fleethq 0.1.0

Train, evaluate, and deploy AI models on Fleet without managing infrastructure

Fleet SDK

Train, evaluate, and deploy AI models on cloud GPUs from your local machine.

Installation

pip install fleethq

Authentication

fleet login

This opens a browser to authenticate. Your credentials are saved to ~/.fleet/credentials.

Quickstart

import fleet

client = fleet.Fleet()

# Load a model, train it, deploy it
model = client.load("./my-model")
job = model.train(dataset="./data.jsonl")
job.wait()
endpoint = model.deploy()
result = endpoint.infer("Hello, world!")

Loading Models

From a local directory

If you have model weights locally (safetensors, .bin, .pt, etc.):

model = client.load("./path/to/model", name="my-model")

From HuggingFace

Download the model locally first, then load into Fleet:

from huggingface_hub import snapshot_download

path = snapshot_download("Qwen/Qwen2.5-7B")
model = client.load(path, name="qwen2.5-7b")

Fleet uploads the weights to its model registry. Subsequent pushes of the same model are instant — Fleet deduplicates by content hash.

Fine-tuned models

After training, the output model is automatically registered and can be deployed or used as a base for further fine-tuning:

job = model.train(dataset="./data.jsonl")
job.wait()
fine_tuned = job.model()   # the output model
endpoint = fine_tuned.deploy()

Training

job = model.train(
    dataset="./data.jsonl",      # local path or R2 key
    hardware="fleet:economy",    # GPU tier (default: fleet:economy)
    method="lora",               # full, lora, qlora (default: full)
)

# Stream logs
for line in job.logs():
    print(line)

# Or just wait
job.wait()
print(job.status)

Hardware tiers

Tier	GPU	Use case
fleet:cpu	CPU only	Testing
fleet:micro	T4 16GB	Small models
fleet:economy	L4 24GB	Mid-size models
fleet:standard	A10G 24GB	Default
fleet:pro	A100 40GB	Large models
fleet:ultra	H100 80GB	Maximum

Inference

endpoint = model.deploy(hardware="fleet:standard")

result = endpoint.infer("What is the capital of France?")
print(result)

CLI

fleet login                          # authenticate
fleet whoami                         # show current user
fleet models                         # list models
fleet jobs                           # list recent jobs
fleet jobs logs <job_id>             # stream job logs
fleet deployments                    # list deployments
fleet deploy <model_id> [hardware]   # deploy a model
fleet infer <deployment_id> <prompt> # run inference