This contains the TorchX Slurm scheduler which can be used to run TorchX components on a Slurm cluster.
- class torchx.schedulers.slurm_scheduler.SlurmScheduler(session_name: str)¶
SlurmScheduler is a TorchX scheduling interface to slurm. TorchX expects that slurm CLI tools are locally installed and job accounting is enabled.
Each app def is scheduled using a heterogenous job via sbatch. Each replica of each role has a unique shell script generated with it’s resource allocations and args and then sbatch is used to launch all of them together.
Logs are available in combined form via
torchx log, the programmatic API as well as in the job launch directory as
slurm-<jobid>-<role>-<replica_id>.out. If TorchX is running in a different directory than where the job was created the logs won’t be able to be found.
Some of the config options passed to it are added as SBATCH arguments to each replica. See https://slurm.schedmd.com/sbatch.html#SECTION_OPTIONS for info on the arguments.
Slurm jobs inherit the currently active
virtualenvand run in the current working directory. This matches the behavior of the
For more info see:
$ torchx run --scheduler slurm utils.echo --msg hello slurm://torchx_user/1234 $ torchx status slurm://torchx_user/1234 $ less slurm-1234.out ...
usage: [partition=PARTITION],[time=TIME],[comment=COMMENT],[constraint=CONSTRAINT],[mail-user=MAIL-USER],[mail-type=MAIL-TYPE],[job_dir=JOB_DIR] optional arguments: partition=PARTITION (str, None) The partition to run the job in. time=TIME (str, None) The maximum time the job is allowed to run for. Formats: "minutes", "minutes:seconds", "hours:minutes:seconds", "days-hours", "days-hours:minutes" or "days-hours:minutes:seconds" comment=COMMENT (str, None) Comment to set on the slurm job. constraint=CONSTRAINT (str, None) Constraint to use for the slurm job. mail-user=MAIL-USER (str, None) User to mail on job end. mail-type=MAIL-TYPE (str, None) What events to mail users on. job_dir=JOB_DIR (str, None) The directory to place the job code and outputs. The directory must not exist and will be created. To enable log iteration, jobs will be tracked in ``.torchxslurmjobdirs``.
Partial support. SlurmScheduler will return job and replica status but does not provide the complete original AppSpec.
Workspaces / Patching
If ``job_dir`` is specified the DirWorkspaceMixin will create a new isolated directory with a snapshot of the workspace.
If a partition has less than 1GB of RealMemory configured we disable memory requests to workaround https://github.com/aws/aws-parallelcluster/issues/2198.
- describe(app_id: str) Optional[DescribeAppResponse] ¶
Describes the specified application.
AppDef description or
Noneif the app does not exist.
- list() List[ListAppResponse] ¶
For apps launched on the scheduler, this API returns a list of ListAppResponse objects each of which have app id and its status. Note: This API is in prototype phase and is subject to change.
- log_iter(app_id: str, role_name: str, k: int = 0, regex: Optional[str] = None, since: Optional[datetime] = None, until: Optional[datetime] = None, should_tail: bool = False, streams: Optional[Stream] = None) Iterable[str] ¶
Returns an iterator to the log lines of the
k``th replica of the ``role. The iterator ends when all qualifying log lines have been read.
If the scheduler supports time-based cursors fetching log lines for custom time ranges, then the
untilfields are honored, otherwise they are ignored. Not specifying
untilis equivalent to getting all available log lines. If the
untilis empty, then the iterator behaves like
tail -f, following the log output until the job reaches a terminal state.
The exact definition of what constitutes a log is scheduler specific. Some schedulers may consider stderr or stdout as the log, others may read the logs from a log file.
Behaviors and assumptions:
Produces an undefined-behavior if called on an app that does not exist The caller should check that the app exists using
exists(app_id)prior to calling this method.
Is not stateful, calling this method twice with same parameters returns a new iterator. Prior iteration progress is lost.
Does not always support log-tailing. Not all schedulers support live log iteration (e.g. tailing logs while the app is running). Refer to the specific scheduler’s documentation for the iterator’s behavior.
- 3.1 If the scheduler supports log-tailing, it should be controlled
Does not guarantee log retention. It is possible that by the time this method is called, the underlying scheduler may have purged the log records for this application. If so this method raises an arbitrary exception.
should_tailis True, the method only raises a
StopIterationexception when the accessible log lines have been fully exhausted and the app has reached a final state. For instance, if the app gets stuck and does not produce any log lines, then the iterator blocks until the app eventually gets killed (either via timeout or manually) at which point it raises a
should_tailis False, the method raises
StopIterationwhen there are no more logs.
Need not be supported by all schedulers.
Some schedulers may support line cursors by supporting
iterseeks to the 50th log line).
- Whitespace is preserved, each new line should include
support interactive progress bars the returned lines don’t need to include
\nbut should then be printed without a newline to correctly handle
- Whitespace is preserved, each new line should include
streams – The IO output streams to select. One of: combined, stdout, stderr. If the selected stream isn’t supported by the scheduler it will throw an ValueError.
Iteratorover log lines of the specified role replica
NotImplementedError – if the scheduler does not support log iteration
- schedule(dryrun_info: AppDryRunInfo[SlurmBatchRequest]) str ¶
submitexcept that it takes an
AppDryRunInfo. Implementers are encouraged to implement this method rather than directly implementing
submitcan be trivially implemented by:
dryrun_info = self.submit_dryrun(app, cfg) return schedule(dryrun_info)
- torchx.schedulers.slurm_scheduler.create_scheduler(session_name: str, **kwargs: Any) SlurmScheduler ¶
- class torchx.schedulers.slurm_scheduler.SlurmBatchRequest(cmd: List[str], replicas: Dict[str, SlurmReplicaRequest], job_dir: Optional[str], max_retries: int)¶
Holds parameters used to launch a slurm job via sbatch.
- class torchx.schedulers.slurm_scheduler.SlurmReplicaRequest(name: str, entrypoint: str, args: List[str], srun_opts: Dict[str, str], sbatch_opts: Dict[str, Optional[str]], env: Dict[str, str])¶
Holds parameters for a single replica running on slurm and can be materialized down to a bash script.
- classmethod from_role(name: str, role: Role, cfg: SlurmOpts, nomem: bool) SlurmReplicaRequest ¶
from_rolecreates a SlurmReplicaRequest for the specific role and name.