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by maciej on June 7, 2016

Deploy Bitfusion Boost on AWS faster than ever

Enabling development, deployment, and acceleration of multi-node GPU applications from deep learning to oil exploration.

Back in March, we first described how to deploy Bitfusion Boost on AWS to create a 16 GPU cluster. We received a lot of customer feedback since then, in particular we paid attention to issues that tripped you up in order to make the experience more seamless. With that in mind, we engaged the AWS Marketplace team to integrate Bitfusion Boost directly into our products, enabling you to spin-up Bitfusion Boost GPU clusters directly from the AWS Marketplace with just a few clicks. Some of the major improvements include:

  • Run multi-gpu enabled applications across multiple GPU instances without any additional configurations or code changes
  • Boost enabled AMIs can be launched in cluster-mode directly from the AWS Marketplace
  • Boost enabled AMI clusters can be launched in all AWS regions that contain GPU instances
  • AMI opt-in process is identical for single-instance and cluster-mode AMI launches
  • Monthly cluster cost estimates are provided directly in the AWS Marketplace
  • Simplified cluster launch parameters for CFNs enable easier cluster customization

 

Summary


Launching a Bitfusion Boost Cluster now entails only 4 easy steps:

  1. Locate a Bitfusion Boost enabled AMI in the AWS Marketplace
  2. Select a Bitfusion Boost Cluster configuration
  3. Fine-tune the Bitfusion Boost Cluster launch parameters
  4. Launch the Bitfusion Boost Cluster and verify proper operation

 

Detailed Instructions


  1. Locate a Bitfusion Boost enabled AMI

    You can locate all Bitfusion Boost enabled AMIs in the AWS marketplace by clicking here. Alternatively, below are direct links to our AMIs which are presently Boost enabled. If you don't already have an AWS account, you can create one by clicking here.

  2. Select a Bitfusion Boost cluster configuration

    For this example we are using the Bitfusion Boost Ubuntu Cuda 7.5 AMI, and we will launch an 8 GPU cluster. The image below has several color-coded boxes:

    • Blue Box: Shows detailed descriptions of the available deployment (delivery) options for this AMI.
    • Green Box: Selection box where you can pick the cluster you want to create. Pick the GPU Optimized Cluster here.
    • Yellow Box: Estimated costs for the cluster if you were going to run it 24/7 for an entire month. Even though the cost of the infrastructure is shown for a month, the actual charges will be calculated based on hourly usage.

    Bitfusion Boost Cluster Selection
    Once you have selected the GPU Optimized Cluster option, click on the large Continue button above it, and you will be forwarded to the Launch on EC2 page shown below. The important sections are once again highlighted by color-coded boxes:

    • Blue Box: Select the AWS region in which you would like to launch the Bitfusion Boost Cluster.
    • Green Box: Click this button to proceed and fine-tune the cluster parameters.

    Bitfusion Boost Cluster Region

  3. Fine-Tune the Bitfusion Boost Cluster parameters

    After you click the Launch with CloudFormation Console button you will be taken to the Select Template AWS page. Simply click the Next button on the bottom right and you will be presented with several options to fine-tune the cluster you are about to launch. All the available options are described in detail in our Boost on AWS Documentation, however, to launch the 8 GPU cluster we only need to specify two options as highlighted in the figure below:

    • Blue Box: Select a key name which you will use to SSH into the instance. If you have not create an AWS key before you can create one by following the AWS directions here. After you create the key, return to the fine-tuning page where the key needs to be selected, refresh the page, and then select they key you just created.
    • Green Box: You must enter here the IP address from which you will be connecting to the EC2 instance. For now enter 0.0.0.0/0 to keeps things simple, however, for future clusters consider setting a specific IP from which you will be connecting to increase the security of the cluster even further.

    Bitfusion Boost Cluster Details

    Once you set these two fields, click the Next button on the bottom right and you will be forwarded to the Options pages. Nothing needs to be set here, so simply click the Next button again to go to the Review page.

  4. Launch the Bitfusion Boost Cluster

    One the Review page you must click the check-box next to the "I acknowledge that this template might cause AWS CloudFormation to create IAM resources" text at the very bottom of the page to enable our template to provision the cluster for you. Only thing left to do is clicking the Create button, and your cluster will be created!

    At this point you are forwarded to the Stack Management page on AWS. It will most likely be blank initially, but after a couple minutes you will see a stack being created as shown in the image below. You can click the check-box next to the stack to obtain additional information about the stack.

    Bitfusion Boost Cluster Create

    You will see that the status is shown as CREATE_IN_PROGRESS. The creation of the cluster can take anywhere from 5 to 10 minutes. If you are curious about all the details that we are taking care of simply click on the events tab. Eventually you will see the status change to CREATE_COMPLETE - time to log in to the cluster and verify that everything is working as expected.

    To log in to the instance you need to obtain the instance IP address. You can find this information by navigating to your AWS Console, clicking on EC2, and then clicking on running instances. In case you have other instance running, filter the instances by "bitfusion-boost" and you should see two instances as shown below.

    • Blue Box: Select the AWS instance that contains the cuda75 in the name. This is the application instance into which you will log in, and from which you will execute your Cuda / GPU applications. The instances below it, with gpunode in the name, is the instance hosting the additional GPUs. Depending on how many additional GPUs you selected when creating your cluster, you may have multiple of these instances.
    • Green Box: Note down the Public DNS address listed in this box for your instance. You will use this address in the commands below to access the instance and execute applications.

    Bitfusion Boost Cluster Instance Details

    To access the instance application instance execute the following command:

    ssh -i {path to your pem file} ubuntu@{public dns address}

    Once you are logged in execute the following command to verify that all 8 GPUs are available to your application:

    bfboost client  /usr/local/cuda-7.5/samples/bin/x86_64/linux/release/deviceQuery

    You should see obtain the following output:

    deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 8, Device0 = GRID K520, Device1 = GRID K520, Device2 = GRID K520, Device3 = GRID K520, Device4 = GRID K520, Device5 = GRID K520, Device6 = GRID K520, Device7 = GRID K520
    Result = PASS
    BFBoost run complete.

    You are all set. Happy coding and development on your 8+ GPU Bitfusion Boost Cluster.

Topics: aws

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