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climate_ref.executor.hpc #

HPC-based Executor to use job schedulers.

If you want to - run REF under the HPC workflows - run REF in multiple nodes

The HPCExecutor requires the optional parsl dependency. This dependency (and therefore this executor) is not available on Windows.

HPCExecutor #

Run diagnostics by submitting a job script

Source code in packages/climate-ref/src/climate_ref/executor/hpc.py
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class HPCExecutor:
    """
    Run diagnostics by submitting a job script

    """

    name = "hpc"

    def __init__(
        self,
        *,
        database: Database | None = None,
        config: Config | None = None,
        **executor_config: str | float | int,
    ) -> None:
        config = config or Config.default()
        database = database or Database.from_config(config, run_migrations=False)

        self.config = config
        self.database = database

        self.scheduler = executor_config.get("scheduler", "slurm")
        self.account = str(executor_config.get("account", os.environ.get("USER")))
        self.username = executor_config.get("username", os.environ.get("USER"))
        self.partition = str(executor_config.get("partition")) if executor_config.get("partition") else None
        self.queue = str(executor_config.get("queue")) if executor_config.get("queue") else None
        self.qos = str(executor_config.get("qos")) if executor_config.get("qos") else None
        self.req_nodes = int(executor_config.get("req_nodes", 1)) if self.scheduler == "slurm" else 1
        self.walltime = str(executor_config.get("walltime", "00:10:00"))
        self.log_dir = str(executor_config.get("log_dir", "runinfo"))

        self.extra_slurm_provider: dict[str, Any] = cast(
            dict[str, Any], executor_config.get("extra_slurm_provider") or {}
        )
        self.extra_executor: dict[str, Any] = cast(
            dict[str, Any], executor_config.get("extra_executor") or {}
        )

        self.cores_per_worker = _to_int(executor_config.get("cores_per_worker"))
        self.mem_per_worker = _to_float(executor_config.get("mem_per_worker"))

        self.parsl_provider_base = {
            "account",
            "partition",
            "qos",
            "nodes_per_block",
            "max_blocks",
            "worker_init",
            "walltime",
            "cmd_timeout",
        }

        self.parsl_executor_base = {
            "label",
            "cores_per_worker",
            "mem_per_worker",
            "max_workers_per_node",
            "cpu_affinity",
        }

        self._validate_extras()

        if self.scheduler == "slurm":
            self.slurm_config = SlurmConfig.model_validate(executor_config)
            hours, minutes, seconds = map(int, self.slurm_config.walltime.split(":"))

            if self.slurm_config.validation and HAS_REAL_SLURM:
                self._validate_slurm_params()
        else:
            hours, minutes, seconds = map(int, self.walltime.split(":"))

        total_minutes = hours * 60 + minutes + seconds / 60
        self.total_minutes = total_minutes

        self._initialize_parsl()

        self.parsl_results: list[ExecutionFuture] = []

    def _validate_extras(self) -> None:

        self._provider_extra = {}
        self._executor_extra = {}

        if self.scheduler == "slurm" and self.extra_slurm_provider:
            self._provider_extra = type(self)._filter_extras(
                self.extra_slurm_provider,
                list(self.parsl_provider_base),
                SlurmProvider,
                "SlurmProvider",
            )

        if self.extra_executor:
            self._executor_extra = type(self)._filter_extras(
                self.extra_executor,
                list(self.parsl_executor_base),
                HighThroughputExecutor,
                "HighThroughputExecutor",
            )

    @staticmethod
    def _filter_extras(
        extras: dict[str, Any], used_keys: list[str], cls: type[T], name: str = ""
    ) -> dict[str, Any]:

        # drop duplicates
        duplicates = used_keys & extras.keys()
        if duplicates:
            logger.info(f"Ignoring duplicate {name} params: {sorted(duplicates)}")

        filtered = {k: v for k, v in extras.items() if k not in used_keys}

        # validate keys
        sig = inspect.signature(cls.__init__)
        has_var_kw = any(p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values())

        if not has_var_kw:
            valid = {k for k in sig.parameters if k != "self"}
            invalid = set(filtered) - valid
            if invalid:
                logger.warning(f"Warning: Removing invalid {name} params: {sorted(invalid)}")
                filtered = {k: v for k, v in filtered.items() if k in valid}

        return filtered

    def _validate_slurm_params(self) -> None:
        """Validate the Slurm configuration using SlurmChecker.

        Raises
        ------
            ValueError: If account, partition or QOS are invalid or inaccessible.
        """
        slurm_checker = SlurmChecker()
        if self.slurm_config.account and not slurm_checker.get_account_info(self.slurm_config.account):
            raise ValueError(f"Account: {self.slurm_config.account} not valid")

        partition_limits = None
        node_info = None

        if self.slurm_config.partition:
            if not slurm_checker.get_partition_info(self.slurm_config.partition):
                raise ValueError(f"Partition: {self.slurm_config.partition} not valid")

            if not slurm_checker.can_account_use_partition(
                self.slurm_config.account, self.slurm_config.partition
            ):
                raise ValueError(
                    f"Account: {self.slurm_config.account}"
                    f" cannot access partiton: {self.slurm_config.partition}"
                )

            partition_limits = slurm_checker.get_partition_limits(self.slurm_config.partition)
            node_info = slurm_checker.get_node_from_partition(self.slurm_config.partition)

        qos_limits = None
        if self.slurm_config.qos:
            if not slurm_checker.get_qos_info(self.slurm_config.qos):
                raise ValueError(f"QOS: {self.slurm_config.qos} not valid")

            if not slurm_checker.can_account_use_qos(self.slurm_config.account, self.slurm_config.qos):
                raise ValueError(
                    f"Account: {self.slurm_config.account} cannot access qos: {self.slurm_config.qos}"
                )

            qos_limits = slurm_checker.get_qos_limits(self.slurm_config.qos)

        max_cores_per_node = int(node_info["cpus"]) if node_info else None
        if max_cores_per_node and self.slurm_config.cores_per_worker:
            if self.slurm_config.cores_per_worker > max_cores_per_node:
                raise ValueError(
                    f"cores_per_work:{self.slurm_config.cores_per_worker}"
                    f"larger than the maximum in a node {max_cores_per_node}"
                )

        max_mem_per_node = float(node_info["real_memory"]) if node_info else None
        if max_mem_per_node and self.slurm_config.mem_per_worker:
            if self.slurm_config.mem_per_worker > max_mem_per_node:
                raise ValueError(
                    f"mem_per_work:{self.slurm_config.mem_per_worker}"
                    f"larger than the maximum mem in a node {max_mem_per_node}"
                )

        max_walltime_partition = (
            partition_limits["max_time_minutes"] if partition_limits else self.total_minutes
        )
        max_walltime_qos = qos_limits["max_time_minutes"] if qos_limits else self.total_minutes

        max_walltime_minutes = min(float(max_walltime_partition), float(max_walltime_qos))

        if self.total_minutes > float(max_walltime_minutes):
            raise ValueError(
                f"Walltime: {self.slurm_config.walltime} exceed the maximum time "
                f"{max_walltime_minutes} allowed by {self.slurm_config.partition} and {self.slurm_config.qos}"
            )

    def _initialize_parsl(self) -> None:
        executor_config = self.config.executor.config

        provider: SlurmProvider | SmartPBSProvider
        if self.scheduler == "slurm":
            provider = SlurmProvider(
                account=self.slurm_config.account,
                partition=self.slurm_config.partition,
                qos=self.slurm_config.qos,
                nodes_per_block=self.slurm_config.req_nodes,
                max_blocks=self.slurm_config.max_blocks,
                scheduler_options=self.slurm_config.scheduler_options,
                worker_init=self.slurm_config.worker_init,
                launcher=SrunLauncher(
                    debug=True,
                    overrides=self.slurm_config.overrides,
                ),
                walltime=self.slurm_config.walltime,
                cmd_timeout=self.slurm_config.cmd_timeout,
                **self._provider_extra or {},
            )

            executor = HighThroughputExecutor(
                label="ref_hpc_executor",
                cores_per_worker=self.slurm_config.cores_per_worker,
                mem_per_worker=self.slurm_config.mem_per_worker,
                max_workers_per_node=self.slurm_config.max_workers_per_node,
                cpu_affinity=self.slurm_config.cpu_affinity,
                provider=provider,
                **self._executor_extra or {},
            )

            hpc_config = ParslConfig(
                run_dir=self.slurm_config.log_dir,
                executors=[executor],
                retries=self.slurm_config.retries,
            )

        elif self.scheduler == "pbs":
            provider = SmartPBSProvider(
                account=self.account,
                queue=self.queue,
                worker_init=executor_config.get("worker_init", "source .venv/bin/activate"),
                nodes_per_block=_to_int(executor_config.get("nodes_per_block", 1)),
                cpus_per_node=_to_int(executor_config.get("cpus_per_node", None)),
                ncpus=_to_int(executor_config.get("ncpus", None)),
                mem=executor_config.get("mem", "4GB"),
                jobfs=executor_config.get("jobfs", "10GB"),
                storage=executor_config.get("storage", ""),
                init_blocks=executor_config.get("init_blocks", 1),
                min_blocks=executor_config.get("min_blocks", 0),
                max_blocks=executor_config.get("max_blocks", 1),
                parallelism=executor_config.get("parallelism", 1),
                scheduler_options=executor_config.get("scheduler_options", ""),
                launcher=SimpleLauncher(),
                walltime=self.walltime,
                cmd_timeout=int(executor_config.get("cmd_timeout", 120)),
            )

            executor = HighThroughputExecutor(
                label="ref_hpc_executor",
                cores_per_worker=self.cores_per_worker if self.cores_per_worker else 1,
                mem_per_worker=self.mem_per_worker,
                max_workers_per_node=_to_int(executor_config.get("max_workers_per_node", 16)),
                cpu_affinity=str(executor_config.get("cpu_affinity")),
                provider=provider,
                **self._executor_extra or {},
            )

            hpc_config = ParslConfig(
                run_dir=self.log_dir,
                executors=[executor],
                retries=int(executor_config.get("retries", 2)),
            )

        else:
            raise ValueError(f"Unsupported scheduler: {self.scheduler}")

        parsl.load(hpc_config)

    def run(
        self,
        definition: ExecutionDefinition,
        execution: Execution | None = None,
    ) -> None:
        """
        Run a diagnostic in process

        Parameters
        ----------
        definition
            A description of the information needed for this execution of the diagnostic
        execution
            A database model representing the execution of the diagnostic.
            If provided, the result will be updated in the database when completed.
        """
        # Submit the execution to the process pool
        # and track the future so we can wait for it to complete
        future = _process_run(
            definition=definition,
            log_level=self.config.log_level,
        )

        self.parsl_results.append(
            ExecutionFuture(
                future=future,
                definition=definition,
                execution_id=execution.id if execution else None,
            )
        )

    def join(self, timeout: float) -> None:  # noqa: PLR0912
        """
        Wait for all diagnostics to finish

        This will block until all diagnostics have completed or the timeout is reached.

        The effective timeout is the smaller of the caller-provided ``timeout`` and
        the configured Slurm/PBS walltime. When it is reached, any outstanding
        executions are marked as failed (retryable) so the next solve can pick
        them up rather than leaving them stuck with ``successful=None``.

        Parameters
        ----------
        timeout
            Timeout in seconds.
            Positive values are capped by the configured Slurm/PBS walltime.
            ``0`` or negative values disable the caller-provided timeout,
            but this method still waits at most until the configured walltime if one is set.

        Raises
        ------
        TimeoutError
            If the timeout is reached before all executions complete
        """
        start_time = time.time()
        refresh_time = 0.5

        walltime_seconds = self.total_minutes * 60
        if timeout and timeout > 0:
            effective_timeout = min(timeout, walltime_seconds) if walltime_seconds else timeout
        else:
            effective_timeout = walltime_seconds

        results = self.parsl_results
        t = tqdm(total=len(results), desc="Waiting for executions to complete", unit="execution")

        try:
            while results:
                # Iterate over a copy of the list and remove finished tasks
                for result in results[:]:
                    if not result.future.done():
                        continue

                    err = result.future.exception()
                    execution_result: ExecutionResult | None
                    if err is None:
                        try:
                            execution_result = result.future.result(timeout=0)
                        except Exception:
                            # Result retrieval failed even though the future
                            # reported done; treat as retryable system failure.
                            logger.exception(
                                f"Failed to retrieve result for {result.definition.execution_slug()!r}"
                            )
                            execution_result = ExecutionResult.build_from_failure(
                                result.definition, retryable=True
                            )
                    elif isinstance(err, DiagnosticError):
                        execution_result = err.result
                    else:
                        # Walltime kill, ExecutionLost, OOM, segfault, etc.
                        # Mark retryable so the next solve picks it up.
                        logger.error(
                            f"System-level failure for {result.definition.execution_slug()!r}: {err!r}"
                        )
                        execution_result = ExecutionResult.build_from_failure(
                            result.definition, retryable=True
                        )

                    assert isinstance(execution_result, ExecutionResult), (
                        "Execution result should be of type ExecutionResult"
                    )
                    # Process the result in the main process
                    # The results should be committed after each execution
                    with self.database.session.begin():
                        execution = (
                            self.database.session.get(Execution, result.execution_id)
                            if result.execution_id
                            else None
                        )
                        process_result(self.config, self.database, execution_result, execution)
                    logger.debug(f"Execution completed: {result}")
                    t.update(n=1)
                    results.remove(result)

                # Break early to avoid waiting for one more sleep cycle
                if len(results) == 0:
                    break

                elapsed_time = time.time() - start_time

                if effective_timeout and elapsed_time > effective_timeout:
                    self._fail_outstanding(results, t)
                    raise TimeoutError(f"Not all HPC executions completed within {effective_timeout}s")

                # Wait for a short time before checking for completed executions
                time.sleep(refresh_time)
        finally:
            t.close()
            if parsl.dfk():
                parsl.dfk().cleanup()

    def _fail_outstanding(self, results: list[ExecutionFuture], progress: Any) -> None:
        """Mark every outstanding execution as failed-retryable before raising."""
        for outstanding in list(results):
            logger.warning(
                f"HPC execution {outstanding.definition.execution_slug()!r} did not complete in time"
            )
            mark_execution_failed(
                self.database,
                self.config,
                outstanding.definition,
                outstanding.execution_id,
                retryable=True,
            )
            progress.update(n=1)
            results.remove(outstanding)

join(timeout) #

Wait for all diagnostics to finish

This will block until all diagnostics have completed or the timeout is reached.

The effective timeout is the smaller of the caller-provided timeout and the configured Slurm/PBS walltime. When it is reached, any outstanding executions are marked as failed (retryable) so the next solve can pick them up rather than leaving them stuck with successful=None.

Parameters:

Name Type Description Default
timeout float

Timeout in seconds. Positive values are capped by the configured Slurm/PBS walltime. 0 or negative values disable the caller-provided timeout, but this method still waits at most until the configured walltime if one is set.

required

Raises:

Type Description
TimeoutError

If the timeout is reached before all executions complete

Source code in packages/climate-ref/src/climate_ref/executor/hpc.py
def join(self, timeout: float) -> None:  # noqa: PLR0912
    """
    Wait for all diagnostics to finish

    This will block until all diagnostics have completed or the timeout is reached.

    The effective timeout is the smaller of the caller-provided ``timeout`` and
    the configured Slurm/PBS walltime. When it is reached, any outstanding
    executions are marked as failed (retryable) so the next solve can pick
    them up rather than leaving them stuck with ``successful=None``.

    Parameters
    ----------
    timeout
        Timeout in seconds.
        Positive values are capped by the configured Slurm/PBS walltime.
        ``0`` or negative values disable the caller-provided timeout,
        but this method still waits at most until the configured walltime if one is set.

    Raises
    ------
    TimeoutError
        If the timeout is reached before all executions complete
    """
    start_time = time.time()
    refresh_time = 0.5

    walltime_seconds = self.total_minutes * 60
    if timeout and timeout > 0:
        effective_timeout = min(timeout, walltime_seconds) if walltime_seconds else timeout
    else:
        effective_timeout = walltime_seconds

    results = self.parsl_results
    t = tqdm(total=len(results), desc="Waiting for executions to complete", unit="execution")

    try:
        while results:
            # Iterate over a copy of the list and remove finished tasks
            for result in results[:]:
                if not result.future.done():
                    continue

                err = result.future.exception()
                execution_result: ExecutionResult | None
                if err is None:
                    try:
                        execution_result = result.future.result(timeout=0)
                    except Exception:
                        # Result retrieval failed even though the future
                        # reported done; treat as retryable system failure.
                        logger.exception(
                            f"Failed to retrieve result for {result.definition.execution_slug()!r}"
                        )
                        execution_result = ExecutionResult.build_from_failure(
                            result.definition, retryable=True
                        )
                elif isinstance(err, DiagnosticError):
                    execution_result = err.result
                else:
                    # Walltime kill, ExecutionLost, OOM, segfault, etc.
                    # Mark retryable so the next solve picks it up.
                    logger.error(
                        f"System-level failure for {result.definition.execution_slug()!r}: {err!r}"
                    )
                    execution_result = ExecutionResult.build_from_failure(
                        result.definition, retryable=True
                    )

                assert isinstance(execution_result, ExecutionResult), (
                    "Execution result should be of type ExecutionResult"
                )
                # Process the result in the main process
                # The results should be committed after each execution
                with self.database.session.begin():
                    execution = (
                        self.database.session.get(Execution, result.execution_id)
                        if result.execution_id
                        else None
                    )
                    process_result(self.config, self.database, execution_result, execution)
                logger.debug(f"Execution completed: {result}")
                t.update(n=1)
                results.remove(result)

            # Break early to avoid waiting for one more sleep cycle
            if len(results) == 0:
                break

            elapsed_time = time.time() - start_time

            if effective_timeout and elapsed_time > effective_timeout:
                self._fail_outstanding(results, t)
                raise TimeoutError(f"Not all HPC executions completed within {effective_timeout}s")

            # Wait for a short time before checking for completed executions
            time.sleep(refresh_time)
    finally:
        t.close()
        if parsl.dfk():
            parsl.dfk().cleanup()

run(definition, execution=None) #

Run a diagnostic in process

Parameters:

Name Type Description Default
definition ExecutionDefinition

A description of the information needed for this execution of the diagnostic

required
execution Execution | None

A database model representing the execution of the diagnostic. If provided, the result will be updated in the database when completed.

None
Source code in packages/climate-ref/src/climate_ref/executor/hpc.py
def run(
    self,
    definition: ExecutionDefinition,
    execution: Execution | None = None,
) -> None:
    """
    Run a diagnostic in process

    Parameters
    ----------
    definition
        A description of the information needed for this execution of the diagnostic
    execution
        A database model representing the execution of the diagnostic.
        If provided, the result will be updated in the database when completed.
    """
    # Submit the execution to the process pool
    # and track the future so we can wait for it to complete
    future = _process_run(
        definition=definition,
        log_level=self.config.log_level,
    )

    self.parsl_results.append(
        ExecutionFuture(
            future=future,
            definition=definition,
            execution_id=execution.id if execution else None,
        )
    )

SlurmConfig #

Bases: BaseModel

Slurm Configurations

Source code in packages/climate-ref/src/climate_ref/executor/hpc.py
class SlurmConfig(BaseModel):
    """Slurm Configurations"""

    scheduler: Literal["slurm"]
    account: str
    username: str
    partition: str | None = None
    log_dir: str = "runinfo"
    qos: str | None = None
    req_nodes: Annotated[int, Field(strict=True, ge=1, le=1000)] = 1
    cores_per_worker: Annotated[int, Field(strict=True, ge=1, le=1000)] = 1
    mem_per_worker: Annotated[float, Field(strict=True, gt=0, lt=1000.0)] | None = None
    max_workers_per_node: Annotated[int, Field(strict=True, ge=1, le=1000)] = 16
    validation: StrictBool = False
    walltime: str = "00:30:00"
    scheduler_options: str = ""
    retries: Annotated[int, Field(strict=True, ge=0, le=3)] = 2
    max_blocks: Annotated[int, Field(strict=True, ge=1)] = 1  # one block mean one job?
    worker_init: str = ""
    overrides: str = ""
    cmd_timeout: Annotated[int, Field(strict=True, ge=0)] = 120
    cpu_affinity: str = "none"

    @model_validator(mode="before")
    def _check_parition_qos(cls, data: Any) -> Any:
        if not ("partition" in data or "qos" in data):
            raise ValueError("partition or qos is needed")
        return data

    @field_validator("scheduler_options")
    def _validate_sbatch_syntax(cls, v: str | None) -> Any:
        if not v:
            return v

        sbatch_pattern = re.compile(
            r"^\s*#SBATCH\s+"  # Start with #SBATCH
            r"(?:-\w+\s+[^\s]+"  # Option-value pairs
            r"(?:\s+-\w+\s+[^\s]+)*)"  # Additional options
            r"\s*$",
            re.IGNORECASE | re.MULTILINE,
        )

        invalid_lines = [
            line
            for line in v.split("\n")
            if not (line.strip().upper().startswith("#SBATCH") and sbatch_pattern.match(line.strip()))
        ]

        if invalid_lines:
            error_msg = (
                "Invalid SBATCH directives:\n"
                + "\n".join(invalid_lines)
                + "\n"
                + "Expected format: '#SBATCH -option value [-option value ...]'"
            )
            raise ValueError(error_msg)
        return v

    @field_validator("walltime")
    def _validate_walltime(cls, v: str) -> str:
        pattern = r"^(\d+-)?\d{1,5}:[0-5][0-9]:[0-5][0-9]$"
        if not re.match(pattern, v):
            raise ValueError("Walltime must be in `D-HH:MM:SS/HH:MM:SS` format")
        return v

limit_from_env(*args, **kwargs) #

Get the memory limits from env variables

Source code in packages/climate-ref/src/climate_ref/executor/hpc.py
def limit_from_env(*args: Any, **kwargs: Any) -> float | None:
    """Get the memory limits from env variables"""
    val = os.getenv("MEMORY_LIMIT_PARSL_JOB_GB")
    if not val:
        return None
    try:
        return float(val)
    except ValueError:
        return None

with_memory_limit(limit_gb) #

Set memory limit for a parsl worker

Source code in packages/climate-ref/src/climate_ref/executor/hpc.py
def with_memory_limit(limit_gb: float | Callable[..., float | None]) -> Callable[[F], F]:
    """Set memory limit for a parsl worker"""

    def decorator(func: F) -> F:
        def wrapper(*args: Any, **kwargs: Any) -> Any:
            try:
                current_limit = limit_gb(*args, **kwargs) if callable(limit_gb) else limit_gb
            except Exception:
                current_limit = None

            if current_limit is not None and current_limit > 0:
                bytes_limit = int(current_limit * 1024 * 1024 * 1024)
                _, hard0 = resource.getrlimit(resource.RLIMIT_AS)
                soft = min(bytes_limit, hard0) if hard0 > 0 else bytes_limit
                resource.setrlimit(resource.RLIMIT_AS, (soft, hard0))
            return func(*args, **kwargs)

        return cast(F, wrapper)

    return decorator