PyTriton 0.2.4

• new: Introduced strict flag in Triton.bind which enables data types and shapes validation of inference callable outputs
  against model config
• new: AsyncioModelClient which works in FastAPI and other async frameworks
• fix: FuturesModelClient do not raise gevent.exceptions.InvalidThreadUseError
• fix: Do not throw TimeoutError if could not connect to server during model verification

• Version of Triton Inference Server embedded in wheel: 2.33.0

PyTriton 0.2.3

• Improved verification of Proxy Backend environment when running under same Python interpreter
• Fixed pytriton.version to represent currently installed version

• Version of Triton Inference Server embedded in wheel: 2.33.0

PyTriton 0.2.2

• Added inference_timeout_s parameters to client classes
• Renamed PyTritonClientUrlParseError to PyTritonClientInvalidUrlError
• ModelClient and FuturesModelClient methods raise PyTritonClientClosedError when used after client is closed
• Pinned tritonclient dependency due to issues with tritonclient >= 2.34 on systems with glibc version lower than 2.34
• Added warning after Triton Server setup and teardown while using too verbose logging level as it may cause a significant performance drop in model inference

• Version of Triton Inference Server embedded in wheel: 2.33.0

PyTriton 0.2.1

• Fixed handling TritonConfig.cache_directory option - the directory was always overwritten with the default value.
• Fixed tritonclient dependency - PyTriton need tritonclient supporting http headers and parameters
• Improved shared memory usage to match 64MB limit (default value for Docker, Kubernetes) reducing the initial size for PyTriton Proxy Backend.

• Version of Triton Inference Server embedded in wheel: 2.33.0

PyTriton 0.2.0

• Added support for using custom HTTP/gRPC request headers and parameters.
  See docs/custom_params.md for further information

  This change breaks backward compatibility of the inference function signature.
  The undecorated inference function now accepts a list of Request instances instead
  of a list of dictionaries. The Request class contains data for inputs and parameters
  for combined parameters and headers. For details see docs/infrence_callable.md.

• Added FuturesModelClient which enables sending inference requests in a parallel manner.

• Added displaying documentation link after models are loaded.

• Version of Triton Inference Server embedded in wheel: 2.33.0

PyTriton 0.1.5

• Improved pytriton.decorators.group_by_values function
  • Modified the function to avoid calling the inference callable on each individual sample when grouping by string/bytes input
  • Added pad_fn argument for easy padding and combining of the inference results
• Fixed Triton binaries search
• Improved Workspace management (remove workspace on shutdown)

• Version of external components used during testing:
  • Triton Inference Server: 2.29.0
  • Other component versions depend on the used framework and Triton Inference Server containers versions.
    Refer to its support matrix
    for a detailed summary.

PyTriton v0.1.4

0.1.4 (2023-03-16)

• Add validation of the model name passed to Triton bind method.
• Add monkey patching of InferenceServerClient.__del__ method to prevent unhandled exceptions.

• Version of external components used during testing:
  • Triton Inference Server: 2.29.0
  • Other component versions depend on the used framework and Triton Inference Server containers versions.
    Refer to its support matrix
    for a detailed summary.

PyTriton v0.1.3

0.1.3 (2023-02-20)

• Fixed getting model config in fill_optionals decorator.

• Version of external components used during testing:
  • Triton Inference Server: 2.29.0
  • Other component versions depend on the used framework and Triton Inference Server containers versions.
    Refer to its support matrix
    for a detailed summary.

PyTriton v0.1.2

• Fixed wheel build to support installations on operating systems with glibc version 2.31 or higher.
• Updated the documentation on custom builds of the package.
• Change: TritonContext instance is shared across bound models and contains model_configs dictionary.
• Fixed support of binding multiple models that uses methods of the same class.

• Version of external components used during testing:
  • Triton Inference Server: 2.29.0
  • Other component versions depend on the used framework and Triton Inference Server containers versions.
    Refer to its support matrix
    for a detailed summary.

PyTriton v0.1.1

• Change: The @first_value decorator has been updated with new features:
  • Renamed from @first_values to @first_value
  • Added a strict flag to toggle the checking of equality of values on a single selected input of the request. Default is True
  • Added a squeeze_single_values flag to toggle the squeezing of single value ND arrays to scalars. Default is True
• Fix: @fill_optionals now supports non-batching models
• Fix: @first_value fixed to work with optional inputs
• Fix: @group_by_values fixed to work with string inputs
• Fix: @group_by_values fixed to work per sample-wise

• Version of external components used during testing:
  • Triton Inference Server: 2.29.0
  • Other component versions depend on the used framework and Triton Inference Server containers versions.
    Refer to its support matrix
    for a detailed summary.