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Changelog

0.4.2 (2023-12-05)

  • New: You can create client from existing client instance or model configuration to avoid loading model configuration from server.
  • New: Introduced warning system using the warnings module.
  • Fix: Experimental client for decoupled models prevents sending another request, when responses from previous request are not consumed, blocks close until stream is stopped.
  • Fix: Leak of ModelClient during Triton creation
  • Fix: Fixed non-declared project dependencies (removed from use in code or added to package dependencies)
  • Fix: Remote model is being unloaded from Triton when RemoteTriton is closed.

0.4.1 (2023-11-09)

  • New: Place where workspaces with temporary Triton model repositories and communication file sockets can be configured by $PYTRITON_HOME environment variable
  • Fix: Recover handling KeyboardInterrupt in triton.serve()
  • Fix: Remove limit for handling bytes dtype tensors
  • Build scripts update
    • Added support for arm64 platform builds

0.4.0 (2023-10-20)

  • New: Remote Mode - PyTriton can be used to connect to a remote Triton Inference Server
    • Introduced RemoteTriton class which can be used to connect to a remote Triton Inference Server running on the same machine, by passing triton url.
    • Changed Triton lifecycle - now the Triton Inference Server is started while entering the context. This allows to load models dynamically to the running server while calling the bind method. It is still allowed to create Triton instance without entering the context and bind models before starting the server (in this case the models are lazy loaded when calling run or serve method like it worked before).
    • In RemoteTriton class, calling enter or connect method connects to triton server, so we can safely load models while binding inference functions (if RemoteTriton is used without context manager, models are lazy loaded when calling connect or serve method).
  • Change: @batch decorator raises a ValueError if any of the outputs have a different batch size than expected.
  • fix: gevent resources leak in FuturesModelClient

0.3.1 (2023-09-26)

  • Change: KeyboardInterrupt is now handled in triton.serve(). PyTriton hosting scripts return an exit code of 0 instead of 130 when they receive a SIGINT signal.
  • Fix: Addressed potential instability in shared memory management.

0.3.0 (2023-09-05)

  • new: Support for multiple Python versions starting from 3.8+
  • new: Added support for decoupled models enabling to support streaming models (alpha state)
  • change: Upgraded Triton Inference Server binaries to version 2.36.0. Note that this Triton Inference Server requires glibc 2.35+ or a more recent version.

0.2.5 (2023-08-24)

  • new: Allow to execute multiple PyTriton instances in the same process and/or host
  • fix: Invalid flags for Proxy Backend configuration passed to Triton

0.2.4 (2023-08-10)

  • 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

0.2.3 (2023-07-21)

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

0.2.2 (2023-07-19)

  • 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

0.2.1 (2023-06-28)

  • 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.

0.2.0 (2023-05-30)

  • Added support for using custom HTTP/gRPC request headers and parameters.

    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.

    See docs/custom_params.md for further information

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

  • Added displaying documentation link after models are loaded.

0.1.5 (2023-05-12)

  • 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.

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.

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.

0.1.2 (2023-02-14)

  • 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.

0.1.1 (2023-01-31)

  • 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.

0.1.0 (2023-01-12)

  • Initial release of PyTriton
  • 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.