Module blindai.client

Ancestors

• contextlib.AbstractContextManager
• abc.ABC

Methods

def close(self)

def delete_model(self, model_id: str)

Delete a model in the inference server.

This may be used to free up some memory. If you did not specify that you wanted your model to be saved on the server, please note that the model will only be present in memory, and will disappear when the server close.

Security & confidentiality warnings: model_id: The deletion of a model does only relies on the model_id. It doesn't relies on a session token or anything, hence if the model_id is known, it's deletion is possible.

Args

model_id : str

The id of the model to remove.

Raises

HttpError

raised by the requests lib to relay server side errors

ValueError

raised when inputs sanity checks fail

def run_model(self, model_id: str = '', model_hash: str = '', input_tensors: Union[List, Dict, ForwardRef(None)] = None, dtypes: Optional[List[ModelDatumType]] = None, shapes: Union[List[List[int]], List[int], ForwardRef(None)] = None) ‑> RunModelResponse

Send data to the server to make a secure inference.

The data provided must be in a list, as the tensor will be rebuilt inside the server.

Security & confidentiality warnings: model_id: hash of the Onnx model uploaded. the given hash is return via gRPC through the proto files. It's a SHA-256 hash that is generated each time a model is uploaded. tensors: protected in transit and protected when running it on the secure enclave. In the case of a compromised OS, the data is isolated and confidential by SGX design.

Args

model_id : str

If set, will run a specific model.

model_hash : str

hash of the Onnx model uploaded. If no uuid was provided, the server will try to find a model matching this hash input_tensors (Union[List[Any], List[List[Any]]))): The input data. It must be an array of numpy, tensors or flat list of the same type datum_type specified in upload_model.

dtypes : Union[List[ModelDatumType], ModelDatumType], optional

The type of data of the data you want to upload. Only required if you are uploading flat lists, will be ignored if you are uploading numpy or tensors (this info will be extracted directly from the tensors/numpys).

shapes : Union[List[List[int]], List[int]], optional

The shape of the data you want to upload. Only required if you are uploading flat lists, will be ignored if you are uploading numpy or tensors (this info will be extracted directly from the tensors/numpys).

Raises

HttpError

raised by the requests lib to relay server side errors

ValueError

raised when inputs sanity checks fail

Returns

RunModelResponse

The response object.

def upload_model(self, model: str, model_name: Optional[str] = None, optimize: bool = True) ‑> UploadResponse

Upload an inference model to the server.

The provided model needs to be in the Onnx format.

Security & confidentiality warnings: model: The model sent on a Onnx format is encrypted in transit via TLS (as all connections). It may be subject to inference Attacks if an adversary is able to query the trained model repeatedly to determine whether or not a particular example is part of the trained dataset model.

Args

model : str

Path to Onnx model file.

model_name : Optional[str], optional

Name of the model. Used for you to identify the model, but won't be used by the server (a random UUID will be assigned to your model for the inferences).

optimize : bool

Whether tract (our inference engine) should optimize the model or not. Optimzing should only be turned off when you are encountering issues loading your model.

Raises

HttpError

raised by the requests lib to relay server side errors

ValueError

raised when inputs sanity checks fail

Returns

UploadResponse

The response object.

Class variables

var model_id : str

Ancestors

• enum.IntEnum
• builtins.int
• enum.Enum

Class variables

var Bool

var F32

var F64

var I16

var I32

var I64

var I8

var U16

var U32

var U64

var U8

Class variables

var client_info : Optional[blindai.client._ClientInfo]

var inputs : List[Tensor]

var model_hash : str

var model_id : str

Class variables

var outputs : List[Any]

Class variables

var output : List[Tensor]

Class variables

var hash : bytes

var model_id : str

Ancestors

• builtins.Warning
• builtins.Exception
• builtins.BaseException

Class variables

var bytes_data : bytes

var info : Union[TensorInfo, dict]

Instance variables

var datum_type : ModelDatumType

var shape : tuple

Methods

def as_flat(self) ‑> list

Convert the prediction calculated by the server to a flat python list.

def as_numpy(self)

Convert the prediction calculated by the server to a numpy array.

def as_torch(self)

Convert the prediction calculated by the server to a Torch Tensor.

As torch is heavy it's an optional dependency of the project, and is imported only when needed.

Raises: ImportError if torch isn't installed

Class variables

var datum_type : ModelDatumType

var fact : List[int]

var node_name : str

Class variables

var client_info : blindai.client._ClientInfo

var length : int

var model : List[int]

var model_name : str

var optimize : bool

Class variables

var hash : bytes

var model_id : str