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distributed-computing
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In our API docs we currently use
.. autosummary::
Client
Client.call_stack
Client.cancel
...
To generate a table of Client
methods at the top of the page. Later on we use
.. autoclass:: Client
:members:
to display the docstrings for all the public methods on Client
(here an example for
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If enter_data()
is called with the same train_path
twice in a row and the data itself hasn't changed, a new Dataset does not need to be created.
We should add a column which stores some kind of hash of the actual data. When a Dataset would be created, if the metadata and data hash are exactly the same as an existing Dataset, nothing should be added to the ModelHub database and the existing
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This could be example that uses these supported syntax and APIs:
https://github.com/couler-proj/couler/tree/d34a690/couler/core/syntax
It seems that the number of joining clients (not the num of computing clients) is fixed in fedml_api/data_preprocessing/**/data_loader and cannot be changed except CIFAR10 datasets.
Here I mean that it seems the total clients is decided by the datasets, rather the input from run_fedavg_distributed_pytorch.sh.
https://github.com/FedML-AI/FedML/blob/3d9fda8d149c95f25ec4898e31df76f035a33b5d/fed
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Describe the bug
I found that some names agruments in framework aren't consistent.
So for example: