remove reference of DeepNumPy

apache · Jun 15, 2020 · 8b368ac · 8b368ac
1 parent d3ba629
commit 8b368ac
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Showing 5 changed files with 7 additions and 7 deletions.
diff --git a/python/mxnet/ndarray/numpy/_op.py b/python/mxnet/ndarray/numpy/_op.py
@@ -8048,7 +8048,7 @@ def shares_memory(a, b, max_work=None):
     the following way(s):
 
     - Does not support `max_work`, it is a dummy argument
-    - Actually it is same as `may_share_memory` in MXNet DeepNumPy
+    - Actually it is same as `may_share_memory` in MXNet np
     """
     return _api_internal.share_memory(a, b).item()
 
@@ -8089,7 +8089,7 @@ def may_share_memory(a, b, max_work=None):
     the following way(s):
 
     - Does not support `max_work`, it is a dummy argument
-    - Actually it is same as `shares_memory` in MXNet DeepNumPy
+    - Actually it is same as `shares_memory` in MXNet np
     """
     return _api_internal.share_memory(a, b).item()
 

diff --git a/python/mxnet/ndarray/numpy/random.py b/python/mxnet/ndarray/numpy/random.py
@@ -390,7 +390,7 @@ def multivariate_normal(mean, cov, size=None, check_valid=None, tol=None):
 
     This operator is a little different from the one in official NumPy.
     The official NumPy operator only accepts 1-D ndarray as mean and 2-D ndarray as cov,
-    whereas the operator in DeepNumPy supports batch operation and auto-broadcasting.
+    whereas the operator in MXNet np supports batch operation and auto-broadcasting.
 
     Both `mean` and `cov` may have any number of leading dimensions, which correspond
     to a batch shape. They are not necessarily assumed to have the same batch shape,

diff --git a/python/mxnet/numpy/multiarray.py b/python/mxnet/numpy/multiarray.py
@@ -10093,7 +10093,7 @@ def shares_memory(a, b, max_work=None):
     the following way(s):
 
     - Does not support `max_work`, it is a dummy argument
-    - Actually it is same as `may_share_memory` in MXNet DeepNumPy
+    - Actually it is same as `may_share_memory` in MXNet np
     """
     return _mx_nd_np.shares_memory(a, b, max_work)
 
@@ -10134,7 +10134,7 @@ def may_share_memory(a, b, max_work=None):
     the following way(s):
 
     - Does not support `max_work`, it is a dummy argument
-    - Actually it is same as `shares_memory` in MXNet DeepNumPy
+    - Actually it is same as `shares_memory` in MXNet np
     """
     return _mx_nd_np.may_share_memory(a, b, max_work)
 

diff --git a/python/mxnet/numpy/random.py b/python/mxnet/numpy/random.py
@@ -430,7 +430,7 @@ def multivariate_normal(mean, cov, size=None, check_valid=None, tol=None):
 
     This operator is a little different from the one in official NumPy.
     The official NumPy operator only accepts 1-D ndarray as mean and 2-D ndarray as cov,
-    whereas the operator in DeepNumPy supports batch operation and auto-broadcasting.
+    whereas the operator in MXNet np supports batch operation and auto-broadcasting.
 
     Both `mean` and `cov` may have any number of leading dimensions, which correspond
     to a batch shape. They are not necessarily assumed to have the same batch shape,

diff --git a/python/mxnet/symbol/numpy/random.py b/python/mxnet/symbol/numpy/random.py
@@ -926,7 +926,7 @@ def multivariate_normal(mean, cov, size=None, check_valid=None, tol=None):
 
     This operator is a little different from the one in official NumPy.
     The official NumPy operator only accepts 1-D ndarray as mean and 2-D ndarray as cov,
-    whereas the operator in DeepNumPy supports batch operation and auto-broadcasting.
+    whereas the operator in MXNet np supports batch operation and auto-broadcasting.
 
     Both `mean` and `cov` may have any number of leading dimensions, which correspond
     to a batch shape. They are not necessarily assumed to have the same batch shape,