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Lengthy Model Training Period Delays the Progress of Getting AI Projects into Production

Challenges of deep learning that you should know

Deep learning and neural networks are increasingly popular due to the enhanced performance computing resources. Many companies, from big enterprises to small startups, are trying to introduce artificial intelligence techniques, to achieve business success. However, the lengthy development cycle prevents deep learning projects from being applied to the market. To accelerate the buildout of artificial intelligence projects, it is important to identify the root causes that delay the progress and learn to solve those problems.

One of the time-consuming parts of an artificial intelligence project is model training. 76% of data scientists have the same pain point that they spend most of their working time waiting for computing at the stage of building and training models. For data scientists, it is distressful to wait for a long time, especially at the stage of testing and conducting hyperparameter optimization. They could wait for several hours but only figure out a few times whether the adjustment of hyperparameters increases the performance of the model.

Why is model training so time-consuming? The common reasons are as follow: 

1.  Environment settings are not optimized 

Numerous environment settings affect the speed of model training.  Practitioners have to spend a lot of time monitoring and analyzing performance profilers to diagnose the bottleneck. From environment settings to deep learning frameworks, the optimization of the environment reduces the time needed for model training.

2. Computing resources are limited 

A low GPU utilization rate leads to the inefficiency of computing, and the insufficient GPU memory directs to the limited input of datasets. Sometimes, insufficient GPU resource allocation causes a longer time when sharing GPUs resources among multiple users within the data team.

To solve the above issues and accelerate model training, several methods could be considered. For instance, training models in parallel or distribution with multiple GPUs allows a shorter time for model training. To improve the GPU utilization rate, data scientists could consider increasing the batch size and system throughput. 

However, data scientists rarely apply those methods. In fact, to successfully accelerate model training, data scientists should spend lots of time studying lots of research papers and surveying technical guidelines provided by the GPU suppliers.  As data scientists devote a majority of their time to enhance model performance, it is difficult for them to spend extra effort in investigating how to accelerate model training. 

Under this circumstance, data scientists could consider using artificial intelligence tools. The artificial intelligence tool, FAST-AI, aims at providing an optimized environment for data scientists. FAST-AI provides a platform for data scientists to train and deploy models, and it significantly decreases the time needed for model training. FAST-AI optimizes the environment settings and builds distributed training as default. It supports different deep learning frameworks such as Tensorflow, Pytorch, as well as different types of GPU, from cloud to edge devices.  In this way, the artificial intelligence software FAST-AI accelerates the model training and decreases the workload for data scientists. 

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