amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

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Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

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Jan 14	2019-01-14 00:00:00 UTC	2019-01-21 00:00:00 UTC	0	0	0
Jan 21	2019-01-21 00:00:00 UTC	2019-01-28 00:00:00 UTC	0	0	0
Jan 28	2019-01-28 00:00:00 UTC	2019-02-04 00:00:00 UTC	0	0	0
Feb 4	2019-02-04 00:00:00 UTC	2019-02-11 00:00:00 UTC	0	0	0
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Feb 18	2019-02-18 00:00:00 UTC	2019-02-25 00:00:00 UTC	0	0	0
Feb 25	2019-02-25 00:00:00 UTC	2019-03-04 00:00:00 UTC	0	0	0
Mar 4	2019-03-04 00:00:00 UTC	2019-03-11 00:00:00 UTC	0	0	0
Mar 11	2019-03-11 00:00:00 UTC	2019-03-18 00:00:00 UTC	0	0	0
Mar 18	2019-03-18 00:00:00 UTC	2019-03-25 00:00:00 UTC	1	0	1

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You require AWS to at least activate and set up DeepRacer [0]. You don't strictly speaking need the cloud for training a DeepRacer model, although cloud access (via the DeepRacer console) could be the path of least friction. Much of the source code for training DeepRacer on AWS can be found on the amazon-sagemaker-examples github repo [1]. The sample training environment relies on Gazebo [2] which, while freely available, has a managed AWS offering [3]. But ultimately, you should just be able to deploy an RL Coach model to the device [4]. [0] https://docs.aws.amazon.com/deepracer/latest/developerguide/... [1] https://github.com/awslabs/amazon-sagemaker-examples/tree/ma... [2] http://gazebosim.org/ [3] https://docs.aws.amazon.com/robomaker/latest/dg/simulation-t... [4] https://docs.aws.amazon.com/deepracer/latest/developerguide/...