Caffe vs TensorFlow: What’s Best for Enterprise Machine Learning?

The variety of open-source machine learning frameworks suitable for enterprise projects has consolidated into a handful of candidates over the last ten years. Among them are Keras, TensorFlow, Caffe, PyTorch, Microsoft Cognitive Toolkit (CNTK) and Apache MXNet.

Due to their open-source nature, academic provenance, and varying levels of interoperability with each other, these are not discrete or 'standalone' products. Neither are they even distinct competitors in a traditional sense: Microsoft has brought backend support for Keras into its CNTK; Facebook has integrated a new iteration of Caffe into PyTorch; and Google's TensorFlow has adopted Keras as its default API—among many other unusual relationships and interdependencies in the current machine learning research culture.

Yet all of these projects still exist in their own right as prospective base structures for enterprise software production. By what criteria then can you decide on the core technologies for your own machine learning workflows?

TensorFlow is a symbolic programmatic library developed from earlier internal research at Google Brain. It has gained widespread industry and academic adoption in the machine/deep learning sector since being released to open source in 2015.

Developed in C++ and Python, TensorFlow uses dataflow graphs to map dependencies between individual operations. This representative approach allows high-volume implementations that can access diverse algorithms and models via a common layer. TensorFlow uses a readable syntax that is ably documented.

TensorFlow's strong application areas include Time Series Forecasting, Natural Language Processing (NLP), computer vision, text classification and speech recognition.

Besides boasting notable adherents such as Airbnb, China Mobile and Intel, TensorFlow is used to power or augment many of Google's core offerings, including Search, Translate and Gmail.

TensorFlow provides dedicated APIs for C and Python, and experimental APIs for Swift, Go, C++, Java and JavaScript. Third-party API resources are available for other languages including C#, Rust and Scala. However, as we'll see shortly, the latest version of TensorFlow has committed to Keras as its API of choice.

TensorFlow offers low-level hardware support via CUDA and cloud-based Tensor Processing Units (TPUs), dedicated application-specific integrated circuits (ASICs) designed to exploit the architecture.

Recent research indicates that TensorFlow is a little ahead of Facebook's PyTorch as the fastest-growing deep learning framework. It is used primarily in research and server production.

TensorFlow pipelines are programmatic rather than pre-specified at the layer level, allowing for flexibility in design and rapid prototyping. Recurrent Neural Networks and other types of machine learning protocols can be instantiated with very little code, albeit at the expense of an interpretive framework complex and rich enough to respond to a wide variety of tasks.

Caffe pipelines, designed primarily for production edge deployment, carry with them the burden of specifically pre-defined network layers, in a codebase that can be hard to navigate. However, this increased upfront development time has historically resulted in notable performance benefits over TensorFlow.

Iflexion recommends: If the scope of your project involves Caffe's core strength of deep learning in image-related domains, and your objectives are clear and fixed, it's still possible to outperform TensorFlow by engaging with Caffe's greater initial design and implementation requirements. Developing in Caffe2 further removes some of the original project's most-criticized barriers to entry.

TensorFlow has historically offered a wide range of flexible high-level APIs to speed up initial development. Although this has contributed in part to its popularity and uptake, the release of TensorFlow 2 removes a great deal of the framework's API redundancy and duplication, committing to a custom fork of the increasingly popular Keras framework. Keras offers an extensible, user-friendly and modular interface to TensorFlow's capabilities.

Caffe must be developed through mid or low-level APIs, which limits the configurability of the workflow model and restricts most of the development time to a C++ environment that discourages experimentation and requires greater initial architectural mapping.

Iflexion recommends: By now, Caffe's trade-off between flexibility and high-performing, optimized code is coming into focus. Since TensorFlow 2 addresses so many long-term criticisms regarding bloat and scope, one would need an extraordinarily lean brief in order to favor Caffe's more arcane approach to development.

This is a more complicated subject when comparing Caffe vs TensorFlow. Both have fragmented into multiple branches and missions, making direct speed comparisons increasingly difficult. Even in the rare instances where objective and/or recent benchmarking is available, the most meaningful use cases are hidden behind the proprietary internal politics of Google and Facebook. After all, they are the highest-volume creators and consumers of both frameworks, respectively.

In the case of TensorFlow, one has to evaluate both the data center/desktop/VM environment and the recently-launched Lite iteration, which is arguably more comparable to the mobile-centric mission of many aspects of Facebook's Caffe2.

Over the last five years, TensorFlow has been popularly estimated to lag behind even the original version of Caffe, since the latter has been perceived to benefit from its low-level streamlined approach to neural network design. However, this judgement doesn't account for the improved performance of TensorFlow 2, the impact of which is likely to become clearer over the next 18 months.

Neither does it account for the speed advantages that scale and uptake bring to the table: when a framework reaches a certain level of diffusion, ubiquity tends to compensate for architectural bloat via dedicated industry workarounds, which come into existence through an increased market demand. These include ASIC solutions (in this case, via Google's own TPUs), edge caching and other case-specific resources available through low-latency local data centers and bespoke on-premises hardware solutions.

Like-for-like speed testing between TensorFlow and Caffe is a problem at the moment, due to increased recent activity in their release cycles, the difference in scope between various versions of both frameworks, and the fact that Caffe is still primarily used for vision-related tasks—which is an important but not pivotal element in TensorFlow.

Iflexion recommends: If your project is centered around vision-based research, and particularly if it incorporates facial recognition, it's a reasonable bet that a PyTorch/Caffe2 basis will provide a more performant solution. Facebook's vanguard position in facial recognition technologies, facilitated by its unrivalled access to high-volume social network facial image data, has inclined it toward Caffe for a reason. You'll also benefit from an improved API and more configurability and flexibility than the original Caffe.

For all other purposes, TensorFlow's scalability, pace of innovation and industry support suggests a likely edge in production-level performance across all phases of development and deployment.