chariots.ops¶
operations are the atomic computation element of Chariots, you can use them to train models, preprocess your data, extract features and much more.
to create your own operations, you will need to subclass one of the base op classes:
create a minimalist operation by subclassing the BaseOp class
create an op that supports loading and saving by subclassing the LoadableOp class
create a machine learning operation by subclassing on of the machine learning ops (depending on your framework) like an sklearn op
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class
chariots.ops.LoadableOp(op_callbacks: Optional[List[chariots.callbacks._op_callback.OpCallBack]] = None)[source]¶ Bases:
chariots.base._base_op.BaseOpan operation that supports loading and saving. This means that when a pipeline tries to load a node using this kind of op, it will try to find the serialized bytes of the last saved version of this op and pass them to the load method of the op.
Similarly when the pipeline will try to save a node using this kind of operation, it will get the op’s serialized bytes by calling it’s serialize method (along with the op’s version)
to create your own loadable op, you will need to: - define the load and serialize method - define the execute method as for a normal op to define the behavior of your op
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execute(*args, **kwargs)[source]¶ main method to override. it defines the behavior of the op. In the pipeline the argument of the pipeline will be passed from the node with one argument per input (in the order of the input nodes)
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