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Bumps Microsoft.ML.OnnxRuntime from 1.22.0 to 1.24.2 Bumps Microsoft.ML.Tokenizers from 1.0.2 to 2.0.0 --- updated-dependencies: - dependency-name: Microsoft.ML.OnnxRuntime dependency-version: 1.24.2 dependency-type: direct:production update-type: version-update:semver-minor dependency-group: microsoft-packages - dependency-name: Microsoft.ML.Tokenizers dependency-version: 2.0.0 dependency-type: direct:production update-type: version-update:semver-major dependency-group: microsoft-packages ... Signed-off-by: dependabot[bot] <support@github.com>
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Updated Microsoft.ML.OnnxRuntime from 1.22.0 to 1.24.2.
Release notes
Sourced from Microsoft.ML.OnnxRuntime's releases.
1.24.2
This is a patch release for ONNX Runtime 1.24, containing several bug fixes, security improvements, and execution provider updates.
Bug Fixes
SparseTensorProtoToDenseTensorPrototo improve robustness. (#27323)ArrayFeatureExtractor. (#27275)Execution Provider Updates
LazyReleasefor prepack allocator. (#27077)ConvTransposebias validation in both TypeScript and C++ implementations. (#27213)Build and Infrastructure
Microsoft.ML.OnnxRuntime.Foundrypackage for Windows ARM64 support and NuGet signing. (#27294)BaseTesterto support plugin EPs with both compiled nodes and registered kernels. (#27176)Full Changelog: v1.24.1...v1.24.2
Contributors
@tianleiwu, @hariharans29, @edgchen1, @xiaofeihan1, @adrianlizarraga, @angelser, @angelserMS, @ankitm3k, @baijumeswani, @bmehta001, @ericcraw, @eserscor, @fs-eire, @guschmue, @mc-nv, @qjia7, @qti-monumeen, @titaiwangms, @yuslepukhin
1.24.1
📢 Announcements & Breaking Changes
Platform Support Changes
API Version
✨ New Features
🤖 Execution Provider (EP) Plugin API
A major infrastructure enhancement enabling plugin-based EPs with dynamic loading:
OrtKernelInfoAPIs for kernel-based plugin EPs (#26803)🔧 Core APIs
OrtApi::CreateEnvWithOptions()andOrtEpApi::GetEnvConfigEntries()(#26971)KernelInfo(#26589)📊 Dependencies & Integration
🖥️ Execution Provider Updates
NVIDIA
Qualcomm QNN EP
Intel & AMD
... (truncated)
1.23.2
1.23.1
What's Changed
Full Changelog: microsoft/onnxruntime@v1.23.0...v1.23.1
1.23.0
Announcements
This release introduces Execution Provider (EP) Plugin API, which is a new infrastructure for building plugin-based EPs. (#24887 , #25137, #25124, #25147, #25127, #25159, #25191, #2524)
This release introduces the ability to dynamically download and install execution providers. This feature is exclusively available in the WinML build and requires Windows 11 version 25H2 or later. To leverage this new capability, C/C++/C# users should use the builds distributed through the Windows App SDK, and Python users should install the onnxruntime-winml package(will be published soon). We encourage users who can upgrade to the latest Windows 11 to utilize the WinML build to take advantage of this enhancement.
Upcoming Changes
Execution & Core Optimizations
Shutdown logic on Windows is simplified
Now on Windows some global object will be not destroyed if we detect that the process is being shutting down(#24891) . It will not cause memory leak as when a process ends all the memory will be returned to the operating system. This change can reduce the chance of having crashes on process exit.
AutoEP/Device Management
Now ONNX Runtime has the ability to automatically discovery computing devices and select the best EPs to download and register. The EP downloading feature currently only works on Windows 11 version 25H2 or later.
Execution Provider (EP) Updates
ROCM EP was removed from the source tree. Users are recommended to use Migraphx or Vitis AI EPs from AMD.
A new EP, Nvidia TensorRT RTX, was added.
Web
EMDSK is upgraded from 4.0.4 to 4.0.8
WebGPU EP
Added WGSL template support.
QNN EP
SDK Update: Added support for QNN SDK 2.37.
KleidiAI
Enhanced performance for SGEMM, IGEMM, and Dynamic Quantized MatMul operations, especially for Conv2D operators on hardware that supports SME2 (Scalable Matrix Extension v2).
Known Problems
Contributions
Contributors to ONNX Runtime include members across teams at Microsoft, along with our community members:
@1duo, @Akupadhye, @amarin16, @AndreyOrb, @ankan-ban, @ankitm3k, @anujj, @aparmp-quic, @arnej27959, @bachelor-dou, @benjamin-hodgson, @Bonoy0328, @chenweng-quic, @chuteng-quic, @clementperon, @co63oc, @daijh, @damdoo01-arm, @danyue333, @fanchenkong1, @gedoensmax, @genarks, @gnedanur, @Honry, @huaychou, @ianfhunter, @ishwar-raut1, @jing-bao, @joeyearsley, @johnpaultaken, @jordanozang, @JulienMaille, @keshavv27, @kevinch-nv, @khoover, @krahenbuhl, @kuanyul-quic, @mauriciocm9, @mc-nv, @minfhong-quic, @mingyueliuh, @MQ-mengqing, @NingW101, @notken12, @omarhass47, @peishenyan, @pkubaj, @qc-tbhardwa, @qti-jkilpatrick, @qti-yuduo, @quic-ankus, @quic-ashigarg, @quic-ashwshan, @quic-calvnguy, @quic-hungjuiw, @quic-tirupath, @qwu16, @ranjitshs, @saurabhkale17, @schuermans-slx, @sfatimar, @stefantalpalaru, @sunnyshu-intel, @TedThemistokleous, @thevishalagarwal, @toothache, @umangb-09, @vatlark, @VishalX, @wcy123, @xhcao, @xuke537, @zhaoxul-qti
1.22.2
What's new?
This release adds an optimized CPU/MLAS implementation of DequantizeLinear (8 bit) and introduces the build option client_package_build, which enables default options that are more appropriate for client/on-device workloads (e.g., disable thread spinning by default).
Build System & Packages
CPU EP
QNN EP
1.22.1
What's new?
This release replaces static linking of dxcore.lib with optional runtime loading, lowering the minimum supported version from Windows 10 22H2 (10.0.22621) to 20H1 (10.0.19041). This enables compatibility with Windows Server 2019 (10.0.17763), where dxcore.dll may be absent.
Commits viewable in compare view.
Updated Microsoft.ML.Tokenizers from 1.0.2 to 2.0.0.
Release notes
Sourced from Microsoft.ML.Tokenizers's releases.
1.7.1
Minor servicing update with dependency updates and PFI bug fix for correctly finding the correct transformer to use.
1.7.0-rc.1
ML.NET 1.7.0 RC 1
Moving forward, we are going to be aligning more with the overall .NET release schedule. As such, this is a smaller release since we had a larger one just about 3 months ago but it aligns us with the release of .NET 6.
New Features
ML.NET
DataFrame
Enhancements
ML.NET
DataFrame
Bug Fixes
Build / Test updates
Documentation Updates
Breaking Changes
1.6.0
ML.NET 1.6.0
New Features
Enhancements
Bug Fixes
... (truncated)
1.5.5
New Features
. A new API has been added to accept double type for the confidence level. This helps when you need to have higher precision than an int will allow for. (Thank you @esso23)
Enhancements
Bug Fixes
Build / Test updates
Documentation Updates
Breaking Changes
1.5.4
New Features
Enhancements
Bug Fixes
Build / Test updates
Documentation Updates
Breaking Changes
1.5.2
New Features
Enhancements
Modeas a replacement method. (#5205)Bug Fixes
... (truncated)
1.5.0
New Features
DetectEntireAnomalyBySrCnnthat computes anomalies by considering the entire dataset and also supports the ability to set sensitivity and output margin.Enhancements
Bug Fixes
In this release we have traced down every bug that would occur randomly and sporadically and fixed many subtle bugs. As a result, we have also re-enabled a lot of tests listed in the Test Updates section below.
Onnx bug fixes
AutoML fixes
... (truncated)
1.5.0-preview2
New Features (IN-PREVIEW, please provide feedback)
Bug Fixes
Enhancements
... (truncated)
1.5.0-preview
New Features (IN-PREVIEW, please provide feedback)
Export-to-ONNX for below components:
DateTime Transformer (#4521)
Loader and Saver for SVMLight file format (#4190)
Sample
Expression transformer (#4548)
The expression transformer takes the expression in the form of text using syntax of a simple expression language, and performs the operation defined in the expression on the input columns in each row of the data. The transformer supports having a vector input column, in which case it applies the expression to each slot of the vector independently. The expression language is extendable to user defined operations.
Sample
Bug Fixes
Stability fixes by Sam Harwell
... (truncated)
1.4.0
New Features
General Availability of Image Classification API
Introduces
Microsoft.ML.Visionpackage that enables image classification by leveraging an existing pre-trained deep neural network model. Here the API trains the last classification layer using TensorFlow by using its C# bindings from TensorFlow .NET. This is a high level API that is simple yet powerful. Below are some of the key features:GPU training: Supported on Windows and Linux, more information here.Early stopping: Saves time by stopping training automatically when model has been stabelized.Learning rate scheduler: Learning rate is an integral and potentially difficult part of deep learning. By providing learning rate schedulers, we give users a way to optimize the learning rate with high initial values which can decay over time. High initial learning rate helps to introduce randomness into the system, allowing the Loss function to better find the global minima. While the decayed learning rate helps to stabilize the loss over time. We have implemented Exponential Decay Learning rate scheduler and Polynomial Decay Learning rate scheduler.Pre-trained DNN Architectures: The supported DNN architectures used internally fortransfer learningare below:Example code:
Samples
Defaults
Learning rate scheduling
Early stopping
ResNet V2 101 train-test split
End-to-End
General Availability of Database Loader
The database loader enables to load data from databases into the
IDataViewand therefore enables model training directly against relational databases. This loader supports any relational database provider supported by System.Data in .NET Core or .NET Framework, meaning that you can use any RDBMS such as SQL Server, Azure SQL Database, Oracle, SQLite, PostgreSQL, MySQL, Progress, etc.It is important to highlight that in the same way as when training from files, when training with a database ML .NET also supports data streaming, meaning that the whole database doesn’t need to fit into memory, it’ll be reading from the database as it needs so you can handle very large databases (i.e. 50GB, 100GB or larger).
Example code:
... (truncated)
1.4.0-preview2
New Features
Deep Neural Networks Training (0.16.0-preview2)
Improves the in-preview
ImageClassificationAPI further:PredictedLabeloutput column now contains actual class labels instead ofuint32class index values (#4228)In-memory image inferencing sample
Early stopping sample
GPU samples
New ONNX Exporters (1.4.0-preview2)
Bug Fixes
IsSavedModelreturns true when loaded TensorFlow model is a frozen model (#4262)OnnxSequenceTypeattribute directly without specify sequence type (#4272, #4297)Samples
Breaking Changes
None.
Obsolete API
OnnxSequenceTypeattribute that doesn't take a type (#4272, #4297)Enhancements
CLI and AutoML API
None.
Remarks
... (truncated)
1.4.0-preview
New Features
Deep Neural Networks Training (0.16.0-preview) (#4151)
Improves the in-preview
ImageClassificationAPI further:Design specification
Sample
Database Loader (0.16.0-preview) (#4070,#4091,#4138)
Additional DatabaseLoader support:
CreateDatabaseLoader<TInput>to map columns from a .NET Type.Design specification
... (truncated)
1.3.1
New Features
Deep Neural Networks Training (PREVIEW) (#4057)
Introduces in-preview 0.15.1
Microsoft.ML.DNNpackage that enables full DNN model retraining and transfer learning in .NET using C# bindings for tensorflow provided by Tensorflow .NET. The goal of this package is to allow high level DNN training and scoring tasks such as image classification, text classification, object detection, etc using simple yet powerful APIs that are framework agnostic but currently they only uses Tensorflow as the backend. The below APIs are in early preview and we hope to get customer feedback that we can incorporate in the next iteration.![DNN stack](https://github.com/dotnet/machinelearning/blob/master/docs/release-notes/1.3.1/dnn_s...
...
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