zylora
Serverless GPU functions — deploy ML models with a decorator, scale to zero, pay per GPU-second.
Quickstart
pip install zylora
Define a GPU function
import zylora
@zylora.fn(gpu="H100")
def embed(text: str) -> list[float]:
from transformers import AutoModel
model = AutoModel.from_pretrained("BAAI/bge-large-en-v1.5")
return model.encode(text).tolist()
Deploy
zy deploy
Invoke
# Local (development)
result = embed("hello world")
# Remote (deployed)
result = embed.remote("hello world")
# Batch
results = embed.map(["hello", "world", "foo"])
# Streaming (for LLMs)
for token in generate.stream("Tell me a joke"):
print(token, end="", flush=True)
# Async
job = embed.remote_async("hello")
result = job.result()
Client API (without decorator)
from zylora import Zylora
zy = Zylora()
result = zy.invoke("embed", {"text": "hello"})
Authentication
Set your API key via environment variable:
export ZYLORA_API_KEY="zy_live_..."
Or use the CLI:
zy login
Configuration
@zylora.fn() options
| Parameter | Type | Default | Description |
|---|---|---|---|
gpu |
str |
required | GPU type: t4, l4, rtx4090, a100_40gb, a100_80gb, l40s, h100, h200, b200, mi300x |
name |
str |
function name | Custom function name |
packages |
list[str] |
[] |
pip packages to install |
model |
str |
None |
Model to download during build |
timeout |
int |
300 |
Execution timeout (seconds) |
min_instances |
int |
0 |
Minimum warm instances |
max_instances |
int |
10 |
Maximum concurrent instances |
concurrency |
int |
1 |
Requests per instance |
image |
str |
None |
Custom base Docker image |
runtime |
str |
"python312" |
Python runtime version |
routing |
str |
"cost_optimized" |
Routing strategy |
visibility |
str |
"private" |
Function visibility |
Documentation
Full documentation: docs.zylora.dev
License
MIT
1 maintainer
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