ezpz.dist

• See: ezpz.dist

dist.py

Contains methods for initializing distributed communication.

barrier(device=None, group=torch.distributed.GroupMember.WORLD, async_op=False, device_ids=None)

Barrier for all processes in the group.

This collective blocks processes until the whole group enters this function, if async_op is False, or if async work handle is called on wait().

Parameters:

Name	Type	Description	Default
device	device | int | str	The device to synchronize. If None, the default device will be used. Defaults to None.	None
group	ProcessGroup | None	The process group to work on. If None, the default process group (WORLD) will be used. Defaults to torch.distributed.GroupMember.WORLD.	WORLD
async_op	bool	If True, the barrier will be asynchronous.	False
device_ids	str | Iterable | None	The device IDs to synchronize.	None

Returns:

Type	Description
Work | None	torch.distributed.Work | None: If async_op is True, returns a work handle. If async_op is False, returns None.

Source code in src/ezpz/dist.py

def barrier(
    device: Optional[torch.device | int | str] = None,
    group: (
        torch.distributed.ProcessGroup | None  # type:ignore
    ) = torch.distributed.GroupMember.WORLD,  # type:ignore
    async_op: bool = False,
    device_ids: str | Iterable | None = None,
) -> torch.distributed.Work | None:  # type:ignore
    """
    Barrier for all processes in the group.

    This collective blocks processes until the whole group enters this function,
    if async_op is False, or if async work handle is called on wait().

    Args:
        device (torch.device | int | str, optional): The device to synchronize.
            If None, the default device will be used. Defaults to None.
        group (torch.distributed.ProcessGroup | None, optional): The process group to work on.
            If None, the default process group (WORLD) will be used.
            Defaults to torch.distributed.GroupMember.WORLD.
        async_op (bool, optional): If True, the barrier will be asynchronous.
        device_ids (str | Iterable | None, optional): The device IDs to synchronize.

    Returns:
        torch.distributed.Work | None: If async_op is True, returns a work handle.
            If async_op is False, returns None.
    """
    try:
        torch.distributed.barrier(
            group=group, async_op=async_op, device_ids=device_ids
        )
    except Exception:
        logger.warning(
            "Unable to use `torch.distributed.barrier` "
            "for this process group. "
            "Falling back to `mpi4py` barrier."
        )
        MPI.COMM_WORLD.barrier()

check(framework='pytorch', backend='deepspeed', port='5432')

Check if the framework is installed and working

Source code in src/ezpz/dist.py

def check(
    framework: str = "pytorch",
    backend: str = "deepspeed",
    port: int | str = "5432",
):
    """Check if the framework is installed and working"""
    from ezpz.configs import FRAMEWORKS

    if framework in FRAMEWORKS["pytorch"]:
        _ = setup_torch(
            backend=backend,
            port=str(port),
        )
    elif framework in FRAMEWORKS["tensorflow"]:
        _ = setup_tensorflow()
    else:
        raise ValueError(f"Unable to parse framework: {framework}")

cleanup()

Cleanup the distributed environment. This function destroys the process group if it is initialized.

Example

cleanup()

Source code in src/ezpz/dist.py

def cleanup() -> None:
    """
    Cleanup the distributed environment.
    This function destroys the process group if it is initialized.

    Example:
        >>> cleanup()
    """
    if torch.distributed.is_initialized():
        torch.distributed.destroy_process_group()

get_cpus_per_node()

Get the number of CPUs per node

Source code in src/ezpz/dist.py

def get_cpus_per_node() -> int:
    """Get the number of CPUs per node"""
    from sh import getconf as sh_getconf  # type:ignore noqa

    return int(sh_getconf("_NPROCESSORS_ONLN").rstrip("\n"))

get_dist_info(framework=None, verbose=None, hostfile=None)

Get distributed info.

Parameters:

Name	Type	Description	Default
framework	str	Framework to use. Defaults to None.	None
verbose	bool	Whether to print the info. Defaults to None.	None
hostfile	PathLike	Path to the hostfile. Defaults to None.	None

Returns:

Name	Type	Description
dict	dict[str, str | int | list]	Dictionary containing the distributed info.

Source code in src/ezpz/dist.py

def get_dist_info(
    framework: Optional[str] = None,
    verbose: Optional[bool] = None,
    hostfile: Optional[PathLike] = None,
) -> dict[str, str | int | list]:
    """Get distributed info.

    Args:
        framework (str, optional): Framework to use. Defaults to None.
        verbose (bool, optional): Whether to print the info. Defaults to None.
        hostfile (PathLike, optional): Path to the hostfile. Defaults to None.

    Returns:
        dict: Dictionary containing the distributed info.
    """
    dist_info = _get_dist_info(
        hostfile=hostfile,
        framework=framework,
    )
    if verbose:
        import json

        logger.info(
            f"DistInfo={json.dumps(dist_info, indent=4, sort_keys=True)}"
        )
    return dist_info

get_free_port()

Get a free port on the local machine.

Returns:

Name	Type	Description
int		A free port number that can be used for communication.

Source code in src/ezpz/dist.py

def get_free_port():
    """
    Get a free port on the local machine.

    Returns:
        int: A free port number that can be used for communication.
    """
    sock = socket.socket()
    sock.bind(
        ("127.0.0.1", 0)
    )  # Bind to an available port on the loopback interface
    port = sock.getsockname()[1]
    sock.close()
    return port

get_gpus_per_node()

Get the number of GPUs per node

Source code in src/ezpz/dist.py

def get_gpus_per_node() -> int:
    """Get the number of GPUs per node"""
    # return torch.cuda.device_count() if torch.cuda.is_available() else (
    #     (
    #         ipex.xpu.device_count() if ipex is not None else (
    #             get_cpus_per_node()
    #         )
    #     )
    # )
    # if _assert:
    #     raise RuntimeError(
    #         'No {X, G}pus found; but _assert specified. Returning !!'
    #     )
    # logger.warning('No {x,g}-pus found, returning' + f'{cpus_per_node}')
    ngpu_per_host = os.environ.get("NGPU_PER_HOST", None)
    if ngpu_per_host is not None:
        return int(ngpu_per_host)
    if torch.cuda.is_available():
        return torch.cuda.device_count()
    if torch.xpu.is_available():
        return torch.xpu.device_count()
    if ipex is not None and torch.xpu.is_available():
        return ipex.xpu.device_count()
    if torch.backends.mps.is_available():
        # XXX: Maybe we're running MPI with multiple MPS devices?
        return get_world_size_in_use()
    return 0

get_hostfile_with_fallback(hostfile=None)

Get the hostfile from the environment or create one if it doesn't exist

Source code in src/ezpz/dist.py

def get_hostfile_with_fallback(hostfile: Optional[PathLike] = None) -> Path:
    """Get the hostfile from the environment or create one if it doesn't exist"""
    from ezpz.configs import get_scheduler

    scheduler = get_scheduler()
    if scheduler.lower() == "unknown":
        logger.debug("Unknown scheduler")
        hostfile = Path(os.getcwd()).joinpath("hostfile")
    if scheduler.lower() == "slurm":
        hostfile = make_hostfile_from_slurm_env()
        assert Path(hostfile).is_file()
    if hostfile is None:
        hfp = os.environ.get(
            "PBS_NODEFILE",
            os.environ.get(
                "HOSTFILE",
                None,  # fallback_hostfile.as_posix()
            ),
        )
        if (
            hfp is None or not Path(hfp).is_file()
            # and scheduler == 'PBS'
        ):
            if scheduler == "PBS":
                # hfp = Path(get_pbs_nodefile_from_qstat())
                nodefile = ezpz.pbs.get_pbs_nodefile()
                assert nodefile is not None, (
                    "Unable to get PBS_NODEFILE from `qstat` or `ezpz.pbs`!"
                )
                hfp = Path(nodefile)
            else:
                # create makeshift hostfile containing 'localhost'
                hfp = Path(os.getcwd()).joinpath("hostfile")
                hfp.touch(exist_ok=True)
                write_localhost_to_hostfile(hfp)
    else:
        hfp = Path(hostfile)
    assert hfp is not None and Path(hfp).is_file()
    assert Path(hfp).is_file()
    hostfile = Path(hfp).as_posix()
    # if hfp is not None:
    # hostfile, hosts = get_hosts_from_hostfile(hostfile)
    # hosts = [h.split('.')[0] for h in hosts]
    # if scheduler == 'PBS':
    #     os.environ['PBS_NODEFILE'] = hostfile  # hfp.as_posix()
    hfname = f"{scheduler.upper()}_NODEFILE"
    if hfname not in os.environ:
        os.environ |= {hfname: hostfile}
    # os.environ[f'{scheduler.upper()}_NODEFILE'] = hostfile
    return Path(hfp)

get_hostname()

Get the hostname of the current machine.

Returns:

Name	Type	Description
str	str	The hostname of the current machine.

Source code in src/ezpz/dist.py

def get_hostname() -> str:
    """Get the hostname of the current machine.

    Returns:
        str: The hostname of the current machine.
    """
    import socket

    try:
        hostname = socket.gethostbyaddr(socket.gethostname())[0].lower()
    # except socket.herror as exc:
    except Exception:
        from sh import hostname as sh_hostname  # type:ignore[missingTypeStubs]

        hostname = sh_hostname()
        # if get_rank() == 0:
        #     logger.debug('Unable to determine hostname with `socket`.')
        #     logger.debug(f'hostname from`sh`: {hostname}')
        #     # logger.exception(exc)
    return hostname.rstrip("\n")

get_hosts_from_hostfile(hostfile=None)

Get hosts from the hostfile or environment variables.

Parameters:

Name	Type	Description	Default
hostfile	str | Path	Path to the hostfile. Defaults to None.	None

Returns:

Type	Description
tuple[str, list[str]]	tuple[str, list[str]]: Tuple containing the hostfile path and a list of hosts.

Example

get_hosts_from_hostfile("/path/to/hostfile") ('/path/to/hostfile', ['host1', 'host2', ...])

Source code in src/ezpz/dist.py

def get_hosts_from_hostfile(
    hostfile: Optional[str | Path] = None,  # type:ignore[reportDeprecated]
) -> tuple[str, list[str]]:
    """
    Get hosts from the hostfile or environment variables.

    Args:
        hostfile (str | Path, optional): Path to the hostfile. Defaults to None.

    Returns:
        tuple[str, list[str]]: Tuple containing the hostfile path and a list of hosts.

    Example:
        >>> get_hosts_from_hostfile("/path/to/hostfile")
        ('/path/to/hostfile', ['host1', 'host2', ...])
    """
    # hostfile = '' if hostfile is None else hostfile
    hostname = get_hostname()
    hostfile = os.environ.get(
        "HOSTFILE",
        os.environ.get(
            "PBS_NODEFILE",
            os.environ.get(
                "COBALT_NODEFILE",
                None,
            ),
        ),
    )
    hosts: list[str] = []
    assert hostfile is not None
    if Path(hostfile).is_file():
        if get_rank() == 0:
            logger.debug(f"Reading hosts from {hostfile}")
        hpath = Path(hostfile).resolve().absolute()
        with hpath.open("r") as f:
            hosts.extend([h.rstrip("\n") for h in f.readlines()])
    else:
        hosts.append(hostname)
    return Path(hostfile).as_posix(), hosts

get_local_rank()

Return get_rank() % get_gpus_per_node()

Returns:

Name	Type	Description
int	int	The local rank of the current process within its node. This is calculated as the current rank modulo the number of GPUs per node.

Example

local_rank = get_local_rank() print(f"Local rank of the current process: {local_rank}")

Source code in src/ezpz/dist.py

def get_local_rank() -> int:
    """Return `get_rank() % get_gpus_per_node()`

    Returns:
        int: The local rank of the current process within its node.
            This is calculated as the current rank modulo the number of GPUs per node.

    Example:
        >>> local_rank = get_local_rank()
        >>> print(f"Local rank of the current process: {local_rank}")
    """
    return int(get_rank() % get_gpus_per_node()) if get_world_size() > 1 else 0

get_machine(hostname=None)

Get the machine name from the hostname.

Parameters:

Name	Type	Description	Default
hostname	str	The hostname to check. Defaults to None.	None

Returns:

Name	Type	Description
str	str	The machine name.

Example

get_machine("frontier") "Frontier"

Source code in src/ezpz/dist.py

def get_machine(hostname: Optional[str] = None) -> str:
    """Get the machine name from the hostname.

    Args:
        hostname (str, optional): The hostname to check. Defaults to None.

    Returns:
        str: The machine name.

    Example:
        >>> get_machine("frontier")
        "Frontier"
    """

    if hostname is None:
        try:
            hostname = socket.gethostbyaddr(socket.gethostname())[0]
        except Exception:
            try:
                hostname = socket.gethostname()
            except Exception:
                logger.warning("Unable to determine hostname!")
                hostname = "unknown"
    if hostname.startswith("frontier"):
        return "Frontier"
    if hostname.startswith("sophia"):
        return "Sophia"
    if hostname.startswith("theta"):
        return "ThetaGPU"
    if hostname.startswith("x1"):
        return "SunSpot"
    if hostname.startswith("x3"):
        if (pbs_host := os.environ.get("PBS_O_HOST", None)) is not None:
            if str(pbs_host).startswith("sirius"):
                return "Sirius"
            return "Polaris"
        return "Polaris"
    if hostname.startswith("x4"):
        return "Aurora"
    if hostname.startswith("login"):
        return "Perlmutter"
    if hostname.startswith("nid"):
        return "Perlmutter"
    return f"{hostname}"

get_node_index()

Get the index of the current node in the hostfile

Source code in src/ezpz/dist.py

def get_node_index() -> int:
    """Get the index of the current node in the hostfile"""
    return get_rank() % get_num_nodes()

get_nodes_from_hostfile(hostfile)

Get the nodes from the hostfile.

Parameters:

Name	Type	Description	Default
hostfile	PathLike	The path to the hostfile.	required

Returns:

Type	Description
list[str]	list[str]: A list of nodes from the hostfile.

Source code in src/ezpz/dist.py

def get_nodes_from_hostfile(
    hostfile: PathLike,
) -> list[str]:
    """Get the nodes from the hostfile.

    Args:
        hostfile (PathLike): The path to the hostfile.

    Returns:
        list[str]: A list of nodes from the hostfile.
    """
    # cobalt_nodefile = get_cobalt_nodefile()
    fpath = Path(hostfile)
    assert fpath.is_file()
    with fpath.open("r") as f:
        nodes = [i.rstrip("\n") for i in f.readlines()]
    return nodes

get_num_nodes(hostfile=None)

Get the number of nodes from the hostfile

Source code in src/ezpz/dist.py

def get_num_nodes(hostfile: Optional[PathLike] = None) -> int:
    """Get the number of nodes from the hostfile"""
    num_nodes = os.environ.get("SLURM_NNODES", None)
    if num_nodes is not None:
        return int(num_nodes)
    hfp = get_hostfile_with_fallback(hostfile)
    hosts = [h.split(".")[0] for h in get_nodes_from_hostfile(hfp)]
    return len(hosts)

get_rank()

Get current MPI rank.

Returns:

Name	Type	Description
int	int	The rank of the current process in the MPI world.

Example

rank = get_rank() print(f"Current MPI rank: {rank}")

Source code in src/ezpz/dist.py

def get_rank() -> int:
    """Get current MPI rank.

    Returns:
        int: The rank of the current process in the MPI world.

    Example:
        >>> rank = get_rank()
        >>> print(f"Current MPI rank: {rank}")
    """
    return int(MPI.COMM_WORLD.Get_rank())

get_torch_backend()

Get the current PyTorch backend.

Returns:

Name	Type	Description
str	str	The current PyTorch backend.

Source code in src/ezpz/dist.py

def get_torch_backend() -> str:
    """
    Get the current PyTorch backend.

    Returns:
        str: The current PyTorch backend.
    """
    backend_from_env = os.environ.get("TORCH_BACKEND", None)
    if backend_from_env is not None:
        return backend_from_env
    return (
        "nccl"
        if torch.cuda.is_available()
        else (
            get_torch_backend_on_xpu() if torch.xpu.is_available() else "gloo"
        )
    )

get_torch_backend_on_xpu()

Deal with breaking change introduced in torch 2.6:

See: https://github.com/pytorch/pytorch/pull/141856

Example:

```python
>>> torch_version = float('.'join(torch.__version__.split('.')[:2]))
>>> if torch_version > 2.5:
>>>     backend = 'xccl'
>>> else:
>>>     backend = 'ccl'
```

Source code in src/ezpz/dist.py

def get_torch_backend_on_xpu() -> str:
    """Deal with breaking change introduced in torch 2.6:

    See: https://github.com/pytorch/pytorch/pull/141856

    Example:

        ```python
        >>> torch_version = float('.'join(torch.__version__.split('.')[:2]))
        >>> if torch_version > 2.5:
        >>>     backend = 'xccl'
        >>> else:
        >>>     backend = 'ccl'
        ```
    """
    torch_version = get_torch_version_as_float()
    assert torch.xpu.is_available()
    return "xccl" if torch_version > 2.5 else "ccl"

get_torch_device(*, device_type=None, as_torch_device=None)

Get the current PyTorch device.

Parameters:

Name	Type	Description	Default
device_type	str	The type of device to return. If None, it will be determined automatically. Defaults to None.	None
as_torch_device	bool	If True, return a torch.device object. If False, return a string representing the device type. Defaults to False.	None

Returns:

Type	Description
str | device	str | torch.device: The current PyTorch device. If as_torch_device is True, returns a torch.device object. If as_torch_device is False, returns a string representing the device type.

Example

get_torch_device() # Returns the current device type as a string

Source code in src/ezpz/dist.py

def get_torch_device(
    *,
    device_type: Optional[str] = None,
    as_torch_device: Optional[bool] = None,
) -> str | torch.device:
    """Get the current PyTorch device.

    Args:
        device_type (str, optional): The type of device to return.
            If None, it will be determined automatically.
            Defaults to None.
        as_torch_device (bool, optional): If True, return a torch.device object.
            If False, return a string representing the device type.
            Defaults to False.

    Returns:
        str | torch.device: The current PyTorch device.
            If as_torch_device is True, returns a torch.device object.
            If as_torch_device is False, returns a string representing the device type.

    Example:
        >>> get_torch_device()  # Returns the current device type as a string
    """
    device_type = get_torch_device_type(device_type)
    return torch.device(device_type) if as_torch_device else device_type

get_torch_device_type(device_type=None)

Get the current PyTorch device type.

Parameters:

Name	Type	Description	Default
device_type	str	The type of device to return. If None, it will be determined automatically. Defaults to None.	None

Returns:

Name	Type	Description
str	str	The current PyTorch device type. Possible values are "cpu", "mps", "xpu", or "cuda".

Example

get_torch_device_type() # Returns the current device type as a string

Source code in src/ezpz/dist.py

def get_torch_device_type(device_type: Optional[str] = None) -> str:
    """Get the current PyTorch device type.

    Args:
        device_type (str, optional): The type of device to return.
            If None, it will be determined automatically.
            Defaults to None.

    Returns:
        str: The current PyTorch device type.
            Possible values are "cpu", "mps", "xpu", or "cuda".

    Example:
        >>> get_torch_device_type()  # Returns the current device type as a string
    """
    if device_type is not None:
        assert device_type in (
            "cpu",
            "mps",
            "xpu",
            "cuda",
        )
        logger.warning(
            " ".join(
                [
                    f"device_type: {device_type} passed to",
                    "ezpz.dist.get_torch_device_type",
                ]
            )
        )
        return device_type
    if (tdevice := os.environ.get("TORCH_DEVICE")) is not None:
        if get_rank() == 0:
            logger.warning(f"Caught TORCH_DEVICE={tdevice} from environment!")
        tdevice = tdevice.lower()
        assert tdevice is not None and tdevice in (
            "cpu",
            "mps",
            "xpu",
            "cuda",
        )
        return tdevice.lower()
    return (
        "xpu"
        if torch.xpu.is_available()
        else (
            "cuda"
            if torch.cuda.is_available()
            else (
                "mps"
                if (
                    torch.backends.mps.is_available()
                    and torch.get_default_dtype() != torch.float64
                )
                else "cpu"
            )
        )
    )

get_torch_version_as_float()

Get the PyTorch version as a float.

Returns:

Name	Type	Description
float	float	The PyTorch version as a float, e.g., 1.10.0 -> 1.10

Source code in src/ezpz/dist.py

def get_torch_version_as_float() -> float:
    """Get the PyTorch version as a float.

    Returns:
        float: The PyTorch version as a float, e.g., 1.10.0 -> 1.10
    """
    return float(".".join(torch.__version__.split(".")[:2]))

get_world_size(total=None, in_use=None)

Get the total number of *PUs available or currently in use. Args: total (bool, optional): If True, return the total number of *PUs available. Defaults to None. in_use (bool, optional): If True, return the number of *PUs currently in use. Defaults to None.

Returns:

Name	Type	Description
int	int	The total number of *PUs available or currently in use. If both total and in_use are None, it returns the number of *PUs currently in use by the MPI communicator.

Example

world_size = get_world_size(total=True) print(f"Total number of *PUs available: {world_size}") world_size_in_use = get_world_size(in_use=True) print(f"Number of *PUs currently in use: {world_size_in_use}")

Source code in src/ezpz/dist.py

def get_world_size(
    total: Optional[bool] = None,
    in_use: Optional[bool] = None,
) -> int:
    """
    Get the total number of *PUs available or currently in use.
    Args:
        total (bool, optional): If True, return the total number of *PUs available.
            Defaults to None.
        in_use (bool, optional): If True, return the number of *PUs currently in use.
            Defaults to None.

    Returns:
        int: The total number of *PUs available or currently in use.
            If both `total` and `in_use` are None, it returns the number of *PUs
            currently in use by the MPI communicator.

    Example:
        >>> world_size = get_world_size(total=True)
        >>> print(f"Total number of *PUs available: {world_size}")
        >>> world_size_in_use = get_world_size(in_use=True)
        >>> print(f"Number of *PUs currently in use: {world_size_in_use}")
    """
    if total:
        return get_world_size_total()
    if in_use:
        return get_world_size_in_use()
    # TODO: Deal with subtlety between:
    # 1. 'world_size' == total AVAILABLE gpus (for record keeping)
    # 2. 'world_size' == number of gpus CURRENTLY IN USE (from {`mpi`, ...})
    # ¯\_(ツ)_/¯
    try:
        world_size = int(MPI.COMM_WORLD.Get_size())
    except Exception:
        num_nodes = get_num_nodes()
        gpus_per_node = get_gpus_per_node()
        world_size = num_nodes * gpus_per_node
        logger.warning(
            "MPI not initialized !!"
            "Calculating (and using!! ??) "
            "[world_size]=[(num_nodes) x (num_*pus_per_node)]=[num_*pus_total]"
            f"[{world_size}]=[({num_nodes}) x ({gpus_per_node})]"
        )
    # if world_size == 1:
    #     gpus_per_node = get_gpus_per_node()
    #     num_nodes = get_num_nodes()
    #     world_size = num_nodes * gpus_per_node
    return world_size

get_world_size_in_use()

Get number of currently in use MPI ranks

Returns:

Name	Type	Description
int	int	The number of currently in use MPI ranks. This is the size of the MPI communicator. It is the number of processes that are currently running and participating in the distributed computation.

Example

world_size_in_use = get_world_size_in_use() print(f"Number of currently in use MPI ranks: {world_size_in_use}")

Source code in src/ezpz/dist.py

def get_world_size_in_use() -> int:
    """Get number of currently in use MPI ranks

    Returns:
        int: The number of currently in use MPI ranks.
            This is the size of the MPI communicator.
            It is the number of processes that are currently running
            and participating in the distributed computation.

    Example:
        >>> world_size_in_use = get_world_size_in_use()
        >>> print(f"Number of currently in use MPI ranks: {world_size_in_use}")
    """
    return int(MPI.COMM_WORLD.Get_size())

get_world_size_total()

Calculate total AVAILABLE *PUs as:

total = [num_hosts] * [num_*pu_per_host]

Returns:

Name	Type	Description
int	int	The total number of available *PUs across all nodes. This is the product of the number of nodes and the number of *PUs per node.

Example

total_pus = get_world_size_total() print(f"Total available *PUs: {total_pus}")

Source code in src/ezpz/dist.py

def get_world_size_total() -> int:
    """Calculate total AVAILABLE *PUs as:

    total = [num_hosts] * [num_*pu_per_host]

    Returns:
        int: The total number of available *PUs across all nodes.
            This is the product of the number of nodes and the number of *PUs per node.

    Example:
        >>> total_pus = get_world_size_total()
        >>> print(f"Total available *PUs: {total_pus}")
    """
    # nhosts = get_num_nodes()
    # ngpu_per_host = get_gpus_per_node()
    # return ngpu_per_host * nhosts
    return get_gpus_per_node() * get_num_nodes()

include_file(f)

Check if a file should be included based on its extension.

Parameters:

Name	Type	Description	Default
f	PathLike	The file path to check.	required

Returns:

Name	Type	Description
bool		True if the file should be included, False otherwise.

Source code in src/ezpz/dist.py

def include_file(f: PathLike):
    """
    Check if a file should be included based on its extension.

    Args:
        f (PathLike): The file path to check.

    Returns:
        bool: True if the file should be included, False otherwise.
    """
    fpath = Path(f)
    return fpath.suffix in {
        ".py",
        ".yaml",
        ".sh",
        ".md",
        ".qmd",
        ".yml",
        ".toml",
    }

init_deepspeed(dist_backend=None, auto_mpi_discovery=True, distributed_port=29500, verbose=True, timeout=None, init_method=None, dist_init_required=None, config=None, rank=None, world_size=None)

Initialize DeepSpeed distributed environment.

Parameters:

Name	Type	Description	Default
dist_backend	str	The distributed backend to use. Defaults to None.	None
auto_mpi_discovery	bool	Whether to automatically discover MPI. Defaults to True.	True
distributed_port	int | str	The port for distributed communication. Defaults to 29500.	29500
verbose	bool	Whether to print verbose logs. Defaults to True.	True
timeout	int | None	Timeout in seconds for distributed initialization. Defaults to None.	None
init_method	str	Initialization method for distributed training. Defaults to None.	None
dist_init_required	bool	Whether distributed initialization is required. Defaults to None.	None
config	dict	DeepSpeed configuration dictionary. Defaults to None.	None
rank	int | None	Rank of the current process. Defaults to None.	None
world_size	int | None	Total number of processes. Defaults to None.	None

Raises:

Type	Description
ImportError	If DeepSpeed is not installed.
Exception	If there is an error during DeepSpeed initialization.

Example

init_deepspeed( ... dist_backend="nccl", ... distributed_port=29500, ... verbose=True, ... timeout=3600, ... rank=0, ... world_size=4, ... )

Source code in src/ezpz/dist.py

def init_deepspeed(
    dist_backend: Optional[str] = None,
    auto_mpi_discovery: bool = True,
    distributed_port: int | str = 29500,
    verbose: bool = True,
    timeout: Optional[int] = None,
    init_method: Optional[str] = None,
    dist_init_required: Optional[bool] = None,
    config: Optional[dict] = None,
    rank: Optional[int] = None,
    world_size: Optional[int] = None,
):
    """
    Initialize DeepSpeed distributed environment.

    Args:
        dist_backend (str, optional): The distributed backend to use.
            Defaults to None.
        auto_mpi_discovery (bool, optional): Whether to automatically discover MPI.
            Defaults to True.
        distributed_port (int | str, optional): The port for distributed communication.
            Defaults to 29500.
        verbose (bool, optional): Whether to print verbose logs. Defaults to True.
        timeout (int | None, optional): Timeout in seconds for distributed initialization.
            Defaults to None.
        init_method (str, optional): Initialization method for distributed training.
            Defaults to None.
        dist_init_required (bool, optional): Whether distributed initialization is required.
            Defaults to None.
        config (dict, optional): DeepSpeed configuration dictionary. Defaults to None.
        rank (int | None, optional): Rank of the current process. Defaults to None.
        world_size (int | None, optional): Total number of processes. Defaults to None.

    Raises:
        ImportError: If DeepSpeed is not installed.
        Exception: If there is an error during DeepSpeed initialization.

    Example:
        >>> init_deepspeed(
        ...     dist_backend="nccl",
        ...     distributed_port=29500,
        ...     verbose=True,
        ...     timeout=3600,
        ...     rank=0,
        ...     world_size=4,
        ... )
    """
    try:
        import deepspeed  # noqa type:ignore

        os.environ["DEEPSPEED_VERSION"] = deepspeed.__version__
    except ImportError as e:
        logger.warning(
            "Unable to import deepspeed. Please install it to use DeepSpeed features."
        )
        raise ImportError(
            "DeepSpeed is not installed. Install with 'pip install deepspeed'"
        ) from e

    rank = get_rank() if rank is None else rank
    world_size = get_world_size() if world_size is None else world_size
    os.environ["WORLD_SIZE"] = str(world_size)
    try:
        import deepspeed  # type:ignore

        # logger.warning(f'Setting {timeout=}')
        dt = 3600 if timeout is None else timeout
        deepspeed.init_distributed(
            dist_backend=dist_backend,
            auto_mpi_discovery=auto_mpi_discovery,
            distributed_port=int(distributed_port),
            verbose=verbose,
            timeout=datetime.timedelta(seconds=dt),
            init_method=init_method,
            dist_init_required=dist_init_required,
            config=config,
            rank=rank,
            world_size=world_size,
        )
    except Exception as exc:
        logger.warning("Unable to `import deepspeed`. Exiting!")
        logger.exception(exc)
        raise exc

init_process_group(rank, world_size, timeout, backend=None)

Initialize the PyTorch distributed process group.

Parameters:

Name	Type	Description	Default
rank	int | str	The rank of the current process.	required
world_size	int | str	The total number of processes.	required
timeout	str | int | timedelta	Timeout for the process group initialization.	required
backend	str	The backend to use for distributed training. Defaults to None, which will use the default backend based on the device.	None

Source code in src/ezpz/dist.py

def init_process_group(
    rank: int | str,
    world_size: int | str,
    timeout: str | int | timedelta,
    backend: Optional[str] = None,
) -> None:
    """
    Initialize the PyTorch distributed process group.

    Args:
        rank (int | str): The rank of the current process.
        world_size (int | str): The total number of processes.
        timeout (str | int | timedelta): Timeout for the process group initialization.
        backend (str, optional): The backend to use for distributed training.
            Defaults to None, which will use the default backend based on the device.
    """
    backend = get_torch_backend() if backend is None else backend
    if get_rank() == 0:
        logger.info(
            " ".join(
                [
                    "Calling torch.distributed.init_process_group_with:",
                    f"rank={rank}",
                    f"world_size={world_size}",
                    f"backend={backend}",
                ]
            )
        )
    if not isinstance(timeout, timedelta):
        timeout = timedelta(
            seconds=int(timeout),
        )
    if not torch.distributed.is_initialized():  # type:ignore
        torch.distributed.init_process_group(  # type:ignore
            backend=backend,
            timeout=timeout,
            rank=int(rank),
            world_size=int(world_size),
            init_method="env://",
        )

log_dict_as_bulleted_list(d, name=None)

Print a dict as bullets.

Source code in src/ezpz/dist.py

def log_dict_as_bulleted_list(d: dict, name: Optional[str] = None):
    """Print a dict as bullets."""
    tag = name or getattr(d, "__qualname__", "dict")
    lines = [f"[{tag}]:"] + [f"  • {k}={v}" for k, v in d.items()]
    logger.info("\n\n" + "\n".join(lines) + "\n")

make_hostfile_from_slurm_env(outfile=None)

Make a hostfile from the SLURM_NODELIST environment variable

Source code in src/ezpz/dist.py

def make_hostfile_from_slurm_env(outfile: Optional[PathLike] = None) -> Path:
    """Make a hostfile from the SLURM_NODELIST environment variable"""
    nodes = os.environ.get("SLURM_NODELIST", None)
    # if nodes is not None:
    assert nodes is not None
    # machine = get_machine()
    prefix, idxs = nodes.split("[")
    idxs = idxs.rstrip("]")
    idxs = "-".join(idxs.split(",")).split("-")
    nodelist = [f"{prefix}{i}" for i in idxs]
    # idxs = (
    #     nodes.split
    # )
    # idxs = (
    #     nodes.lstrip('frontier').replace('[', '').replace(']', '').split('-')
    # )
    # nodelist = [f'frontier{i}' for i in idxs]
    if outfile is None:
        outfile = Path(os.getcwd()).joinpath("hostfile")
    else:
        outfile = Path(outfile)
    with outfile.open("w") as f:
        for node in nodelist:
            f.write(f"{node}\n")
    return outfile

print_dist_setup(framework=None, hostfile=None)

Print distributed setup.

Parameters:

Name	Type	Description	Default
framework	str	Framework to use. Defaults to None.	None
hostfile	PathLike	Path to the hostfile. Defaults to None.	None

Returns:

Name	Type	Description
str	str	String containing the distributed setup.

Source code in src/ezpz/dist.py

def print_dist_setup(
    framework: Optional[str] = None,
    hostfile: Optional[PathLike] = None,
) -> str:
    """Print distributed setup.

    Args:
        framework (str, optional): Framework to use. Defaults to None.
        hostfile (PathLike, optional): Path to the hostfile. Defaults to None.

    Returns:
        str: String containing the distributed setup.
    """
    rank = get_rank()
    wst = get_world_size(total=True)
    wsa = get_world_size(in_use=True)
    # world_size = get_world_size()
    local_rank = get_local_rank()
    gpus_per_node = get_gpus_per_node()
    hostfile = get_hostfile_with_fallback(hostfile)
    # NOTE:
    # We ensure that num_nodes is AT LEAST 1
    # since if gpus_per_node > wsa, wsa // gpus_per_node = 0
    # if gpus_per_node > wsa, wsa // gpus_per_node = 0
    num_nodes = max((wsa // gpus_per_node, 1))
    num_nodes_from_hostfile = get_num_nodes()
    # assert num_nodes_from_hostfile == num_nodes
    # if num_nodes != num_nodes_from_hostfile:
    #     logger.critical(f'{num_nodes=} vs. {num_nodes_from_hostfile=} ??')
    node = get_node_index()
    device = None
    # if framework.lower() in {'pt', 'torch', 'pytorch'}:
    device = get_torch_device_type()
    rank_len = len(str(rank))
    ws_len = len(str(wsa))
    lr_len = len(str(local_rank))
    gpn_len = len(str(gpus_per_node))
    node_len = len(str(node))
    num_nodes_len = len(str(num_nodes))
    dist_list = [
        f"[{device=}]",
        f"[{rank=:>{rank_len}}/{(wsa - 1):<{ws_len}}]",
        f"[{local_rank=:>{lr_len}}/{gpus_per_node - 1:<{gpn_len}}]",
        f"[{node=:>{node_len}}/{(num_nodes - 1):<{num_nodes_len}}]",
    ]
    if framework is not None:
        dist_list.append(f"[{framework=}]")
    dist_str = "".join(dist_list)
    logger.info(f"{dist_str}")
    if rank == 0:
        if wsa > 1000:
            logger.warning(
                f"WORLD_SIZE={wsa} > 1000, only printing on RANK={rank}"
            )
        logger.warning(
            f'Using [{wsa} / {wst}] available "{device}" devices !!'
        )
        if num_nodes_from_hostfile != num_nodes:
            logger.critical(
                f"num_nodes_from_hostfile = [{num_nodes_from_hostfile=}]"
                f"vs."
                f"[{wsa=} // {gpus_per_node=}] = {num_nodes}"
                r"¯\_(ツ)_/¯ ??"
            )
    return dist_str

query_environment()

Query environment variables for info about distributed setup

Returns:

Type	Description
dict[str, int]	dict[str, int]: A dictionary containing the distributed setup information. Includes keys like 'world_size', 'rank', and 'local_rank'. If the environment variables are not set, it falls back to using get_world_size(), get_rank(), and get_local_rank() functions.

Example

env_info = query_environment() print(env_info)

Source code in src/ezpz/dist.py

def query_environment() -> dict[str, int]:
    """Query environment variables for info about distributed setup

    Returns:
        dict[str, int]: A dictionary containing the distributed setup information.
            Includes keys like 'world_size', 'rank', and 'local_rank'.
            If the environment variables are not set, it falls back to using
            `get_world_size()`, `get_rank()`, and `get_local_rank()` functions.

    Example:
        >>> env_info = query_environment()
        >>> print(env_info)
        {'world_size': 4, 'rank': 0, 'local_rank': 0}
    """
    ws = os.environ.get("WORLD_SIZE", None)
    r = os.environ.get("RANK", None)
    lr = os.environ.get("LOCAL_RANK", None)
    if ws is not None and r is not None and lr is not None:
        return {
            "world_size": int(ws),
            "rank": int(r),
            "local_rank": int(lr),
            # 'machine': machine,
        }
    return {
        "world_size": int(get_world_size()),
        "rank": int(get_rank()),
        "local_rank": int(get_local_rank()),
    }

run_bash_command(cmd)

Run a bash command and return the output. Args: cmd (str): The command to run.

Returns:

Name	Type	Description
Any	Any	The output of the command.

Source code in src/ezpz/dist.py

def run_bash_command(cmd: str) -> Any:
    """
    Run a bash command and return the output.
    Args:
        cmd (str): The command to run.

    Returns:
        Any: The output of the command.
    """
    import subprocess
    import shlex

    process = subprocess.Popen(
        shlex.split(cmd, posix=True),
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE
    )
    output, error = process.communicate()
    if process.returncode != 0:
        raise Exception(
            f"Command failed with return code {process.returncode}.\n"
                f"stdout: {output.decode().strip()}\n"
                f"stderr: {error.decode().strip()}"
        )
    if error:
        raise Exception(error.decode())
    else:
        return output

run_ddp(fn, world_size)

Run a function in a distributed data parallel (DDP) setup.

Parameters:

Name	Type	Description	Default
fn	Callable	The function to run in DDP.	required
world_size	int	The total number of processes to run.	required

Source code in src/ezpz/dist.py

def run_ddp(fn: Callable, world_size: int) -> None:
    """
    Run a function in a distributed data parallel (DDP) setup.

    Args:
        fn (Callable): The function to run in DDP.
        world_size (int): The total number of processes to run.
    """
    import torch.multiprocessing as mp

    mp.spawn(  # type:ignore
        fn, args=(world_size,), nprocs=world_size, join=True
    )

seed_everything(seed)

Set random seed for reproducibility.

Parameters:

Name	Type	Description	Default
seed	int	Random seed to set.	required

Source code in src/ezpz/dist.py

def seed_everything(seed: int) -> None:
    """Set random seed for reproducibility.

    Args:
        seed (int): Random seed to set.
    """
    import torch
    import numpy as np
    import random

    random.seed(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    np.random.seed(seed)
    _ = torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
    if torch.xpu.is_available():
        torch.xpu.manual_seed(seed)

setup(framework='pytorch', backend='DDP', port='5432', seed=None, precision=None, ngpus=None)

Setup distributed environment for the specified framework.

Parameters:

Name	Type	Description	Default
framework	str	The framework to use for distributed training. Defaults to "pytorch".	'pytorch'
backend	str	The backend to use for distributed training. Defaults to "DDP".	'DDP'
port	str	The port to use for distributed communication. Defaults to "5432".	'5432'
seed	int	Random seed for reproducibility. Defaults to None.	None
precision	str	Precision to use for training. Defaults to None.	None
ngpus	int	Number of GPUs to use. Defaults to None.	None

Returns:

Type	Description
	None

Source code in src/ezpz/dist.py

def setup(
    framework: str = "pytorch",
    backend: str = "DDP",
    port: str = "5432",
    seed: Optional[int] = None,
    precision: Optional[str] = None,
    ngpus: Optional[int] = None,
):
    """
    Setup distributed environment for the specified framework.

    Args:
        framework (str): The framework to use for distributed training.
            Defaults to "pytorch".
        backend (str): The backend to use for distributed training.
            Defaults to "DDP".
        port (str): The port to use for distributed communication.
            Defaults to "5432".
        seed (int, optional): Random seed for reproducibility. Defaults to None.
        precision (str, optional): Precision to use for training. Defaults to None.
        ngpus (int, optional): Number of GPUs to use. Defaults to None.

    Returns:
        None
    """
    return (
        setup_tensorflow(precision=precision, ngpus=ngpus)
        if framework in {"tensorflow", "tf", "t"}
        else setup_torch(backend=backend, port=port, seed=seed)
    )

setup_tensorflow(precision=None, ngpus=None)

Initialize TensorFlow + Horovod for Distributed Training

Source code in src/ezpz/dist.py

def setup_tensorflow(
    precision: Optional[str] = None,
    ngpus: Optional[int] = None,
) -> int:
    """Initialize TensorFlow + Horovod for Distributed Training"""
    try:
        import tensorflow as tf  # type:ignore noqa

        os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
        import horovod.tensorflow as hvd  # type:ignore noqa
    except Exception:
        logger.warning(
            "Unable to import `tensorflow` or `horovod.tensorflow`. "
            "Install with `pip install tensorflow horovod`"
        )
        raise

    _ = None if hvd.is_initialized() else hvd.init()
    # hvd.init() if not hvd.is_initialized() else None
    if precision in [
        "fp16",
        "float16",
        "half",
        "16",
        "mixed_float16",
        # 'mixed_bfloat16'
    ]:
        tf.keras.mixed_precision.set_global_policy("mixed_float16")  # pyright:ignore
    TF_FLOAT = tf.keras.backend.floatx()  # pyright:ignore
    eager_mode = os.environ.get("TF_EAGER", None)
    if eager_mode is not None:
        logger.info("Detected `TF_EAGER` from env. Running eagerly.")
        tf.config.run_functions_eagerly(True)

    gpus = tf.config.experimental.list_physical_devices("GPU")
    cpus = tf.config.experimental.list_physical_devices("CPU")
    if gpus:
        try:
            # Currently memory growth needs to be the same across GPUs
            if ngpus is not None:
                gpus = gpus[-ngpus:]

            for gpu in gpus:
                tf.config.experimental.set_memory_growth(gpu, True)
            tf.config.experimental.set_visible_devices(
                gpus[hvd.local_rank()],
                "GPU",
            )
            _ = tf.config.experimental.list_logical_devices("GPU")  # pyright:ignore
        except RuntimeError as e:
            logger.info(e)
    elif cpus:
        try:
            # Currently, memory growth needs to be the same across GPUs
            logical_cpus = tf.config.experimental.list_logical_devices("CPU")
            logger.info(
                f"{len(cpus)}, Physical CPUs and "
                f"{len(logical_cpus)} Logical CPUs"
            )
        except RuntimeError as e:
            # Memory growth must be set before GPUs have been initialized
            logger.info(e)
    RANK = hvd.rank()
    WORLD_SIZE = hvd.size()
    LOCAL_RANK = hvd.local_rank()
    # LOCAL_SIZE = hvd.local_size()
    os.environ["RANK"] = str(RANK)
    os.environ["WORLD_SIZE"] = str(WORLD_SIZE)
    os.environ["LOCAL_RANK"] = str(LOCAL_RANK)
    # logger.info(f'RANK: {RANK} / {WORLD_SIZE-1}')
    if RANK == 0:
        logger.info(f"Using {TF_FLOAT} precision")
    return RANK

setup_torch(framework=None, backend=None, port=None, seed=None, timeout=None, verbose=False, tensor_parallel_size=1, pipeline_parallel_size=1, context_parallel_size=1, tensor_parallel_backend=None, pipeline_parallel_backend=None, context_parallel_backend=None, data_parallel_backend=None)

Setup torch.

Parameters:

Name	Type	Description	Default
backend	str	Backend to use. Defaults to None.	None
port	str | int	Port to use. Defaults to None.	None
seed	int	Seed to use. Defaults to None.	None
timeout	str | int	Timeout to use. Defaults to None.	None
verbose	bool	Whether to print the info. Defaults to False.	False
tensor_parallel_size	int	Tensor parallel size. Defaults to 1.	1
pipeline_parallel_size	int	Pipeline parallel size. Defaults to 1.	1
context_parallel_size	int	Context parallel size. Defaults to 1.	1
tensor_parallel_backend	str	Tensor parallel backend. Defaults to None.	None
pipeline_parallel_backend	str	Pipeline parallel backend. Defaults to None.	None
context_parallel_backend	str	Context parallel backend. Defaults to None.	None
data_parallel_backend	str	Data parallel backend. Defaults to None.	None

Returns:

Name	Type	Description
int	int	Rank of the process.

Source code in src/ezpz/dist.py

def setup_torch(
    framework: Optional[str] = None,
    backend: Optional[str] = None,
    port: Optional[str | int] = None,
    seed: Optional[int] = None,
    timeout: Optional[str | int] = None,
    verbose: Optional[bool] = False,
    tensor_parallel_size: int = 1,
    pipeline_parallel_size: int = 1,
    context_parallel_size: int = 1,
    tensor_parallel_backend: Optional[str] = None,
    pipeline_parallel_backend: Optional[str] = None,
    context_parallel_backend: Optional[str] = None,
    data_parallel_backend: Optional[str] = None,
) -> int:
    """Setup torch.

    Args:
        backend (str, optional): Backend to use. Defaults to None.
        port (str | int, optional): Port to use. Defaults to None.
        seed (int, optional): Seed to use. Defaults to None.
        timeout (str | int, optional): Timeout to use. Defaults to None.
        verbose (bool, optional): Whether to print the info. Defaults to False.
        tensor_parallel_size (int, optional): Tensor parallel size. Defaults to 1.
        pipeline_parallel_size (int, optional): Pipeline parallel size. Defaults to 1.
        context_parallel_size (int, optional): Context parallel size. Defaults to 1.
        tensor_parallel_backend (str, optional): Tensor parallel backend. Defaults to None.
        pipeline_parallel_backend (str, optional): Pipeline parallel backend. Defaults to None.
        context_parallel_backend (str, optional): Context parallel backend. Defaults to None.
        data_parallel_backend (str, optional): Data parallel backend. Defaults to None.

    Returns:
        int: Rank of the process.
    """
    device = get_torch_device()
    # if ACCELERATOR_TYPE == 'NvidiaGPU' and device == 'cuda':
    #     os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':4096:8'
    #     torch.backends.cudnn.deterministic = True     # type:ignore
    #     torch.backends.cudnn.benchmark = True         # type:ignore
    #     torch.backends.cudnn.allow_tf32 = True        # type:ignore
    #     torch.backends.cuda.matmul.allow_tf32 = True  # type:ignore
    # torch.use_deterministic_algorithms(True)
    ws_from_env = os.environ.get("WORLD_SIZE", None)
    framework = "DDP" if framework is None else framework
    framework = framework.lower()
    backend = str(get_torch_backend()).lower()
    if ws_from_env is not None and ws_from_env == "1":
        logger.info(
            f"Running on a single {device}, not initializing torch.distributed!"
        )
        rank = 0
        world_size = 1
        local_rank = 0
        local_size = 1
        num_nodes = 1
    else:
        dsetup = setup_torch_distributed(
            framework=framework,
            backend=backend,
            port=port,
            timeout=timeout,
            tensor_parallel_size=int(tensor_parallel_size),
            pipeline_parallel_size=int(pipeline_parallel_size),
            context_parallel_size=int(context_parallel_size),
            tensor_parallel_backend=tensor_parallel_backend,
            pipeline_parallel_backend=pipeline_parallel_backend,
            context_parallel_backend=context_parallel_backend,
            data_parallel_backend=data_parallel_backend,
        )
        rank = dsetup["rank"]
        world_size = dsetup["world_size"]
        local_rank = dsetup["local_rank"]
        try:
            local_size = get_gpus_per_node()
        except Exception:
            local_size = 1

        try:
            num_nodes = get_num_nodes()
        except Exception:
            num_nodes = 1
    os.environ["RANK"] = str(rank)
    os.environ["LOCAL_RANK"] = str(local_rank)
    os.environ["NUM_NODES"] = str(num_nodes)
    os.environ["LOCAL_SIZE"] = str(local_size)
    os.environ["WORLD_SIZE"] = str(world_size)
    # nthreads = os.environ.get('OMP_NUM_THREADS', None)
    # if ACCELERATOR_TYPE == "IntelGPU" and device == "xpu":
    if torch.xpu.is_available():
        torch.xpu.set_device(local_rank)
        # try:
        #     import intel_extension_for_pytorch as ipex  # type:ignore[missingTypeStubs]
        # except Exception:
        #     ipex = None
        # if ipex is not None:
        #     logger.debug(f"Using ipex from: {ipex.__file__}")
        #
        # try:
        #     import oneccl_bindings_for_pytorch as oneccl_bpt  # type:ignore[missingTypeStubs]
        # except Exception:
        #     oneccl_bpt = None
        # if oneccl_bpt is not None:
        #     logger.debug(f"Using oneccl_bindings from: {oneccl_bpt.__file__}")
        #
        #     # logger.warning(f'Using {get_torch_device()}:{get_local_rank()}')
        #     # os.environ['CCL_LOCAL_RANK'] = str(local_rank)
        #     # os.environ['CCL_LOCAL_SIZE'] = str(local_size)
    if seed is not None:
        seed_everything(seed * (rank + 1) * (local_rank + 1))
    if rank == 0:
        if backend in {"ds", "deepspeed", "dspeed"}:
            from ezpz.configs import git_ds_info

            git_ds_info()
        _ = get_dist_info(verbose=verbose)
        if verbose:
            _ = print_dist_setup()
    if world_size > 1:
        barrier()

    if rank == 0:
        logger.info(
            f"Using {device=} with {backend=} "
            f"+ '{get_torch_backend()}' "
            "for distributed training."
        )
    lrank = len(str(world_size - 1))
    # nz = lrank - len(str(rank))
    hn = socket.gethostname()
    psizes = [f"['{hn}']" + f"[{rank:>{lrank}}/{world_size - 1:<{lrank}}] "]
    if (
        tensor_parallel_size > 1
        or context_parallel_size > 1
        or pipeline_parallel_size > 1
    ):
        import ezpz.tp

        tpsize = ezpz.tp.get_tensor_parallel_world_size()
        cpsize = ezpz.tp.get_context_parallel_world_size()
        ppsize = ezpz.tp.get_pipeline_parallel_world_size()
        dpsize = ezpz.tp.get_data_parallel_world_size()
        if cpsize > 1 or ppsize > 1 or tpsize > 1:
            if cpsize > 1:
                lcp = len(str(cpsize - 1))
                cprank = ezpz.tp.get_context_parallel_rank()
                # cpranks = ezpz.tp.get_context_parallel_ranks()
                psizes.append(f"[cp:{cprank:>{lcp}}/{cpsize - 1:<{lcp}}]")
                barrier(group=ezpz.tp.get_context_parallel_group())
            if ppsize > 1:
                pprank = ezpz.tp.get_pipeline_parallel_rank()
                # ppranks = ezpz.tp.get_pipeline_parallel_ranks()
                lpp = len(str(ppsize - 1))
                psizes.append(f"[pp:{pprank:>{lpp}}/{ppsize - 1:<{lpp}}]")
                barrier(group=ezpz.tp.get_pipeline_parallel_group())
            if tpsize > 1:
                ltp = len(str(tpsize - 1))
                tprank = ezpz.tp.get_tensor_parallel_rank()
                # tpranks = ezpz.tp.get_tensor_parallel_ranks()
                psizes.append(f"[tp:{tprank:>{ltp}}/{tpsize - 1:<{ltp}}]")
                barrier(group=ezpz.tp.get_tensor_parallel_group())
            if dpsize > 1:
                ldp = len(str(dpsize - 1))
                dprank = ezpz.tp.get_data_parallel_rank()
                # dpranks = ezpz.tp.get_data_parallel_ranks()
                psizes.append(f"[dp:{dprank:>{ldp}}/{dpsize - 1:<{ldp}}]")
                barrier(group=ezpz.tp.get_data_parallel_group())
    logger.info("".join(psizes))
    barrier()
    return rank

setup_torch_DDP(port='2345', timeout=3600, backend=None)

Setup PyTorch Distributed Data Parallel (DDP) environment. Args: port (str, optional): The port to use for distributed communication. Defaults to "2345". timeout (int | str | timedelta, optional): Timeout for the process group initialization. Defaults to 3600 seconds. backend (str, optional): The backend to use for distributed training. Defaults to None, which will use the default backend based on the device.

Returns:

Type	Description
dict[str, int]	dict[str, int]: A dictionary containing the distributed setup information. Includes keys like 'world_size', 'rank', and 'local_rank'.

Source code in src/ezpz/dist.py

def setup_torch_DDP(
    port: str = "2345",
    timeout: int | str | timedelta = 3600,
    backend: Optional[str] = None,
) -> dict[str, int]:
    """
    Setup PyTorch Distributed Data Parallel (DDP) environment.
    Args:
        port (str, optional): The port to use for distributed communication.
            Defaults to "2345".
        timeout (int | str | timedelta, optional): Timeout for the process group initialization.
            Defaults to 3600 seconds.
        backend (str, optional): The backend to use for distributed training.
            Defaults to None, which will use the default backend based on the device.

    Returns:
        dict[str, int]: A dictionary containing the distributed setup information.
            Includes keys like 'world_size', 'rank', and 'local_rank'.
    """
    if not isinstance(timeout, timedelta):
        timeout = timedelta(seconds=int(timeout))
    os_rank = os.environ.get("RANK", None)
    os_world_size = os.environ.get("WORLD_SIZE", None)
    os_local_rank = os.environ.get("LOCAL_RANK", None)
    world_size = int(get_world_size())
    rank = int(get_rank())
    local_rank = int(get_local_rank())
    # ensure there is no funny business going on
    if os_rank and int(os_rank) != int(rank):
        logger.warning(f"Mismatch between {os_rank=} and {rank=}")
    if os_world_size and int(os_world_size) != int(world_size):
        logger.warning(f"Mismatch between {os_world_size=} and {world_size=}")
    if os_local_rank and int(os_local_rank) != int(local_rank):
        logger.warning(f"Mismatch between {os_local_rank=} and {local_rank=}")
    # now, set these variables explicitly in the process' environment
    os.environ["LOCAL_RANK"] = str(local_rank)
    os.environ["RANK"] = str(rank)
    os.environ["WORLD_SIZE"] = str(world_size)
    # get `hostname` ONLY from rank 0
    master_addr = socket.gethostname() if rank == 0 else None
    if (mn := ezpz.get_machine().lower()) in {
        "aurora",
        "polaris",
        "sirius",
    }:
        master_addr = f"{master_addr}.hsn.cm.{mn}.alcf.anl.gov"
    elif mn == "sophia":
        master_addr = f"{master_addr}.lab.alcf.anl.gov"
    # check if we have specified a 'MASTER_PORT' explicitly, if so, use this
    free_port = str(get_free_port()) if rank == 0 else None
    eport = os.environ.get("MASTER_PORT", free_port)
    if eport is not None:
        _ = (
            logger.info(f"Caught MASTER_PORT={eport} from environment!")
            if rank == 0
            else None
        )
    else:
        eport = port
    # grab it from rank 0
    master_port = eport if rank == 0 else None
    # broadcast it to make sure everyones tapped in
    master_port = MPI.COMM_WORLD.bcast(master_port, root=0)
    master_addr = MPI.COMM_WORLD.bcast(master_addr, root=0)
    # set it explicitly in each process' environment
    os.environ["MASTER_ADDR"] = master_addr
    os.environ["MASTER_PORT"] = master_port
    # now, torch is ready for us
    if rank == 0:
        logger.info(
            "\n".join(
                [
                    "Using torch.distributed.init_process_group with",
                    f"- {master_addr=}",
                    f"- {master_port=}",
                    f"- {world_size=}",
                    f"- {rank=}",
                    f"- {local_rank=}",
                    f"- {timeout=}",
                    f"- {backend=}",
                ]
            )
        )
    init_process_group(
        rank=rank,
        world_size=world_size,
        timeout=timeout,
        backend=backend,
    )
    return {"world_size": world_size, "rank": rank, "local_rank": local_rank}

setup_torch_distributed(framework=None, backend=None, tensor_parallel_size=1, pipeline_parallel_size=1, context_parallel_size=1, tensor_parallel_backend=None, pipeline_parallel_backend=None, context_parallel_backend=None, data_parallel_backend=None, port=None, timeout=None)

Setup distributed environment for PyTorch.

Parameters:

Name	Type	Description	Default
framework	str	The framework to use for distributed training. Defaults to None, which will use "ddp".	None
backend	str	The backend to use for distributed training. Defaults to None, which will use the default backend based on the device.	None
tensor_parallel_size	int	Size of tensor parallelism. Defaults to 1.	1
pipeline_parallel_size	int	Size of pipeline parallelism. Defaults to 1.	1
context_parallel_size	int	Size of context parallelism. Defaults to 1.	1
tensor_parallel_backend	str	Backend for tensor parallelism. Defaults to None.	None
pipeline_parallel_backend	str	Backend for pipeline parallelism. Defaults to None.	None
context_parallel_backend	str	Backend for context parallelism. Defaults to None.	None
data_parallel_backend	str	Backend for data parallelism. Defaults to None.	None
port	str | int	Port for distributed communication. Defaults to "1234".	None
timeout	str | int	Timeout for distributed initialization. Defaults to 3600 seconds.	None

Returns:

Type	Description
dict[str, int]	dict[str, int]: A dictionary containing the distributed setup information. Includes keys like 'world_size', 'rank', and 'local_rank'.

Raises:

Type	Description
AssertionError	If the framework is not one of the supported frameworks. Supported frameworks are "ddp", "ds", "deepspeed", "horovod", and "hvd".
ValueError	If the backend is not one of the supported backends. Supported backends are "ddp", "ds", "deepspeed", "horovod", and "hvd".

Example

setup_torch_distributed( ... framework="ddp", ... backend="nccl", ... tensor_parallel_size=2, ... pipeline_parallel_size=1, ... context_parallel_size=1, ... port=1234, ... timeout=3600, ... )

Source code in src/ezpz/dist.py

def setup_torch_distributed(
    framework: Optional[str] = None,
    backend: Optional[str] = None,
    tensor_parallel_size: int = 1,
    pipeline_parallel_size: int = 1,
    context_parallel_size: int = 1,
    tensor_parallel_backend: Optional[str] = None,
    pipeline_parallel_backend: Optional[str] = None,
    context_parallel_backend: Optional[str] = None,
    data_parallel_backend: Optional[str] = None,
    port: Optional[str | int] = None,
    timeout: Optional[str | int] = None,
) -> dict[str, int]:
    """
    Setup distributed environment for PyTorch.

    Args:
        framework (str, optional): The framework to use for distributed training.
            Defaults to None, which will use "ddp".
        backend (str, optional): The backend to use for distributed training.
            Defaults to None, which will use the default backend based on the device.
        tensor_parallel_size (int, optional): Size of tensor parallelism. Defaults to 1.
        pipeline_parallel_size (int, optional): Size of pipeline parallelism. Defaults to 1.
        context_parallel_size (int, optional): Size of context parallelism. Defaults to 1.
        tensor_parallel_backend (str, optional): Backend for tensor parallelism. Defaults to None.
        pipeline_parallel_backend (str, optional): Backend for pipeline parallelism. Defaults to None.
        context_parallel_backend (str, optional): Backend for context parallelism. Defaults to None.
        data_parallel_backend (str, optional): Backend for data parallelism. Defaults to None.
        port (str | int, optional): Port for distributed communication. Defaults to "1234".
        timeout (str | int, optional): Timeout for distributed initialization. Defaults to 3600 seconds.

    Returns:
        dict[str, int]: A dictionary containing the distributed setup information.
            Includes keys like 'world_size', 'rank', and 'local_rank'.

    Raises:
        AssertionError: If the framework is not one of the supported frameworks.
            Supported frameworks are "ddp", "ds", "deepspeed", "horovod", and "hvd".
        ValueError: If the backend is not one of the supported backends.
            Supported backends are "ddp", "ds", "deepspeed", "horovod", and "hvd".

    Example:
        >>> setup_torch_distributed(
        ...     framework="ddp",
        ...     backend="nccl",
        ...     tensor_parallel_size=2,
        ...     pipeline_parallel_size=1,
        ...     context_parallel_size=1,
        ...     port=1234,
        ...     timeout=3600,
        ... )
    """
    framework = "ddp" if framework is None else framework
    # if str(framework).lower() not in {"ddp", "ds", "deepspeed", "horovod", "hvd"}:
    assert str(framework).lower() in {
        "ddp",
        "ds",
        "deepspeed",
        "horovod",
        "hvd",
    }, (
        f"Invalid framework: {framework=}, expected one of "
        f"{'ddp', 'ds', 'deepspeed', 'horovod', 'hvd'}"
    )

    timeout = (
        3600
        if timeout is None
        else int(timeout)
        if isinstance(timeout, str)
        else timeout
    )
    port = (
        "1234"
        if port is None
        else str(port)
        if isinstance(port, int)
        else port
    )
    rank = get_rank()
    world_size = get_world_size()
    local_rank = get_local_rank()
    fw = str(framework).lower()
    be = (
        str(get_torch_backend()).lower()
        if backend is None
        else str(backend).lower()
    )
    # be = str(framework).lower()
    # assert fw in {"ds", "deepspeed", "ddp", "horovod", "hvd"}, (
    #     f"Invalid backend: {framework=}, expected one of "
    #     f"{'ds', 'deepspeed', 'ddp', 'horovod', 'hvd'}"
    # )
    # assert be in BACKENDS['pytorch']
    if rank == 0:
        logger.info(
            " ".join(
                [
                    f"Using {fw=} with",
                    "torch_{device,backend}={",
                    f"{get_torch_device_type()}, {be}" + "}",
                ]
            )
        )
    if fw == "ddp":
        dsetup = setup_torch_DDP(port, timeout, backend=be)
        world_size = dsetup["world_size"]
        rank = dsetup["rank"]
        local_rank = dsetup["local_rank"]
        if torch.cuda.is_available():
            torch.cuda.set_device(local_rank)
    elif fw in {"deepspeed", "ds"}:
        init_deepspeed(timeout=timeout)
        world_size = get_world_size()
        rank = get_rank()
        local_rank = get_local_rank()
    elif fw in {"horovod", "hvd"}:
        import horovod.torch as hvd  # type:ignore noqa

        _ = None if hvd.is_initialized() else hvd.init()  # type:ignore
        # hvd.init() if not hvd.is_initialized() else None
        rank = hvd.rank()  # type:ignore
        world_size = hvd.size()  # type:ignore
        local_rank = hvd.local_rank()  # type:ignore
        if torch.cuda.is_available():
            torch.cuda.set_device(hvd.local_rank())  # type:ignore
    else:
        raise ValueError(f"Unable to parse backend: {be=}")

    if (
        tensor_parallel_size > 1
        or context_parallel_size > 1
        or pipeline_parallel_size > 1
    ):
        ezpz.tp.initialize_tensor_parallel(
            tensor_parallel_size=tensor_parallel_size,
            pipeline_parallel_size=pipeline_parallel_size,
            context_parallel_size=context_parallel_size,
            tensor_parallel_backend=tensor_parallel_backend,
            pipeline_parallel_backend=pipeline_parallel_backend,
            context_parallel_backend=context_parallel_backend,
            data_parallel_backend=data_parallel_backend,
            timeout=timedelta(seconds=timeout),
        )

    os.environ["world_size"] = str(world_size)
    os.environ["RANK"] = str(rank)
    os.environ["LOCAL_RANK"] = str(local_rank)

    return {"world_size": world_size, "rank": rank, "local_rank": local_rank}

setup_wandb(project_name=None, entity=None, config=None, start_method='thread', outdir=None, init_timeout=300)

Setup wandb for logging.

Parameters:

Name	Type	Description	Default
project_name	str	The name of the project. Defaults to None.	None
entity	str	The entity name. Defaults to None.	None
config	dict | DictConfig	The configuration dictionary. Defaults to None.	None
start_method	str	The start method for wandb. Defaults to "thread".	'thread'
outdir	str | Path | PathLike	The output directory. Defaults to None.	None
init_timeout	int	The timeout for wandb initialization. Defaults to 300.	300

Example

setup_wandb(project_name="my_project", entity="my_entity")

Source code in src/ezpz/dist.py

def setup_wandb(
    project_name: Optional[str] = None,
    entity: Optional[str] = None,
    config: Optional[dict | DictConfig] = None,
    start_method: str = "thread",
    outdir: Optional[str | Path | os.PathLike] = None,
    init_timeout: int = 300,
):
    """Setup wandb for logging.

    Args:
        project_name (str, optional): The name of the project. Defaults to None.
        entity (str, optional): The entity name. Defaults to None.
        config (dict | DictConfig, optional): The configuration dictionary. Defaults to None.
        start_method (str, optional): The start method for wandb. Defaults to "thread".
        outdir (str | Path | os.PathLike, optional): The output directory. Defaults to None.
        init_timeout (int, optional): The timeout for wandb initialization. Defaults to 300.

    Example:
        >>> setup_wandb(project_name="my_project", entity="my_entity")
    """
    # try:
    #     import wandb
    # except Exception:
    #     wandb = None
    # try:
    #     import wandb
    #
    WANDB_DISABLED = os.environ.get("WANDB_DISABLED", False)
    WANDB_MODE = os.environ.get("WANDB_MODE", "").lower()

    # except Exception:
    #     wandb = None
    #     WANDB_DISABLED = True

    if WANDB_DISABLED or WANDB_MODE == "disabled":
        logger.warning(
            f"Logging with W&B is disabled!, caught: {WANDB_DISABLED=}"
        )
        return None

    try:
        import wandb
    except (ImportError, ModuleNotFoundError) as e:
        logger.warning(
            "Unable to import `wandb`. Install with `pip install wandb`"
        )
        raise e

    outdir = (
        Path(os.getcwd()).as_posix()
        if outdir is None
        else Path(outdir).as_posix()
    )
    rank = get_rank()
    project_name = (
        project_name
        if project_name is not None
        else os.environ.get(
            "WB_PROJECT",
            os.environ.get(
                "WANDB_PROJECT",
                os.environ.get("WB_PROJECT_NAME", None),
            ),
        )
    )
    if project_name is None:
        import sys

        frame = sys._getframe().f_back
        assert frame is not None
        calling_module = frame.f_code.co_filename
        fp = Path(calling_module)
        project_name = f"{fp.parent.stem}.{fp.stem}"

    logger.info(f"Setting up wandb from {rank=}")
    logger.info(f"Using WB_PROJECT={project_name}")
    tensorboard_dir = (
        os.environ.get("TENSORBOARD_DIR", None)
        if config is None
        else config.get("tensorboard_dir", None)
    )
    if tensorboard_dir is not None:
        logger.info(f"Patching tensorboard from {tensorboard_dir}")
        try:
            wandb.tensorboard.patch(root_logdir=tensorboard_dir)  # type:ignore
        except Exception as exc:
            logger.exception(exc)
    # wbrun_id = wandb.util.generate_id()
    now = datetime.datetime.now()
    dstr = now.strftime("%Y-%m-%d-%H%M%S")
    run = wandb.init(
        entity=entity,
        # resume='allow',
        dir=outdir,
        sync_tensorboard=(tensorboard_dir is not None),  # True,
        project=(project_name if project_name is not None else None),
        # dir=(tensorboard_dir if tensorboard_dir is not None else None),
        settings=wandb.Settings(
            start_method=start_method, init_timeout=init_timeout
        ),
    )
    assert run is not None and run is wandb.run
    # run.log_code(HERE.as_posix(), include_fn=include_file)
    logger.info(f"wandb.run=[{run.name}]({run.url})")
    if (
        wandb is not None
        and wandb.run is not None
        and "DIST_INFO" not in wandb.run.config
    ):
        wandb.run.config.update({"DIST_INFO": get_dist_info()})
    torch_version = torch.__version__
    torch_file = torch.__file__
    run.config.update(
        {
            "created_at": dstr,
            "day": ezpz.get_timestamp("%d"),
            "month": ezpz.get_timestamp("%m"),
            "outdir": os.getcwd(),
            "torch_version": torch_version,
            "torch_file": torch_file,
            "world_size": get_world_size(),
            "year": ezpz.get_timestamp("%Y"),
            "ezpz_version": ezpz.__version__,
            "ezpz_file": ezpz.__file__,
        }
    )
    if config is not None:
        if isinstance(config, DictConfig):
            cfg = OmegaConf.to_container(
                config, resolve=True, throw_on_missing=True
            )
            run.config.update({"config": cfg})
        else:
            run.config.update({"config": config})
    env = {
        k: v
        for k, v in dict(os.environ).items()
        if not k.startswith("_ModuleTable")
    }
    _ = env.pop("LS_COLORS", None)
    _ = env.pop("PS1", None)
    run.config.update({"env": env})
    machine = get_machine()
    logger.info(f"Running on {machine=}")
    run.config.update({"machine": machine})
    model_size = os.environ.get("MODEL_SIZE", None)
    if model_size is not None:
        run.config.update({"MODEL_SIZE": model_size})
    return wandb.run

synchronize(device=None)

Synchronize the given device.

Parameters:

Name	Type	Description	Default
device	device | int | str	The device to synchronize. If None, the default device will be used. Defaults to None.	None

Returns:

Type	Description
	None

Source code in src/ezpz/dist.py

def synchronize(device: Optional[torch.device | int | str] = None):
    """
    Synchronize the given device.

    Args:
        device (torch.device | int | str, optional): The device to synchronize.
            If None, the default device will be used. Defaults to None.

    Returns:
        None
    """
    return (
        torch.cuda.synchronize(device)
        if torch.cuda.is_available()
        else (
            torch.xpu.synchronize(device)
            if torch.xpu.is_available()
            else (
                torch.mps.synchronize()
                if torch.backends.mps.is_available()
                else torch.cpu.synchronize(device)
            )
        )
    )

timeit(func)

Decorator to time a function and log the time taken.

Parameters:

Name	Type	Description	Default
func	Callable	Function to be timed.	required

Example

@timeit def my_function(arg1, arg2): # Function implementation pass

Source code in src/ezpz/dist.py

def timeit(func: Callable):
    """
    Decorator to time a function and log the time taken.

    Args:
        func (Callable): Function to be timed.

    Example:
        @timeit
        def my_function(arg1, arg2):
            # Function implementation
            pass
    """
    try:
        import wandb
    except Exception:
        wandb = None  # type:ignore

    @wraps(func)
    def wrapper(*args, **kwargs):
        t0 = time.perf_counter()
        result = func(*args, **kwargs)
        dt = time.perf_counter() - t0
        fname = getattr(
            func, "__qualname__", getattr(func, "__name__", "unknown")
        )
        logger.info(f"{fname}({args}, {kwargs}) took: {dt=:.4f}s")
        if wandb is not None and wandb.run is not None:
            wandb.log({f"timeit/{fname}": dt})
        return result

    return wrapper

timeitlogit(rank=None, verbose=True)

Decorator to time a function and log the time taken.

Parameters:

Name	Type	Description	Default
rank	int	Rank of the process. Defaults to None.	None
verbose	bool	Whether to log the time taken. Defaults to True.	True

Example

@timeitlogit(rank=0, verbose=True) def my_function(arg1, arg2): # Function implementation pass

Source code in src/ezpz/dist.py

def timeitlogit(rank: Optional[int] = None, verbose: bool = True):
    """Decorator to time a function and log the time taken.

    Args:
        rank (int, optional): Rank of the process. Defaults to None.
        verbose (bool, optional): Whether to log the time taken. Defaults to True.

    Example:
        @timeitlogit(rank=0, verbose=True)
        def my_function(arg1, arg2):
            # Function implementation
            pass
    """
    rank = get_rank() if rank is None else rank
    try:
        import wandb
    except Exception:
        wandb = None  # type:ignore

    def decorator(func: Callable):
        """Decorator to time a function and log the time taken.

        Args:
            func (Callable): Function to be timed.
        """

        @wraps(func)
        def wrapper(*args, **kwargs):
            t0 = time.perf_counter()
            assert isinstance(rank, int)
            result = func(*args, **kwargs)
            dt = time.perf_counter() - t0
            fname = getattr(
                func, "__qualname__", getattr(func, "__name__", "unknown")
            )
            if verbose and rank == 0:
                arg_str = ", ".join(map(str, args))
                kw_str = ", ".join(f"{k}={v}" for k, v in kwargs.items())
                inner = ", ".join(filter(None, [arg_str, kw_str]))
                logger.info(f"{fname}({inner}) took {dt:.4f} s")
                if wandb is not None and wandb.run is not None:
                    wandb.log({f"timeit/{fname}": dt}, commit=False)
            # if verbose:
            #     if rank == 0:
            #         astr = []
            #         if len(args) > 0:
            #             astr.append(f"({args}")
            #         _ = (
            #             astr.append(f", {kwargs})")
            #             if len(kwargs) > 0
            #             else (astr.append(")") if len(args) > 0 else "")
            #         )
            #         zstr = [f"Called: '{fname}' with arguments:"]
            #         if len(astr) > 0:
            #             zstr.append(f"{''.join(astr)}")
            #         zstr.append(f"'{fname}' took: {dt=:.4f} s")
            #         logger.info("\n".join(zstr))
            return result

        return wrapper

    return decorator

write_hostfile_from_list_of_hosts(hosts, hostfile=None, rank_zero_only=True)

Write a list of hosts to the hostfile.

Parameters:

Name	Type	Description	Default
hosts	list[str]	A list of hostnames to write to the hostfile.	required
hostfile	PathLike	The path to the hostfile. Defaults to None.	None
rank_zero_only	bool	If True, only rank 0 will write the hostfile. Defaults to True.	True

Source code in src/ezpz/dist.py

def write_hostfile_from_list_of_hosts(
    hosts: list[str],
    hostfile: Optional[PathLike] = None,
    rank_zero_only: bool = True,
):
    """Write a list of hosts to the hostfile.

    Args:
        hosts (list[str]): A list of hostnames to write to the hostfile.
        hostfile (PathLike, optional): The path to the hostfile. Defaults to None.
        rank_zero_only (bool, optional): If True, only rank 0 will write the hostfile.
            Defaults to True.
    """
    hostfile = (
        Path(hostfile).as_posix()
        if hostfile is not None
        else Path(os.getcwd()).joinpath("hostfile").as_posix()
    )
    if (rank_zero_only and get_rank() == 0) or not rank_zero_only:
        logger.info(f"Writing to {hostfile}")
        with Path(hostfile).open("w") as f:
            for host in hosts:
                f.write(f"{host}\n")

write_localhost_to_hostfile(hostfile)

Write 'localhost' to the hostfile

Source code in src/ezpz/dist.py

def write_localhost_to_hostfile(hostfile: PathLike):
    """Write 'localhost' to the hostfile"""
    if get_rank() == 0:
        logger.debug(
            f"Writing {(hostname := get_hostname())} "
            f"to {Path(hostfile).as_posix()}"
        )
        hostname = get_hostname()
        with Path(hostfile).open("w") as f:
            f.write(f"{hostname}")