# Python API for creating Ray autoscaling cluster? **URL:** \ **Category:** Ray Clusters\ **Created:** [April 29, 2021, 5:54pm UTC](https://discuss.ray.io/t/python-api-for-creating-ray-autoscaling-cluster/1980 "2021-04-29T17:54:42Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![jennakwon06](https://avatars.discourse-cdn.com/v4/letter/j/b19c9b/32.png) [@jennakwon06](https://discuss.ray.io/u/jennakwon06)\ **Post date:** [April 29, 2021, 5:54pm UTC](https://discuss.ray.io/t/python-api-for-creating-ray-autoscaling-cluster/1980/1 "2021-04-29T17:54:42Z")
Hello! I was wondering if there’s a way to, or if there’s a plan to, supporting Python APIs for spinning up multi-node autoscaling Ray clusters. I’m looking for something like… > script.py: ```auto cluster_info = ray.create_cluster("cluster.yaml") # call blocks until cluster is instantiated head_node_ip = cluster_info["head_node_ip"] ray.init(address="auto") # connect to cluster created in this script # do things with Ray API so that compute happens on the distributed Ray cluster ``` ---
**Author:** ![lshao-ts](https://avatars.discourse-cdn.com/v4/letter/l/5f8ce5/32.png) [@lshao-ts](https://discuss.ray.io/u/lshao-ts)\ **Post date:** [September 24, 2021, 9:53pm UTC](https://discuss.ray.io/t/python-api-for-creating-ray-autoscaling-cluster/1980/2 "2021-09-24T21:53:43Z")
The following works: - In a python subprocess, run `ray.scripts.scripts.start` with the arguments as if you call `ray start --head` in the commandline. Add the `--block` flag to make it blocking in the sub process. - In the main process, wait for the `ray_current_cluster` file to appear in the temp directory. It contains the ip and port to connect to ray. The temp directory can be specified when you call `ray start --head` above. - Read the `ray_current_cluster` file and then call `ray.init(address=...)`. - Run your workflow. - Call `ray.shutdown()` in the main process, then kill the subprocess.