Use a base AMI - Amazon Elastic Compute Cloud

Use a base AMI

The following steps help you to get started with Elastic Fabric Adapter using one of the supported base AMIs.

Note
  • Only the p3dn.24xlarge and p4d.24xlarge instance types are supported.

  • Only Amazon Linux 2, RHEL 7/8, CentOS 7, and Ubuntu 18.04/20.04 base AMIs are supported.

Step 1: Prepare an EFA-enabled security group

An EFA requires a security group that allows all inbound and outbound traffic to and from the security group itself. The following procedure allows all inbound and outbound traffic for testing purposes only. For other scenarios, see Security group rules for different use cases.

To create an EFA-enabled security group

  1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.

  2. In the navigation pane, choose Security Groups and then choose Create security group.

  3. In the Create security group window, do the following:

    1. For Security group name, enter a descriptive name for the security group, such as EFA-enabled security group.

    2. (Optional) For Description, enter a brief description of the security group.

    3. For VPC, select the VPC into which you intend to launch your EFA-enabled instances.

    4. Choose Create security group.

  4. Select the security group that you created, and on the Details tab, copy the Security group ID.

  5. With the security group still selected, choose Actions, Edit inbound rules, and then do the following:

    1. Choose Add rule.

    2. For Type, choose All traffic.

    3. For Source type, choose Custom and paste the security group ID that you copied into the field.

    4. Choose Add rule.

    5. For Type, choose SSH.

    6. For Source type, choose Anywhere-IPv4.

    7. Choose Save rules.

  6. With the security group still selected, choose Actions, Edit outbound rules, and then do the following:

    1. Choose Add rule.

    2. For Type, choose All traffic.

    3. For Destination type, choose Custom and paste the security group ID that you copied into the field.

    4. Choose Save rules.

Step 2: Launch a temporary instance

Launch a temporary instance that you can use to install and configure the EFA software components. You use this instance to create an EFA-enabled AMI from which you can launch your EFA-enabled instances.

New console

To launch a temporary instance

  1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.

  2. In the navigation panel, choose Instances, and then choose Launch Instances to open the new launch instance wizard.

  3. (Optional) In the Name and tags section, provide a name for the instance, such as EFA-instance. The name is assigned to the instance as a resource tag (Name=EFA-instance).

  4. In the Application and OS Images section, select a supported AMI.

  5. In the Instance type section, select either p3dn.24xlarge or p4d.24xlarge.

  6. In the Key pair section, select the key pair to use for the instance.

  7. In the Network settings section, choose Edit, and then do the following:

    1. For Subnet, choose the subnet in which to launch the instance. If you do not select a subnet, you can't enable the instance for EFA.

    2. For Firewall (security groups), choose Select existing security group, and then select the security group that you created in the previous step.

    3. Expand the Advanced network configuration section, and for Elastic Fabric Adapter, select Enable.

  8. In the Storage section, configure the volumes as needed.

    Note

    You must provision an additional 10 to 20 GiB of storage for the Nvidia CUDA Toolkit. If you do not provision enough storage, you will receive an insufficient disk space error when attempting to install the Nvidia drivers and CUDA toolkit.

  9. In the Summary panel on the right, choose Launch instance.

Old console

To launch a temporary instance

  1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.

  2. Choose Launch Instance.

  3. On the Choose an AMI page, choose one of the supported AMIs.

  4. On the Choose an Instance Type page, select p3dn.24xlarge or p4d.24xlarge and then choose Next: Configure Instance Details.

  5. On the Configure Instance Details page, do the following:

    1. For Subnet, choose the subnet in which to launch the instance. If you do not select a subnet, you can't enable the instance for EFA.

    2. For Elastic Fabric Adapter, choose Enable.

    3. In the Network Interfaces section, for device eth0, choose New network interface.

    4. Choose Next: Add Storage.

  6. On the Add Storage page, specify the volumes to attach to the instances, in addition to the volumes specified by the AMI (such as the root device volume). Ensure that you provision enough storage for the Nvidia CUDA Toolkit. Then choose Next: Add Tags.

    Note

    You must provision an additional 10 to 20 GiB of storage for the Nvidia CUDA Toolkit. If you do not provision enough storage, you will receive an insufficient disk space error when attempting to install the Nvidia drivers and CUDA toolkit.

  7. On the Add Tags page, specify a tag that you can use to identify the temporary instance, and then choose Next: Configure Security Group.

  8. On the Configure Security Group page, for Assign a security group, select Select an existing security group. Then select the security group that you created in Step 1.

  9. On the Review Instance Launch page, review the settings, and then choose Launch to choose a key pair and to launch your instance.

Step 3: Install Nvidia GPU drivers, Nvidia CUDA toolkit, and cuDNN

Amazon Linux 2

To install the Nvidia GPU drivers, Nvidia CUDA toolkit, and cuDNN

  1. Install the utilities that are needed to install the Nvidia GPU drivers and the Nvidia CUDA toolkit.

    $ sudo yum groupinstall 'Development Tools' -y
  2. Disable the nouveau open source drivers.

    1. Install the required utilities and the kernel headers package for the version of the kernel that you are currently running.

      $ sudo yum install -y wget kernel-devel-$(uname -r) kernel-headers-$(uname -r)
    2. Add nouveau to the /etc/modprobe.d/blacklist.conf deny list file.

      $ cat << EOF | sudo tee --append /etc/modprobe.d/blacklist.conf blacklist vga16fb blacklist nouveau blacklist rivafb blacklist nvidiafb blacklist rivatv EOF
    3. Append GRUB_CMDLINE_LINUX="rdblacklist=nouveau" to the grub file and rebuild the Grub configuration.

      $ echo 'GRUB_CMDLINE_LINUX="rdblacklist=nouveau"' | sudo tee -a /etc/default/grub \ && sudo grub2-mkconfig -o /boot/grub2/grub.cfg
  3. Reboot the instance and reconnect to it.

  4. Prepare the required repositories

    1. Install the EPEL repository for DKMS and enable any optional repos for your Linux distribution.

      $ sudo yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
    2. Install the CUDA repository public GPG key.

      $ distribution='rhel7'
    3. Set up the CUDA network repository and update the repository cache.

      $ ARCH=$( /bin/arch ) \ && sudo yum-config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/$distribution/${ARCH}/cuda-$distribution.repo \ && sudo yum clean expire-cache
    4. (Kernel version 5.10 only) Perform these steps only if you are using Amazon Linux 2 with kernel version 5.10. If you are using Amazon Linux 2 with kernel version 4.12, skip these steps. To check your kernel version, run uname -r.

      1. Add the amzn2-nvidia repository. Create a new repo file named /etc/yum.repos.d/amzn2-nvidia.repo, and using your preferred text editor, add the following to the file.

        [amzn2-nvidia] name=Amazon Linux 2 Nvidia repository mirrorlist=http://amazonlinux.$awsregion.$awsdomain/$releasever/amzn2-nvidia/$target/$basearch/mirror.list priority=20 gpgcheck=0 gpgkey=https://developer.download.nvidia.com/compute/cuda/repos/rhel7/x86_64/7fa2af80.pub enabled=1 metadata_expire=300 mirrorlist_expire=300 report_instanceid=yes
      2. Install the system-release-nvidia package from the repo you added in the previous step.

        $ sudo yum install system-release-nvidia
      3. Create the Nvidia driver configuration file named /etc/dkms/nvidia.conf.

        $ sudo mkdir -p /etc/dkms \ && echo "MAKE[0]=\"'make' -j2 module SYSSRC=\${kernel_source_dir} IGNORE_XEN_PRESENCE=1 IGNORE_PREEMPT_RT_PRESENCE=1 IGNORE_CC_MISMATCH=1 CC=/usr/bin/gcc10-gcc\"" | sudo tee /etc/dkms/nvidia.conf
  5. Install the Nvidia GPU drivers, NVIDIA CUDA toolkit, and cuDNN.

    $ sudo yum clean all \ && sudo yum -y install nvidia-driver-latest-dkms \ && sudo yum -y install cuda-drivers-fabricmanager cuda libcudnn8-devel
  6. Reboot the instance and reconnect to it.

  7. (p4d.24xlarge instances only) Start the Nvidia Fabric Manager service, and ensure that it starts automatically when the instance starts. Nvidia Fabric Manager is required for NV Switch Management.

    $ sudo systemctl enable nvidia-fabricmanager && sudo systemctl start nvidia-fabricmanager
  8. Ensure that the CUDA paths are set each time that the instance starts.

    • For bash shells, add the following statements to /home/username/.bashrc and /home/username/.bash_profile.

      export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
    • For tcsh shells, add the following statements to /home/username/.cshrc.

      setenv PATH=/usr/local/cuda/bin:$PATH setenv LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
  9. To confirm that the Nvidia GPU drivers are functional, run the following command.

    $ nvidia-smi -q | head

    The command should return information about the Nvidia GPUs, Nvidia GPU drivers, and Nvidia CUDA toolkit.

CentOS 7

To install the Nvidia GPU drivers, Nvidia CUDA toolkit, and cuDNN

  1. To ensure that all of your software packages are up to date, perform a quick software update on your instance.

    $ sudo yum upgrade -y && sudo reboot

    After the instance has rebooted, reconnect to it.

  2. Install the utilities that are needed to install the Nvidia GPU drivers and the Nvidia CUDA toolkit.

    $ sudo yum groupinstall 'Development Tools' -y \ && sudo yum install -y tar bzip2 make automake pciutils elfutils-libelf-devel libglvnd-devel iptables firewalld vim bind-utils
  3. To use the Nvidia GPU driver, you must first disable the nouveau open source drivers.

    1. Install the required utilities and the kernel headers package for the version of the kernel that you are currently running.

      $ sudo yum install -y wget kernel-devel-$(uname -r) kernel-headers-$(uname -r)
    2. Add nouveau to the /etc/modprobe.d/blacklist.conf deny list file.

      $ cat << EOF | sudo tee --append /etc/modprobe.d/blacklist.conf blacklist vga16fb blacklist nouveau blacklist rivafb blacklist nvidiafb blacklist rivatv EOF
    3. Open /etc/default/grub using your preferred text editor and add the following.

      GRUB_CMDLINE_LINUX="rdblacklist=nouveau"
    4. Rebuild the Grub configuration.

      $ sudo grub2-mkconfig -o /boot/grub2/grub.cfg
  4. Reboot the instance and reconnect to it.

  5. Install the Nvidia GPU drivers, NVIDIA CUDA toolkit, and cuDNN.

    1. Install the EPEL repository for DKMS and enable any optional repos for your Linux distribution.

      $ sudo yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
    2. Install the CUDA repository public GPG key.

      $ distribution='rhel7'
    3. Set up the CUDA network repository and update the repository cache.

      $ ARCH=$( /bin/arch ) \ && sudo yum-config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/$distribution/${ARCH}/cuda-$distribution.repo \ && sudo yum clean expire-cache
    4. Install the NVIDIA, CUDA drivers and cuDNN.

      $ sudo yum clean all \ && sudo yum -y install cuda-drivers-fabricmanager cuda libcudnn8-devel
  6. Reboot the instance and reconnect to it.

  7. (p4d.24xlarge instances only) Start the Nvidia Fabric Manager service, and ensure that it starts automatically when the instance starts. Nvidia Fabric Manager is required for NV Switch Management.

    $ sudo systemctl start nvidia-fabricmanager \ && sudo systemctl enable nvidia-fabricmanager
  8. Ensure that the CUDA paths are set each time that the instance starts.

    • For bash shells, add the following statements to /home/username/.bashrc and /home/username/.bash_profile.

      export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
    • For tcsh shells, add the following statements to /home/username/.cshrc.

      setenv PATH=/usr/local/cuda/bin:$PATH setenv LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
  9. To confirm that the Nvidia GPU drivers are functional, run the following command.

    $ nvidia-smi -q | head

    The command should return information about the Nvidia GPUs, Nvidia GPU drivers, and Nvidia CUDA toolkit.

RHEL 7/8

To install the Nvidia GPU drivers, Nvidia CUDA toolkit, and cuDNN

  1. Install the utilities that are needed to install the Nvidia GPU drivers and the Nvidia CUDA toolkit.

    $ sudo yum groupinstall 'Development Tools' -y
  2. To use the Nvidia GPU driver, you must first disable the nouveau open source drivers.

    1. Install the required utilities and the kernel headers package for the version of the kernel that you are currently running.

      $ sudo yum install -y wget kernel-devel-$(uname -r) kernel-headers-$(uname -r)
    2. Add nouveau to the /etc/modprobe.d/blacklist.conf deny list file.

      $ cat << EOF | sudo tee --append /etc/modprobe.d/blacklist.conf blacklist vga16fb blacklist nouveau blacklist rivafb blacklist nvidiafb blacklist rivatv EOF
    3. Open /etc/default/grub using your preferred text editor and add the following.

      GRUB_CMDLINE_LINUX="rdblacklist=nouveau"
    4. Rebuild the Grub configuration.

      $ sudo grub2-mkconfig -o /boot/grub2/grub.cfg
  3. Reboot the instance and reconnect to it.

  4. Install the Nvidia GPU drivers, NVIDIA CUDA toolkit, and cuDNN.

    1. Install the EPEL repository for DKMS and enable any optional repos for your Linux distribution.

      • RHEL 7

        $ sudo yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-7.noarch.rpm
      • RHEL 8

        $ sudo yum install -y https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm
    2. Install the CUDA repository public GPG key.

      $ distribution=$(. /etc/os-release;echo $ID`rpm -E "%{?rhel}%{?fedora}"`)
    3. Set up the CUDA network repository and update the repository cache.

      $ ARCH=$( /bin/arch ) \ && sudo yum-config-manager --add-repo http://developer.download.nvidia.com/compute/cuda/repos/$distribution/${ARCH}/cuda-$distribution.repo \ && sudo yum clean expire-cache
    4. Install the NVIDIA, CUDA drivers and cuDNN.

      $ sudo yum clean all \ && sudo yum -y install cuda-drivers-fabricmanager cuda libcudnn8-devel
  5. Reboot the instance and reconnect to it.

  6. (p4d.24xlarge instances only) Start the Nvidia Fabric Manager service, and ensure that it starts automatically when the instance starts. Nvidia Fabric Manager is required for NV Switch Management.

    $ sudo systemctl start nvidia-fabricmanager \ && sudo systemctl enable nvidia-fabricmanager
  7. Ensure that the CUDA paths are set each time that the instance starts.

    • For bash shells, add the following statements to /home/username/.bashrc and /home/username/.bash_profile.

      export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
    • For tcsh shells, add the following statements to /home/username/.cshrc.

      setenv PATH=/usr/local/cuda/bin:$PATH setenv LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
  8. To confirm that the Nvidia GPU drivers are functional, run the following command.

    $ nvidia-smi -q | head

    The command should return information about the Nvidia GPUs, Nvidia GPU drivers, and Nvidia CUDA toolkit.

Ubuntu 18.04/20.04

To install the Nvidia GPU drivers, Nvidia CUDA toolkit, and cuDNN

  1. Install the utilities that are needed to install the Nvidia GPU drivers and the Nvidia CUDA toolkit.

    $ sudo apt-get update && sudo apt-get install build-essential -y
  2. To use the Nvidia GPU driver, you must first disable the nouveau open source drivers.

    1. Install the required utilities and the kernel headers package for the version of the kernel that you are currently running.

      $ sudo apt-get install -y gcc make linux-headers-$(uname -r)
    2. Add nouveau to the /etc/modprobe.d/blacklist.conf deny list file.

      $ cat << EOF | sudo tee --append /etc/modprobe.d/blacklist.conf blacklist vga16fb blacklist nouveau blacklist rivafb blacklist nvidiafb blacklist rivatv EOF
    3. Open /etc/default/grub using your preferred text editor and add the following.

      GRUB_CMDLINE_LINUX="rdblacklist=nouveau"
    4. Rebuild the Grub configuration.

      $ sudo update-grub
  3. Reboot the instance and reconnect to it.

  4. Install the Nvidia GPU drivers, NVIDIA CUDA toolkit, and cuDNN.

    1. Download and install the additional dependencies and add the CUDA repository.

      • Ubuntu 18.04

        $ sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub \ && wget -O /tmp/deeplearning.deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb \ && sudo dpkg -i /tmp/deeplearning.deb \ && wget -O /tmp/cuda.pin https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin \ && sudo mv /tmp/cuda.pin /etc/apt/preferences.d/cuda-repository-pin-600 \ && sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub \ && sudo add-apt-repository 'deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/ /' \ && sudo apt update
      • Ubuntu 20.04

        $ sudo apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/7fa2af80.pub \ && wget -O /tmp/deeplearning.deb http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/nvidia-machine-learning-repo-ubuntu2004_1.0.0-1_amd64.deb \ && sudo dpkg -i /tmp/deeplearning.deb \ && wget -O /tmp/cuda.pin https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin \ && sudo mv /tmp/cuda.pin /etc/apt/preferences.d/cuda-repository-pin-600 \ && sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/3bf863cc.pub \ && sudo add-apt-repository 'deb http://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /' \ && sudo apt update
    2. Install the NVIDIA, CUDA drivers and cuDNN.

      $ sudo apt install -o Dpkg::Options::='--force-overwrite' cuda-drivers cuda-toolkit-11-0 libcudnn8 libcudnn8-dev -y
  5. Reboot the instance and reconnect to it.

  6. (p4d.24xlarge instances only) Install the Nvidia Fabric Manager.

    1. You must install the version of the Nvidia Fabric Manager that matches the version of the Nvidia kernel module that you installed in the previous step.

      Run the following command to determine the version of the Nvidia kernel module.

      $ cat /proc/driver/nvidia/version | grep "Kernel Module"

      The following is example output.

      NVRM version: NVIDIA UNIX x86_64 Kernel Module 450.42.01 Tue Jun 15 21:26:37 UTC 2021

      In the example above, major version 450 of the kernel module was installed. This means that you need to install Nvidia Fabric Manager version 450.

    2. Install the Nvidia Fabric Manager. Run the following command and specify the major version identified in the previous step.

      $ sudo apt install -o Dpkg::Options::='--force-overwrite' nvidia-fabricmanager-major_version_number

      For example, if major version 450 of the kernel module was installed, use the following command to install the matching version of Nvidia Fabric Manager.

      $ sudo apt install -o Dpkg::Options::='--force-overwrite' nvidia-fabricmanager-450
    3. Start the service, and ensure that it starts automatically when the instance starts. Nvidia Fabric Manager is required for NV Switch Management.

      $ sudo systemctl start nvidia-fabricmanager && sudo systemctl enable nvidia-fabricmanager
  7. Ensure that the CUDA paths are set each time that the instance starts.

    • For bash shells, add the following statements to /home/username/.bashrc and /home/username/.bash_profile.

      export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
    • For tcsh shells, add the following statements to /home/username/.cshrc.

      setenv PATH=/usr/local/cuda/bin:$PATH setenv LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
  8. To confirm that the Nvidia GPU drivers are functional, run the following command.

    $ nvidia-smi -q | head

    The command should return information about the Nvidia GPUs, Nvidia GPU drivers, and Nvidia CUDA toolkit.

Step 4: Install the EFA software

Install the EFA-enabled kernel, EFA drivers, Libfabric, and Open MPI stack that is required to support EFA on your temporary instance.

To install the EFA software

  1. Connect to the instance you launched. For more information, see Connect to your Linux instance.

  2. To ensure that all of your software packages are up to date, perform a quick software update on your instance. This process may take a few minutes.

    • Amazon Linux 2, RHEL 7/8, and CentOS 7

      $ sudo yum update -y
    • Ubuntu 18.04/20.04

      $ sudo apt-get update && sudo apt-get upgrade -y
  3. Download the EFA software installation files. The software installation files are packaged into a compressed tarball (.tar.gz) file. To download the latest stable version, use the following command.

    $ curl -O https://efa-installer.amazonaws.com/aws-efa-installer-1.18.0.tar.gz

    You can also get the latest version by replacing the version number with latest in the preceding command.

  4. (Optional) Verify the authenticity and integrity of the EFA tarball (.tar.gz) file. We recommend that you do this to verify the identity of the software publisher and to check that the file has not been altered or corrupted since it was published. If you do not want to verify the tarball file, skip this step.

    Note

    Alternatively, if you prefer to verify the tarball file by using an MD5 or SHA256 checksum instead, see Verify the EFA installer using a checksum.

    1. Download the public GPG key and import it into your keyring.

      $ wget https://efa-installer.amazonaws.com/aws-efa-installer.key && gpg --import aws-efa-installer.key

      The command should return a key value. Make a note of the key value, because you need it in the next step.

    2. Verify the GPG key's fingerprint. Run the following command and specify the key value from the previous step.

      $ gpg --fingerprint key_value

      The command should return a fingerprint that is identical to 4E90 91BC BB97 A96B 26B1 5E59 A054 80B1 DD2D 3CCC. If the fingerprint does not match, don't run the EFA installation script, and contact AWS Support.

    3. Download the signature file and verify the signature of the EFA tarball file.

      $ wget https://efa-installer.amazonaws.com/aws-efa-installer-1.18.0.tar.gz.sig && gpg --verify ./aws-efa-installer-1.18.0.tar.gz.sig

      The following shows example output.

      gpg: Signature made Wed 29 Jul 2020 12:50:13 AM UTC using RSA key ID DD2D3CCC gpg: Good signature from "Amazon EC2 EFA <ec2-efa-maintainers@amazon.com>" gpg: WARNING: This key is not certified with a trusted signature! gpg: There is no indication that the signature belongs to the owner. Primary key fingerprint: 4E90 91BC BB97 A96B 26B1 5E59 A054 80B1 DD2D 3CCC

      If the result includes Good signature, and the fingerprint matches the fingerprint returned in the previous step, proceed to the next step. If not, don't run the EFA installation script, and contact AWS Support.

  5. Extract the files from the compressed .tar.gz file and navigate into the extracted directory.

    $ tar -xf aws-efa-installer-1.18.0.tar.gz && cd aws-efa-installer
  6. Run the EFA software installation script.

    $ sudo ./efa_installer.sh -y

    Libfabric is installed in the /opt/amazon/efa directory, while Open MPI is installed in the /opt/amazon/openmpi directory.

  7. If the EFA installer prompts you to reboot the instance, do so and then reconnect to the instance. Otherwise, log out of the instance and then log back in to complete the installation.

  8. Confirm that the EFA software components were successfully installed.

    $ fi_info -p efa -t FI_EP_RDM

    The command should return information about the Libfabric EFA interfaces. The following example shows the command output.

    • p3dn.24xlarge with single network interface

      provider: efa fabric: EFA-fe80::94:3dff:fe89:1b70 domain: efa_0-rdm version: 2.0 type: FI_EP_RDM protocol: FI_PROTO_EFA
    • p4d.24xlarge with multiple network interfaces

      provider: efa fabric: EFA-fe80::c6e:8fff:fef6:e7ff domain: efa_0-rdm version: 111.0 type: FI_EP_RDM protocol: FI_PROTO_EFA provider: efa fabric: EFA-fe80::c34:3eff:feb2:3c35 domain: efa_1-rdm version: 111.0 type: FI_EP_RDM protocol: FI_PROTO_EFA provider: efa fabric: EFA-fe80::c0f:7bff:fe68:a775 domain: efa_2-rdm version: 111.0 type: FI_EP_RDM protocol: FI_PROTO_EFA provider: efa fabric: EFA-fe80::ca7:b0ff:fea6:5e99 domain: efa_3-rdm version: 111.0 type: FI_EP_RDM protocol: FI_PROTO_EFA

Step 5: Install NCCL

Install NCCL. For more information about NCCL, see the NCCL repository.

To install NCCL

  1. Navigate to the /opt directory.

    $ cd /opt
  2. Clone the official NCCL repository to the instance and navigate into the local cloned repository.

    $ sudo git clone https://github.com/NVIDIA/nccl.git && cd nccl
  3. Build and install NCCL and specify the CUDA installation directory.

    $ sudo make -j src.build CUDA_HOME=/usr/local/cuda

Step 6: Install the aws-ofi-nccl plugin

The aws-ofi-nccl plugin maps NCCL's connection-oriented transport APIs to Libfabric's connection-less reliable interface. This enables you to use Libfabric as a network provider while running NCCL-based applications. For more information about the aws-ofi-nccl plugin, see the aws-ofi-nccl repository.

To install the aws-ofi-nccl plugin

  1. Navigate to your home directory.

    $ cd $HOME
  2. (Ubuntu only) Install the utilities that are required to install the aws-ofi-nccl plugin. To install the required utilities, run the following command.

    $ sudo apt-get install libtool autoconf -y
  3. Clone the aws branch of the official AWS aws-ofi-nccl repository to the instance and navigate into the local cloned repository.

    $ git clone https://github.com/aws/aws-ofi-nccl.git -b aws && cd aws-ofi-nccl
  4. To generate the configure script, run the autogen.sh script.

    $ ./autogen.sh
  5. To generate the make files, run the configure script and specify the MPI, Libfabric, NCCL, and CUDA installation directories.

    $ ./configure --prefix=/opt/aws-ofi-nccl --with-mpi=/opt/amazon/openmpi \ --with-libfabric=/opt/amazon/efa --with-nccl=/opt/nccl/build \ --with-cuda=/usr/local/cuda
  6. Add the Open MPI directory to the PATH variable.

    $ export PATH=/opt/amazon/openmpi/bin/:$PATH
  7. Install the aws-ofi-nccl plugin.

    $ make && sudo make install

Step 7: Install the NCCL tests

Install the NCCL tests. The NCCL tests enable you to confirm that NCCL is properly installed and that it is operating as expected. For more information about the NCCL tests, see the nccl-tests repository.

To install the NCCL tests

  1. Navigate to your home directory.

    $ cd $HOME
  2. Clone the official nccl-tests repository to the instance and navigate into the local cloned repository.

    $ git clone https://github.com/NVIDIA/nccl-tests.git && cd nccl-tests
  3. Add the Libfabric directory to the LD_LIBRARY_PATH variable.

    • Amazon Linux, Amazon Linux 2, RHEL , and CentOS

      $ export LD_LIBRARY_PATH=/opt/amazon/efa/lib64:$LD_LIBRARY_PATH
    • Ubuntu 18.04/20.04

      $ export LD_LIBRARY_PATH=/opt/amazon/efa/lib:$LD_LIBRARY_PATH
  4. Install the NCCL tests and specify the MPI, NCCL, and CUDA installation directories.

    $ make MPI=1 MPI_HOME=/opt/amazon/openmpi NCCL_HOME=/opt/nccl/build CUDA_HOME=/usr/local/cuda

Step 8: Test your EFA and NCCL configuration

Run a test to ensure that your temporary instance is properly configured for EFA and NCCL.

To test your EFA and NCCL configuration

  1. Create a host file that specifies the hosts on which to run the tests. The following command creates a host file named my-hosts that includes a reference to the instance itself.

    IMDSv2
    [ec2-user ~]$ TOKEN=`curl -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600"` \ && curl -H "X-aws-ec2-metadata-token: $TOKEN" –v http://169.254.169.254/latest/meta-data/local-ipv4 >> my-hosts
    IMDSv1
    [ec2-user ~]$ curl http://169.254.169.254/latest/meta-data/local-ipv4 >> my-hosts
  2. Run the test and specify the host file (--hostfile) and the number of GPUs to use (-n). The following command runs the all_reduce_perf test on 8 GPUs on the instance itself, and specifies the following environment variables.

    • FI_PROVIDER="efa"—specifies the fabric interface provider. This must be set to "efa".

    • FI_EFA_USE_DEVICE_RDMA=1—uses the device's RDMA functionality for one-sided and two-sided transfer.

    • NCCL_DEBUG=INFO—enables detailed debugging output. You can also specify VERSION to print only the NCCL version at the start of the test, or WARN to receive only error messages.

    • NCCL_ALGO=ring—enables ring algorithm for collective operations.

    • NCCL_PROTO=simple—instructs NCCL to use a simple protocol for communication. Currently, the EFA provider does not support LL protocols. Enabling them could lead to data corruption.

    For more information about the NCCL test arguments, see the NCCL Tests README in the official nccl-tests repository.

    $ /opt/amazon/openmpi/bin/mpirun \ -x FI_PROVIDER="efa" \ -x FI_EFA_USE_DEVICE_RDMA=1 \ -x LD_LIBRARY_PATH=/opt/nccl/build/lib:/usr/local/cuda/lib64:/opt/amazon/efa/lib:/opt/amazon/openmpi/lib:/opt/aws-ofi-nccl/lib:$LD_LIBRARY_PATH \ -x NCCL_DEBUG=INFO \ -x NCCL_ALGO=ring \ -x NCCL_PROTO=simple \ --hostfile my-hosts -n 8 -N 8 \ --mca pml ^cm --mca btl tcp,self --mca btl_tcp_if_exclude lo,docker0 --bind-to none \ $HOME/nccl-tests/build/all_reduce_perf -b 8 -e 1G -f 2 -g 1 -c 1 -n 100
  3. You can confirm that EFA is active as the underlying provider for NCCL when the NCCL_DEBUG log is printed.

    ip-192-168-2-54:14:14 [0] NCCL INFO NET/OFI Selected Provider is efa*

    The following additional information is displayed when using a p4d.24xlarge instance.

    ip-192-168-2-54:14:14 [0] NCCL INFO NET/OFI Running on P4d platform, Setting NCCL_TOPO_FILE environment variable to /home/ec2-user/install/plugin/share/aws-ofi-nccl/xml/p4d-24xl-topo.xml

Step 9: Install your machine learning applications

Install the machine learning applications on the temporary instance. The installation procedure varies depending on the specific machine learning application. For more information about installing software on your Linux instance, see Managing Software on Your Linux Instance.

Note

You might need to refer to your machine learning application’s documentation for installation instructions.

Step 10: Create an EFA and NCCL-enabled AMI

After you have installed the required software components, you create an AMI that you can reuse to launch your EFA-enabled instances.

To create an AMI from your temporary instance

  1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.

  2. In the navigation pane, choose Instances.

  3. Select the temporary instance that you created and choose Actions, Image, Create image.

  4. For Create image, do the following:

    1. For Image name, enter a descriptive name for the AMI.

    2. (Optional) For Image description, enter a brief description of the purpose of the AMI.

    3. Choose Create image.

  5. In the navigation pane, choose AMIs.

  6. Locate the AMI tht you created in the list. Wait for the status to change from pending to available before continuing to the next step.

Step 11: Terminate the temporary instance

At this point, you no longer need the temporary instance that you launched. You can terminate the instance to stop incurring charges for it.

To terminate the temporary instance

  1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.

  2. In the navigation pane, choose Instances.

  3. Select the temporary instance that you created and then choose Actions, Instance state, Terminate instance.

  4. When prompted for confirmation, choose Terminate.

Step 12: Launch EFA and NCCL-enabled instances into a cluster placement group

Launch your EFA and NCCL-enabled instances into a cluster placement group using the EFA-enabled AMI and the EFA-enabled security group that you created earlier.

Note
  • It is not an absolute requirement to launch your EFA-enabled instances into a cluster placementgroup. However, we do recommend running your EFA-enabled instances in a cluster placement group as it launches the instances into a low-latency group in a single Availability Zone.

  • To ensure that capacity is available as you scale your cluster’s instances, you can create a Capacity Reservation for your cluster placement group. For more information, see Capacity Reservations in cluster placement groups.

New console

To launch a temporary instance

  1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.

  2. In the navigation panel, choose Instances, and then choose Launch Instances to open the new launch instance wizard.

  3. (Optional) In the Name and tags section, provide a name for the instance, such as EFA-instance. The name is assigned to the instance as a resource tag (Name=EFA-instance).

  4. In the Application and OS Images section, choose My AMIs, and then select the AMI that you created in the previous step.

  5. In the Instance type section, select either p3dn.24xlarge or p4d.24xlarge.

  6. In the Key pair section, select the key pair to use for the instance.

  7. In the Network settings section, choose Edit, and then do the following:

    1. For Subnet, choose the subnet in which to launch the instance. If you do not select a subnet, you can't enable the instance for EFA.

    2. For Firewall (security groups), choose Select existing security group, and then select the security group that you created in the previous step.

    3. Expand the Advanced network configuration section, and for Elastic Fabric Adapter, select Enable.

  8. (Optional) In the Storage section, configure the volumes as needed.

  9. In the Advanced details section, for Placement group name, select the cluster placement group into which to launch the instance. If you need to create a new cluster placement group, choose Create new placement group.

  10. In the Summary panel on the right, for Number of instances, enter the number of EFA-enabled instances that you want to launch, and then choose Launch instance.

Old console

To launch your EFA and NCCL-enabled instances into a cluster placement group

  1. Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.

  2. Choose Launch Instance.

  3. On the Choose an AMI page, choose My AMIs, find the AMI that you created earlier, and then choose Select.

  4. On the Choose an Instance Type page, select p3dn.24xlarge and then choose Next: Configure Instance Details.

  5. On the Configure Instance Details page, do the following:

    1. For Number of instances, enter the number of EFA and NCCL-enabled instances that you want to launch.

    2. For Network and Subnet, select the VPC and subnet into which to launch the instances.

    3. For Placement group, select Add instance to placement group.

    4. For Placement group name, select Add to a new placement group, and then enter a descriptive name for the placement group. Then for Placement group strategy, select cluster.

    5. For EFA, choose Enable.

    6. In the Network Interfaces section, for device eth0, choose New network interface. You can optionally specify a primary IPv4 address and one or more secondary IPv4 addresses. If you are launching the instance into a subnet that has an associated IPv6 CIDR block, you can optionally specify a primary IPv6 address and one or more secondary IPv6 addresses.

    7. Choose Next: Add Storage.

  6. On the Add Storage page, specify the volumes to attach to the instances in addition to the volumes specified by the AMI (such as the root device volume). Then choose Next: Add Tags.

  7. On the Add Tags page, specify tags for the instances, such as a user-friendly name, and then choose Next: Configure Security Group.

  8. On the Configure Security Group page, for Assign a security group, select Select an existing security group, and then select the security group that you created earlier.

  9. Choose Review and Launch.

  10. On the Review Instance Launch page, review the settings, and then choose Launch to choose a key pair and to launch your instances.

Step 13: Enable passwordless SSH

To enable your applications to run across all of the instances in your cluster, you must enable passwordless SSH access from the leader node to the member nodes. The leader node is the instance from which you run your applications. The remaining instances in the cluster are the member nodes.

To enable passwordless SSH between the instances in the cluster

  1. Select one instance in the cluster as the leader node, and connect to it.

  2. Disable strictHostKeyChecking and enable ForwardAgent on the leader node. Open ~/.ssh/config using your preferred text editor and add the following.

    Host * ForwardAgent yes Host * StrictHostKeyChecking no
  3. Generate an RSA key pair.

    $ ssh-keygen -t rsa -N "" -f ~/.ssh/id_rsa

    The key pair is created in the $HOME/.ssh/ directory.

  4. Change the permissions of the private key on the leader node.

    $ chmod 600 ~/.ssh/id_rsa chmod 600 ~/.ssh/config
  5. Open ~/.ssh/id_rsa.pub using your preferred text editor and copy the key.

  6. For each member node in the cluster, do the following:

    1. Connect to the instance.

    2. Open ~/.ssh/authorized_keys using your preferred text editor and add the public key that you copied earlier.

  7. To test that the passwordless SSH is functioning as expected, connect to your leader node and run the following command.

    $ ssh member_node_private_ip

    You should connect to the member node without being prompted for a key or password.