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Currently, the support for object storage like s3 and hdfs are "best-effort", meaning we write code using dmlc::Stream most of the time but with an exception in external memory support due to the use of mmap. We have no test for related features on our CI. I suggest that we deprecate the use of these features inside XGBoost and let third-party libraries handle them. For instance, to load a model in Python, one can first use a different library to fetch the model from s3 then use Booster to load from a byte buffer. This way, we can limit the scope of the code base to be more aligned with machine learning and have less concern with system programming.
The text was updated successfully, but these errors were encountered:
Currently, the support for object storage like
s3
andhdfs
are "best-effort", meaning we write code usingdmlc::Stream
most of the time but with an exception in external memory support due to the use ofmmap
. We have no test for related features on our CI. I suggest that we deprecate the use of these features inside XGBoost and let third-party libraries handle them. For instance, to load a model in Python, one can first use a different library to fetch the model from s3 then useBooster
to load from a byte buffer. This way, we can limit the scope of the code base to be more aligned with machine learning and have less concern with system programming.The text was updated successfully, but these errors were encountered: