bgawrych commented on code in PR #21090: URL: https://github.com/apache/incubator-mxnet/pull/21090#discussion_r917653470
########## 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@@ -19,9 +19,9 @@ # Gluon2.0: Migration Guide ## Overview -Since the introduction of the Gluon API in MXNet 1.x, it has superseded commonly used symbolic, module and model APIs for model development.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 +Since the introduction of the Gluon API in MXNet 1.x, it has superseded commonly used symbolic, module and model APIs for model development.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: Review Comment: Shouldn't numpy be written as NumPy? Also there is inconsistency in ndarray - sometimes it's ndarray and sometimes NDArray ########## docs/python_docs/python/tutorials/getting-started/gluon_migration_guide.md: ########## @@ -33,41 +33,41 @@ In Gluon2.0, we extend the support to MXNet numpy and numpy extension with simpl - **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 [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. Review Comment: ```suggestion 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. ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
