In that case, you can add "dashboard-address": "0.0.0.0" to ray_args when creating the cluster, to bind the dashboard to all interfaces, or select a visible address if one knows it. If the port forwarding fails, it is possible that the interface is not reachable. PORT_NUMBER (by adding "dashboard-port": str(PORT_NUMBER) to ray_args when creating the cluster) and the command changed accordingly. If that port is not free, we can bind the dashboard to another port, e.g. We are using 8265 as a port, which is the default dashboard port. There are two things to know if something does not work: Then, from a browser on the local system, we can go to the address to see the dashboard. For previous versions, you have to add password=None to the Ra圜luster constructor. Note that this approach only works with ray>=1.6. On a Cray XC, for example, this will be ipogif0. ib, eth0, …), we can specify to which one the Ray nodes should bind by setting the interface argument it is recommended to always choose the one offering the best performances. If the hosts are attached to multiple interfaces (e.g. Notice that the cluster will be password-protected (the password, generated internally, will be shared with worker nodes). If we wanted to let it use all the CPUs, it would suffice not to pass ray_args. We are limiting the number each ray node can use to CPUS_PER_WORKER. Next, we will instance a Ra圜luster to connect to the cluster. The output files generated by the experment will be located in the ray-cluster directory. By doing so we will create a ray-cluster directory (relative to the path from where we are executing this notebook). Now, we instance a SmartSim experiment with the name "ray-cluster", which we will spin up the Ray cluster. Import numpy as np import os import ray from ray import tune import ray.util from smartsim import Experiment from import Ra圜luster NUM_NODES = 3 CPUS_PER_WORKER = 18 LAUNCHER = 'slurm'
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