How severe does this issue affect your experience of using Ray?
- Low: It annoys or frustrates me for a moment.
Our project to schedule 100 process on 1 machine into 1000 process on 10 machine.
Each process is to run a c++ execution binary with different params.
here is a sample of code.
from multiprocessing import Pool import subprocess def run_binary(params): subprocess.run([binary_path, params], stdout=output_fd, stderr=error_fd) with Pool(100) as p: p.map(run_binary, params)
I try to change the code into something like this.
with Pool(ray_address='ray://head_server_Ip:10001') as p: p.map(run_binary, params)
I try to follow this Environment Dependencies — Ray 1.13.0 to config the path for the binary and local environment.
The issue I meet is, the code is stuck on the sync the environment. and never run the binary on the worker after I wait for 20 mins.