# Release Notes

### Version 1.2.1 (3/19/2026)

**New Features and Modifications**

1. DEEPX runtime version 3.2.0 is supported. Corresponding driver version should be 2.1.0-2.

**Bug Fixes**

1. "Python postprocessor: configure\_worker: wrong worker\_id" error occurs intermittently when running models with Python postprocessor.
2. `degirum download-zoo` CLI command does not honor token installed by `degirum token install` or `degirum token create`.

***

### Version 1.2.0 (3/6/2026)

**New Features and Modifications**

1. `update_if_newer` property is added to `degirum.model._ClientModel` class. This class is returned by `degirum.load_model` function when `@local` inference is requested. This property affects how cloud models are handled by local model cache in case of `@local` inference. When the cloud model is requested for the inference and this property is set to `True`, the local model cache always queries the cloud zoo for the model checksum and downloads the model from the cloud zoo to the local cache if checksums mismatch. When set to `False` (this is default setting) the cloud model is downloaded once and the model checksum is never queried. Please note that in previous releases model checksums are always queried, so the default behavior is changed.

**Bug Fixes**

1. When `input_shape` model property is set to the same value, model parameters get invalidated causing model reload. Model parameters invalidation and model reload must happen only when a model parameter is changed to some new value.
2. DeepX runtime agent: when a model compiled with older version of DeepX model compiler is loaded for inference, inference hangs in DeepX runtime. Now the model version is validated against supported versions and the error message is generated in case of not supported version.
3. Fixed bugs in Audio pre-processor for YAMNET models.

***

### Version 1.1.0 (2/17/2026)

**New Features and Modifications**

1. ONNX runtime plugin is redesigned to improve inference performance.
2. DEEPX runtime version 3.1.0 is supported.
3. `--token` parameter is added to `degirum server start` command to be able to install token prior to server launch to enable proper license activation.
4. `degirum machine-id` CLI command is added to print host machine-id
5. `degirum token create` command is modified to generate meaningful token description like `Created by PySDK for <username>@<hostname> (<host IP>)`
6. `degirum download-llm` CLI command is added to download and install LLM models from DeGirum model zoo for OpenVino GenAI plugin.

**Bug Fixes**

1. Automatic renewal of expired token is now performed prior to the PySDK license verification. The absence of such automatic renewal caused license verification error in case when installed token has finite duration and is expired.
2. Proper import of `pyseccomp` is implemented in Python post-processor engine: plain `import pyseccomp` fails on some systems causing `ModuleNotFoundError: No module named 'pyseccomp'` error.

### Version 1.0.0 (1/26/2026)

**This is the first production release of PySDK.**

> :warning: IMPORTANT:

We have updated the PySDK release distribution process. Pre-production releases (prior to ver. 1.0.0) were published on PyPI (pypi.org). Production releases are distributed through the [DeGirum Package Service](https://pkg.degirum.com) **and** pypi.org.

To install the production version of PySDK from DeGirum Package Service, use the following command:

```
pip install -i https://pkg.degirum.com degirum
```

> :warning: IMPORTANT:

Starting from ver. 1.0.0, the usage of premium runtime plugins requires license. The license is obtained automatically by PySDK from DeGirum AI Hub if you have AI Hub token installed on your system. Once requested, the plugin license is stored locally and automatically renewed on expiration. Default expiration period is 10 days. The license is node-locked.

The Free plan allows you to use PySDK premium runtimes on **one host**. If you need to use PySDK premium runtimes on more than one host, you need to upgrade your AI Hub workspace to [Professional or Enterprise plans](https://degirum.com/pricing).

To install existing AI Hub token, you run degirum CLI command: `degirum token install <TOKEN>` where `<TOKEN>` is the AI Hub token string which you generate on [AI Hub](https://docs.degirum.com/ai-hub/workspaces/workspace-tokens).

To create new token and install it, you run degirum CLI command: `degirum token create`. If you run this command on a system having graphical desktop, it will open token generation page in your default browser for you. Otherwise it will print URL which you need to paste in any browser.

To upgrade your plan follow [these instructions](https://docs.degirum.com/ai-hub/workspace-plans#upgrade-your-workspace-plan).

The premium plugins include:

* Akida (Brainchip)
* Axelera
* DeepX
* EdgeCortix
* Hailo
* MemryX
* ONNX
* OpenVINO (Intel)
* Renesas
* RKNN (RockChip)
* TensorRT (nVidia)

Free plugins include DeGirum N2X Orca and Google TFLite.

**New Features and Modifications**

1. YOLO26 models are initially supported by PySDK.
2. Axelera runtime version 1.5.3 is supported. Please note that models compiled by Axelera compiler ver. prior to 1.5.x are not compatible with 1.5.x.
3. Double-buffering and DMA buffers are supported for Axelera runtime. This is performance optimization.
4. `degirum token install` CLI command now accepts token string without `--token` keyword.

   Before: `degirum token install --token <TOKEN>`

   Now: `degirum token install <TOKEN>`

**Bug Fixes**

1. `PythonFile` model parameter is not assigned properly on model loading: the value from model JSON file always overwrites the value assigned in run-time.

***

### Version 0.20.0 (12/16/2025)

**New Features and Modifications**

1. Minimum supported Ubuntu version for ARM64 platforms is now 22.04 (bumped from 20.04). For x86-64 platforms it is still 20.04.
2. Python 3.13 is supported.
3. Renesas AI accelerators are initially supported for Linux OS. The runtime/device designator for these devices is `"RENESAS/RZ-V2*"` where `*` is the particular device model (L, M, MA, H, and N). The supported runtime version is 2.5.1.
4. `model.predict_batch()` latency is improved. Previous versions yield only one ready result for each consumed input frame. This led to accumulated latency which never reduced. With new change the code yields results until no more ready results are available.
5. Audio pre-processor quality and performance for Whisper models is improved.
6. Cloud model zoo cache is now shared between all processes which use PySDK on a given system. In previous versions for `@local` inferences temporary non-persistent cloud zoo cache was created for each process.
7. `PythonFile` model parameter is now can be modified in runtime. For example, to temporary disable Python post-processor assign `model._model_parameters.PythonFile = ""`
8. Startup time of Python postprocessor worker processes is significantly improved due to parallelization.
9. TensorRT runtime: added JIT compilation of pre-quantized ONNX models.
10. TensorRT runtime: implemented loading .engine files supplied by the model JSON. This bypasses the JIT entirely.
11. TensorRT runtime: new `extra_device_parameters` are supported for TensorRT runtime models: `UseFP16` and `UseINT8`, You may set them by assigning `model.extra_device_params.UseFP16` and `model.extra_device_params.UseINT8` respectively.
12. Token creation procedure via `degirum token create` is improved for headless systems (systems without GUI browser).
13. Empty zoo\_url for `@local` inference type is now treated as default public cloud zoo.
14. `degirum.get_supported_devices()`: `zoo_url` and `token` parameters are deprecated as extraneous - you may now omit them.

**Bug Fixes**

1. Timing information was stripped from inference results only **after** creation of inference result object, which exposed that timing data to custom post-processor constructors. This led to single fake result containing only timing information to appear when no actual results are reported by the model. Now it is stripped from inference results before that.
2. YOLO detection result processors incorrectly quantized confidence threshold when output score tensor quantization parameters produce out-of range results. Now clamping is applied to prevent wrap over.
3. Proper detection of MemryX device count is implemented for Windows platform. In previous versions it was just hardcoded to one device.

***

### Version 0.19.2 (11/01/2025)

> ATTENTION: this release contains critical bug fixes. We strongly recommend to upgrade to this release from 0.19.0 and 0.19.1 versions.

**Bug Fixes**

1. **Critical bug fix:** YOLOv8 pose detection and object detection+segmentation models produce incorrect results.
2. **Critical bug fix:** when Numpy package version 2.0 or newer is installed, then all models reporting raw tensors like ReID embedding models, pure segmentation models, or models with "None" postprocessor type produce incorrect results. This bug is due to incompatibility of pybind11 library ver. 2.10.3 using in PuSDK build and Numpy ver 2.0 and above.
3. When Hailo runtime ver. 4.20.1 is installed and Hailo multiprocess service is **not** running, then PySDK produces HAILO\_INVALID\_OPERATION error.
4. When running inferences of models for MemryX runtime, switching models causes segmentation fault.

***

### Version 0.19.1 (10/28/2025)

> ATTENTION: this release has critical bugs. Please upgrade to newer release 0.19.2 or above!

**New Features and Modifications**

1. Improved performance of Detection post-processors, especially on ARM hosts.
2. Audio preprocessor improvements: now supports different tensor layouts and input dimensions for Whisper preprocessing.
3. HailoRT versions 4.23.0 and 4.20.1 are supported.
4. MemryX runtime version 2.0 is supported.

**Bug Fixes**

1. Implemented a timeout bypass mechanism for the Axelera runtime plugin to prevent indefinite stalling when the Axelera API fails.
2. Incorrect inference results appear intermittently when performing streaming predictions using TensorRT plugin due to race condition in TensorRT plugin implementation.

***

### Version 0.19.0 (10/13/2025)

> ATTENTION: this release has critical bugs. Please upgrade to newer release 0.19.2 or above!

**New Features and Modifications**

1. [EdgeCortix](https://www.edgecortix.com/en/) AI accelerators are initially supported for Linux OS. The runtime/device designator for these devices is `"EDGECORTIX/SAKURA2"`.
2. Axelera runtime version 1.4.1 is supported.
3. Error handling in Axelera runtime plugin is improved:

* Waits with infinite timeouts are replaced with finite.
* Errors in inference stream no longer disable device operations.

4. PySDK image pre-processor throughput is significantly improved by reducing the amount of double-buffering along the processing pipeline. This improvement is more significant for slower hosts.
5. Introduced a new model parameter: `ExtraDeviceParams`. This parameter is used for hardware-specific configurations.

* Inside the Model JSON, it is a JSON object under the key `ExtraDeviceParams` in `DEVICE` section.
* Is is exposed as `degirum.model.Model.extra_device_params` property in PySDK for setting these parameters.
* Use `model.extra_device_params.KEY = VALUE` syntax for setting these parameters.

6. Hailo Runtime Agent no longer uses `degirum.model.Model.eager_batch_size` property for internal Hailo batching control. Instead, now it uses `HAILO_BATCH_SIZE` inside the model's `ExtraDeviceParams`. Now, to set a model's batch size for Hailo devices, use `model.extra_device_params.HAILO_BATCH_SIZE = <desired-batch-size>`
7. `numpy` package maximum version limitation in PySDK package requirements is relaxed to `< 3.0`.
8. `degirum.model.Model.model.output_class_set` and `degirum.model.Model.model.overlay_blur` property setters now accept list, set, or string arguments interchangeably. Property getters now return lists.
9. Now `degirum.get_supported_devices()` function requires only one argument: `inference_host_address`. There is no need to pass `zoo_url` and `token` parameters anymore but they left for backward-compatibility.
10. `degirum.model.Model.model.devices_selected` property is now updated when `degirum.model.Model.model.device_type` property changes so the selected device list remains valid for newly selected device type. Particularly, when `devices_selected` list becomes empty after device type change, it is automatically assigned with `devices_available` value.

**Bug Fixes**

1. `"Tensor"` preprocessor type now allows data types other than `DG_FLT` or `DG_UINT8` to pass through unmodified.
2. ONNX runtime agent now correctly supports conversion for all input tensor data types, not only `DG_FLT` or `DG_UINT8`.
3. Segmentation results renderer fails on bounding boxes with zero area in mask resize.

***

### Version 0.18.3 (09/19/2025)

**New Features and Modifications**

1. Axelera runtime version 1.4 is supported.
2. OpenVINO GenAI runtime agent is initially supported. This runtime agent allows running inferences of LLM models in PySDK using OpenVINO GenAI runtime.
3. Integrated Intel GPU is now available as inference device for OPENVINO runtime, when it is the only GPU in the system (if you have both discrete and integrated GPUs, the integrated GPU is still not available). The device designator is "OPENVINO/GPU" as for discrete GPU.
4. Timeouts are implemented for all AI server commands. Before that modification, some commands, like system information request, may wait indefinitely for AI server response when AI server process already exited for whatever reason.
5. The `degirum install-runtime` CLI commands have been updated to better support operation in root environments.
6. The performance of rendering of segmentation results is greatly improved. The memory consumption to store segmentation masks is also reduced.
7. Tensor Preprocessor: quantization/dequantization functionality is implemented. When input tensor data type (as specified by `InputRawDataType` model parameter) does not match model input data type (as deduced from `InputQuantEn` model parameter), either quantization or dequantization of the input tensor happens. When `InputRawDataType` is DG\_UINT8 and `InputQuantEn` is false, then dequantization is performed. When `InputRawDataType` is DG\_FLT and `InputQuantEn` is true, then quantization is performed. Quantization/dequantization parameters are specified by `InputQuantScale` and `InputQuantOffset` model parameters.

**Bug Fixes**

1. Cloud inference started from a separate thread may produce the error `"got Future <Future pending> attached to a different loop"`.
2. The `degirum server cache-dump` command now always outputs valid JSON.
3. Floating point overflow in bounding box coordinate decoding computation is fixed in YOLOv8 Detection post-processor.

***

### Version 0.18.2 (08/12/2025)

**New Features and Modifications**

Axelera runtime is now supported on ARM64 platforms.

***

### Version 0.18.1 (08/11/2025)

**New Features and Modifications**

1. New post-processor types are supported:
   * `Null` post-processor. This post-processor effectively disables all post-processing, which is useful for model bechmarking. The post-processor tag is `"Null"`.
   * `Dequantization` post-processor. This post-processor performs dequantization of all integer-type output tensor results (compare it with `None` post-processor, which just returns all output tensor results as-is). The post-processor tag is `"Dequantization"`.
2. Verbosity of `degirum token` CLI commands is improved. Now all commands produce some confirmation message on completion.

**Bug Fixes**

1. When cloud API token with unlimited lifetime is installed in PySDK using `degirum token install` CLI command, the following `dg.connect()` call raises error `"Invalid isoformat string"`.

***

### Version 0.18.0 (08/05/2025)

**New Features and Modifications**

1. `degirum token` command tree has been added to PySDK CLI. Using these commands you can manage cloud access API tokens in PySDK. You can install existing token into PySDK internal storage, you can create and install new token, you can query the status of the currently installed token, you can renew currently installed token, when it is expired, and you can delete currently installed token from the PySDK internal storage. The PySDK internal storage is arranged as JSON file in user-specific directory: %APPDATA%\DeGirum for Windows and \~/.local/share/DeGirum for Linux/MacOS. The following commands are supported:
   * `degirum token status`: prints the status of the currently installed token. If Internet connection is available, it queries the most current token status from the DeGirum AI Hub otherwise it prints latest known token status.
   * `degirum token install --token <token string>`: installs the provided token into the PySDK internal storage. You need to obtain the token string from DeGirum AI Hub Web GUI.
   * `degirum token create`: opens automatic token creation URL in your default browser (when available) or prints this URL into console. Using provided URL you need to authenticate in DeGirum AI Hub. Meanwhile, PySDK CLI will wait until you open provided URL and authenticate in DeGirum AI Hub. Once authenticated, the new token with 2-week expiration will be created automatically, and PySDK CLI will pick it up and install into PySDK internal storage.
   * `degirum token renew`: renews currently installed token. Renewal happens even if the token is already expired. Renewed token is then installed into the PySDK internal storage replacing the currently installed token.
   * `degirum token clear`: removes the currently installed token from the PySDK internal storage. It does not delete the token from the AI Hub.
2. `degirum.connect` function now tries to use currently installed token, if no token is provided as `token` argument (see `degirum token` CLI description above). It will try to renew this token automatically if it is about to be expired or already expired. This feature is intended to simplify the token handling in PySDK, when you install the time-limited token once, using PySDK CLI, and then use it indefinitely on this system from all your scripts.
3. `degirum install-runtime` command tree has been added to PySDK CLI. Using these commands you can install third-party AI accelerator runtime libraries on your system greatly simplifying system housekeeping. The following commands are supported:
   * `degirum install-runtime --list`: list all runtimes available for installation. Using this command you may determine arguments for the following command.
   * `degirum install-runtime <runtime> <version>`: install particular version of particular runtime. You may specify `ALL` instead of the version number to install all supported versions of that runtime.
   * The following runtimes are supported for Linux Ubuntu/Debian OS in this release (more runtimes will be supported in the future releases):

     | Designator | Runtime                 | Versions                     |
     | ---------- | ----------------------- | ---------------------------- |
     | `akida`    | Brainchip Akida runtime | 2.11.0                       |
     | `axelera`  | Axelera Voyager runtime | 1.3.3                        |
     | `memryx`   | MemryX runtime          | 1.2.3-1                      |
     | `onnx`     | ONNX runtime            | 1.20.1                       |
     | `openvino` | Intel OpenVINO runtime  | 2024.6.0, 2024.2.0, 2023.3.0 |
     | `rknn`     | Rockchip RKNN runtime   | 2.3.0                        |
4. HAILO runtime agent now supports HAILORT runtime version 4.22.
5. Axelera runtime agent now supports Voyager SDK runtime version 1.3.3.
6. Preprocessing functionality for Whisper models is implemented for audio preprocessor. The `InputType` model parameter value for such models is `Audio`. The audio preprocessor distinguishes Whisper models by combination of `InputFrameSize` equal to 400 and `InputFrameHopStepSize` equal to 160.
7. Now `degirum.connect()` checks the cloud token and cloud zoo validity for non-public zoo access. If you pass an empty or incorrect token, you will get an error `"Unable to connect to server hub.degirum.com: your cloud API access token is not valid"`. If you pass correct token, but the cloud zoo does not exist or not accessible to you, the you will get an error `"Cloud model zoo '<space>/<zoo>' either does not exist or you do not have access to it."`
8. When calling `degirum.connect(degirum.LOCAL)` function to connect for local inference with an empty string passed as `zoo_url` argument, the error `"ZooManager: incorrect local model zoo URL"` is raised. In previous releases empty `zoo_url` argument led to local zoo selection in the current working directory causing a lot of confusion.

**Bug Fixes**

1. Multiple bug fixes of intermittent failures of ORCA USB accelerator inferences.

***

### Version 0.17.3 (07/15/2025)

**Bug Fixes**

Automatic reconnect on critical error functionality in the cloud inference protocol has been broken due to recent changes in this protocol made in 0.17.2 release.

***

### Version 0.17.2 (07/14/2025)

**New Features and Modifications**

1. [Axelera](https://axelera.ai) AI accelerators are initially supported for Linux OS. The runtime/device designator for these devices is `"AXELERA/METIS"`.
2. DEEPX runtime inference performance is improved by avoiding extra memory copy of inference result tensors.

**Bug Fixes**

1. Frequent websocket disconnects happen during streaming cloud inferences in PySDK, significantly reducing streaming frame rate. This bug was due to the race condition in the websocket-client package used by python-socketio synchronous client. Now PySDK uses python-socketio asynchronous client, which does not use websocket-client package.

***

### Version 0.17.1 (06/25/2025)

**New Features and Modifications**

1. ONNX runtime agent now supports ONNX runtime version 1.20.1.

**Bug Fixes**

1. Hailo models with hardware accelerated NMS layer did not work with PySDK and HailoRT ver. 4.21. Now this bug is fixed.
2. Once the model is loaded by Hailo agent, it remembers model batch size and any attempt to change the batch size of already loaded model has no effect.

***

### Version 0.17.0 (06/20/2025)

**Known Issues**

> Hailo models with hardware accelerated NMS layer do not work with PySDK and HailoRT ver. 4.21. We recommend to use HailoRT ver. 4.20 for such models until the PySDK fix will be released in the next PySDK version.

**New Features and Modifications**

1. Batching is supported for HailoRT runtime. You control the batch size via `degirum.model.Model.eager_batch_size` property. For single-context models (models which fully fit into accelerator memory) the Hailo runtime batch size is set to auto to improve performance, because such models do not benefit from batching. For multi-context models which do benefit from batching the Hailo runtime batch size is set equal to `degirum.model.Model.eager_batch_size`.
2. DEEPX runtime version 2.9.5 is supported. This version supports production version of DEEPX M.2 accelerator card.
3. Multi-device support was enabled for DEEPX runtime. Now PySDK can operate with multiple DEEPX M.2 accelerator cards.
4. Models with built-in NMS layer are supported for HailoRT runtime.
5. Maximum supported model parameters configuration version is increased from 10 to 11.
6. The post-processor for DamoYolo models is implemented. The post-processor tag is `"DetectionDamoYolo"`.
7. Custom landmark shapes (other than 2) are supported in PoseDetectionYoloV8 post-processor. Now `landmark` key in detection results may contain coordinate list with more than 2 elements, for example `[x,y,score]`.
8. AI annotation renderers now support zero-size line width. You set it via `degirum.model.Model.overlay_line_width` property. When it is set to zero, no lines are drawn.
9. The URL parsing logic of `degirum.connect()` is made more reliable. Now in the case of local inference or AI server inference the zoo URL is first checked is it a cloud zoo URL. The URL is considered as cloud zoo URL if it starts with `http://` or `https://` scheme or it contains exactly one slash like `workspace/zoo`. Only if provided URL does not look like cloud zoo URL it is then checked is it local zoo URL.
   * In the case of local inference it is checked is it a valid local path; and if it is not, then the error message `"incorrect local model zoo URL: path does not exist"` is raised. You may use explicit `file://` scheme for local paths.
   * In the case of AI server inference it is considered as local model zoo URL if it is empty string or it starts with `aiserver://` scheme. Otherwise the error `"incorrect cloud model zoo URL"` is raised.
10. Runtime plugin loading whitelist is implemented. The list of runtime plugins allowed to load can be specified in DG\_PLUGINS\_ALLOWED environment variable using the following syntax: plugin prefixes separated by any non alphanumeric separator. For example: `DG_PLUGINS_ALLOWED=n2x_runtime_agent;onnx_runtime_agent`. When DG\_PLUGINS\_ALLOWED is not defined, all plugins will be loaded as before.

**Bug Fixes**

1. When there is no connection to the cloud zoo, the model from AI server model zoo cache can be loaded without token authorization.

***

### Version 0.16.2 (05/09/2025)

**New Features and Modifications**

1. C++ SDK example has been modified to support inference of models from cloud zoos.
2. HAILO runtime agent now supports HAILORT runtime version 4.21.
3. MEMRYX runtime agent is supported on Windows 10/11 OS.
4. TENSORRT runtime agent is supported on Windows 10/11 OS.
5. Dequantization post-processor is implemented. This post-processor performs dequantization of model output tensors according to model dequantization settings for those tensors. The post-processor tag is `"Dequantization"`.
6. New `InferenceResultsType` model parameter is added. This model parameter specifies objects of which PySDK result class (a class derived from `InferenceResults` base class) will be returned as inference results. When the `InferenceResultsType` model parameter is not defined then the `OutputPostprocessType` model parameter is used instead, as before. Please note that in previous releases the `OutputPostprocessType` model parameter was used for both selecting which Core-level post-processor is applied to the model output tensors and objects of what PySDK result class to return as inference results. Having separate parameter to select PySDK result class allows flexibility of matching different Core-level post-processors with different PySDK result classes. For example, "Dequantization" Core-level post-processor can be used with "Classification" PySDK result class.
7. `degirum.inference_results_type` model property is added to control `InferenceResultsType` model parameter in run-time.
8. `degirum.get_inference_results_class` method is added to return PySDK result class which objects are returned as inference results for this
9. `degirum postprocessor.register_postprocessor` function is added. It registers your own PySDK result class for a specific inference result type (as defined by `InferenceResultsType` model parameter, see above). This function accepts two arguments: inference result type string and PySDK result class. PySDK result class must inherit `degirum postprocessor.InferenceResults` base class. Once your class is registered, objects of this class will be returned as inference results for models with `InferenceResultsType` model parameter equal to the inference result type string you passed when calling `register_postprocessor` function. This allows developing new custom PySDK result classes and seamlessly integrating them into PySDK.

**Bug Fixes**

1. Various error messages appear when trying to do inference of more than 32 different models using HAILORT runtime agent on a single Hailo accelerator device.
2. Inference multiplexing on all available devices was not performed for the TFLITE and OPENVINO runtime agents: only single device with index 0 was always used. This bug was introduced in ver. 0.16.0.

***

### Version 0.16.1 (04/29/2025)

**New Features and Modifications**

MemryX runtime agent now supports MemryX runtime version 1.2.

**Bug Fixes**

TensorRT runtime agent failed to run inferences on DLA devices producing `"Cuda failure: invalid device ordinal"` error.

***

### Version 0.16.0 (04/17/2025)

**New Features and Modifications**

1. [DEEPX](https://deepx.ai) AI accelerators are initially supported for Linux OS. The runtime/device designator for these devices is `"DEEPX/M1A"`.

   > NOTE: due to current limitations of DEEPX runtime the number of supported devices is limited to one.
2. Error handling is improved for Hailo runtime agent: now all errors reported by HailoRT runtime are treated as critical.
3. TensorRT runtime agent performance is improved due to implementation of asynchronous pipelined inference.
4. Hailo runtime agent is supported on Windows 10/11 OS.
5. `postprocess_type` keyword argument is added to `degirum.zoo_manager.ZooManager.list_models` method. It allows filtering models by post-processor type. Possible values for this argument are: `"Classification"`, `"Detection"`, `"Segmentation"` etc. They correspond to `OutputPostprocessType` model parameter values.

**Bug Fixes**

1. Oriented bounding box post-processor incorrectly interpreted rotation angle tensors for certain OBB models, which led to the error message `"Execution failed. Condition '<N1> == <N2>' is not met"`.