For example, if your code loads a Machine Learning model with **1 GB in size**, when you run one process with your API, it will consume at least 1 GB of RAM. And if you start **4 processes** (4 workers), each will consume 1 GB of RAM. So in total, your API will consume **4 GB of RAM**.

And if your remote server or virtual machine only has 3 GB of RAM, trying to load more than 4 GB of RAM will cause problems. 🚨

### Multiple Processes - An Example

                    "Event": {
                        "title": "Event",
                        "required": ["name", "total"],
                        "type": "object",
                        "properties": {
                            "name": {"title": "Name", "type": "string"},
                            "total": {"title": "Total", "type": "number"},
                        },
                    },
                    "HTTPValidationError": {

# Ref: https://json-schema.org/draft/2020-12/json-schema-core.html#name-json-schema-documents
# A JSON Schema MUST be an object or a boolean.
SchemaOrBool = Union[Schema, bool]


class Example(TypedDict, total=False):
    summary: Optional[str]
    description: Optional[str]
    value: Optional[Any]
    externalValue: Optional[AnyUrl]

    if PYDANTIC_V2:  # type: ignore [misc]

    bytes: lambda o: o.decode(),
    Color: str,
    datetime.date: isoformat,
    datetime.datetime: isoformat,
    datetime.time: isoformat,
    datetime.timedelta: lambda td: td.total_seconds(),
    Decimal: decimal_encoder,
    Enum: lambda o: o.value,
    frozenset: list,
    deque: list,
    GeneratorType: list,
    IPv4Address: str,
    IPv4Interface: str,
    IPv4Network: str,