wehub-resource-sync
593b94c120
pytest / Unit Tests (push) Has been cancelled
pytest / Integration (integration_tests_a) (push) Has been cancelled
pytest / Integration (integration_tests_b) (push) Has been cancelled
pytest / Integration (integration_tests_c) (push) Has been cancelled
pytest / Integration (integration_tests_d) (push) Has been cancelled
pytest / Integration (integration_tests_e) (push) Has been cancelled
pytest / Integration (integration_tests_f) (push) Has been cancelled
pytest / Integration (integration_tests_g) (push) Has been cancelled
pytest / Integration (integration_tests_h) (push) Has been cancelled
pytest / Integration (integration_tests_i) (push) Has been cancelled
pytest / Integration (integration_tests_j) (push) Has been cancelled
pytest / Distributed (distributed_a) (push) Has been cancelled
pytest / Distributed (distributed_b) (push) Has been cancelled
pytest / Distributed (distributed_c) (push) Has been cancelled
pytest / Distributed (distributed_d) (push) Has been cancelled
pytest / Distributed (distributed_e) (push) Has been cancelled
pytest / Distributed (distributed_f) (push) Has been cancelled
pytest / Minimal Install (push) Has been cancelled
pytest / Event File (push) Has been cancelled
pytest (slow) / py-slow (push) Has been cancelled
Publish JSON Schema / publish-schema (push) Has been cancelled
88 lines
2.6 KiB
Python
88 lines
2.6 KiB
Python
"""Deploy a trained Ludwig model with Ray Serve.
|
|
|
|
Usage:
|
|
# Single replica, CPU
|
|
python deploy.py --model_path ./results/my_model
|
|
|
|
# Two GPU replicas
|
|
python deploy.py \
|
|
--model_path ./results/my_model \
|
|
--num_replicas 2 \
|
|
--gpu
|
|
|
|
# Custom name / port
|
|
python deploy.py \
|
|
--model_path ./results/my_model \
|
|
--name sentiment \
|
|
--port 8080
|
|
|
|
After deploying, send predictions:
|
|
|
|
# Single record
|
|
curl -s -X POST http://localhost:8000/ludwig \\
|
|
-H "Content-Type: application/json" \\
|
|
-d '{"text": "I love this product!", "stars": 5}'
|
|
|
|
# Batch (list of records)
|
|
curl -s -X POST http://localhost:8000/ludwig \\
|
|
-H "Content-Type: application/json" \\
|
|
-d '[{"text": "great"}, {"text": "terrible"}]'
|
|
"""
|
|
|
|
import argparse
|
|
import sys
|
|
import time
|
|
|
|
|
|
def parse_args(argv=None):
|
|
p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
|
|
p.add_argument("--model_path", required=True, help="Path to trained Ludwig model directory")
|
|
p.add_argument("--name", default="ludwig", help="Ray Serve application name (also URL prefix)")
|
|
p.add_argument("--num_replicas", type=int, default=1, help="Number of Ray Serve replicas")
|
|
p.add_argument("--gpu", action="store_true", help="Request 1 GPU per replica")
|
|
p.add_argument("--port", type=int, default=8000, help="Ray dashboard / Serve port")
|
|
p.add_argument("--block", action="store_true", help="Keep running until interrupted")
|
|
return p.parse_args(argv)
|
|
|
|
|
|
def main(argv=None):
|
|
args = parse_args(argv)
|
|
|
|
import ray
|
|
from ray import serve
|
|
|
|
from ludwig.serve_ray_serve import deploy_ludwig_model
|
|
|
|
ray_actor_options = {"num_gpus": 1} if args.gpu else {}
|
|
|
|
print("Initialising Ray …")
|
|
ray.init(ignore_reinit_error=True)
|
|
|
|
print(f"Deploying Ludwig model from {args.model_path!r} …")
|
|
deploy_ludwig_model(
|
|
model_path=args.model_path,
|
|
name=args.name,
|
|
num_replicas=args.num_replicas,
|
|
ray_actor_options=ray_actor_options,
|
|
)
|
|
|
|
url = f"http://localhost:{args.port}/{args.name}"
|
|
print(f"\nDeployment live at: {url}")
|
|
print(f" POST {url} with a JSON record or list of records to get predictions.")
|
|
|
|
if args.block:
|
|
print("Press Ctrl-C to stop …")
|
|
try:
|
|
while True:
|
|
time.sleep(1)
|
|
except KeyboardInterrupt:
|
|
print("\nShutting down Ray Serve …")
|
|
serve.shutdown()
|
|
ray.shutdown()
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|