mlsanityData to 7 Oct 2026

TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning

arXiv 1903.01855 ↗First posted 27 Feb 2019cs.PL cs.LGcited by 22

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Abstract (arXiv)

TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production. TensorFlow, which TensorFlow Eager extends, requires users to represent computations as dataflow graphs; this permits compiler optimizations and simplifies deployment but hinders rapid prototyping and run-time dynamism. TensorFlow Eager eliminates these usability costs without sacrificing the benefits furnished by graphs: It provides an imperative front-end to TensorFlow that executes operations immediately and a JIT tracer that translates Python functions composed of TensorFlow operations into executable dataflow graphs. TensorFlow Eager thus offers a multi-stage programming model that makes it easy to interpolate between imperative and staged execution in a single package.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2019.

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Citing papers per year: 2021: 8; 2022: 6; 2023: 1; 2024: 1; 2025: 6.

22 papers.

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  1. 2021CrypTen: Secure Multi-Party Computation Meets Machine Learningcited by 131
  2. 2022Tutorial on amortized optimizationcited by 83
  3. 2022Pathways: Asynchronous Distributed Dataflow for MLcited by 81
  4. 2021Torch.fx: Practical Program Capture and Transformation for Deep Learning in Pythoncited by 38
  5. 2021DKM: Differentiable K-Means Clustering Layer for Neural Network Compressioncited by 31
  6. 2022A domain-specific language for describing machine learning datasetscited by 12
  7. 2021LazyTensor: combining eager execution with domain-specific compilerscited by 11
  8. 2022Closed-Form Diffeomorphic Transformations for Time Series Alignmentcited by 7
  9. 2025Quantum Federated Learning: A Comprehensive Surveycited by 7
  10. 2021Swift for TensorFlow: A portable, flexible platform for deep learningcited by 6
  11. 2024Bundle Adjustment in the Eager Modecited by 6
  12. 2021TensorGP -- Genetic Programming Engine in TensorFlowcited by 5
10 more
  1. 2021Speed Benchmarking of Genetic Programming Frameworkscited by 2
  2. 2022Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Studycited by 2
  3. 2025Data-Driven Adaptive Gradient Recovery for Unstructured Finite Volume Computationscited by 2
  4. 2022Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programscited by 1
  5. 2025Tempo: Compiled Dynamic Deep Learning with Symbolic Dependence Graphscited by 1
  6. 2025veScale: Consistent and Efficient Tensor Programming with Eager-Mode SPMDcited by 1
  7. 2025GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2cited by 1
  8. 2021CLAIMED, a visual and scalable component library for Trusted AIcited by 0
  9. 2023RAMario: Experimental Approach to Reptile Algorithm -- Reinforcement Learning for Mariocited by 0
  10. 2025Speculative Automated Refactoring of Imperative Deep Learning Programs to Graph Executioncited by 0