![]() ![]() Should it be noted that TensorFlow compile from source would also have a learning curve for non dev-ops?Ĭomparing TensorFlow GPU Docker vs. Also, "Docker for deep learning" documentation is a bit sparse (aside from the TensorFlow main w). Docker Learning Curve: Docker can have a bit of a learning curve for a non dev-ops person, which may cause aversion.Reproducibility and Scalability: Spinning up and authoring images and containers is relatively easy, If you want to deploy your model in a real world environment, container orchestration tools like Kubernetes, and swarm can help.Dependency Isolation: Docker allows the end user to utilize multiple containerized deep learning environments and frameworks on a single host machine that may otherwise have conflicting requirements & dependencies when not using Docker. ![]() Negligible Performance Costs: On our test machine (Exxact Workstation using 2x 2080 Ti), performance costs of TensorFlow running on Docker compared to running TensorFlow compiled from source are negligible/close to zero.TensorFlow can be compiled for many different use cases, as with TensorFlow GPU Docker containers. Note: This blog compares only the performance of TensorFlow for the training deep deep neural networks. ![]()
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