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fixed broken link
aaronmarkham 508ebb0
Update docs/python_docs/python/tutorials/deploy/export/onnx.md
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Merge branch 'master' into broken_links_aaron
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update cloud guide
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Merge branch 'broken_links_aaron' of https://github.com/aaronmarkham/…
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@@ -28,7 +28,7 @@ In this tutorial, we will learn how to use MXNet to ONNX exporter on pre-trained | |||||
## Prerequisites | ||||||
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To run the tutorial you will need to have installed the following python modules: | ||||||
- [MXNet >= 1.3.0](http://mxnet.apache.org/install/index.html) | ||||||
- [MXNet >= 1.3.0](/get_started) | ||||||
- [onnx]( https://github.com/onnx/onnx#installation) v1.2.1 (follow the install guide) | ||||||
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*Note:* MXNet-ONNX importer and exporter follows version 7 of ONNX operator set which comes with ONNX v1.2.1. | ||||||
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@@ -147,4 +147,4 @@ checker.check_graph(model_proto.graph) | |||||
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If the converted protobuf format doesn't qualify to ONNX proto specifications, the checker will throw errors, but in this case it successfully passes. | ||||||
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This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference! | ||||||
This method confirms exported model protobuf is valid. Now, the model is ready to be imported in other frameworks for inference! |
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@@ -26,80 +26,8 @@ learning models. Using AWS, we can rapidly fire up multiple machines | |||||
with multiple GPUs each at will and maintain the resources for precisely | ||||||
the amount of time needed. | ||||||
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Set Up an AWS GPU Cluster from Scratch | ||||||
-------------------------------------- | ||||||
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In this document, we provide a step-by-step guide that will teach you | ||||||
how to set up an AWS cluster with *MXNet*. We show how to: | ||||||
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- ``Use Amazon S3 to host data``\ \_ | ||||||
- ``Set up an EC2 GPU instance with all dependencies installed``\ \_ | ||||||
- ``Build and run MXNet on a single computer``\ \_ | ||||||
- ``Set up an EC2 GPU cluster for distributed training``\ \_ | ||||||
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Use Amazon S3 to Host Data | ||||||
:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~`````````\ ~`\ ~~ | ||||||
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Amazon S3 provides distributed data storage which proves especially | ||||||
convenient for hosting large datasets. To use S3, you need | ||||||
``AWS credentials``\ \_, including an ``ACCESS_KEY_ID`` and a | ||||||
``SECRET_ACCESS_KEY``. | ||||||
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To use *MXNet* with S3, set the environment variables | ||||||
``AWS_ACCESS_KEY_ID`` and ``AWS_SECRET_ACCESS_KEY`` by adding the | ||||||
following two lines in ``~/.bashrc`` (replacing the strings with the | ||||||
correct ones): | ||||||
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.. code:: bash | ||||||
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export AWS\_ACCESS\_KEY\_ID=AKIAIOSFODNN7EXAMPLE export | ||||||
AWS\_SECRET\_ACCESS\_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY | ||||||
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There are several ways to upload data to S3. One simple way is to use | ||||||
``s3cmd``\ \_. For example: | ||||||
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.. code:: bash | ||||||
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wget http://data.mxnet.io/mxnet/data/mnist.zip unzip mnist.zip && s3cmd | ||||||
put t\*-ubyte s3://dmlc/mnist/ | ||||||
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Use Pre-installed EC2 GPU Instance | ||||||
:sub:`:sub:`~`\ :sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~`````````````\ ~~ | ||||||
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The ``Deep Learning AMI``\ \_ is an Amazon Linux image supported and | ||||||
maintained by Amazon Web Services for use on Amazon Elastic Compute | ||||||
Cloud (Amazon EC2). It contains ``MXNet-v0.9.3 tag``\ \_ and the | ||||||
necessary components to get going with deep learning, including Nvidia | ||||||
drivers, CUDA, cuDNN, Anaconda, Python2 and Python3. The AMI IDs are the | ||||||
following: | ||||||
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- us-east-1: ami-e7c96af1 | ||||||
- us-west-2: ami-dfb13ebf | ||||||
- eu-west-1: ami-6e5d6808 | ||||||
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Now you can launch *MXNet* directly on an EC2 GPU instance. You can also | ||||||
use ``Jupyter``\ \_ notebook on EC2 machine. Here is a | ||||||
``good tutorial``\ \_ on how to connect to a Jupyter notebook running on | ||||||
an EC2 instance. | ||||||
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Set Up an EC2 GPU Instance from Scratch | ||||||
:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~``````\ :sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~```````\ :sub:`:sub:`~``` | ||||||
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*MXNet* requires the following libraries: | ||||||
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- C++ compiler with C++11 support, such as ``gcc >= 4.8`` | ||||||
- ``CUDA`` (``CUDNN`` in optional) for GPU linear algebra | ||||||
- ``BLAS`` (cblas, open-blas, atblas, mkl, or others) | ||||||
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.. \_Use Amazon S3 to host data: #use-amazon-s3-to-host-data .. \_Set up | ||||||
an EC2 GPU instance with all dependencies installed: | ||||||
#set-up-an-ec2-gpu-instance .. \_Build and run MXNet on a single | ||||||
computer: #build-and-run-mxnet-on-a-gpu-instance .. \_Set up an EC2 GPU | ||||||
cluster for distributed training: | ||||||
#set-up-an-ec2-gpu-cluster-for-distributed-training .. \_AWS | ||||||
credentials: | ||||||
http://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html | ||||||
.. \_s3cmd: http://s3tools.org/s3cmd .. *Deep Learning AMI: | ||||||
https://aws.amazon.com/marketplace/pp/B01M0AXXQB?qid=1475211685369&sr=0-1&ref*\ =srh\_res\_product\_title | ||||||
.. \_MXNet-v0.9.3 tag: https://github.com/apache/incubator-mxnet .. \_Jupyter: | ||||||
http://jupyter.org | ||||||
Here are some ways you can use MXNet on AWS: | ||||||
1. Use [Amazon SageMaker](https://aws.amazon.com/sagemaker/developer-resources/) | ||||||
1. Use the [AWS Deep Learning AMI with Conda](https://docs.aws.amazon.com/dlami/latest/devguide/overview-conda.html) (comes preinstalled!) | ||||||
1. Use an [AWS Deep Learning Container](https://docs.aws.amazon.com/dlami/latest/devguide/deep-learning-containers.html) | ||||||
1. Install MXNet on a [AWS Deep Learning Base AMI](https://docs.aws.amazon.com/dlami/latest/devguide/overview-base.html) | ||||||
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