Intel® Neural Compressor v3.0 Release

• Highlights
• Features
• Improvements
• Examples
• Bug Fixes
• Documentations
• Validated Configurations

Highlights

• FP8 quantization and INT4 model loading support on Intel® Gaudi® AI accelerator
• Framework extension API for quantization, mixed-precision and benchmarking
• Accuracy-aware FP16 mixed precision support on Intel® Xeon® 6 Processors
• Performance optimizations and usability improvements on client-side quantization

Features

• [Quantization] Support FP8 quantization on Gaudi (95197d)
• [Quantization] Support INC and Hugging Face model loading on framework extension API for PyTorch (0eced1, bacc16)
• [Quantization] Support Weight-only Quantization on framework extension API for PyTorch (34f0a9, de43d8, 4a4509, 1a4509, a3a065, 1386ac, a0dee9, 503d9e, 84d705, 099b7a, e3c736, e87c95, 2694bb, ec49a2, e7b4b6, a9bf79, ac717b, 915018, 8447d7, dc9328)
• [Quantization] Support static and dynamic quantization in PT2E path (7a4715, 43c358, 30b36b, 1f58f0, 02958d)
• [Quantization] Support SmoothQuant and static quantization in IPEX path with framework extension API (53e6ee, 72fbce, eaa3a5, 95e67e, 855c10, 9c6102, 5dafe5, a5e5f5, 191383, 776645)
• [Quantization] Support Layer-wise Quantization for RTN/GPTQ on framework extension API for PyTorch (649e6b)
• [Quantization] Support Post Training Quantization on framework extension API for Tensorflow (6c27c1, e22c61, f21afb, 3882e9, 2627d3)
• [Quantization] Support Post Training Quantization on Keras3 (f67e86, 047560)
• [Quantization] Support Weight-only Quantization on Gaudi2 (4b9b44, 14868c, 0a3d4b)
• [Quantization] Improve performance and usability of quantization procedure on client side (16a7b1)
• [Quantization] Support auto-device detection on framework extension API for PyTorch (368ba5, 4b9b44, e81a2d, 0a3d4b, 534300, 2a86ae)
• [Quantization] Support Microscaling(MX) Quant for PyTorch (4a24a6, 455f1e)
• [Quantization] Enable cross-devices Half-Quadratic Quantization(HQQ) for LLMs support (db6164, 07f940)
• [Quantization] Support FP8 cast Weight-only Quantization (57ed61)
• [Mixed-Precision] Support FP16 mixed-precision on framework extension autotune API for PyTorch (2e1cdc)
• [Mixed-Precision] Support mixed INT8 with FP16 in PT2E path (fa961e)
• [AutoTune] Support accuracy-aware tuning on framework extension API (e97659, 7b8aec, 5a0374, a4675c, 3a254e, ac47d9, b8d98e, fb6142, fa8e66, d22df5, 09eb5d, c6a8fa)
• [Benchmarking] Implement incbench command for ease-of-use benchmark (2fc725)

Improvements

• [Quantization] Integrate AutoRound v0.3 (bfa27e, [fd9...

Intel® Neural Compressor v2.6 Release

• Highlights
• Features
• Improvements
• Examples
• Bug Fixes
• External Contributions
• Validated Configurations

Highlights

• Integrated recent AutoRound with lm-head quantization support and calibration process optimizations
• Migrated ONNX model quantization capability into ONNX project Neural Compressor

Features

• [Quantization] Integrate recent AutoRound with lm-head quantization support and calibration process optimizations (4728fd)
• [Quantization] Support true sequential options in GPTQ (92c942)

Improvements

• [Quantization] Improve WOQ Linear pack/unpack speed with numpy implementation (daa143)
• [Quantization] Auto detect available device when exporting (7be355)
• [Quantization] Refine AutoRound export to support Intel GPU (409231)
• [Benchmarking] Detect the number of sockets when needed (e54b93)

Examples

• Upgrade lm_eval to 0.4.2 in PT and ORT LLM example (fdb509) (54f039)
• Add diffusers/dreambooth example with IPEX (ba4798)

Bug Fixes

• Fix incorrect dtype of unpacked tensor issue in PT (29fdec)
• Fix TF LLM SQ legacy Keras environment variable issue (276449)
• Fix TF estimator issue by adding version check on TF2.16 (855b98)
• Fix missing tokenizer issue in run_clm_no_trainer.py after using lm-eval 0.4.2 (d64029)
• Fix AWQ padding issue in ORT (903da4)
• Fix recover function issue in ORT (ee24db)
• Update model ckpt download url in prepare_model.py (0ba573)
• Fix case where pad_max_length set to None (960bd2)
• Fix a failure for GPU backend (71a9f3)
• Fix numpy versions for rnnt and 3d-unet examples (12b8f4)
• Fix CVEs (5b5579) (25c71a) (47d73b) (41da74)

External Contributions

• Update model ckpt download url in prepare_model.py (0ba573)
• Fix case where pad_max_length set to None (960bd2)
• Add diffusers/dreambooth example with IPEX (ba4798)

Validated Configurations

• Centos 8.4 & Ubuntu 22.04 & Win 11 & MacOS Ventura 13.5
• Python 3.8, 3.9, 3.10, 3.11
• PyTorch/IPEX 2.1, 2.2, 2.3
• TensorFlow 2.14, 2.15, 2.16
• ITEX 2.13.0, 2.14.0, 2.15.0
• ONNX Runtime 1.16, 1.17, 1.18

Intel® Neural Compressor v2.5.1 Release

• Improvement
• Bug Fixes
• Validated Configurations

Improvement

• Improve WOQ AutoRound export (409231, 7ee721)
• Adapt ITREX v1.4 release for example evaluate (9d7a05)
• Update more supported LLM recipes (ce9b16)

Bug Fixes

• Fix WOQ RTN supported layer checking condition (079177)
• Fix in-place processing error in quant_weight function (92533a)

Validated Configurations

• Centos 8.4 & Ubuntu 22.04
• Python 3.10
• TensorFlow 2.15
• ITEX 2.14.0
• PyTorch/IPEX 2.2
• ONNX Runtime 1.17

Intel® Neural Compressor v2.5 Release

• Highlights
• Features
• Improvement
• Productivity
• Bug Fixes
• External Contributes
• Validated Configurations

Highlights

• Integrated Weight-Only Quantization algorithm AutoRound and verified on Gaudi2, Intel CPU, NV GPU
• Applied SmoothQuant & Weight-Only Quantization algorithms with 15+ popular LLMs for INT8 & INT4 quantization and published the recipes

Features

• [Quantization] Integrate Weight-Only Quantization algorithm AutoRound (5c7f33, dfd083, 9a7ddd, cf1de7)
• [Quantization] Quantize weight with in-place mode in Weight-Only Quantization (deb1ed)
• [Pruning] Enable SNIP on multiple cards using DeepSpeed ZeRO-3 (49ab28)
• [Pruning] Support new pruning approach Wanda and DSNOT for PyTorch LLM (7a3671)

Improvement

• [Quantization] SmoothQuant code structure refactor (a8d81c)
• [Quantization] Optimize the workflow of parsing Keras model (b816d7)
• [Quantization] Support static_groups options in GPTQ API (1c426a)
• [Quantization] Update TEQ train dataloader (d1e994)
• [Quantization] WeightOnlyLinear keeps self.weight after recover (2835bd)
• [Quantization] Add version condition for IPEX prepare init (d96e14)
• [Quantization] Enhance the ORT node name checking (f1597a)
• [Pruning] Stop the tuning process early when enabling smooth quant (844a03)

Productivity

• ORT LLM examples support latest optimum version (26b260)
• Add coding style docs and recommended VS Code setting (c1f23c)
• Adapt transformers 4.37 loading (6133f4)
• Upgrade pre-commit checker for black/blacken-docs/ruff (7763ed)
• Support CI summary in PR comments (d4bcdd))
• Notebook example update to install latest INC & TF, add metric in fit (4239d3)

Bug Fixes

• Fix QA IPEX example fp32 input issue (c4de19)
• Update Conditions of Getting min-max during TF MatMul Requantize (d07175)
• Fix TF saved_model issues (d8e60b)
• Fix comparison of module_type and MulLinear (ba3aba)
• Fix ORT calibration issue (cd6d24)
• Fix ORT example bart export failure (b0dc0d)
• Fix TF example accuracy diff during benchmark and quantization (5943ea)
• Fix bugs for GPTQ exporting with static_groups (b4e37b)
• Fix ORT quant issue caused by tensors having same name (0a20f3)
• Fix Neural Solution SQL/CMD injection (14b7b0)
• Fix the best qmodel recovery issue (f2d9b7)
• Fix logger issue (83bc77)
• Store token in protected file (c6f9cc)
• Define the default SSL context (b08725)
• Fix IPEX stats bug (5af383)
• Fix ORT calibration for Dml EP (c58aea)
• Fix wrong socket number retrieval for non-english system (5b2a88)
• Fix trust remote for llm examples (2f2c9a)

External Contributes

• Intel Mac support (21cfeb)
• Add PTQ example for PyTorch CV Segment Anything Model (bd5e69)

Validated Configurations

• Centos 8.4 & Ubuntu 22.04 & Win 11 & MacOS Ventura 13.5
• Python 3.8, 3.9, 3.10, 3.11
• TensorFlow 2.13, 2.14, 2.15
• ITEX 2.13.0, 2.14.0
• PyTorch/IPEX 2.0, 2.1, 2.2
• ONNX Runtime 1.15, 1.16, 1.17

Intel® Neural Compressor v2.4.1 Release

• Improvement
• Bug Fixes
• Examples
• Validated Configurations

Improvement

• Narrow down the tuning space of SmoothQuant auto-tune (9600e1)
• Support ONNXRT Weight-Only Quantization with different dtypes (5119fc)
• Add progress bar for ONNXRT Weight-Only Quantization and SmoothQuant (4d26e3)

Bug Fixes

• Fix SmoothQuant alpha-space generation (33ece9)
• Fix inputs error for SmoothQuant example_inputs (39f63a)
• Fix LLMs accuracy regression with IPEX 2.1.100 (3cb6d3)
• Fix quantizable add ops detection on IPEX backend (4c004d)
• Fix range step bug in ORTSmoothQuant (40275c)
• Fix unit test bugs and update CI versions (6c78df, 835805)
• Fix notebook issues (08221e)

Examples

• Add verified LLMs list and recipes for SmoothQuant and Weight-Only Quantization (f19cc9)
• Add code-generaion evaluation for Weight-Only Quantization GPTQ (763440)

Validated Configurations

• Centos 8.4 & Ubuntu 22.04
• Python 3.10
• TensorFlow 2.14
• ITEX 2.14.0.1
• PyTorch/IPEX 2.1.0
• ONNX Runtime 1.16.3

Intel® Neural Compressor v2.4 Release

• Highlights
• Features
• Improvement
• Productivity
• Bug Fixes
• Examples
• Validated Configurations

Highlights

• Supported layer-wise quantization for PyTorch RTN/GPTQ Weight-Only Quantization and ONNX Runtime W8A8 quantization.
• Supported Weight-Only Quantization tuning for ONNX Runtime backend.
• Supported GGML double quant on RTN/GPTQ Weight-Only Quantization with FW extension API
• Supported SmoothQuant of Big Saved Model for TensorFlow Backend.

Features

• [Quantization] Support GGML double quant in Weight-Only Quantization for RTN and GPTQ (05c15a)
• [Quantization] Support Weight-Only Quantization tuning for ONNX Runtime backend (6d4ea5, 934ba0, 4fcfdf)
• [Quantization] Support SmoothQuant block-wise alpha-tuning (ee6bc2)
• [Quantization] Support SmoothQuant of Big Saved Model for TensorFlow Backend (3b2925, 4f2c35)
• [Quantization] Support PyTorch layer-wise quantization for GPTQ (ee5450)
• [Quantization] support PyTorch layer-wise quantization for RTN (ebd1e2)
• [Quantization] Support ONNX Runtime layer-wise W8A8 quantization (6142e4, 5d33a5)
• [Common] [Experimental] FW extension API implement (76b8b3, 8447d7, 258236)
• [Quantization] [Experimental] FW extension API for PT backend support Weight-Only Quantization (915018, dc9328)
• [Quantization] [Experimental] FW extension API for TF backend support Keras Quantization (2627d3)
• [Quantization] IPEX 2.1 XPU (CPU+GPU) support (af0b50, cf847c)

Improvement

• [Quantization] Add use_optimum_format for export_compressed_model in Weight-Only Quantization (5179da, 0a0644)
• [Quantization] Enhance ONNX Runtime quantization with DirectML EP (db0fef, d13183, 098401, 6cad50)
• [Quantization] Support restore ipex model from json (c3214c)
• [Quantization] ONNX Runtime add attr to MatMulNBits (7057e3)
• [Quantization] Increase SmoothQuant auto alpha running speed (173c18)
• [Quantization] Add SmoothQuant alpha search space as a config argument (f9663d)
• [Quantization] Add SmoothQuant weight_clipping as a default_on option (1f4aec)
• [Quantization] Support SmoothQuant with MinMaxObserver (45b496)
• [Quantization] Support Weight-Only Quantization with fp16 for PyTorch backend (d5cb56)
• [Quantization] Support trace with dictionary type example_inputs (afe315)
• [Quantization] Support falcon Weight-Only Quantization (595d3a)
• [Common] Add deprecation decorator in experimental fold (aeb3ed)
• [Common] Remove 1.x API dependency (ee617a)
• [Mixed Precision] Support PyTorch eager mode BF16 MixedPrecision (3bfb76)

Productivity

• Support quantization and benchmark on macOS (16d6a0)
• Support ONNX Runtime 1.16.0 (d81732, 299af9, 753783)
• Support TensorFlow new API for gnr-base (8160c7)

Bug Fixes

• Fix GraphModule object has no attribute bias (7f53d1)
• Fix ONNX model export issue (af0aea, eaa57f)
• Add clip for ONNX Runtime SmoothQuant (cbb69b)
• Fix SmoothQuant minmax observer init (b1db1c)
• Fix SmoothQuant issue in get/set_module (dffcfe)
• Align sparsity with block-wise masks in progressive pruning (fcdc29)

Examples

• Support peft model with SmoothQuant (5e21b7)
• Enable two ONNX Runtime examples table-transformer-detection (550cee), BEiT (7265df)

Validated Configurations

• Centos 8.4 & Ubuntu 22.04 & Win10 & MacOS Ventura 13.5
• Python 3.8, 3.9, 3.10, 3.11
• TensorFlow 2.13, 2.14, 2.15
• ITEX 1.2.0, 2.13.0.0, 2.14.0.1
• PyTorch/IPEX 1.13.0+cpu, 2.0.1+cpu, 2.1.0
• ONNX Runtime 1.14.1, 1.15.1, 1.16.3
• MXNet 1.9.1

Intel® Neural Compressor v2.3.2 Release

• Features
• Bug Fixes

Features

• Reduce memory consumption in ONNXRT adaptor (f64833)
• Support MatMulFpQ4 for onnxruntime 1.16 (1beb43)
• Support MatMulNBits for onnxruntime 1.17 (67a31b)

Bug Fixes

• Update ITREX version in ONNXRT WOQ example and fix bugs in hf models (0ca51a)
• Update ONNXRT WOQ example into llama-2-7b (7f2063)
• Fix ONNXRT WOQ failed with None model_path (cbd0a4)

Validated Configurations

• Centos 8.4 & Ubuntu 22.04
• Python 3.10
• TensorFlow 2.13
• ITEX 2.13
• PyTorch/IPEX 2.0.1+cpu
• ONNX Runtime 1.15.1
• MXNet 1.9.1

Intel® Neural Compressor v2.3.1 Release

• Bug Fixes
• Productivity

Bug Fixes

• Fix PyTorch SmoothQuant for auto alpha (e9c14a, 35def7)
• Fix PyTorch SmoothQuant calibration memory overhead (49e950)
• Fix PyTorch SmoothQuant issue in get/set_module (Issue #1265)(6de9ce)
• Support falcon Weight-Only Quantization (bf7b5c)
• Remove Conv2d in Weight-Only Quantization adaptor white list (1a6526)
• Fix TensorFlow ssd_resnet50_v1 Example for TF New API (c63fc5)

Productivity

• Adapt Example for TensorFlow 2.14 AutoTrackable API Change (424cf3)

Validated Configurations

• Centos 8.4 & Ubuntu 22.04
• Python 3.10
• TensorFlow 2.13, 2.14
• ITEX 2.13
• PyTorch/IPEX 2.0.1+cpu
• ONNX Runtime 1.15.1
• MXNet 1.9.1

Intel® Neural Compressor v2.3 Release

• Highlights
• Features
• Improvement
• Productivity
• Bug Fixes
• Examples
• Validated Configurations

Highlights

• Integrate Intel Neural Compressor into MSFT ONNX Runtime (#16288) and Olive (#411, #412, #469).
• Supported low precision (INT4, NF4, FP4) and Weight-Only Quantization algorithms including RTN, AWQ, GPTQ and TEQ on ONNX Runtime and PyTorch for LLMs optimization.
• Supported sparseGPT pruner (88adfc).
• Supported quantization for ONNX Runtime DML EP and DNNL EP, and verified inference on Intel NPU (e.g., Meteor Lake) and Intel CPU (e.g., Sapphire Rapids).

Features

• [Quantization] Support ONNX Runtime quantization and inference for DNNL EP (79be8b)
• [Quantization] [Experimental] Support ONNX Runtime quantization and inference for DirectML EP (750bb9)
• [Quantization] Support low precision and Weight-Only Quantization (WOQ) algorithms, including RTN (501440, 19ab16, 859315), AWQ (2562f2, 641d42),
  GPTQ (b5ac3c, 6ba783) and TEQ (d2f995, 9ff7f0) for PyTorch
• [Quantization] Support NF4 and FP4 data type for PyTorch Weight-Only Quantization (3d11b5)
• [Quantization] Support low precision and Weight-Only Quantization algorithms, including RTN, AWQ and GPTQ for ONNX Runtime (da4c92)
• [Quantization] Support layer-wise quantization (d9d1fc) and enable with SmoothQuant (ec9ae9)
• [Pruning] Add sparseGPT pruner and refactor pruning class (88adfc)
• [Pruning] Add Hyper-parameter Optimization algorithm for pruning (6613cf)
• [Model Export] Support PT2ONNX dynamic quantization export (165532)

Improvement

• [Common] Clean up dataloader usage in examples (1044d8,
  a2931e, 447cc7)
• [Common] Enhance ONNX Runtime backend check (4ce9de)
• [Strategy] Add block-wise distributed fallback in basic strategy (ea309f)
• [Strategy] Enhance strategy exit policy (d19b42)
• [Quantization] Add WeightOnlyLinear for Weight-Only approach to allow low memory inference (00bbf8)
• [Quantization] Support more ONNX Runtime direct INT8 ops (b9ce61)
• [Quantization] Support TensorFlow per-channel MatMul quantization (cf5589)
• [Quantization] Implement a new method to perform alpha auto-tuning in SmoothQuant (084eda)
• [Quantization] Enhance ONNX SmoothQuant tuning structure (f0d51c)
• [Quantization] Enhance PyTorch SmoothQuant tuning structure (81da40)
• [Quantization] Update PyTorch examples dataloader to support transformers 4.31.x (59371f)
• [Quantization] Enhance ONNX Runtime backend setting for GPU EP support (295535)
• [Pruning] Refactor pruning (92d14d)
• [Mixed Precision] Update the list of supported layers for Keras mix-precision (692c8b)
• [Mixed Precision] Introduce quant_level into mixed precision (0dc6a9)

Productivity

• [Ecosystem] MSFT Olive integrate SmoothQuant and 3 LLM examples (#411, #412, #469)
• [Ecosystem] MSFT ONNX Runtime integrate SmoothQuant static quantization (#16288)
• [Neural Insights] Support PyTorch FX inspect tensor and integrate with Neural Insights (775def, 74a785)
• [Neural Insights] Add step-by-step diagnosis cases (99c3b0)
• [Neural Solution] Resource management and user-facing API enhancement (fbba10)
• [Auto CI] Integrate auto CI code scan bug fix tools (f77a2c, 06cc38)

Bug Fixes

• Fix bugs in PyTorch SmoothQuant (0349b9, 8f3645)
• Fix pytorch dataloader batch size issue (6a98d0)
• Fix bugs for ONNX Runtime CUDA EP (a1b566, d1f315)
• Fix bug in ONNX Runtime adapter where _rename_node function fails with model size > 2 GB (1f6b1a)
• Fix ONNX Runtime diagnosis bug (f10e26)
• Update Neural Solution example and fix grpc port issue (528868)
• Fix the objective initialization issue (9d7546)
• Fix reshape issue for bayesian strategy (77cb83)
• Fix CVEs (d86922, 2bbfcd, fc71fa)

Examples

• Add Weight-Only LLM examples for PyTorch (4b24be, 66f7c1, aa457a)
• Add Weight-Only LLM examples for ONNX Runtime ([10c133](https://github.c...

Intel® Neural Compressor v2.2 Release

• Highlights
• Features
• Improvement
• Productivity
• Bug Fixes
• Examples
• External Contributes

Highlights

• Expanded SmoothQuant support on mainstream frameworks including PyTorch/IPEX, TensorFlow/ITEX, ONNX Runtime, and validated popular large language models (LLMs) such as GPT-J, LLaMA, OPT, BLOOM, Dolly, MPT, LaMini-LM and RedPajama-INCITE.
• Innovated two productivity components Neural Solution for distributed quantization and Neural Insights for quantization accuracy debugging.
• Successfully integrated Intel Neural Compressor into MSFT Olive (#157) and DeepSpeed (#3300).

Features

• [Quantization] Support TensorFlow SmoothQuant (1f4127)
• [Quantization] Support ITEX SmoothQuant (1f4127)
• [Quantization] Support PyTorch FX SmoothQuant (6a39f6, 603811)
• [Quantization] Support ONNX Runtime SmoothQuant (3df647, 1e1d70)
• [Quantization] Support dictionary inputs for IPEX quantization (4ba233)
• [Quantization] Enable calibration algorithm Entropy/KL & Percentile for ONNX Runtime (dae494)
• [MixedPrecision] Support mixed precision op name/type dict option (a9c2cb)
• [Strategy] Support block wise tuning (9c26ed)
• [Strategy] Enable mse_v2 for ONNX Runtime (62122d)
• [Pruning] Support retrain free sparse (d29aa0)
• [Pruning] Support TensorFlow pruning with 2.x API (072c13)

Improvement

• [Quantization] Enhance Keras functional model quantization with Keras model in, quantized Keras model out (699751)
• [Quantization] Enhance MatMul and Gather quantization for ONNX Runtime (1f9c4f)
• [Quantization] Add new recipe for ONNX Runtime NLP models (10d82c)
• [MixedPrecision] Add more FP16 OPs support for ONNX Runtime (15d551)
• [MixedPrecision] Add more BF16 OPs support for TensorFlow (369b9d)
• [Pruning] Enhance multihead-attention slim (f3de50)
• [Pruning] Enable progressive pruning in N:M pattern (483e80)
• [Model Export] Refine PT2ONNX export (877adb)
• Remove redundant classes for quantization, benchmark and mixed precision (c51096)

Productivity

• [Neural Solution] Support multi-node distribute tuning model-level parallelism (ee049c)
• [Neural Insights] Support quantization and benchmark diagnosis with GUI (5dc9ea, 3bde2e, 898344)
• [Neural Coder] Migrate Neural Coder support into 2.x API (113ca1, e74a8a)
• [Ecosystem] MSFT Olive integration (#157)
• [Ecosystem] MSFT DeepSpeed integration (#3300)
• Support ITEX 1.2 (5519e2)
• Support Python 3.11 (6fa053)
• Enhance documentations for mixed precision, diagnosis, dataloader, metric, etc.

Bug Fixes

• Fix ONNX Runtime SmoothQuant issues (85c6a0, 1b26c0)
• Fix bug in IPEX fallback (b4f9c7)
• Fix ITEX quantize/dequantize before BN u8 issue (5519e2)
• Fix example inputs issue for IPEX smoothquant (c8b753)
• Fix IPEX mixed precision (d1e734)
• Fix inspect tensor (8f5f5d)
• Fix PyTorch model peleenet, 3dunet accuracy issue after migrate into 2.x API
• Fix CVEs (04c482, efcd98, 6e9f7b, 7abe32)

Examples

• Enable 4 ONNX Runtime examples, layoutlmv3, layoutlmft, deberta-v3, GPTJ-6B.
• Enable 2 TensorFlow LLMs with SmoothQuant, facebook-opt-125m, gpt2-medium.

External Contributes

• Add a mathematical check for SmoothQuant transform (5c04ac)
• Fix mismatch absorb layers due to tracing and named modules for SmoothQuant (bccc89)
• Fix trace issue when input is dictionary for SmoothQuant (6a3c64)
• Allow dictionary model inputs for ONNX export (17b642)

Validated Configurations

• Centos 8.4 & Ubuntu 22.04
• Python 3.7, 3.8, 3.9, 3.10, 3.11
• TensorFlow 2.10.0, 2.11.0, 2.12.0
• ITEX 1.1.0, 1.2.0
• PyTorch/IPEX 1.12.1+cpu, 1.13.0+cpu, 2.0.1+cpu
• ONNX Runtime 1.13.1, 1.14.1, 1.15.0
• MXNet 1.9.1