john-andrilla commented on a change in pull request #16500: Fixing broken links URL: https://github.com/apache/incubator-mxnet/pull/16500#discussion_r335749991
########## File path: docs/python_docs/python/tutorials/deploy/run-on-aws/cloud.rst ########## @@ -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. -Set Up an AWS GPU Cluster from Scratch --------------------------------------- - -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: - -- ``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``\ \_ - -Use Amazon S3 to Host Data -:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~`````````\ ~`\ ~~ - -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``. - -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): - -.. code:: bash - -export AWS\_ACCESS\_KEY\_ID=AKIAIOSFODNN7EXAMPLE export -AWS\_SECRET\_ACCESS\_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY - -There are several ways to upload data to S3. One simple way is to use -``s3cmd``\ \_. For example: - -.. code:: bash - -wget http://data.mxnet.io/mxnet/data/mnist.zip unzip mnist.zip && s3cmd -put t\*-ubyte s3://dmlc/mnist/ - -Use Pre-installed EC2 GPU Instance -:sub:`:sub:`~`\ :sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~`````````````\ ~~ - -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: - -- us-east-1: ami-e7c96af1 -- us-west-2: ami-dfb13ebf -- eu-west-1: ami-6e5d6808 - -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. - -Set Up an EC2 GPU Instance from Scratch -:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~``````\ :sub:`:sub:`:sub:`:sub:`:sub:`:sub:`:sub:`~```````\ :sub:`:sub:`~``` - -*MXNet* requires the following libraries: - -- 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) - -.. \_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) Review comment: ```suggestion 1. Install MXNet on an [AWS Deep Learning Base AMI](https://docs.aws.amazon.com/dlami/latest/devguide/overview-base.html) ``` ---------------------------------------------------------------- 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. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
