Yangqing Jia (Alibaba)

Yangqing is currently the VP AI / Big Data at Alibaba, and was formerly Director of AI Platform at Facebook. He co-created the Caffe2 and Caffe deep learning frameworks.
What'd Ya Mean by Frameworks?

The Progress of Deep Learning Frameworks

  • 2008: Theano

  • 2012: Torch7

  • 2013: Caffe

  • 2015: Keras, TensorFlow

  • 2017: Caffe2, PyTorch, ONNX

What Are The Deciding Factors?

  • A framework is intended for model development.

  • A framework helps to improve developer efficiency - trying out ideas faster (debugging, interactive development, simplicity, intuitiveness).

  • A framework helps to improve infrastructure efficiency - running computation faster (implementation, scalability, model definition, cross-platform requirements).

  • A good framework makes a balance between developer efficiency and infrastructure efficiency.

Declarative Toolkits

  • Examples include Theano, Caffe, MXNet, TensorFlow, and Caffe2.

  • In these frameworks, we declare and compile models, then repeatedly execute the models in a Virtual Machine.

  • Advantages:

    • Easy to optimize.

    • Easy to serialize for production deployment.

  • Disadvantages:

    • Non-intuitive programming model.

    • Difficult to design and maintain.

Imperative Toolkits

  • Examples include PyTorch and Chainer.

  • In these frameworks, we define and construct the models by running computation. There is no separate execution engine.

  • Advantages:

    • Intuitive to write programs.

    • Easy to design, debug, and iterate.

  • Disadvantages:

    • Difficult to optimize - no domain-specific languages.

    • Hard to deploy on multiple platforms.

Facebook Example

  • Research to Production at Facebook:

    • PyTorch → Caffe2 (2017): Reimplementation took weeks or months.

    • PyTorch → ONNX → Caffe2 (2018): Enabling model or model fragment transfer.

    • PyTorch + Caffe2 (2019-Present): Combining both the advantages of developer efficiency and infrastructure efficiency.

  • Many frameworks start adopting such a combination:

    • Keras/TF-Eager + TensorFlow

    • Gluon + MXNet

How To Choose Frameworks

  • Understand your need:

    • Developer Efficiency: Algorithm Research? Startup? Proof of Concept?

    • Infrastructure Efficiency: System Research? Cross-Platform? Scale?

  • Learn one framework and focus on your problem.

  • It's fine to switch.

  • The goal of frameworks is to improve productivity.

Beyond Frameworks

  • Within the tech stack, there is the libraries layer on top of frameworks: TF-Serving, CoreML, Clipper, Ray, etc.

  • On top of libraries, we have the applications layer: Detectron, FairSeq, Magenta, GluonNLP, etc.

  • Below the frameworks, we have the layer of runtime, compilers, and optimizers: CuDNN, NNPack, TVM, ONXX, etc.

  • The lowest layer is hardware: CPU, GPU, DSP, FPGA/ASIC

Thoughts Across The Stack

  • Do NOT use AlexNet!

  • Unifications help everyone: ONNX bridges the gap between high-level API & framework frontends with hardware vendor libraries & devices.

  • Invest in experiment management.

  • Use Computer Science conventional wisdom: programming language, compilers, scientific computation, databases, etc.

  • Things change across the stack:

    • Applications layer: quantization brings a balance between speed and accuracy.

    • Libraries layer: auto-quantization interfaces increase ease of use.

    • Frameworks layer: quantized training, auto-scaling, etc.

    • Runtime, compilers, optimizers layer: high-performance fixed-point math.

    • Hardware layer: quantized computation primitives.