### Memory per Process

Now, when the program loads things in memory, for example, a machine learning model in a variable, or the contents of a large file in a variable, all that **consumes a bit of the memory (RAM)** of the server.

* You don't have to use `File()` in the default value of the parameter.
* It uses a "spooled" file:
    * A file stored in memory up to a maximum size limit, and after passing this limit it will be stored in disk.
* This means that it will work well for large files like images, videos, large binaries, etc. without consuming all the memory.
* You can get metadata from the uploaded file.

If you have a file-like object (e.g. the object returned by `open()`), you can create a generator function to iterate over that file-like object.

That way, you don't have to read it all first in memory, and you can pass that generator function to the `StreamingResponse`, and return it.

This includes many libraries to interact with cloud storage, video processing, and others.

That "wait for something else" normally refers to <abbr title="Input and Output">I/O</abbr> operations that are relatively "slow" (compared to the speed of the processor and the RAM memory), like waiting for:

* the data from the client to be sent through the network
* the data sent by your program to be received by the client through the network

* Restarts
* Replication (the number of processes running)
* Memory
* Previous steps before starting

## Memory

If you run **a single process per container** you will have a more or less well-defined, stable, and limited amount of memory consumed by each of those containers (more than one if they are replicated).

This was changed in version 0.110.0 to fix unhandled memory consumption from forwarded exceptions without a handler (internal server errors), and to make it consistent with the behavior of regular Python code.

### Background Tasks and Dependencies with `yield`, Technical Details

das Programm nun Dinge in den Arbeitsspeicher lädt, zum Beispiel ein Modell für maschinelles Lernen in einer Variablen oder den Inhalt einer großen Datei in einer Variablen, verbraucht das alles **einen Teil des Arbeitsspeichers (RAM – Random Access Memory)** des Servers.

Und mehrere Prozesse teilen sich normalerweise keinen Speicher. Das bedeutet, dass jeder laufende Prozess seine eigenen Dinge, eigenen Variablen und eigenen Speicher hat. Und wenn Sie in Ihrem Code viel Speicher verbrauchen,...