Data loading is a critical aspect of deep learning workflows, whether you’re focused on training or inference. However, it often presents a paradox: the need for a highly convenient solution that is simultaneously customizable. These two goals are notoriously difficult to reconcile. One of the traditional solutions to this problem is to scale out the processing and parallelize the user…
]]>This post is an update to an older post. Deep learning models require training with vast amounts of data to achieve accurate results. Raw data usually cannot be directly fed into a neural network due to various reasons such as different storage formats, compression, data format and size, and limited amount of high-quality data. Addressing these issues requires extensive data preparation…
]]>Join the NVIDIA Triton and NVIDIA TensorRT community to stay current on the latest product updates, bug fixes, content, best practices, and more. When you are working on optimizing inference scenarios for the best performance, you may underestimate the effect of data preprocessing. These are the operations required before forwarding an input sample through the model. This post highlights the…
]]>Today, smartphones, the most popular device for taking pictures, can capture images as large as 4K UHD (3840×2160 image), more than 25 MB of raw data. Even considering the embarrassingly low HD resolution (1280×720), a raw image requires more than 2.5 MB of storage. Storing as few as 100 UHD images would require almost 3 GB of free space. Clearly, if you store data this way…
]]>Let’s imagine a situation. You buy a brand-new, cutting-edge, Volta-powered DGX-2 server. You’ve done your math right, expecting a 2x performance increase in ResNet50 training over the DGX-1 you had before. You plug it into your rack cabinet and run the training. That’s when an unpleasant surprise pops up. Even though your math is correct, the speedup you’re getting lower than expected. Why?
]]>Editor’s Note: This post has been updated. Here is the revised post. Training deep learning models with vast amounts of data is necessary to achieve accurate results. Data in the wild, or even prepared data sets, is usually not in the form that can be directly fed into neural network. This is where NVIDIA DALI data preprocessing comes into play. There are various reasons for that…
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