Guide¶

Getting started¶

A 60-minute Gluon crash coursecrash-course/index.html

A quick overview of the core concepts of MXNet using the Gluon API.

Moving from other frameworksto-mxnet/index.html

Guides that ease your transition to MXNet from other framework.

Packages & Modules¶

Gluonpackages/gluon/index.html

MXNet’s imperative interface for Python. If you’re new to MXNet, start here!

NDArray APIpackages/ndarray/index.html

How to use the NDArray API to manipulate data. A useful set of tutorials for beginners.

Symbol APIpackages/symbol/index.html

How to use MXNet’s Symbol API.

Autograd APIpackages/autograd.html

How to use Automatic Differentiation with the Autograd API.

Learning Ratepackages/lr_scheduler.html

How to use the Learning Rate Scheduler.

Performance¶

Improving Performanceperformance/perf.html

How to get the best performance from MXNet.

Profilerperformance/profiler.html

How to profile MXNet models.

Tuning Numpy Operationsperformance/numpy.html

Gotchas using NumPy in MXNet.

Compression: float16performance/float16.html

How to use float16 in your model to boost training speed.

Compression: int8performance/index.html

How to use int8 in your model to boost training speed.

Gradient Compressionperformance/gradient_compression.html

How to use gradient compression to reduce communication bandwidth and increase speed.

MKL-DNNperformance/mkl-dnn.html

How to get the most from your CPU by using Intel’s MKL-DNN.

TensorRTperformance/index.html

How to use NVIDIA’s TensorRT to boost inference performance.

TVMperformance/tvm.html

How to use TVM to boost performance.

Deployment¶

MXNet on EC2deploy/run-on-aws/use_ec2.html

How to deploy MXNet on an Amazon EC2 instance.

MXNet on SageMakerdeploy/run-on-aws/use_sagemaker.html

How to run MXNet using Amazon SageMaker.

Training with Data from S3deploy/run-on-aws/use_s3.html

How to train with data from Amazon S3 buckets.

ONNX Modelsdeploy/onnx.html

How to export an MXNet model to the ONNX model format.

Customization¶

Custom Layers for Gluonextend/custom_layer.html

How to add new layer functionality to MXNet’s imperative interface.

Custom Operators Using Numpyextend/custom_op.html

How to use Numpy to create custom MXNet operators.

New Layer Creationextend/new_op.html

How to create new MXNet operators.

Next steps¶

To learn more about using MXNet to implement various deep learning algorithms from scratch, we recommend the Dive into Deep Learning book.
If you are interested in building your projects based on state-of-the-art deep learning algorithms and/or pre-trained models, please refer to the toolkits in the MXNet ecosystem.
Check out the API Reference docs.