Highlighting the difference between client node and training client. #583
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Wrede
requested changes
Apr 23, 2024
| @@ -19,6 +19,11 @@ Start by creating an account in FEDn Studio and set up a project by following th | |||
| Install FEDn | |||
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| Client node: A physical or virtual machine (MacOSx Linux or Windows) used to perform initial steps. | |||
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this just makes it more complex for the user I think, Also, it sound to much "hadoop"
| @@ -41,17 +46,18 @@ It is recommended to use a virtual environment when installing FEDn. | |||
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| .. _package-creation: | |||
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| Initialize FEDn with the client code bundle and seed model | |||
| Initialize FEDn with the compute package and seed model | |||
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We want to move away from calling it compute package and instead refer to is as a FEDn project which can be bundled into a package which then can be used by the fedn package manager.
| code that contains entrypoints for training and (optionally) validating a model update on the client. | ||
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| [!NOTE] The preparation of the compute package and seed model is a one-time requirement. All training clients use the same compute package and seed model. |
| Locate into ``examples/mnist-pytorch`` and familiarize yourself with the project structure. The dependencies needed in the client environment are specified | ||
| in ``client/python_env.yaml``. | ||
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| In order to train a federated model using FEDn, your Studio project needs to be initialized with a compute package and a seed model. The compute package is a bundle | ||
| of the client specification, and the seed model is a first version of the global model. | ||
| In order to train a federated model using FEDn, your Studio project needs to be initialized with a compute package and a seed model. The compute package is a bundle of the client specification, and the seed model is a first version of the global model. |
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I think this has been superseeded with some of our more recent updates. |
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