Sandboxes

from muna import compile, Sandbox

# Install numpy and sklearn
sandbox = (Sandbox()
  .pip_install("numpy", "scikit-learn")
)

# Compile your function with the sandbox
@compile(..., sandbox=sandbox)
def predict() -> np.ndarray:
    ...

from muna import compile, Sandbox

# Install git and wget
sandbox = (Sandbox()
  .apt_install("git", "wget")
)

# Compile your function with the sandbox
@compile(..., sandbox=sandbox)
def predict() -> BytesIO:
    ...

from muna import compile, Sandbox

# Define an environment variable
sandbox = (Sandbox()
  .env({ "MUNA_WEBSITE": "https://muna.ai" })
)

# Compile your function with the sandbox
@compile(..., sandbox=sandbox)
def predict(prompt: str) -> str:
    ...

from muna import compile, Sandbox

# Upload a model weight to the sandbox
sandbox = (Sandbox()
  .upload_file("DeepSeek-R1.gguf", "/Deepseek-R1.gguf")
)

# Compile your function with the sandbox
@compile(..., sandbox=sandbox)
def predict(prompt: str) -> str:
    ...

from muna import compile, Sandbox

# Upload a directory to the sandbox
sandbox = (Sandbox()
  .upload_file("resources/", "/resources")
)

# Compile your function with the sandbox
@compile(..., sandbox=sandbox)
def predict(prompt: str) -> str:
    ...