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Intel® Transfer Learning Tool v0.3.0

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@ashahba ashahba released this 16 Mar 05:21
· 314 commits to main since this release
v0.3.0
e80c1e3

Initial release of the Intel® Transfer Learning Tool (TLT). TLT makes transfer learning workflows easier and faster by providing a command-line interface (CLI) and a Python library (API) that leverages public model hubs, Intel optimized deep learning frameworks, and your custom dataset to efficiently generate new deep learning models optimized for deployment on Intel CPUs.

Features

  • Low-code API and no-code CLI for:
    • Image classification transfer learning using Intel® Optimization for TensorFlow
      • Support for 19 models from TF Hub
      • Support for custom datasets and the TensorFlow datasets catalog
      • Post-training quantization and benchmarking using Intel® Neural Compressor, when using custom datasets
      • FP32 graph optimization using Intel® Neural Compressor
      • Reduced training time with auto-mixed precision on Intel® third or fourth generation Xeon processors
    • Image classification transfer learning using PyTorch and Intel® Extension for PyTorch
      • Support for 60 models from Torchvision
      • Support for custom datasets and select Torchvision datasets
      • Post-training quantization using Intel® Neural Compressor, when using custom datasets
    • Text Classification transfer learning using Intel Optimization for TensorFlow
      • Support for 26 models from TF Hub
      • Support for custom datasets and the TensorFlow datasets catalog
      • Post-training quantization and benchmarking using Intel® Neural Compressor, when using custom datasets
      • Reduced training time with auto-mixed precision on Intel® third or fourth generation Xeon processors
    • Text Classification transfer learning using PyTorch and Intel® Extension for PyTorch
      • Support for 4 models from HuggingFace
      • Support for custom datasets and select Hugging Face datasets
      • Post-training quantization using Intel® Neural Compressor, when using custom datasets
    • Anomaly Detection transfer learning using PyTorch
      • Support for 5 models from Torchvision
      • Support for custom datasets
  • Additional Features:
    • Dataset scaling, cropping, batching, splitting, and augmentation
    • APIs for training, prediction, and evaluation
    • Export model for deployment or resume training from checkpoints
    • Reproducible experiments
    • Support for user-provided pre-trained models
    • Model customization options:
      • Additional dense layers
      • Selective layer freezing and unfreezing for PyTorch models
    • Advanced training options:
      • Early stopping
      • Custom learning rate and learning rate decay
      • Auto-evaluation at the end of each training epoch
      • Configurable optimizers & loss functions

New Notebooks:

  • Tutorials:

    • BERT Binary Text Classification with TF Hub
    • BERT Binary Text Classification with PyTorch
    • Image Classification with TensorFlow
    • Image Classification with TensorFlow using Graph Optimization
    • Image Classification with PyTorch

  • End-to-End notebooks:

    • Document-Level Sentiment Analysis (SST2) using PyTorch
    • Medical Imaging Classification (Colorectal histology) using TensorFlow
    • Remote Sensing Image Scene Classification (Resisc) using TensorFlow
    • Multimodal Cancer Detection using TensorFlow and PyTorch
    • Anomaly Detection using PyTorch

Validated configuration

  • Ubuntu 20.04 LTS
  • Intel® Optimization for TensorFlow 2.10.0
  • Intel® Extension for PyTorch 1.13.0
  • PyTorch 1.13.1
  • Intel® Neural Compressor 1.14.2
  • TensorFlow Hub 0.12.0
  • Torchvision 0.14.1
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