remove mxnet for now

README.rst

@@ -78,7 +78,7 @@ Contents
 Overview
 --------
 
-Ivy is an ML framework that currently supports JAX, TensorFlow, PyTorch, MXNet, and Numpy.
+Ivy is an ML framework that currently supports JAX, TensorFlow, PyTorch, and Numpy.
 We’re very excited for you to try it out!
 
 Next on our roadmap is to support automatic code conversions between all frameworks 🔄,
@@ -144,7 +144,7 @@ You can immediately use Ivy to train a neural network, using your favorite frame
     print('Finished training!')
 
 This example uses PyTorch as a backend framework,
-but the backend can easily be changed to your favorite frameworks, such as TensorFlow, JAX, or MXNet.
+but the backend can easily be changed to your favorite frameworks, such as TensorFlow, or JAX.
 
 **Framework Agnostic Functions**
 
@@ -156,15 +156,13 @@ This is the same for ALL Ivy functions. They can accept tensors from any framewo
     import jax.numpy as jnp
     import tensorflow as tf
     import numpy as np
-    import mxnet as mx
     import torch
 
     import ivy
 
     jax_concatted   = ivy.concat((jnp.ones((1,)), jnp.ones((1,))), -1)
     tf_concatted    = ivy.concat((tf.ones((1,)), tf.ones((1,))), -1)
     np_concatted    = ivy.concat((np.ones((1,)), np.ones((1,))), -1)
-    mx_concatted    = ivy.concat((mx.nd.ones((1,)), mx.nd.ones((1,))), -1)
     torch_concatted = ivy.concat((torch.ones((1,)), torch.ones((1,))), -1)
 
 To see a list of all Ivy methods, type :code:`ivy.` into a python command prompt and press :code:`tab`.

docs/partial_source/background/standardization.rst

@@ -6,7 +6,7 @@ Skepticism
 
 With our central goal being to unify all ML frameworks, you would be entirely forgiven for raising an eyebrow 🤨
 
-“You want to try and somehow unify: TensorFlow, PyTorch, JAX, NumPy, MXNet and others, all of which have strong industrial backing, huge user momentum, and significant API differences?”
+“You want to try and somehow unify: TensorFlow, PyTorch, JAX, NumPy and others, all of which have strong industrial backing, huge user momentum, and significant API differences?”
 
 Won’t adding a new “unified” framework just make the problem even worse…
 

docs/partial_source/deep_dive/7_data_types.rst

@@ -270,17 +270,6 @@ Jax:
         device: jaxlib.xla_extension.Device,
     ) -> JaxArray:
 
-MXNet:
-
-.. code-block:: python
-
-    def zeros(
-        shape: Union[int, Sequence[int]],
-        *,
-        dtype: type,
-        device: mx.context.Context,
-    ) -> mx.nd.NDArray:
-
 NumPy:
 
 .. code-block:: python

docs/partial_source/deep_dive/8_devices.rst

@@ -125,17 +125,6 @@ Jax:
         device: jaxlib.xla_extension.Device,
     ) -> JaxArray:
 
-MXNet:
-
-.. code-block:: python
-
-    def zeros(
-        shape: Union[int, Tuple[int], List[int]],
-        *,
-        dtype: type,
-        device: mx.context.Context,
-    ) -> mx.nd.NDArray:
-
 NumPy:
 
 .. code-block:: python

docs/partial_source/deep_dive/9_inplace_updates.rst

@@ -66,27 +66,6 @@ JAX **does not** natively support inplace updates,
 and so there is no way of actually inplace updating the :code:`JaxArray` instance :code:`x_native`.
 Therefore, an inplace update is only performed on :code:`ivy.Array` instances provided in the input.
 
-**MXNet**:
-
-.. code-block:: python
-
-    def inplace_update(
-        x: Union[ivy.Array, mx.nd.NDArray],
-        val: Union[ivy.Array, mx.nd.NDArray],
-        ensure_in_backend: bool = False,
-    ) -> ivy.Array:
-        (x_native, val_native), _ = ivy.args_to_native(x, val)
-        x_native[:] = val_native
-        if ivy.is_ivy_array(x):
-            x.data = x_native
-        else:
-            x = ivy.Array(x_native)
-        return x
-
-MXNet **does** natively support inplace updates,
-and so :code:`x_native` is updated inplace with :code:`val_native`.
-Following this, an inplace update is then also performed on the :code:`ivy.Array` instance, if provided in the input.
-
 **NumPy**:
 
 .. code-block:: python
@@ -213,13 +192,6 @@ The implementations of :code:`ivy.tan` for each backend are as follows.
     def tan(x: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
         return jnp.tan(x)
 
-**MXNet** (no :code:`support_native_out` attribute):
-
-.. code-block:: python
-
-    def tan(x: mx.nd.NDArray, /, *, out: Optional[mx.nd.NDArray] = None) -> mx.nd.NDArray:
-        return mx.nd.tan(x)
-
 **NumPy** (includes :code:`support_native_out` attribute):
 
 .. code-block:: python

ivy/__init__.py

@@ -4,7 +4,6 @@
 import jax
 import jaxlib
 from jaxlib.xla_extension import Buffer
-import mxnet as mx
 import numpy as np
 import tensorflow as tf
 from tensorflow.python.types.core import Tensor
@@ -19,7 +18,7 @@
 
 class FrameworkStr(str):
     def __new__(cls, fw_str):
-        assert fw_str in ["jax", "tensorflow", "torch", "mxnet", "numpy"]
+        assert fw_str in ["jax", "tensorflow", "torch", "numpy"]
         return str.__new__(cls, fw_str)
 
 
@@ -31,19 +30,18 @@ class Framework:
     jax.interpreters.xla._DeviceArray,
     jaxlib.xla_extension.DeviceArray,
     Buffer,
-    mx.nd.NDArray,
     np.ndarray,
     Tensor,
     torch.Tensor,
 ]
 
 
 NativeVariable = Union[
-    jax.interpreters.xla._DeviceArray, mx.nd.NDArray, np.ndarray, Tensor, torch.Tensor
+    jax.interpreters.xla._DeviceArray, np.ndarray, Tensor, torch.Tensor
 ]
 
 
-NativeDevice = Union[jaxlib.xla_extension.Device, mx.context.Context, str, torch.device]
+NativeDevice = Union[jaxlib.xla_extension.Device, str, torch.device]
 
 
 NativeDtype = Union[jnp.dtype, np.dtype, tf.DType, torch.dtype]
@@ -399,7 +397,6 @@ class Node(str):
     try_import_ivy_jax,
     try_import_ivy_tf,
     try_import_ivy_torch,
-    try_import_ivy_mxnet,
     try_import_ivy_numpy,
     clear_backend_stack,
 )

ivy/backend_handler.py

@@ -33,21 +33,18 @@ def __exit__(self, exc_type, exc_val, exc_tb):
 _array_types["jaxlib.xla_extension"] = "ivy.functional.backends.jax"
 _array_types["tensorflow.python.framework.ops"] = "ivy.functional.backends.tensorflow"
 _array_types["torch"] = "ivy.functional.backends.torch"
-_array_types["mxnet.ndarray.ndarray"] = "ivy.functional.backends.mxnet"
 
 _backend_dict = dict()
 _backend_dict["numpy"] = "ivy.functional.backends.numpy"
 _backend_dict["jax"] = "ivy.functional.backends.jax"
 _backend_dict["tensorflow"] = "ivy.functional.backends.tensorflow"
 _backend_dict["torch"] = "ivy.functional.backends.torch"
-_backend_dict["mxnet"] = "ivy.functional.backends.mxnet"
 
 _backend_reverse_dict = dict()
 _backend_reverse_dict["ivy.functional.backends.numpy"] = "numpy"
 _backend_reverse_dict["ivy.functional.backends.jax"] = "jax"
 _backend_reverse_dict["ivy.functional.backends.tensorflow"] = "tensorflow"
 _backend_reverse_dict["ivy.functional.backends.torch"] = "torch"
-_backend_reverse_dict["ivy.functional.backends.mxnet"] = "mxnet"
 
 
 # Backend Getting/Setting #
@@ -284,7 +281,7 @@ def get_backend(backend: Optional[str] = None):
     ----------
     backend
         The backend for which we want to retrieve Ivy's backend i.e. one of 'jax',
-        'torch', 'tensorflow', 'numpy', 'mxnet'.
+        'torch', 'tensorflow', 'numpy'.
 
     Returns
     -------
@@ -435,20 +432,6 @@ def try_import_ivy_torch(warn=False):
         )
 
 
-def try_import_ivy_mxnet(warn=False):
-    try:
-        import ivy.functional.backends.mxnet
-
-        return ivy.functional.backends.mxnet
-    except (ImportError, ModuleNotFoundError) as e:
-        if not warn:
-            return
-        logging.warning(
-            "{}\n\nmxnet does not appear to be installed, "
-            "ivy.functional.backends.mxnet can therefore not be imported.\n".format(e)
-        )
-
-
 def try_import_ivy_numpy(warn=False):
     try:
         import ivy.functional.backends.numpy
@@ -467,15 +450,14 @@ def try_import_ivy_numpy(warn=False):
     "jax": try_import_ivy_jax,
     "tensorflow": try_import_ivy_tf,
     "torch": try_import_ivy_torch,
-    "mxnet": try_import_ivy_mxnet,
     "numpy": try_import_ivy_numpy,
 }
 
 
 def choose_random_backend(excluded=None):
     excluded = list() if excluded is None else excluded
     while True:
-        if len(excluded) == 5:
+        if len(excluded) == 4:
             raise Exception(
                 "Unable to select backend, all backends are either excluded "
                 "or not installed."

ivy/func_wrapper.py

@@ -8,7 +8,6 @@
     "jax": [],
     "tensorflow": [],
     "torch": [],
-    "mxnet": ["ndarray"],
 }
 
 NATIVE_KEYS_TO_SKIP = {
@@ -27,7 +26,6 @@
         "type",
         "requires_grad_",
     ],
-    "mxnet": [],
 }
 
 # Helpers #