Leaping Forward and Learning: Overcoming the Challenges of TensorFlow

When a new program is introduced to a community, it naturally takes some time to get used to. This is the case with TensorFlow, a relatively new program designed to assist machine learning engineers with their coding and programming. Thankfully, the community has begun to work out the kinks in the TensorFlow system, and things are looking up for engineers and programmers looking to try out the new software for themselves. Google’s very own machine learning team has chosen to move forward with TensorFlow, and this is a great sign. There are still some problems, of course, so here are a handful of the pros and cons of using TensorFlow.

Ease of Use Has Improved

Many features of TensorFlow have enabled machine learning enthusiasts to streamline their projects and keep track of critical developments. TensorFlow supports multiple GPUs, which makes multi-tasking much simpler once you have designated tasks to each GPU. You may set up queues for specific programs and operations so the workflow moves the way you like, and the TensorBoard feature allows you to visualize your project’s flows and cycles using graphs. Along with this visualization comes memory, as you may log outputs and workflow with the graphs. Finally, you can create checkpoints throughout your project’s processes in order to go back and try new things. The ability to test new ideas and tweak old ones makes TensorFlow great for problem-solving.

Documentation and Data Comparison Could Use Some Work

Many of TensorFlow’s example datasets come from academic sources. Although this isn’t such a terrible thing, it sometimes limits the lengths the software can go when processing real-world problems that research has not yet extrapolated. This also slows down the learning process when TensorFlow is taking in entirely new deep learning information, and it causes the program to struggle with deviation and data anomalies. Additionally, the documentation of the machine learning processes on TensorFlow is skewed to the extremes – with more simplistic conceptual models on the one hand, and cutting-edge, real-world models on the other – without the middle ground that connects these two realms. This means the learning curve drops off, and it can be difficult to get beyond the initial stumbling blocks. If more realistic data models and comprehensive documentation are added to TensorFlow, it will improve dramatically.

Sharing Space and Resources Is Difficult

TensorFlow likes to have things to itself at this time, so it attempts to utilize your entire GPU when it starts up. This is useful in certain contexts, but most of the time, it simply bogs down your machines and creates obstacles when you need to run several programs at once. In the same vein, the popular program Theano is incredibly helpful to many machine learning enthusiasts, but when it is used in conjunction with TensorFlow, they each fight for GPU resources and cause problems. It is possible to reroute Theano to CPU using Python, but it would prevent future headaches to get both programs to work together.

TensorFlow is not perfect, but what program launches without kinks? The improvements that have been made – and continue to be made – are fantastic, and working knowledge of TensorFlow is indispensable to current and aspiring machine learning engineers.

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