Ranked in these QuestionsQuestion Ranking
Pro Ensured continued support
TensorFlow is developed and maintained by Google. It's the engine behind a lot of features found in Google applications, such as:
- recognizing spoken words
- translating from one language to another
- improving Internet search results
Making it a crucial component in a lot of Google applications. As such, continued support and development is ensured in the long-term, considering how important it is to the current maintainers.
Pro Easily spin up sessions without restarting the program
TensorFlow can run with multiple GPUs. This makes it easy to spin up sessions and run the code on different machines without having to stop or restart the program.
Pro Python has a lot of powerful scientific libraries available
Other than having an easy syntax, using Python also gives developers a wide range of some of the most powerful libraries for scientific calculations (NumPy, SciPy, Pandas) without having to switch languages.
Pro Visualization suite available
Google has made a powerful suite of visualizations available for both network topology and performance.
Pro Written in Python, which is regarded as a really pleasant language to read and develop in
TensorFlow is written in Python, with the parts that are crucial for performance implemented in C++. But all of the high-level abstractions and development is done in Python.
Pro Great debugging potential
You can introduce and retrieve the results of discretionary data on any edge of the graph. You can also combine this with TensorBoard (suite of visualization tools) to get pretty and easy to understand graph visualizations, making debugging even simpler.
Con Not fully open source
For now, Google has only open sourced parts of the AI engine, namely some algorithms that run atop it. The advanced hardware infrastructure that drives this engine is not "open source".