THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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| Type | Data Granularity | Synchronization Pattern | Bounded / Unbounded | Examples |
|---|---|---|---|---|
| Non-SGD-based | Epoch | Mostly Synchronous | Bounded | K-Means, Apriori, Decision Tree, Random Walk |
SGD-Based Synchronous Offline algorithm | BatchBatch → Epoch* | Synchronous | Bounded | Linear Regression, Logistic Regression, Deep Learning algorithms |
| SGD-Based Asynchronous Offline algorithm | BatchBatch → Epoch* | Asynchronous | Bounded | Same to the above |
| SGD-Based Asynchronous Online algorithm | Batch | Synchronous | Unbounded | Online version of the above algorithm |
| SGD-Based Asynchronous Online algorithm | Batch | Asynchronous | Unbounded | Online version of the above algorithm |
According to how the model is updated, the algorithms could be classified into the following types:
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*Although SGD-based algorithms are also batch-based, it could be implemented with an Epoch-based method if intermediate state is allowed: the subtasks could sample a batch from all the records from the position of the last batch.
Based on the above classification and the replacement implementation for SGD-based algorithms with bounded dataset, we mainly need to support
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Besides, the previous DataStream and DataSet iteration APIs also have some caveats to support algorithm implementation:
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