Release Notes

PlaidML 0.6.2

  • Well defined exports for easier inclusion in other projects & frameworks, e.g., nGraph
  • Initial AMD stripe config
  • Initial stripe CPU support
  • LLVM support in windows
  • Prototype pytorch JIT bridge (limited by pytorch JIT interface)
  • Initial C++ EDSL API support (major revisions expected)

Previous releases

PlaidML 0.5.0

  • Support Keras 2.2.4
  • Several fixes to Metal backend
  • Preliminary release of Stripe
    • New polyhedral IR designed to support modern accelerators
    • Specification, documentation, and paper in progress
    • GPU / OpenCL backend and tutorial coming soon
  • nGraph support (wheels coming soon)
    • Supports tensorflow via tensorflow nGraph bridge.

PlaidML 0.3.3 - 0.3.5

  • Support Keras 2.2.0 - 2.2.2
  • Support ONNX 1.2.1
  • Upgrade kernel scheduling
  • Revise documentation
  • Add HALs for CUDA and Metal
  • Various bugfixes and improvements

PlaidML 0.3.2

  • Now supports ONNX 1.1.0 as a backend through onnx-plaidml
  • Preliminary support for LLVM. Currently only supports CPUs, and only on Linux and macOS. More soon.
  • Support for LSTMs & RNNs with static loop sizes, such as examples/ (from Keras)
    • Training networks with embeddings is especially slow (#96)
    • RNNs are only staticly sized if the input’s sequence length is explicitly specified (#97)
    • Fixes bug related to embeddings (#92)
  • Adds a shared generic op library in python to make creating frontends easier
    • plaidml-keras now uses this library
  • Uses plaidml/toolchain for builds
    • Building for ARM is now simple (–-config=linux_arm_32v7)
  • Various fixes for bugs (#89)