sxjscience commented on a change in pull request #16621: [Numpy] Basic indexing
in symbolic interface of DeepNumpy
URL: https://github.com/apache/incubator-mxnet/pull/16621#discussion_r350507123
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File path: python/mxnet/symbol/numpy/_symbol.py
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@@ -855,6 +972,51 @@ def broadcast_to(self, *args, **kwargs):
def broadcast_like(self, *args, **kwargs):
raise AttributeError('_Symbol object has no attribute broadcast_like')
+ def save(self, fname, remove_amp_cast=True):
+ """Saves symbol to a file.
+ You can also use pickle to do the job if you only work on python.
+ The advantage of `load`/`save` functions is that the file contents are
language agnostic.
+ This means the model saved by one language binding can be loaded by a
different
+ language binding of `MXNet`.
+ You also get the benefit of being able to directly load/save from
cloud storage(S3, HDFS).
+ Parameters
+ ----------
+ fname : str
+ The name of the file.
+ - "s3://my-bucket/path/my-s3-symbol"
+ - "hdfs://my-bucket/path/my-hdfs-symbol"
+ - "/path-to/my-local-symbol"
+ remove_amp_cast : bool, optional
+ Whether to remove the amp_cast and amp_multicast operators, before
saving the model.
+ See Also
+ --------
+ symbol.load : Used to load symbol from file.
+ """
+ if not isinstance(fname, string_types):
+ raise TypeError('fname need to be string')
+
+ handle = self.handle
+ if remove_amp_cast:
+ handle = SymbolHandle()
+ check_call(_LIB.MXSymbolRemoveAmpCast(self.handle,
ctypes.byref(handle)))
+
+ processed_symbol = _Symbol(handle)
+ json_str = processed_symbol.save_json_string()
+ json_data = json.loads(json_str)
+ with open(fname, 'w') as file_out:
+ json.dump(json_data, file_out, indent=2, sort_keys=True)
+
+ def save_json_string(self):
Review comment:
Removed
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