class_docstr: ClusterResolver for system with Slurm workload manager.
  
  This is an implementation of ClusterResolver for Slurm clusters. This allows
  the specification of jobs and task counts, number of tasks per node, number
  of GPUs on each node and number of GPUs for each task. It retrieves system
  attributes by Slurm environment variables, resolves allocated computing node
  names, constructs a cluster and returns a ClusterResolver object which can be
  used for distributed TensorFlow.
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docstr: Creates a new SlurmClusterResolver object.
  
  For any parameter not set it will query the environment for the value.
  It uses those parameters to check which nodes have processes reside on and
  resolves their hostnames.
  With the number tasks per node it offsets the port number for each process.
  With the number of GPUs per node and per task it allocates GPUs to tasks by
  setting environment variables.
  Using the resolver works best (and is easier) with homogeneous tasks but
  heterogeneous tasks (number of tasks varying per node) are also possible as
  long as the number of GPUs per task stays constant.
  
  Used environment variables:
    - SLURM_PROCID
    - (opt) SLURM_STEP_NUM_TASKS
    - (opt) SLURM_STEP_NODELIST
    - (opt) SLURM_STEP_TASKS_PER_NODE
  
  Args:
    jobs: Dictionary with job names as key and number of tasks in the job as
      value. Defaults to as many 'worker's as there are (Slurm) tasks.
    port_base: The first port number to start with for processes on a node.
    gpus_per_node: Number of GPUs available on each node. Defaults to the
      number of GPUs reported by nvidia-smi
    gpus_per_task: Number of GPUs to be used for each task. Default is to
      evenly distribute the gpus_per_node to tasks_per_node.
    tasks_per_node: Number of tasks running on each node. Can be an integer if
      the number of tasks per node is constant or a dictionary mapping
      hostnames to number of tasks on that node. If not set the Slurm
      environment is queried for the correct mapping.
    auto_set_gpu: Set the visible CUDA devices automatically while resolving
      the cluster by setting CUDA_VISIBLE_DEVICES environment variable.
      Defaults to True.
    rpc_layer: The protocol TensorFlow used to communicate between nodes.
      Defaults to 'grpc'.
  
  Returns:
    A ClusterResolver object which can be used with distributed TensorFlow.
  
  Raises:
    RuntimeError: If requested more GPUs per node than available or
      requested more tasks than assigned tasks or
      resolving missing values from the environment failed.
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code: def __init__(
      self,
      jobs=None,
      port_base=8888,
      gpus_per_node=None,
      gpus_per_task=None,
      tasks_per_node=None,
      auto_set_gpu=True,
      rpc_layer="grpc",
  ):
      """Creates a new SlurmClusterResolver object.
  
      For any parameter not set it will query the environment for the value.
      It uses those parameters to check which nodes have processes reside on and
      resolves their hostnames.
      With the number tasks per node it offsets the port number for each process.
      With the number of GPUs per node and per task it allocates GPUs to tasks by
      setting environment variables.
      Using the resolver works best (and is easier) with homogeneous tasks but
      heterogeneous tasks (number of tasks varying per node) are also possible as
      long as the number of GPUs per task stays constant.
  
      Used environment variables:
        - SLURM_PROCID
        - (opt) SLURM_STEP_NUM_TASKS
        - (opt) SLURM_STEP_NODELIST
        - (opt) SLURM_STEP_TASKS_PER_NODE
  
      Args:
        jobs: Dictionary with job names as key and number of tasks in the job as
          value. Defaults to as many 'worker's as there are (Slurm) tasks.
        port_base: The first port number to start with for processes on a node.
        gpus_per_node: Number of GPUs available on each node. Defaults to the
          number of GPUs reported by nvidia-smi
        gpus_per_task: Number of GPUs to be used for each task. Default is to
          evenly distribute the gpus_per_node to tasks_per_node.
        tasks_per_node: Number of tasks running on each node. Can be an integer if
          the number of tasks per node is constant or a dictionary mapping
          hostnames to number of tasks on that node. If not set the Slurm
          environment is queried for the correct mapping.
        auto_set_gpu: Set the visible CUDA devices automatically while resolving
          the cluster by setting CUDA_VISIBLE_DEVICES environment variable.
          Defaults to True.
        rpc_layer: The protocol TensorFlow used to communicate between nodes.
          Defaults to 'grpc'.
  
      Returns:
        A ClusterResolver object which can be used with distributed TensorFlow.
  
      Raises:
        RuntimeError: If requested more GPUs per node than available or
          requested more tasks than assigned tasks or
          resolving missing values from the environment failed.
      """
  
      self._rank = self._resolve_own_rank()
  
      if jobs is None:
          jobs = {"worker": self._resolve_num_tasks()}
  
      self._jobs = jobs
      self._port_base = port_base
  
      if tasks_per_node is None:
          self._task_configuration = self._resolve_task_configuration()
      elif isinstance(tasks_per_node, dict):
          # User can pass in an explicit configuration as a dict
          self._task_configuration = tasks_per_node
      else:
          # User can pass a fixed number of tasks per node
          hostlist = self._resolve_hostlist()
          self._task_configuration = {
              host: int(tasks_per_node) for host in hostlist
          }
  
      max_tasks_per_node = max(self._task_configuration.values())
      num_tasks = sum(self._task_configuration.values())
  
      if gpus_per_node is None:
          gpus_per_node = get_num_gpus()
      if gpus_per_task is None:
          gpus_per_task = gpus_per_node // max_tasks_per_node
      self._gpus_per_node = gpus_per_node
      self._gpus_per_task = gpus_per_task
  
      self._auto_set_gpu = auto_set_gpu
      self.task_type = None
      self.task_id = None
      self.rpc_layer = rpc_layer
  
      self._gpu_allocation = []
      self._cluster_allocation = {}
  
      if max_tasks_per_node * self._gpus_per_task > self._gpus_per_node:
          raise RuntimeError(
              "Requested more GPUs per node than available."
          )
  
      if sum(self._jobs.values()) != num_tasks:
          raise RuntimeError(
              "Requested {} tasks but only {} were assigned.".format(
                  sum(self._jobs.values()), num_tasks
              )
          )
  
imports: import os
  import re
  import subprocess
  from tensorflow.python.distribute.cluster_resolver.cluster_resolver import ClusterResolver
  from tensorflow.python.distribute.cluster_resolver.cluster_resolver import format_master_url
  from tensorflow.python.training.server_lib import ClusterSpec
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 to create a new SlurmClusterResolver object from inherited base classes: ClusterResolver with example: obj = SlurmClusterResolver(jobs, port_base, gpus_per_node, gpus_per_task, tasks_per_node, auto_set_gpu, rpc_layer)