Repository: ray-project/ray
File: python/ray/_private/utils.py (Python)

Code before:
    """
    if object_store_memory is not None:
        return object_store_memory

    avail_memory = estimate_available_memory()
    object_store_memory = int(
        avail_memory * ray_constants.DEFAULT_OBJECT_STORE_MEMORY_PROPORTION
    )

    # Set the object_store_memory size to 2GB on Mac
    # to avoid degraded performance.
    # (https://github.com/ray-project/ray/issues/20388)
    if sys.platform == "darwin":
        object_store_memory = min(
            object_store_memory, ray_constants.MAC_DEGRADED_PERF_MMAP_SIZE_LIMIT
        )

    object_store_memory_cap = ray_constants.DEFAULT_OBJECT_STORE_MAX_MEMORY_BYTES

    # Cap by shm size by default to avoid low performance, but don't
    # go lower than REQUIRE_SHM_SIZE_THRESHOLD.
    if sys.platform == "linux" or sys.platform == "linux2":
        # Multiple by 0.95 to give a bit of wiggle-room.
        # https://github.com/ray-project/ray/pull/23034/files
        shm_avail = get_shared_memory_bytes() * 0.95
        shm_cap = max(ray_constants.REQUIRE_SHM_SIZE_THRESHOLD, shm_avail)

        object_store_memory_cap = min(object_store_memory_cap, shm_cap)

    # Cap memory to avoid memory waste and perf issues on large nodes
    if object_store_memory_cap and object_store_memory > object_store_memory_cap:
        logger.debug(
            "Warning: Capping object memory store to {}GB. ".format(
                object_store_memory_cap // 1e9
            )
            + "To increase this further, specify `object_store_memory` "
            "when calling ray.init() or ray start."
        )
        object_store_memory = object_store_memory_cap

    return object_store_memory


def get_used_memory():
    """Return the currently used system memory in bytes

    Returns:
        The total amount of used memory
    """
    # Try to accurately figure out the memory usage if we are in a docker
    # container.

Review comment (suggestion):
isn't it a little funky to be bigger than `get_shared_memory_bytes`, outside the scope of your pr though

Code after:
    Returns:
        The resolved object store memory size in bytes.
    """
    # Derive default object store memory if not specified
    if object_store_memory is None:
        object_store_memory_cap = ray_constants.DEFAULT_OBJECT_STORE_MAX_MEMORY_BYTES

        # Cap by shm size by default to avoid low performance, but don't
        # go lower than REQUIRE_SHM_SIZE_THRESHOLD.
        if sys.platform == "linux" or sys.platform == "linux2":
            # Multiple by 0.95 to give a bit of wiggle-room.
            # https://github.com/ray-project/ray/pull/23034/files
            shm_avail = get_shared_memory_bytes() * 0.95
            shm_cap = max(ray_constants.REQUIRE_SHM_SIZE_THRESHOLD, shm_avail)

            object_store_memory_cap = min(object_store_memory_cap, shm_cap)

        object_store_memory = int(
            available_memory_bytes
            * ray_constants.DEFAULT_OBJECT_STORE_MEMORY_PROPORTION
        )

        # Set the object_store_memory size to 2GB on Mac
        # to avoid degraded performance.
        # (https://github.com/ray-project/ray/issues/20388)
        if sys.platform == "darwin":
            object_store_memory = min(
                object_store_memory, ray_constants.MAC_DEGRADED_PERF_MMAP_SIZE_LIMIT
            )

        # Cap memory to avoid memory waste and perf issues on large nodes
        if object_store_memory > object_store_memory_cap:
            logger.debug(
                "Warning: Capping object memory store to {}GB. ".format(
                    object_store_memory_cap // 1e9
                )
                + "To increase this further, specify `object_store_memory` "
                "when calling ray.init() or ray start."
            )
            object_store_memory = object_store_memory_cap

    return object_store_memory


def get_used_memory():
    """Return the currently used system memory in bytes

    Returns:
        The total amount of used memory
    """
    # Try to accurately figure out the memory usage if we are in a docker