The State of Machine Learning Frameworks in 2019
6 months ago
- #frameworks
- #deep-learning
- #machine-learning
- Deep learning frameworks have evolved significantly since 2012, with PyTorch and TensorFlow emerging as the main contenders.
- PyTorch has gained dominance in research, with a majority of papers at top conferences now using it, while TensorFlow remains popular in industry.
- Researchers prefer PyTorch for its simplicity, great API, and performance, despite TensorFlow's historical advantages in production environments.
- TensorFlow 2.0 introduced eager mode to compete with PyTorch's ease of use, but challenges remain in seamless deployment and performance.
- PyTorch introduced TorchScript for production needs, but TensorFlow still leads in industry due to better deployment tools like TensorFlow Lite and Serving.
- The future of ML frameworks depends on how well PyTorch can address production needs and how TensorFlow can regain researcher interest.
- Emerging frameworks like Jax offer new capabilities, such as higher-order differentiation, which could influence future research directions.
- The battle between PyTorch and TensorFlow may become irrelevant as new computing models and hardware paradigms emerge.
- Machine learning frameworks shape research by enabling or restricting the ideas researchers can explore easily.
- The community's focus remains on advancing ML research and democratizing AI, regardless of framework preferences.