![]() ![]() ![]() So, if TensorFlow detects both a CPU and a GPU, then GPU-capable code will run on the GPU by default. In terms of how to get your TensorFlow code to run on the GPU, note that operations that are capable of running on a GPU now default to doing so. TensorFlow GPU support is currently available for Ubuntu and Windows systems with CUDA-enabled cards. TensorFlow code, including Keras, will transparently run on a single GPU with no explicit code configuration required. Now let's jump into the main topic of GPU support. Hopefully that provides a bit more clarity about the integration. With that being said, because Keras integrates deeply with low-level TensorFlow functionality, we can actually use the high-level functionality of Keras to do many things without being required to make use of lower-level TensorFlow code. So, when we talk about Keras now, we're talkingĪbout it as an API integrated within TensorFlow, not a separate stand alone library. The standalone version of Keras is no longer being updated or maintained by the Keras team. It's important to understand that as of now, Keras has been completely integrated with TensorFlow. In this episode, we'll discuss GPU support for TensorFlow and the integrated Keras API and how to get your code running with a GPU!īefore jumping into GPU specifics, let's elaborate a bit more on a point from a previous episode.
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