6cd0cd3bd99ec2df47bb571a18e0b70819134504,rllib/agents/impala/impala.py,OverrideDefaultResourceRequest,default_resource_request,#Any#Any#,101

Before Change

            extra_cpu=cf["num_cpus_per_worker"] * cf["num_workers"] +
            cf["num_aggregation_workers"],
            extra_gpu=cf["num_gpus_per_worker"] * cf["num_workers"],
            extra_memory=cf["memory_per_worker"] * cf["num_workers"],
            extra_object_store_memory=cf["object_store_memory_per_worker"] *
            cf["num_workers"])

After Change

        // Return PlacementGroupFactory containing all needed resources
        // (already properly defined as device bundles).
        return PlacementGroupFactory(
            bundles=[{
                // Driver + Aggregation Workers:
                // Force to be on same node to maximize data bandwidth
                // between aggregation workers and the learner (driver).
                // Aggregation workers tree-aggregate experiences collected
                // from RolloutWorkers (n rollout workers map to m
                // aggregation workers, where m < n) and always use 1 CPU
                // each.
                "CPU": cf["num_cpus_for_driver"] +
                cf["num_aggregation_workers"],
                "GPU": cf["num_gpus"]
            }] + [
                {
                    // RolloutWorkers.
                    "CPU": cf["num_cpus_per_worker"],
                    "GPU": cf["num_gpus_per_worker"],
                } for _ in range(cf["num_workers"])
            ] + ([
                {
                    // Evaluation workers (+1 b/c of the additional local
                    // worker)
                    "CPU": eval_config.get("num_cpus_per_worker",
                                           cf["num_cpus_per_worker"]),
                    "GPU": eval_config.get("num_gpus_per_worker",
                                           cf["num_gpus_per_worker"]),
                } for _ in range(cf["evaluation_num_workers"] + 1)
            ] if cf["evaluation_interval"] else []),
            strategy=config.get("placement_strategy", "PACK"))


def make_learner_thread(local_worker, config):
    if config["num_gpus"] > 1 or config["num_data_loader_buffers"] > 1:

Instances