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new e522bea513 [BUGFIX] Fix Gluon2.0 guide (#21090)
e522bea513 is described below
commit e522bea5135f61761a82426d01d9778054d8ae77
Author: bartekkuncer <[email protected]>
AuthorDate: Fri Jul 15 09:50:44 2022 +0200
[BUGFIX] Fix Gluon2.0 guide (#21090)
* Fix Gluon2.0 guide
* Update
docs/python_docs/python/tutorials/getting-started/gluon_migration_guide.md
Co-authored-by: bgawrych <[email protected]>
* Fix NumPy mentions
---
.../getting-started/gluon_migration_guide.md | 40 +++++++++++-----------
1 file changed, 20 insertions(+), 20 deletions(-)
diff --git
a/docs/python_docs/python/tutorials/getting-started/gluon_migration_guide.md
b/docs/python_docs/python/tutorials/getting-started/gluon_migration_guide.md
index e142ccc32a..b1553904f3 100644
--- a/docs/python_docs/python/tutorials/getting-started/gluon_migration_guide.md
+++ b/docs/python_docs/python/tutorials/getting-started/gluon_migration_guide.md
@@ -19,9 +19,9 @@
# Gluon2.0: Migration Guide
## Overview
-Since the introduction of the Gluon API in MXNet 1.x, it has superseded
commonly used symbolic, module and model APIs for model development. In fact,
Gluon was the first in the deep learning community to unify the flexibility of
imperative programming with the performance benefits of symbolic programming,
through just-in-time compilation.
+Since the introduction of the Gluon API in MXNet 1.x, it has superseded
commonly used symbolic, module and model APIs for model development. In fact,
Gluon was the first in the deep learning community to unify the flexibility of
imperative programming with the performance benefits of symbolic programming,
through just-in-time compilation.
-In Gluon2.0, we extend the support to MXNet numpy and numpy extension with
simplified interface and new functionalities:
+In Gluon2.0, we extend the support to MXNet NumPy and NumPy extension with
simplified interface and new functionalities:
- **Simplified hybridization with deferred compute and tracing**: Deferred
compute allows the imperative execution to be used for graph construction,
which allows us to unify the historic divergence of NDArray and Symbol.
Hybridization now works in a simplified hybrid forward interface. Users only
need to specify the computation through imperative programming. Hybridization
also works through tracing, i.e. tracing the data flow of the first input data
to create a graph.
@@ -31,43 +31,43 @@ In Gluon2.0, we extend the support to MXNet numpy and numpy
extension with simpl
- **Gluon Probability**: parameterizable probability distributions and
sampling functions to facilitate more areas of research such as Baysian methods
and AutoML.
-- **Gluon Metrics** and **Optimizers**: refactored with MXNet numpy interface
and addressed legacy issues.
+- **Gluon Metrics** and **Optimizers**: refactored with MXNet NumPy interface
and addressed legacy issues.
-Adopting these new functionalities may or may not require modifications on
your models. But don't worry, this migration guide will go through a high-level
mapping from old functionality to new APIs and make Gluon2.0 migration a
hassle-free experience.
+Adopting these new functionalities may or may not require modifications on
your models. But don't worry, this migration guide will go through a high-level
mapping from old functionality to new APIs and make Gluon2.0 migration a
hassle-free experience.
## Data Pipeline
-**What's new**: In Gluon2.0, `MultithreadingDataLoader` is introduced to speed
up the data loading pipeline. It will use the pure MXNet C++ implementation of
dataloader, datasets and batchify functions. So, you can use either MXNet
internal multithreading mode dataloader or python multiprocessing mode
dataloader in Gluon2.0.
+**What's new**: In Gluon2.0, `MultithreadingDataLoader` is introduced to speed
up the data loading pipeline. It will use the pure MXNet C++ implementation of
dataloader, datasets and batchify functions. So, you can use either MXNet
internal multithreading mode dataloader or python multiprocessing mode
dataloader in Gluon2.0.
-**Migration Guide**: Users can continue with the traditional
gluon.data.Dataloader and the C++ backend will be applied automatically.
+**Migration Guide**: Users can continue with the traditional
gluon.data.Dataloader and the C++ backend will be applied automatically.
-[Gluon2.0
dataloader](../../api/gluon/data/index.rst#mxnet.gluon.data.DataLoader) will
provide a new parameter called `try_nopython`. This parameter takes a default
value of None; when set to `True` the dataloader will compile the python
dataloading pipeline into pure MXNet c++ implementation. The compilation is not
guaranteed to support all use cases, but it will fallback to python in case of
failure:
+[Gluon2.0
dataloader](../../api/gluon/data/index.rst#mxnet.gluon.data.DataLoader) will
provide a new parameter called `try_nopython`. This parameter takes a default
value of None; when set to `True` the dataloader will compile the python
dataloading pipeline into pure MXNet C++ implementation. The compilation is not
guaranteed to support all use cases, but it will fallback to python in case of
failure:
-- The dataset is not fully [supported by the
backend](../../api/gluon/data/index.rst#mxnet.gluon.data.Dataset)(e.g., there
are custom python datasets).
+- The dataset is not fully [supported by the
backend](../../api/gluon/data/index.rst#mxnet.gluon.data.Dataset) (e.g., there
are custom python datasets).
-- Transform is not fully hybridizable.
+- Transform is not fully hybridizable.
-- Bachify is not fully [supported by the
backend](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/data/batchify.py).
+- Bachify is not fully [supported by the
backend](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/data/batchify.py).
-You can refer to [Step5 in Crash
Course](https://mxnet.apache.org/versions/master/api/python/docs/tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders)
for a detailed performance increase with C++ backend.
+You can refer to [Step 5 in Crash
Course](https://mxnet.apache.org/versions/master/api/python/docs/tutorials/getting-started/crash-course/5-datasets.html#New-in-MXNet-2.0:-faster-C++-backend-dataloaders)
for a detailed performance increase with C++ backend.
## Modeling
-In Gluon2.0, users will have a brand new modeling experience with
NumPy-compatible APIs and the deferred compute mechanism.
+In Gluon2.0, users will have a brand new modeling experience with
NumPy-compatible APIs and the deferred compute mechanism.
-- **NumPy-compatible programing experience**: users can build their models
with MXNet implementation with NumPy array library, NumPy-compatible math
operators and some neural network extension operators.
+- **NumPy-compatible programing experience**: users can build their models
with MXNet implementation with NumPy array library, NumPy-compatible math
operators and some neural network extension operators.
-- **Imperative-only coding experience**: with the deferred compute and tracing
being introduced, users only need to specify the computation through imperative
coding but can still make hybridization work. Users will no longer need to
interact with symbol APIs.
+- **Imperative-only coding experience**: with the deferred compute and tracing
being introduced, users only need to specify the computation through imperative
coding but can still make hybridization work. Users will no longer need to
interact with symbol APIs.
-To help users migrate smoothly to use these simplified interfaces, we will
provide the following guidance on how to replace legacy operators with
NumPy-compatible operators, how to build models with `forward` instead of
`hybrid_forward` and how to use `Parameter` class to register your parameters.
+To help users migrate smoothly to use these simplified interfaces, we will
provide the following guidance on how to replace legacy operators with
NumPy-compatible operators, how to build models with `forward` instead of
`hybrid_forward` and how to use `Parameter` class to register your parameters.
### NumPy-compatible Programming Experience
#### NumPy Arrays
-MXNet [NumPy ndarray(i.e. `mx.np.ndarray`)](../../api/np/arrays.ndarray.html)
is a multidimensional container of items of the same type and size. Most of its
properties and attributes are the same as legacy NDArrays(i.e.
`mx.nd.ndarray`), so users can use the NumPy array library just as they did
with legacy NDArrays. But, there are still some changes and deprecations that
need attention, as mentioned below.
+MXNet [NumPy ndarray (i.e. mx.np.ndarray)](../../api/np/arrays.ndarray.rst) is
a multidimensional container of items of the same type and size. Most of its
properties and attributes are the same as legacy NDArrays (i.e.
`mx.nd.ndarray`), so users can use the NumPy array library just as they did
with legacy NDArrays. But, there are still some changes and deprecations that
need attention, as mentioned below.
-**Migration Guide**:
+**Migration Guide**:
-1. Currently, NumPy ndarray only supports `default` storage type, other
storage types, like `row_sparse`, `csr` are not supported. Also, `tostype()`
attribute is deprecated.
+1. Currently, NumPy ndarray only supports `default` storage type, other
storage types, like `row_sparse`, `csr` are not supported. Also, `tostype()`
attribute is deprecated.
2. Users can use `as_np_ndarray` attribute to switch from a legacy NDArray to
NumPy ndarray just like this:
@@ -125,7 +125,7 @@ np_array = nd_array.as_np_ndarray()
#### NumPy and NumPy-extension Operators
-Most of the legacy NDArray operators(`mx.nd.op`) have the equivalent ones in
np/npx namespace. Users can just replace them with `mx.np.op` or `mx.npx.op` to
migrate. Some of the operators will have different inputs and behaviors as
listed in the table below.
+Most of the legacy NDArray operators (`mx.nd.op`) have the equivalent ones in
np/npx namespace. Users can just replace them with `mx.np.op` or `mx.npx.op` to
migrate. Some of the operators will have different inputs and behaviors as
listed in the table below.
**Migration Guide**:
@@ -229,7 +229,7 @@ class SampleBlock(HybridBlock):
# Access constant parameters, which are not iterated during
training
self.weight = self.params.get_constant('const', const_arr)
```
-Now in Gluon 2.0, Block/HybridBlock objects will not maintain the parameter
dictionary(`ParameterDict`). Instead, users can access these parameters via
`Parameter` class and `Constant` class. These parameters will be registered
automatically as part of the Block. Users will no longer need to manage the
name scope for children blocks and hence can remove `with self.name_scope():`
this statement. For example:
+Now in Gluon 2.0, Block/HybridBlock objects will not maintain the parameter
dictionary (`ParameterDict`). Instead, users can access these parameters via
`Parameter` class and `Constant` class. These parameters will be registered
automatically as part of the Block. Users will no longer need to manage the
name scope for children blocks and hence can remove `with self.name_scope():`
this statement. For example:
```{.python}
class SampleBlock(HybridBlock):
def __init__(self):
