# Elastic Agent¶

## Server¶

The elastic agent is the control plane of torchelastic. It is a process that launches and manages underlying worker processes. The agent is responsible for:

1. Working with distributed torch: the workers are started with all the necessary information to successfully and trivially call torch.distributed.init_process_group().

2. Fault tolerance: monitors workers and upon detecting worker failures or unhealthiness, tears down all workers and restarts everyone.

3. Elasticity: Reacts to membership changes and restarts workers with the new members.

The simplest agents are deployed per node and works with local processes. A more advanced agent can launch and manage workers remotely. Agents can be completely decentralized, making decisions based on the workers it manages. Or can be coordinated, communicating to other agents (that manage workers in the same job) to make a collective decision.

Below is a diagram of an agent that manages a local group of workers.

## Concepts¶

This section describes the high-level classes and concepts that are relevant to understanding the role of the agent in torchelastic.

class torchelastic.agent.server.ElasticAgent[source]

Agent process responsible for managing one or more worker processes. The worker processes are assumed to be regular distributed PyTorch scripts. When the worker process is created by the agent, the agent provides the necessary information for the worker processes to properly initialize a torch process group.

The exact deployment topology and ratio of agent-to-worker is dependent on the specific implementation of the agent and the user’s job placement preferences. For instance, to run a distributed training job on GPU with 8 trainers (one per GPU) one can:

1. Use 8 x single GPU instances, place an agent per instance, managing 1 worker per agent.

2. Use 4 x double GPU instances, place an agent per instance, managing 2 workers per agent.

3. Use 2 x quad GPU instances, place an agent per instance, managing 4 workers per agent.

4. Use 1 x 8 GPU instance, place an agent per instance, managing 8 workers per agent.

Usage

try:
results = agent.run()
return results[0] # return rank 0's results
except WorkerGroupFailureException as e:
exceptions = e.get_worker_exceptions()
log.exception(f"worker 0 failed with: {exceptions[0]}")
except Exception as e:
log.exception(f"error while running agent")

abstract get_worker_group(role: str = 'default') → torchelastic.agent.server.api.WorkerGroup[source]
Returns

The WorkerGroup for the given role. Note that the worker group is a mutable object and hence in a multi-threaded/process environment it may change state. Implementors are encouraged (but not required) to return a defensive read-only copy.

abstract run(role: str = 'default') → Dict[int, Any][source]

Runs the agent, retrying the worker group on failures up to max_restarts.

Returns

The return values for each worker mapped by the worker’s global rank. Empty if workers have void signature.

Raises
• WorkerGroupFailureException - workers did not successfully run

• Exception - any other failures NOT related to worker process

class torchelastic.agent.server.WorkerSpec(role: str, local_world_size: int, fn: Callable, args: Tuple, rdzv_handler: torchelastic.rendezvous.api.RendezvousHandler, max_restarts: int = 100, monitor_interval: float = 5.0, master_port=None)[source]

Contains blueprint information about a particular type of worker. For a given role, there must only exist a single worker spec. Worker spec is expected to be homogenous across all nodes (machine), that is each node runs the same number of workers for a particular spec.

class torchelastic.agent.server.WorkerState[source]

State of the WorkerGroup. Workers in a worker group change state as a unit. If a single worker in a worker group fails the entire set is considered failed:

UNKNOWN - agent lost track of worker group state, unrecoverable
INIT - worker group object created not yet started
HEALTHY - workers running and healthy
UNHEALTHY - workers running and unhealthy
STOPPED - workers stopped (interruped) by the agent
SUCCEEDED - workers finished running (exit 0)
FAILED - workers failed to successfully finish (exit !0)


A worker group starts from an initial INIT state, then progresses to HEALTHY or UNHEALTHY states, and finally reaches a terminal SUCCEEDED or FAILED state.

Worker groups can be interrupted and temporarily put into STOPPED state by the agent. Workers in STOPPED state are scheduled to be restarted in the near future by the agent. Some examples of workers being put into STOPPED state are:

1. Worker group failure|unhealthy observed

2. Membership change detected

When actions (start, stop, rdzv, retry, etc) on worker group fails and results in the action being partially applied to the worker group the state will be UNKNOWN. Typically this happens on uncaught/unhandled exceptions during state change events on the agent. The agent is not expected to recover worker groups in UNKNOWN state and is better off self terminating and allowing the job manager to retry the node.

class torchelastic.agent.server.Worker(local_rank: int)[source]

Represents a worker instance. Contrast this with WorkerSpec that represents the specifications of a worker. A Worker is created from a WorkerSpec. A Worker is to a WorkerSpec as an object is to a class.

The id of the worker is interpreted by the specific implementation of ElasticAgent. For a local agent, it could be the pid (int) of the worker, for a remote agent it could be encoded as host:port (string).

Parameters
• id (Any) – uniquely identifies a worker (interpreted by the agent)

• local_rank (int) – local rank of the worker

• global_rank (int) – global rank of the worker

• world_size (int) – number of workers (globally)

class torchelastic.agent.server.WorkerGroup(spec: torchelastic.agent.server.api.WorkerSpec)[source]

Represents the set of Worker instances for the given WorkerSpec managed by ElasticAgent. Whether the worker group contains cross instance workers or not depends on the implementation of the agent.

## Implementations¶

Below are the agent implementations provided by torchelastic.

class torchelastic.agent.server.local_elastic_agent.LocalElasticAgent(spec: torchelastic.agent.server.api.WorkerSpec, start_method='spawn')[source]

An implementation of torchelastic.agent.server.ElasticAgent that handles host-local workers. This agent is deployed per host and is configured to spawn n workers. When using GPUs, n maps to the number of GPUs available on the host.

The local agent does not communicate to other local agents deployed on other hosts, even if the workers may communicate inter-host. The worker id is interpreted to be a local process. The agent starts and stops all worker processes as a single unit.

The worker function and argument passed to the worker function must be python multiprocessing compatible. To pass multiprocessing data structures to the workers you may create the data structure in the same multiprocessing context as the specified start_method and pass it as a function argument.

Example

def trainer(shared_queue):
pass

def main():
start_method="spawn"
shared_queue= multiprocessing.get_context(start_method).Queue()
spec = WorkerSpec(
role="trainer",
local_world_size=nproc_per_process,
fn=trainer,
args=(shared_queue,),
...<OTHER_PARAMS...>)
agent = LocalElasticAgent(spec, start_method)
agent.run()


## Extending the Agent¶

To extend the agent you can implement ElasticAgent directly, however we recommend you extend SimpleElasticAgent instead, which provides most of the scaffolding and leaves you with a few specific abstract methods to implement.

class torchelastic.agent.server.SimpleElasticAgent(spec: torchelastic.agent.server.api.WorkerSpec)[source]

An ElasticAgent that manages workers (WorkerGroup) for a single WorkerSpec (e.g. one particular type of worker role).

_initialize_workers(worker_group: torchelastic.agent.server.api.WorkerGroup) → None[source]

Starts a fresh set of workers for the worker_group. Essentially a rendezvous followed by a start_workers.

The caller should first call _stop_workers() to stop running workers prior to calling this method.

Optimistically sets the state of the worker group that just started as HEALTHY and delegates the actual monitoring of state to _monitor_workers() method

abstract _monitor_workers(worker_group: torchelastic.agent.server.api.WorkerGroup) → torchelastic.agent.server.api.MonitorResult[source]

Checks on the workers for the worker_group and returns the new state of the worker group.

_rendezvous(worker_group: torchelastic.agent.server.api.WorkerGroup) → None[source]

Runs rendezvous for the workers specified by worker spec. Assigns workers a new global rank and world size. Updates the rendezvous store for the worker group.

_restart_workers(worker_group: torchelastic.agent.server.api.WorkerGroup) → None[source]

Restarts (stops, rendezvous, starts) all local workers in the group.

abstract _start_workers(worker_group: torchelastic.agent.server.api.WorkerGroup) → Dict[int, Any][source]

Starts worker_group.spec.local_world_size number of workers according to worker spec for the worker group .

Returns a map of local_rank to worker id.

abstract _stop_workers(worker_group: torchelastic.agent.server.api.WorkerGroup) → None[source]

Stops all workers in the given worker group. Implementors must deal with workers in all states defined by WorkerState. That is, it must gracefully handle stopping non-existent workers, unhealthy (stuck) workers, etc.

get_worker_group() → torchelastic.agent.server.api.WorkerGroup[source]

Returns: The WorkerGroup for the given role. Note that the worker group is a mutable object and hence in a multi-threaded/process environment it may change state. Implementors are encouraged (but not required) to return a defensive read-only copy.

run(role: str = 'default') → Dict[int, Any][source]

Runs the agent, retrying the worker group on failures up to max_restarts.

Returns

The return values for each worker mapped by the worker’s global rank. Empty if workers have void signature.

Raises
• WorkerGroupFailureException - workers did not successfully run

• Exception - any other failures NOT related to worker process

class torchelastic.agent.server.api.MonitorResult(state: torchelastic.agent.server.api.WorkerState, ret_vals: Dict[int, Any] = None, exceptions: Dict[int, Exception] = None)[source]

Returned by the agent’s _monitor_workers API. A holder object that holds information about the monitoring results. The ret_vals and exceptions field map each worker’s return value (output) and exceptions (if any) accordingly by the workers global rank.

state = SUCCEEDED will have ret_val. state = FAILED will have exceptions`. For other states both these fields will be empty.