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<!--- under the License. --><h2>Python API</h2>
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<h4>Python-first API</h4>
<p>Apache MXNet provides a comprehensive and flexible Python API to serve a broad community of developers with different levels of experience and wide ranging requirements. Current efforts are focused on the
<a href="/versions/master/api/python/docs/api"></a>Gluon API. Gluon provides a clear, concise, and simple API for deep learning. It makes it easy to prototype, build, and train deep learning models without sacrificing training speed.</p>
<p>You can checkout the <a href="/versions/master/ecosystem">rich ecosystem</a> built around Apache MXNet Gluon, including <a href="https://d2l.ai">D2L.ai</a>, <a href="https://gluon-cv.mxnet.io">GluonCV</a>,
<a href="https://gluon-nlp.mxnet.io">GluonNLP</a> and <a href="https://gluon-ts.mxnet.io">GluonTS</a>.</p>
<p>While most of the usability improvement around training are focused on the python API, the performance of Apache MXNet is accessible through a variety of different language bindings, checkout their respective API and guides below!</p>
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</div><h2>Other Bindings</h2>
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<h4>Call for Contribution</h4>
The Clojure, Java, Julia, R, and Scala language bindings of <a href="/versions/1.9.1/api">MXNet v1.x</a> were removed in v2.x due to some <a href="https://github.com/apache/incubator-mxnet/issues/17676">C APIs being deprecated</a> and the bindings rely on the deprecated APIs. You can still use these language bindings in v1.x.
MXNet's new C APIs in v2.x can be used to reestablish your preferred language binding. Your contribution is welcome!
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<h2>Apache MXNet Architecture</h2>
<p>
Building a high-performance deep learning library
requires many systems-level design decisions.
In this design note, we share the rationale
for the specific choices made when designing MXNet.
We imagine that these insights may be useful
to both deep learning practitioners
and builders of other deep learning systems.
</p>
<h4>Deep Learning System Design Concepts</h4>
<p>
The following pages address general design concepts for deep learning systems.
Mainly, they focus on the following 3 areas:
abstraction, optimization, and trade-offs between efficiency and flexibility.
Additionally, we provide an overview of the complete MXNet system.
</p>
<ul><li><a href="/versions/master/api/architecture/exception_handling">Exception Handling in Apache MXNet</a></li><li><a href="/versions/master/api/architecture/note_data_loading">Efficient Data Loaders</a></li><li><a href="/versions/master/api/architecture/note_engine">Dependency Engine</a></li><li><a href="/versions/master/api/architecture/note_memory">Memory Consumption</a></li><li><a href="/versions/master/api/architecture/overview">Apache MXNet System Architecture</a></li><li><a href="/versions/master/api/architecture/program_model">Deep Learning Programming Paradigm</a></li></ul>
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<ul><li><a href="/versions/master/api/dev-guide/github_contribution_and_PR_verification_tips">GitHub contribution and PR verification tips</a></li><li><a href="/versions/master/api/dev-guide/debugging_and_performance_optimization_tips">Debugging and performance optimization tips</a></li><li><a href="/versions/master/api/dev-guide/examine_forward_results_with_hooks">Examine forward results with hooks</a></li><li><a href="/versions/master/api/dev-guide/exception_handing_and_custom_error_types">Exception handing and custom error types</a></li><li><a href="/versions/master/api/dev-guide/profiling">Profiling</a></li></ul>
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<ul><h3>Deployment Environments</h3><li><a href="/versions/master/api/faq/cloud">How to run MXNet on AWS?</a></li><li><a href="/versions/master/api/faq/distributed_training">How to do distributed training using MXNet on AWS?</a></li><li><a href="/versions/master/api/faq/env_var">What are MXNet environment variables?</a></li><li><a href="/versions/master/api/faq/s3_integration">How to use data from S3 for training?</a></li><br><h3>Model</h3><li><a href="/versions/master/api/faq/model_parallel_lstm">How can I train using multiple GPUs with model parallelism?</a></li><br><h3>Speed</h3><li><a href="/versions/master/api/faq/float16">How do I use mixed precision (float16) with MXNet or Gluon?</a></li><li><a href="/versions/master/api/faq/gradient_compression">How do I use gradient compression with distributed training?</a></li><li><a href="/versions/master/api/faq/perf">What are the best setup and data-handling tips and tricks for improving speed?</a></li><li><a href="/versions/master/api/faq/recordio">How can I create a .rec dataset ?</a></li><br><h3>Security</h3><li><a href="/versions/master/api/faq/security">How to run MXNet securely?</a></li><br><h3>Extend and Contribute to Apache MXNet</h3><br></ul>
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