Introduction

FlyMyAI CLI (fma) is a command-line tool for deploying custom machine learning models onto FlyMyAI cloud infrastructure. It uses Pydantic with custom field types to define model inputs/outputs, and interacts with FlyMyAI services to upload and run your models on our hardware.

Installation

pip install git+https://github.com/flymyai/fma-custom-models.git

Verify the installation:

fma --help

Quickstart

1. Initialize a project

Create a new empty folder and run:

mkdir my-project && cd my-project
fma init my_model

This creates the following structure:

my-project/
├── my_model/
│   └── model.py
└── metadata.yaml

The generated model.py contains a template:

from typing import List

from pydantic import BaseModel

from fma.toolkit import model as fma_model


class Input(BaseModel):
    pass


class Output(BaseModel):
    pass


class Model(fma_model.Model):
    requirements: List[str] = []

    def initialize(self):
        pass

    def predict(self, input: Input) -> Output:
        return {}

2. Develop your model

Fill in the initialize and predict methods with your inference logic:

• initialize() — runs once when the model starts. Load weights, set up pipelines, etc.
• predict(input) — runs on every inference request. Takes input and returns a result.

All imports should be placed inside the methods. You can store state on self and list Python dependencies in the requirements field.

Important

Even though I/O is described via Pydantic models, the predict method currently accepts and returns plain Python dicts, not Pydantic objects. Full Pydantic support is planned for a future release.

3. Log in and deploy

fma login --username <username> --password <password>
fma deploy

After a successful deploy, model metadata is saved into metadata.yaml.

Field types

All field types can be used in both Input and Output classes.

Type	Schema	Import	Description
str	"string"	built-in	Text data
int	"int"	built-in	Integer numbers
float	"float"	built-in	Floating-point numbers
bool	"bool"	built-in	Boolean values
Image	"image"	fma.toolkit.fields.image	Image data (PIL.Image.Image)
Audio	"audio"	fma.toolkit.fields.audio	Audio data
File	"file"	fma.toolkit.fields.file	Binary data (io.BytesIO)

from fma.toolkit.fields.image import Image
from fma.toolkit.fields.audio import Audio
from fma.toolkit.fields.file import File

Unsupported types

list, dict, bytes, Literal, Enum, and nested Pydantic models are not supported. Using them will raise a ValueError during deployment.

Optional fields

Wrap a type in Optional to mark it as not required:

from typing import Optional

class Input(BaseModel):
    prompt: str                    # required
    width: Optional[int]           # optional, no default
    negative_prompt: Optional[str] # optional, no default

caution

Use Optional[X] from typing, not the X | None syntax (PEP 604). The pipe syntax is not recognized by the CLI and will cause a deployment error.

Default values

You can set default values for fields using Pydantic's Field or plain Python defaults. Default values are serialized and sent to the backend, so the API will use them when a caller omits the field:

from pydantic import BaseModel, Field

class Input(BaseModel):
    prompt: str
    guidance_scale: float = Field(default=7.5)
    num_steps: int = 50

Default values and optionality are independent — a field can have a default without being Optional, and vice versa.

note

None as a default value is not serialized to the backend schema. If you need an optional field with no value by default, use Optional[X] without assigning a default.

Project structure

Your project directory must follow this layout:

<your_model_name>/
    └── model.py
metadata.yaml

There should be no other folders in the same directory alongside your model folder — this will cause an error during deployment.

Supported third-party packages

The infrastructure provides these pre-installed packages at fixed versions:

Package	Version
torch	2.5.0
bitsandbytes	0.44.1
transformers	4.45.2
tokenizers	0.20.1
sentencepiece	0.2.0
accelerate	1.0.1
numpy	≤1.26.4

You can add any other packages via the requirements field in your Model class. The above packages are guaranteed to be available at the specified versions.