LocalSession
sagemaker_session = LocalSession()
role = args.arn
# provide a pre-existing role ARN as an alternative to creating a new role
role_name = role.split(["/"][-1])
print(f"SageMaker Execution Role:{role}")
print(f"The name of the Execution role: {role_name[-1]}")
client = boto3.client("sts")
account = client.get_caller_identity()["Account"]
print(f"AWS account:{account}")
session = boto3.session.Session()
region = session.region_name
print(f"AWS region:{region}")
##########
# Configure the training
##########
base_job_name = f"{args.user.replace('.', '-')}-{NAME}"
checkpoint_local_path = "/opt/ml/checkpoints"
def get_job_name(base):
now = datetime.now()
# Format example: 2023-03-03-10-14-02-324
now_ms_str = f"{now.microsecond // 1000:03d}"
date_str = f"{now.strftime('%Y-%m-%d-%H-%M-%S')}-{now_ms_str}"
job_name = "_".join([base, date_str])
return job_name
job_name = get_job_name(base_job_name)
output_root = f"{args.s3_remote_sync}/sagemaker/{args.user}/{NAME}/"
output_s3 = os.path.join(output_root, job_name)
estimator = PyTorch(
entry_point="open_lm/main.py",
sagemaker_session=sagemaker_session,
base_job_name=base_job_name,
hyperparameters={"config": args.cfg_path},
role=role,
image_uri=image,
instance_count=args.instance_count,
instance_type="local_gpu" if args.local else INSTANCE_MAPPER[args.instance_type],
train_use_spot_instances=args.spot_instance,
output_path=output_s3,
job_name=job_name,
checkpoint_s3_uri=None if args.local else f"{output_s3}/checkpoint",
checkpoint_local_path=None if args.local else checkpoint_local_path,
code_location=output_s3,
# Training using SMDataParallel Distributed Training Framework
distribution={"torch_distributed": {"enabled": True}},
# Max run 5 days
max_run=5 * 24 * 60 * 60,
max_wait=5 * 24 * 60 * 60 if args.spot_instance else None,
input_mode="FastFile",
# environment={"TORCH_DISTRIBUTED_DEBUG": "DETAIL", "TORCH_CPP_LOG_LEVEL": "INFO"},
environment={"SM_USE_RESERVED_CAPACITY": "1"},
keep_alive_period_in_seconds=30 * 60 if not args.spot_instance else None, # 30 minutes
)
estimator.fit()
if __name__ == "__main__":
main()
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