Ray Job Dispatch and Tuning

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import argparse
import ray
import util
from ray.util.scheduling_strategies import NodeAffinitySchedulingStrategy
"""
This script dispatches sub-job(s) (individual jobs, use `tuner.py` for tuning jobs)
to worker(s) on GPU-enabled node(s) of a specific cluster as part of an resource-wrapped aggregate
job. If no desired compute resources for each sub-job are specified,
this script creates one worker per available node for each node with GPU(s) in the cluster.
If the desired resources for each sub-job is specified,
the maximum number of workers possible with the desired resources are created for each node
with GPU(s) in the cluster. It is also possible to split available node resources for each node
into the desired number of workers with the `--num_workers` flag, to be able to easily
parallelize sub-jobs on multi-GPU nodes. Due to Isaac Lab requiring a GPU,
this ignores all CPU only nodes such as loggers.
Sub-jobs are matched with node(s) in a cluster via the following relation:
sorted_nodes = Node sorted by descending GPUs, then descending CPUs, then descending RAM, then node ID
node_submitted_to = sorted_nodes[job_index % total_node_count]
To check the ordering of sorted nodes, supply the `--test` argument and run the script.
Sub-jobs are separated by the + delimiter. The `--sub_jobs` argument must be the last
argument supplied to the script.
If there is more than one available worker, and more than one sub-job,
sub-jobs will be executed in parallel. If there are more sub-jobs than workers, sub-jobs will
be dispatched to workers as they become available. There is no limit on the number
of sub-jobs that can be near-simultaneously submitted.
This script is meant to be executed on a Ray cluster head node as an aggregate cluster job.
To submit aggregate cluster jobs such as this script to one or more remote clusters,
see `../submit_isaac_ray_job.py`.
KubeRay clusters on Google GKE can be created with `../launch.py`
Usage:
~~~bash
# **Ensure that sub-jobs are separated by the `+` delimiter.**
# Generic Templates-----------------------------------
./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py -h
# No resource isolation; no parallelization:
./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py
--sub_jobs <JOB0>+<JOB1>+<JOB2>
# Automatic Resource Isolation; Example A: needed for parallelization
./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py \
--num_workers <NUM_TO_DIVIDE_TOTAL_RESOURCES_BY> \
--sub_jobs <JOB0>+<JOB1>
# Manual Resource Isolation; Example B:  needed for parallelization
./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py --num_cpu_per_worker <CPU> \
--gpu_per_worker <GPU> --ram_gb_per_worker <RAM> --sub_jobs <JOB0>+<JOB1>
# Manual Resource Isolation; Example C: Needed for parallelization, for heterogeneous workloads
./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py --num_cpu_per_worker <CPU> \
--gpu_per_worker <GPU1> <GPU2> --ram_gb_per_worker <RAM> --sub_jobs <JOB0>+<JOB1>
# to see all arguments
./isaaclab.sh -p scripts/reinforcement_learning/ray/wrap_resources.py -h
~~~
"""
def wrap_resources_to_jobs(jobs: list[str], args: argparse.Namespace) -> None:
    """
    Provided a list of jobs, dispatch jobs to one worker per available node,
    unless otherwise specified by resource constraints.
    Args:
        jobs: bash commands to execute on a Ray cluster
        args: The arguments for resource allocation
    """
    if not ray.is_initialized():
        ray.init(address=args.ray_address, log_to_driver=True)
    job_results = []
    gpu_node_resources = util.get_gpu_node_resources(include_id=True, include_gb_ram=True)
    if any([args.gpu_per_worker, args.cpu_per_worker, args.ram_gb_per_worker]) and args.num_workers:
        raise ValueError("Either specify only num_workers or only granular resources(GPU,CPU,RAM_GB).")
    num_nodes = len(gpu_node_resources)
    # Populate arguments
    formatted_node_resources = {
        "gpu_per_worker": [gpu_node_resources[i]["GPU"] for i in range(num_nodes)],
        "cpu_per_worker": [gpu_node_resources[i]["CPU"] for i in range(num_nodes)],
        "ram_gb_per_worker": [gpu_node_resources[i]["ram_gb"] for i in range(num_nodes)],
        "num_workers": args.num_workers,  # By default, 1 worker por node
    }
    args = util.fill_in_missing_resources(args, resources=formatted_node_resources, policy=min)
    print(f"[INFO]: Number of GPU nodes found: {num_nodes}")
    if args.test:
        jobs = ["nvidia-smi"] * num_nodes
    for i, job in enumerate(jobs):
        gpu_node = gpu_node_resources[i % num_nodes]
        print(f"[INFO]: Submitting job {i + 1} of {len(jobs)} with job '{job}' to node {gpu_node}")
        print(
            f"[INFO]: Resource parameters: GPU: {args.gpu_per_worker[i]}"
            f" CPU: {args.cpu_per_worker[i]} RAM {args.ram_gb_per_worker[i]}"
        )
        print(f"[INFO] For the node parameters, creating {args.num_workers[i]} workers")
        num_gpus = args.gpu_per_worker[i] / args.num_workers[i]
        num_cpus = args.cpu_per_worker[i] / args.num_workers[i]
        memory = (args.ram_gb_per_worker[i] * 1024**3) / args.num_workers[i]
        print(f"[INFO]: Requesting {num_gpus=} {num_cpus=} {memory=} id={gpu_node['id']}")
        job = util.remote_execute_job.options(
            num_gpus=num_gpus,
            num_cpus=num_cpus,
            memory=memory,
            scheduling_strategy=NodeAffinitySchedulingStrategy(gpu_node["id"], soft=False),
        ).remote(job, f"Job {i}", args.test)
        job_results.append(job)
    results = ray.get(job_results)
    for i, result in enumerate(results):
        print(f"[INFO]: Job {i} result: {result}")
    print("[INFO]: All jobs completed.")
if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Submit multiple jobs with optional GPU testing.")
    parser = util.add_resource_arguments(arg_parser=parser)
    parser.add_argument("--ray_address", type=str, default="auto", help="the Ray address.")
    parser.add_argument(
        "--test",
        action="store_true",
        help=(
            "Run nvidia-smi test instead of the arbitrary job,"
            "can use as a sanity check prior to any jobs to check "
            "that GPU resources are correctly isolated."
        ),
    )
    parser.add_argument(
        "--sub_jobs",
        type=str,
        nargs=argparse.REMAINDER,
        help="This should be last wrapper argument. Jobs separated by the + delimiter to run on a cluster.",
    )
    args = parser.parse_args()
    if args.sub_jobs is not None:
        jobs = " ".join(args.sub_jobs)
        formatted_jobs = jobs.split("+")
    else:
        formatted_jobs = []
    print(f"[INFO]: Isaac Ray Wrapper received jobs {formatted_jobs=}")
    wrap_resources_to_jobs(jobs=formatted_jobs, args=args)

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import argparse
import importlib.util
import os
import sys
from time import sleep
import ray
import util
from ray import air, tune
from ray.tune.search.optuna import OptunaSearch
from ray.tune.search.repeater import Repeater
"""
This script breaks down an aggregate tuning job, as defined by a hyperparameter sweep configuration,
into individual jobs (shell commands) to run on the GPU-enabled nodes of the cluster.
By default, one worker is created for each GPU-enabled node in the cluster for each individual job.
To use more than one worker per node (likely the case for multi-GPU machines), supply the
num_workers_per_node argument.
Each hyperparameter sweep configuration should include the workflow,
runner arguments, and hydra arguments to vary.
This assumes that all workers in a cluster are homogeneous. For heterogeneous workloads,
create several heterogeneous clusters (with homogeneous nodes in each cluster),
then submit several overall-cluster jobs with `../submit_job.py`.
KubeRay clusters on Google GKE can be created with `../launch.py`
To report tune metrics on clusters, a running MLFlow server with a known URI that the cluster has
access to is required. For KubeRay clusters configured with `../launch.py`, this is included
automatically, and can be easily found with with `grok_cluster_with_kubectl.py`
Usage:
~~~bash
./isaaclab.sh -p scripts/reinforcement_learning/ray/tuner.py -h
# Examples
# Local
./isaaclab.sh -p scripts/reinforcement_learning/ray/tuner.py --run_mode local \
--cfg_file scripts/reinforcement_learning/ray/hyperparameter_tuning/vision_cartpole_cfg.py \
--cfg_class CartpoleTheiaJobCfg
# Remote (run grok cluster or create config file mentioned in `submit_job.py`)
./isaaclab.sh -p scripts/reinforcement_learning/ray/submit_job.py \
--aggregate_jobs tuner.py \
--cfg_file hyperparameter_tuning/vision_cartpole_cfg.py \
--cfg_class CartpoleTheiaJobCfg --mlflow_uri <MLFLOW_URI_FROM_GROK_OR_MANUAL>
~~~
"""
DOCKER_PREFIX = "/workspace/isaaclab/"
BASE_DIR = os.path.expanduser("~")
PYTHON_EXEC = "./isaaclab.sh -p"
WORKFLOW = "scripts/reinforcement_learning/rl_games/train.py"
NUM_WORKERS_PER_NODE = 1  # needed for local parallelism
class IsaacLabTuneTrainable(tune.Trainable):
    """The Isaac Lab Ray Tune Trainable.
    This class uses the standalone workflows to start jobs, along with the hydra integration.
    This class achieves Ray-based logging through reading the tensorboard logs from
    the standalone workflows. This depends on a config generated in the format of
    `JobCfg`
    """
    def setup(self, config: dict) -> None:
        """Get the invocation command, return quick for easy scheduling."""
        self.data = None
        self.invoke_cmd = util.get_invocation_command_from_cfg(cfg=config, python_cmd=PYTHON_EXEC, workflow=WORKFLOW)
        print(f"[INFO]: Recovered invocation with {self.invoke_cmd}")
        self.experiment = None
    def reset_config(self, new_config: dict):
        """Allow environments to be re-used by fetching a new invocation command"""
        self.setup(new_config)
        return True
    def step(self) -> dict:
        if self.experiment is None:  # start experiment
            # When including this as first step instead of setup, experiments get scheduled faster
            # Don't want to block the scheduler while the experiment spins up
            print(f"[INFO]: Invoking experiment as first step with {self.invoke_cmd}...")
            experiment = util.execute_job(
                self.invoke_cmd,
                identifier_string="",
                extract_experiment=True,
                persistent_dir=BASE_DIR,
            )
            self.experiment = experiment
            print(f"[INFO]: Tuner recovered experiment info {experiment}")
            self.proc = experiment["proc"]
            self.experiment_name = experiment["experiment_name"]
            self.isaac_logdir = experiment["logdir"]
            self.tensorboard_logdir = self.isaac_logdir + "/" + self.experiment_name
            self.done = False
        if self.proc is None:
            raise ValueError("Could not start trial.")
        proc_status = self.proc.poll()
        if proc_status is not None:  # process finished, signal finish
            self.data["done"] = True
            print(f"[INFO]: Process finished with {proc_status}, returning...")
        else:  # wait until the logs are ready or fresh
            data = util.load_tensorboard_logs(self.tensorboard_logdir)
            while data is None:
                data = util.load_tensorboard_logs(self.tensorboard_logdir)
                sleep(2)  # Lazy report metrics to avoid performance overhead
            if self.data is not None:
                while util._dicts_equal(data, self.data):
                    data = util.load_tensorboard_logs(self.tensorboard_logdir)
                    sleep(2)  # Lazy report metrics to avoid performance overhead
            self.data = data
            self.data["done"] = False
        return self.data
    def default_resource_request(self):
        """How many resources each trainable uses. Assumes homogeneous resources across gpu nodes,
        and that each trainable is meant for one node, where it uses all available resources."""
        resources = util.get_gpu_node_resources(one_node_only=True)
        if NUM_WORKERS_PER_NODE != 1:
            print("[WARNING]: Splitting node into more than one worker")
        return tune.PlacementGroupFactory(
            [{"CPU": resources["CPU"] / NUM_WORKERS_PER_NODE, "GPU": resources["GPU"] / NUM_WORKERS_PER_NODE}],
            strategy="STRICT_PACK",
        )
def invoke_tuning_run(cfg: dict, args: argparse.Namespace) -> None:
    """Invoke an Isaac-Ray tuning run.
    Log either to a local directory or to MLFlow.
    Args:
        cfg: Configuration dictionary extracted from job setup
        args: Command-line arguments related to tuning.
    """
    # Allow for early exit
    os.environ["TUNE_DISABLE_STRICT_METRIC_CHECKING"] = "1"
    print("[WARNING]: Not saving checkpoints, just running experiment...")
    print("[INFO]: Model parameters and metrics will be preserved.")
    print("[WARNING]: For homogeneous cluster resources only...")
    # Get available resources
    resources = util.get_gpu_node_resources()
    print(f"[INFO]: Available resources {resources}")
    if not ray.is_initialized():
        ray.init(
            address=args.ray_address,
            log_to_driver=True,
            num_gpus=len(resources),
        )
    print(f"[INFO]: Using config {cfg}")
    # Configure the search algorithm and the repeater
    searcher = OptunaSearch(
        metric=args.metric,
        mode=args.mode,
    )
    repeat_search = Repeater(searcher, repeat=args.repeat_run_count)
    if args.run_mode == "local":  # Standard config, to file
        run_config = air.RunConfig(
            storage_path="/tmp/ray",
            name=f"IsaacRay-{args.cfg_class}-tune",
            verbose=1,
            checkpoint_config=air.CheckpointConfig(
                checkpoint_frequency=0,  # Disable periodic checkpointing
                checkpoint_at_end=False,  # Disable final checkpoint
            ),
        )
    elif args.run_mode == "remote":  # MLFlow, to MLFlow server
        mlflow_callback = MLflowLoggerCallback(
            tracking_uri=args.mlflow_uri,
            experiment_name=f"IsaacRay-{args.cfg_class}-tune",
            save_artifact=False,
            tags={"run_mode": "remote", "cfg_class": args.cfg_class},
        )
        run_config = ray.train.RunConfig(
            name="mlflow",
            storage_path="/tmp/ray",
            callbacks=[mlflow_callback],
            checkpoint_config=ray.train.CheckpointConfig(checkpoint_frequency=0, checkpoint_at_end=False),
        )
    else:
        raise ValueError("Unrecognized run mode.")
    # Configure the tuning job
    tuner = tune.Tuner(
        IsaacLabTuneTrainable,
        param_space=cfg,
        tune_config=tune.TuneConfig(
            search_alg=repeat_search,
            num_samples=args.num_samples,
            reuse_actors=True,
        ),
        run_config=run_config,
    )
    # Execute the tuning
    tuner.fit()
    # Save results to mounted volume
    if args.run_mode == "local":
        print("[DONE!]: Check results with tensorboard dashboard")
    else:
        print("[DONE!]: Check results with MLFlow dashboard")
class JobCfg:
    """To be compatible with :meth: invoke_tuning_run and :class:IsaacLabTuneTrainable,
    at a minimum, the tune job should inherit from this class."""
    def __init__(self, cfg: dict):
        """
        Runner args include command line arguments passed to the task.
        For example:
        cfg["runner_args"]["headless_singleton"] = "--headless"
        cfg["runner_args"]["enable_cameras_singleton"] = "--enable_cameras"
        """
        assert "runner_args" in cfg, "No runner arguments specified."
        """
        Task is the desired task to train on. For example:
        cfg["runner_args"]["--task"] = tune.choice(["Isaac-Cartpole-RGB-TheiaTiny-v0"])
        """
        assert "--task" in cfg["runner_args"], "No task specified."
        """
        Hydra args define the hyperparameters varied within the sweep. For example:
        cfg["hydra_args"]["agent.params.network.cnn.activation"] = tune.choice(["relu", "elu"])
        """
        assert "hydra_args" in cfg, "No hyperparameters specified."
        self.cfg = cfg
if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Tune Isaac Lab hyperparameters.")
    parser.add_argument("--ray_address", type=str, default="auto", help="the Ray address.")
    parser.add_argument(
        "--cfg_file",
        type=str,
        default="hyperparameter_tuning/vision_cartpole_cfg.py",
        required=False,
        help="The relative filepath where a hyperparameter sweep is defined",
    )
    parser.add_argument(
        "--cfg_class",
        type=str,
        default="CartpoleRGBNoTuneJobCfg",
        required=False,
        help="Name of the hyperparameter sweep class to use",
    )
    parser.add_argument(
        "--run_mode",
        choices=["local", "remote"],
        default="remote",
        help=(
            "Set to local to use ./isaaclab.sh -p python, set to "
            "remote to use /workspace/isaaclab/isaaclab.sh -p python"
        ),
    )
    parser.add_argument(
        "--workflow",
        default=None,  # populated with RL Games
        help="The absolute path of the workflow to use for the experiment. By default, RL Games is used.",
    )
    parser.add_argument(
        "--mlflow_uri",
        type=str,
        default=None,
        required=False,
        help="The MLFlow Uri.",
    )
    parser.add_argument(
        "--num_workers_per_node",
        type=int,
        default=1,
        help="Number of workers to run on each GPU node. Only supply for parallelism on multi-gpu nodes",
    )
    parser.add_argument("--metric", type=str, default="rewards/time", help="What metric to tune for.")
    parser.add_argument(
        "--mode",
        choices=["max", "min"],
        default="max",
        help="What to optimize the metric to while tuning",
    )
    parser.add_argument(
        "--num_samples",
        type=int,
        default=100,
        help="How many hyperparameter runs to try total.",
    )
    parser.add_argument(
        "--repeat_run_count",
        type=int,
        default=3,
        help="How many times to repeat each hyperparameter config.",
    )
    args = parser.parse_args()
    NUM_WORKERS_PER_NODE = args.num_workers_per_node
    print(f"[INFO]: Using {NUM_WORKERS_PER_NODE} workers per node.")
    if args.run_mode == "remote":
        BASE_DIR = DOCKER_PREFIX  # ensure logs are dumped to persistent location
        PYTHON_EXEC = DOCKER_PREFIX + PYTHON_EXEC[2:]
        if args.workflow is None:
            WORKFLOW = DOCKER_PREFIX + WORKFLOW
        else:
            WORKFLOW = args.workflow
        print(f"[INFO]: Using remote mode {PYTHON_EXEC=} {WORKFLOW=}")
        if args.mlflow_uri is not None:
            import mlflow
            mlflow.set_tracking_uri(args.mlflow_uri)
            from ray.air.integrations.mlflow import MLflowLoggerCallback
        else:
            raise ValueError("Please provide a result MLFLow URI server.")
    else:  # local
        PYTHON_EXEC = os.getcwd() + "/" + PYTHON_EXEC[2:]
        if args.workflow is None:
            WORKFLOW = os.getcwd() + "/" + WORKFLOW
        else:
            WORKFLOW = args.workflow
        BASE_DIR = os.getcwd()
        print(f"[INFO]: Using local mode {PYTHON_EXEC=} {WORKFLOW=}")
    file_path = args.cfg_file
    class_name = args.cfg_class
    print(f"[INFO]: Attempting to use sweep config from {file_path=} {class_name=}")
    module_name = os.path.splitext(os.path.basename(file_path))[0]
    spec = importlib.util.spec_from_file_location(module_name, file_path)
    module = importlib.util.module_from_spec(spec)
    sys.modules[module_name] = module
    spec.loader.exec_module(module)
    print(f"[INFO]: Successfully imported {module_name} from {file_path}")
    if hasattr(module, class_name):
        ClassToInstantiate = getattr(module, class_name)
        print(f"[INFO]: Found correct class {ClassToInstantiate}")
        instance = ClassToInstantiate()
        print(f"[INFO]: Successfully instantiated class '{class_name}' from {file_path}")
        cfg = instance.cfg
        print(f"[INFO]: Grabbed the following hyperparameter sweep config: \n {cfg}")
        invoke_tuning_run(cfg, args)
    else:
        raise AttributeError(f"[ERROR]:Class '{class_name}' not found in {file_path}")

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import argparse
import os
import time
from concurrent.futures import ThreadPoolExecutor
from ray import job_submission
"""
This script submits aggregate job(s) to cluster(s) described in a
config file containing `name: <NAME> address: http://<IP>:<PORT>` on
a new line for each cluster. For KubeRay clusters, this file
can be automatically created with `grok_cluster_with_kubectl.py`
Aggregate job(s) are matched with cluster(s) via the following relation:
cluster_line_index_submitted_to = job_index % total_cluster_count
Aggregate jobs are separated by the * delimiter. The `--aggregate_jobs` argument must be
the last argument supplied to the script.
An aggregate job could be a `../tuner.py` tuning job, which automatically
creates several individual jobs when started on a cluster. Alternatively, an aggregate job
could be a :file:'../wrap_resources.py` resource-wrapped job,
which may contain several individual sub-jobs separated by
the + delimiter.
If there are more aggregate jobs than cluster(s), aggregate jobs will be submitted
as clusters become available via the defined relation above. If there are less aggregate job(s)
than clusters, some clusters will not receive aggregate job(s). The maximum number of
aggregate jobs that can be run simultaneously is equal to the number of workers created by
default by a ThreadPoolExecutor on the machine submitting jobs due to fetching the log output after
jobs finish, which is unlikely to constrain overall-job submission.
Usage:
~~~bash
# Example; submitting a tuning job
python3 scripts/reinforcement_learning/ray/submit_job.py \
--aggregate_jobs /workspace/isaaclab/scripts/reinforcement_learning/ray/tuner.py \
    --cfg_file hyperparameter_tuning/vision_cartpole_cfg.py \
    --cfg_class CartpoleTheiaJobCfg --mlflow_uri <ML_FLOW_URI>
# Example: Submitting resource wrapped job
python3 scripts/reinforcement_learning/ray/submit_job.py --aggregate_jobs wrap_resources.py --test
# For all command line arguments
python3 scripts/reinforcement_learning/ray/submit_job.py -h
~~~
"""
script_directory = os.path.dirname(os.path.abspath(__file__))
CONFIG = {"working_dir": script_directory, "executable": "/workspace/isaaclab/isaaclab.sh -p"}
def read_cluster_spec(fn: str | None = None) -> list[dict]:
    if fn is None:
        cluster_spec_path = os.path.expanduser("~/.cluster_config")
    else:
        cluster_spec_path = os.path.expanduser(fn)
    if not os.path.exists(cluster_spec_path):
        raise FileNotFoundError(f"Cluster spec file not found at {cluster_spec_path}")
    clusters = []
    with open(cluster_spec_path) as f:
        for line in f:
            parts = line.strip().split(" ")
            http_address = parts[3]
            cluster_info = {"name": parts[1], "address": http_address}
            print(f"[INFO] Setting {cluster_info['name']}")  # with {cluster_info['num_gpu']} GPUs.")
            clusters.append(cluster_info)
    return clusters
def submit_job(cluster: dict, job_command: str) -> None:
    """
    Submits a job to a single cluster, prints the final result and Ray dashboard URL at the end.
    """
    address = cluster["address"]
    cluster_name = cluster["name"]
    print(f"[INFO]: Submitting job to cluster '{cluster_name}' at {address}")  # with {num_gpus} GPUs.")
    client = job_submission.JobSubmissionClient(address)
    runtime_env = {"working_dir": CONFIG["working_dir"], "executable": CONFIG["executable"]}
    print(f"[INFO]: Checking contents of the directory: {CONFIG['working_dir']}")
    try:
        dir_contents = os.listdir(CONFIG["working_dir"])
        print(f"[INFO]: Directory contents: {dir_contents}")
    except Exception as e:
        print(f"[INFO]: Failed to list directory contents: {str(e)}")
    entrypoint = f"{CONFIG['executable']} {job_command}"
    print(f"[INFO]: Attempting entrypoint {entrypoint=} in cluster {cluster}")
    job_id = client.submit_job(entrypoint=entrypoint, runtime_env=runtime_env)
    status = client.get_job_status(job_id)
    while status in [job_submission.JobStatus.PENDING, job_submission.JobStatus.RUNNING]:
        time.sleep(5)
        status = client.get_job_status(job_id)
    final_logs = client.get_job_logs(job_id)
    print("----------------------------------------------------")
    print(f"[INFO]: Cluster {cluster_name} Logs: \n")
    print(final_logs)
    print("----------------------------------------------------")
def submit_jobs_to_clusters(jobs: list[str], clusters: list[dict]) -> None:
    """
    Submit all jobs to their respective clusters, cycling through clusters if there are more jobs than clusters.
    """
    if not clusters:
        raise ValueError("No clusters available for job submission.")
    if len(jobs) < len(clusters):
        print("[INFO]: Less jobs than clusters, some clusters will not receive jobs")
    elif len(jobs) == len(clusters):
        print("[INFO]: Exactly one job per cluster")
    else:
        print("[INFO]: More jobs than clusters, jobs submitted as clusters become available.")
    with ThreadPoolExecutor() as executor:
        for idx, job_command in enumerate(jobs):
            # Cycle through clusters using modulus to wrap around if there are more jobs than clusters
            cluster = clusters[idx % len(clusters)]
            executor.submit(submit_job, cluster, job_command)
if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Submit multiple GPU jobs to multiple Ray clusters.")
    parser.add_argument("--config_file", default="~/.cluster_config", help="The cluster config path.")
    parser.add_argument(
        "--aggregate_jobs",
        type=str,
        nargs=argparse.REMAINDER,
        help="This should be last argument. The aggregate jobs to submit separated by the * delimiter.",
    )
    args = parser.parse_args()
    if args.aggregate_jobs is not None:
        jobs = " ".join(args.aggregate_jobs)
        formatted_jobs = jobs.split("*")
        if len(formatted_jobs) > 1:
            print("Warning; Split jobs by cluster with the * delimiter")
    else:
        formatted_jobs = []
    print(f"[INFO]: Isaac Ray Wrapper received jobs {formatted_jobs=}")
    clusters = read_cluster_spec(args.config_file)
    submit_jobs_to_clusters(formatted_jobs, clusters)

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import argparse
import os
import re
import subprocess
import threading
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
"""
This script requires that kubectl is installed and KubeRay was used to create the cluster.
Creates a config file containing `name: <NAME> address: http://<IP>:<PORT>` on
a new line for each cluster, and also fetches the MLFlow URI.
Usage:
~~~bash
python3 scripts/reinforcement_learning/ray/grok_cluster_with_kubectl.py
# For options, supply -h arg
~~~
"""
def get_namespace() -> str:
    """Get the current Kubernetes namespace from the context, fallback to default if not set"""
    try:
        namespace = (
            subprocess.check_output(["kubectl", "config", "view", "--minify", "--output", "jsonpath={..namespace}"])
            .decode()
            .strip()
        )
        if not namespace:
            namespace = "default"
    except subprocess.CalledProcessError:
        namespace = "default"
    return namespace
def get_pods(namespace: str = "default") -> list[tuple]:
    """Get a list of all of the pods in the namespace"""
    cmd = ["kubectl", "get", "pods", "-n", namespace, "--no-headers"]
    output = subprocess.check_output(cmd).decode()
    pods = []
    for line in output.strip().split("\n"):
        fields = line.split()
        pod_name = fields[0]
        status = fields[2]
        pods.append((pod_name, status))
    return pods
def get_clusters(pods: list, cluster_name_prefix: str) -> set:
    """
    Get unique cluster name(s). Works for one or more clusters, based off of the number of head nodes.
    Excludes MLflow deployments.
    """
    clusters = set()
    for pod_name, _ in pods:
        # Skip MLflow pods
        if "-mlflow" in pod_name:
            continue
        match = re.match(r"(" + re.escape(cluster_name_prefix) + r"[-\w]+)", pod_name)
        if match:
            # Get base name without head/worker suffix (skip workers)
            if "head" in pod_name:
                base_name = match.group(1).split("-head")[0]
                clusters.add(base_name)
    return sorted(clusters)
def get_mlflow_info(namespace: str = None, cluster_prefix: str = "isaacray") -> str:
    """
    Get MLflow service information if it exists in the namespace with the given prefix.
    Only works for a single cluster instance.
    Args:
        namespace: Kubernetes namespace
        cluster_prefix: Base cluster name (without -head/-worker suffixes)
    Returns:
        MLflow service URL
    """
    # Strip any -head or -worker suffixes to get base name
    if namespace is None:
        namespace = get_namespace()
    pods = get_pods(namespace=namespace)
    clusters = get_clusters(pods=pods, cluster_name_prefix=cluster_prefix)
    if len(clusters) > 1:
        raise ValueError("More than one cluster matches prefix, could not automatically determine mlflow info.")
    mlflow_name = f"{cluster_prefix}-mlflow"
    cmd = ["kubectl", "get", "svc", mlflow_name, "-n", namespace, "--no-headers"]
    try:
        output = subprocess.check_output(cmd).decode()
        fields = output.strip().split()
        # Get cluster IP
        cluster_ip = fields[2]
        port = "5000"  # Default MLflow port
        # This needs to be http to be resolved. HTTPS can't be resolved
        # This should be fine as it is on a subnet on the cluster regardless
        return f"http://{cluster_ip}:{port}"
    except subprocess.CalledProcessError as e:
        raise ValueError(f"Could not grok MLflow: {e}")  # Fixed f-string
def check_clusters_running(pods: list, clusters: set) -> bool:
    """
    Check that all of the pods in all provided clusters are running.
    Args:
        pods (list): A list of tuples where each tuple contains the pod name and its status.
        clusters (set): A set of cluster names to check.
    Returns:
        bool: True if all pods in any of the clusters are running, False otherwise.
    """
    clusters_running = False
    for cluster in clusters:
        cluster_pods = [p for p in pods if p[0].startswith(cluster)]
        total_pods = len(cluster_pods)
        running_pods = len([p for p in cluster_pods if p[1] == "Running"])
        if running_pods == total_pods and running_pods > 0:
            clusters_running = True
            break
    return clusters_running
def get_ray_address(head_pod: str, namespace: str = "default", ray_head_name: str = "head") -> str:
    """
    Given a cluster head pod, check its logs, which should include the ray address which can accept job requests.
    Args:
        head_pod (str): The name of the head pod.
        namespace (str, optional): The Kubernetes namespace. Defaults to "default".
        ray_head_name (str, optional): The name of the ray head container. Defaults to "head".
    Returns:
        str: The ray address if found, None otherwise.
    Raises:
        ValueError: If the logs cannot be retrieved or the ray address is not found.
    """
    cmd = ["kubectl", "logs", head_pod, "-c", ray_head_name, "-n", namespace]
    try:
        output = subprocess.check_output(cmd).decode()
    except subprocess.CalledProcessError as e:
        raise ValueError(
            f"Could not enter head container with cmd {cmd}: {e}Perhaps try a different namespace or ray head name."
        )
    match = re.search(r"RAY_ADDRESS='([^']+)'", output)
    if match:
        return match.group(1)
    else:
        return None
def process_cluster(cluster_info: dict, ray_head_name: str = "head") -> str:
    """
    For each cluster, check that it is running, and get the Ray head address that will accept jobs.
    Args:
        cluster_info (dict): A dictionary containing cluster information with keys 'cluster', 'pods', and 'namespace'.
        ray_head_name (str, optional): The name of the ray head container. Defaults to "head".
    Returns:
        str: A string containing the cluster name and its Ray head address, or an error message if the head pod or Ray address is not found.
    """
    cluster, pods, namespace = cluster_info
    head_pod = None
    for pod_name, status in pods:
        if pod_name.startswith(cluster + "-head"):
            head_pod = pod_name
            break
    if not head_pod:
        return f"Error: Could not find head pod for cluster {cluster}\n"
    # Get RAY_ADDRESS and status
    ray_address = get_ray_address(head_pod, namespace=namespace, ray_head_name=ray_head_name)
    if not ray_address:
        return f"Error: Could not find RAY_ADDRESS for cluster {cluster}\n"
    # Return only cluster and ray address
    return f"name: {cluster} address: {ray_address}\n"
def main():
    # Parse command-line arguments
    parser = argparse.ArgumentParser(description="Process Ray clusters and save their specifications.")
    parser.add_argument("--prefix", default="isaacray", help="The prefix for the cluster names.")
    parser.add_argument("--output", default="~/.cluster_config", help="The file to save cluster specifications.")
    parser.add_argument("--ray_head_name", default="head", help="The metadata name for the ray head container")
    parser.add_argument(
        "--namespace", help="Kubernetes namespace to use. If not provided, will detect from current context."
    )
    args = parser.parse_args()
    # Get namespace from args or detect it
    current_namespace = args.namespace if args.namespace else get_namespace()
    print(f"Using namespace: {current_namespace}")
    cluster_name_prefix = args.prefix
    cluster_spec_file = os.path.expanduser(args.output)
    # Get all pods
    pods = get_pods(namespace=current_namespace)
    # Get clusters
    clusters = get_clusters(pods, cluster_name_prefix)
    if not clusters:
        print(f"No clusters found with prefix {cluster_name_prefix}")
        return
    # Wait for clusters to be running
    while True:
        pods = get_pods(namespace=current_namespace)
        if check_clusters_running(pods, clusters):
            break
        print("Waiting for all clusters to spin up...")
        time.sleep(5)
    print("Checking for MLflow:")
    # Check MLflow status for each cluster
    for cluster in clusters:
        try:
            mlflow_address = get_mlflow_info(current_namespace, cluster)
            print(f"MLflow address for {cluster}: {mlflow_address}")
        except ValueError as e:
            print(f"ML Flow not located: {e}")
    print()
    # Prepare cluster info for parallel processing
    cluster_infos = []
    for cluster in clusters:
        cluster_pods = [p for p in pods if p[0].startswith(cluster)]
        cluster_infos.append((cluster, cluster_pods, current_namespace))
    # Use ThreadPoolExecutor to process clusters in parallel
    results = []
    results_lock = threading.Lock()
    with ThreadPoolExecutor() as executor:
        future_to_cluster = {
            executor.submit(process_cluster, info, args.ray_head_name): info[0] for info in cluster_infos
        }
        for future in as_completed(future_to_cluster):
            cluster_name = future_to_cluster[future]
            try:
                result = future.result()
                with results_lock:
                    results.append(result)
            except Exception as exc:
                print(f"{cluster_name} generated an exception: {exc}")
    # Sort results alphabetically by cluster name
    results.sort()
    # Write sorted results to the output file (Ray info only)
    with open(cluster_spec_file, "w") as f:
        for result in results:
            f.write(result)
    print(f"Cluster spec information saved to {cluster_spec_file}")
    # Display the contents of the config file
    with open(cluster_spec_file) as f:
        print(f.read())
if __name__ == "__main__":
    main()

# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause
import argparse
import pathlib
import subprocess
import yaml
import util
from jinja2 import Environment, FileSystemLoader
from kubernetes import config
"""This script helps create one or more KubeRay clusters.
Usage:
~~~bash
# If the head node is stuck on container creating, make sure to create a secret
python3 scripts/reinforcement_learning/ray/launch.py -h
# Examples
# The following creates 8 GPUx1 nvidia l4 workers
python3 scripts/reinforcement_learning/ray/launch.py --cluster_host google_cloud \
--namespace <NAMESPACE> --image <YOUR_ISAAC_RAY_IMAGE> \
--num_workers 8 --num_clusters 1 --worker_accelerator nvidia-l4 --gpu_per_worker 1
# The following creates 1 GPUx1 nvidia l4 worker, 2 GPUx2 nvidia-tesla-t4 workers,
# and 2 GPUx4 nvidia-tesla-t4 GPU workers
python3 scripts/reinforcement_learning/ray/launch.py --cluster_host google_cloud \
--namespace <NAMESPACE> --image <YOUR_ISAAC_RAY_IMAGE> \
--num_workers 1 2 --num_clusters 1 \
--worker_accelerator nvidia-l4 nvidia-tesla-t4 --gpu_per_worker 1 2 4
~~~
"""
RAY_DIR = pathlib.Path(__file__).parent
def apply_manifest(args: argparse.Namespace) -> None:
    """Provided a Jinja templated ray.io/v1alpha1 file,
    populate the arguments and create the cluster. Additionally, create
    kubernetes containers for resources separated by '---' from the rest
    of the file.
    Args:
        args: Possible arguments concerning cluster parameters.
    """
    # Load Kubernetes configuration
    config.load_kube_config()
    # Set up Jinja2 environment for loading templates
    templates_dir = RAY_DIR / "cluster_configs" / args.cluster_host
    file_loader = FileSystemLoader(str(templates_dir))
    jinja_env = Environment(loader=file_loader, keep_trailing_newline=True, autoescape=True)
    # Define template filename
    template_file = "kuberay.yaml.jinja"
    # Convert args namespace to a dictionary
    template_params = vars(args)
    # Load and render the template
    template = jinja_env.get_template(template_file)
    file_contents = template.render(template_params)
    # Parse all YAML documents in the rendered template
    all_yamls = []
    for doc in yaml.safe_load_all(file_contents):
        all_yamls.append(doc)
    # Convert back to YAML string, preserving multiple documents
    cleaned_yaml_string = ""
    for i, doc in enumerate(all_yamls):
        if i > 0:
            cleaned_yaml_string += "\n---\n"
        cleaned_yaml_string += yaml.dump(doc)
    # Apply the Kubernetes manifest using kubectl
    try:
        print(cleaned_yaml_string)
        subprocess.run(["kubectl", "apply", "-f", "-"], input=cleaned_yaml_string, text=True, check=True)
    except subprocess.CalledProcessError as e:
        exit(f"An error occurred while running `kubectl`: {e}")
def parse_args() -> argparse.Namespace:
    """
    Parse command-line arguments for Kubernetes deployment script.
    Returns:
        argparse.Namespace: Parsed command-line arguments.
    """
    arg_parser = argparse.ArgumentParser(
        description="Script to apply manifests to create Kubernetes objects for Ray clusters.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    arg_parser.add_argument(
        "--cluster_host",
        type=str,
        default="google_cloud",
        choices=["google_cloud"],
        help=(
            "In the cluster_configs directory, the name of the folder where a tune.yaml.jinja"
            "file exists defining the KubeRay config. Currently only google_cloud is supported."
        ),
    )
    arg_parser.add_argument(
        "--name",
        type=str,
        required=False,
        default="isaacray",
        help="Name of the Kubernetes deployment.",
    )
    arg_parser.add_argument(
        "--namespace",
        type=str,
        required=False,
        default="default",
        help="Kubernetes namespace to deploy the Ray cluster.",
    )
    arg_parser.add_argument(
        "--service_acount_name", type=str, required=False, default="default", help="The service account name to use."
    )
    arg_parser.add_argument(
        "--image",
        type=str,
        required=True,
        help="Docker image for the Ray cluster pods.",
    )
    arg_parser.add_argument(
        "--worker_accelerator",
        nargs="+",
        type=str,
        default=["nvidia-l4"],
        help="GPU accelerator name. Supply more than one for heterogeneous resources.",
    )
    arg_parser = util.add_resource_arguments(arg_parser, cluster_create_defaults=True)
    arg_parser.add_argument(
        "--num_clusters",
        type=int,
        default=1,
        help="How many Ray Clusters to create.",
    )
    arg_parser.add_argument(
        "--num_head_cpu",
        type=float,  # to be able to schedule partial CPU heads
        default=8,
        help="The number of CPUs to give the Ray head.",
    )
    arg_parser.add_argument("--head_ram_gb", type=int, default=8, help="How many gigs of ram to give the Ray head")
    args = arg_parser.parse_args()
    return util.fill_in_missing_resources(args, cluster_creation_flag=True)
def main():
    args = parse_args()
    if "head" in args.name:
        raise ValueError("For compatibility with other scripts, do not include head in the name")
    if args.num_clusters == 1:
        apply_manifest(args)
    else:
        default_name = args.name
        for i in range(args.num_clusters):
            args.name = default_name + "-" + str(i)
            apply_manifest(args)
if __name__ == "__main__":
    main()