The following section describes how to use EFA to run multi-node applications on the AWS Deep Learning AMIs.
Running Multi-Node Applications with EFA
To run an application across a cluster of nodes the following configuration is required
Enable Passwordless SSH
Select one node in your cluster as the leader node. The remaining nodes are referred to as the member nodes.
On the leader node, generate the RSA keypair.
ssh-keygen -t rsa -N "" -f ~/.ssh/id_rsaChange the permissions of the private key on the leader node.
chmod 600 ~/.ssh/id_rsaCopy the public key
~/.ssh/id_rsa.pubto and append it to~/.ssh/authorized_keysof the member nodes in the cluster.You should now be able to directly login to the member nodes from the leader node using the private ip.
ssh <member private ip>Disable strictHostKeyChecking and enable agent forwarding on the leader node by adding the following to the ~/.ssh/config file on the leader node:
Host * ForwardAgent yes Host * StrictHostKeyChecking noOn Amazon Linux 2 instances, run the following command on the leader node to provide correct permissions to the config file:
chmod 600 ~/.ssh/config
Create Hosts File
On the leader node, create a hosts file to identify the nodes in the cluster. The hosts file must have an entry for each node in the cluster. Create a file ~/hosts and add each node using the private ip as follows:
localhost slots=8
<private ip of node 1> slots=8
<private ip of node 2> slots=8NCCL Tests
Note
These tests have been run using EFA version 1.38.0 and OFI NCCL Plugin 1.13.2.
Listed below are a subset of NCCL Tests provided by Nvidia to test both functionality and performance over multiple compute nodes
Supported Instances: P3dn, P4, P5, P5e, P5en
Multi-node NCCL Performance Test on P4d.24xlarge
To check NCCL Performance with EFA, run the standard NCCL Performance test that is
available on the official NCCL-Tests Repo
When constructing your own script, refer to the following guidance:
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Use the complete path to mpirun as shown in the example while running NCCL applications with EFA.
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Change the params np and N based on the number of instances and GPUs in your cluster.
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Add the NCCL_DEBUG=INFO flag and make sure that the logs indicate EFA usage as "Selected Provider is EFA".
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Set the Training Log Location to parse for validation
TRAINING_LOG="testEFA_$(date +"%N").log"
Use the command watch nvidia-smi on any of the member nodes to monitor
GPU usage. The following watch nvidia-smi commands are for a generic CUDA xx.x version and
depend on the Operating System of your instance.
You can run the commands for any available CUDA version in your Amazon EC2 instance by replacing the CUDA version in the script.
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Amazon Linux 2, Amazon Linux 2023:
$ /opt/amazon/openmpi/bin/mpirun -n 16 -N 8 \ -x NCCL_DEBUG=INFO --mca pml ^cm \ -x LD_LIBRARY_PATH=/usr/local/cuda-xx.x/efa/lib:/usr/local/cuda-xx.x/lib:/usr/local/cuda-xx.x/lib64:/usr/local/cuda-xx.x:/opt/amazon/efa/lib64:/opt/amazon/openmpi/lib64:$LD_LIBRARY_PATH \ --hostfile hosts --mca btl tcp,self --mca btl_tcp_if_exclude lo,docker0 --bind-to none \ /usr/local/cuda-xx.x/efa/test-cuda-xx.x/all_reduce_perf -b 8 -e 1G -f 2 -g 1 -c 1 -n 100 | tee ${TRAINING_LOG} -
Ubuntu 20.04, Ubuntu 20.04:
$ /opt/amazon/openmpi/bin/mpirun -n 16 -N 8 \ -x NCCL_DEBUG=INFO --mca pml ^cm \ -x LD_LIBRARY_PATH=/usr/local/cuda-xx.x/efa/lib:/usr/local/cuda-xx.x/lib:/usr/local/cuda-xx.x/lib64:/usr/local/cuda-xx.x:/opt/amazon/efa/lib:/opt/amazon/openmpi/lib:$LD_LIBRARY_PATH \ --hostfile hosts --mca btl tcp,self --mca btl_tcp_if_exclude lo,docker0 --bind-to none \ /usr/local/cuda-xx.x/efa/test-cuda-xx.x/all_reduce_perf -b 8 -e 1G -f 2 -g 1 -c 1 -n 100 | tee ${TRAINING_LOG}
Your output should look like the following:
# nThread 1 nGpus 1 minBytes 8 maxBytes 1073741824 step: 2(factor) warmup iters: 5 iters: 100 agg iters: 1 validation: 1 graph: 0
#
# Using devices
# Rank 0 Group 0 Pid 33378 on ip-172-31-42-25 device 0 [0x10] NVIDIA A100-SXM4-40GB
# Rank 1 Group 0 Pid 33379 on ip-172-31-42-25 device 1 [0x10] NVIDIA A100-SXM4-40GB
# Rank 2 Group 0 Pid 33380 on ip-172-31-42-25 device 2 [0x20] NVIDIA A100-SXM4-40GB
# Rank 3 Group 0 Pid 33381 on ip-172-31-42-25 device 3 [0x20] NVIDIA A100-SXM4-40GB
# Rank 4 Group 0 Pid 33382 on ip-172-31-42-25 device 4 [0x90] NVIDIA A100-SXM4-40GB
# Rank 5 Group 0 Pid 33383 on ip-172-31-42-25 device 5 [0x90] NVIDIA A100-SXM4-40GB
# Rank 6 Group 0 Pid 33384 on ip-172-31-42-25 device 6 [0xa0] NVIDIA A100-SXM4-40GB
# Rank 7 Group 0 Pid 33385 on ip-172-31-42-25 device 7 [0xa0] NVIDIA A100-SXM4-40GB
# Rank 8 Group 0 Pid 30378 on ip-172-31-43-8 device 0 [0x10] NVIDIA A100-SXM4-40GB
# Rank 9 Group 0 Pid 30379 on ip-172-31-43-8 device 1 [0x10] NVIDIA A100-SXM4-40GB
# Rank 10 Group 0 Pid 30380 on ip-172-31-43-8 device 2 [0x20] NVIDIA A100-SXM4-40GB
# Rank 11 Group 0 Pid 30381 on ip-172-31-43-8 device 3 [0x20] NVIDIA A100-SXM4-40GB
# Rank 12 Group 0 Pid 30382 on ip-172-31-43-8 device 4 [0x90] NVIDIA A100-SXM4-40GB
# Rank 13 Group 0 Pid 30383 on ip-172-31-43-8 device 5 [0x90] NVIDIA A100-SXM4-40GB
# Rank 14 Group 0 Pid 30384 on ip-172-31-43-8 device 6 [0xa0] NVIDIA A100-SXM4-40GB
# Rank 15 Group 0 Pid 30385 on ip-172-31-43-8 device 7 [0xa0] NVIDIA A100-SXM4-40GB
ip-172-31-42-25:33385:33385 [7] NCCL INFO cudaDriverVersion 12060
ip-172-31-43-8:30383:30383 [5] NCCL INFO Bootstrap : Using ens32:172.31.43.8
ip-172-31-43-8:30383:30383 [5] NCCL INFO NCCL version 2.23.4+cuda12.5
...
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Initializing aws-ofi-nccl 1.13.2-aws
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Using Libfabric version 1.22
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Using CUDA driver version 12060 with runtime 12050
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Configuring AWS-specific options
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Setting provider_filter to efa
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Setting FI_EFA_FORK_SAFE environment variable to 1
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Setting NCCL_NVLSTREE_MAX_CHUNKSIZE to 512KiB
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Setting NCCL_NVLS_CHUNKSIZE to 512KiB
ip-172-31-42-25:33384:33451 [6] NCCL INFO NET/OFI Running on p4d.24xlarge platform, Setting NCCL_TOPO_FILE environment variable to /opt/amazon/ofi-nccl/share/aws-ofi-nccl/xml/p4d-24xl-topo.xml
...
-----------------------------some output truncated-----------------------------------
# out-of-place in-place
# size count type redop root time algbw busbw #wrong time algbw busbw #wrong
# (B) (elements) (us) (GB/s) (GB/s) (us) (GB/s) (GB/s)
8 2 float sum -1 180.3 0.00 0.00 0 179.3 0.00 0.00 0
16 4 float sum -1 178.1 0.00 0.00 0 177.6 0.00 0.00 0
32 8 float sum -1 178.5 0.00 0.00 0 177.9 0.00 0.00 0
64 16 float sum -1 178.8 0.00 0.00 0 178.7 0.00 0.00 0
128 32 float sum -1 178.2 0.00 0.00 0 177.8 0.00 0.00 0
256 64 float sum -1 178.6 0.00 0.00 0 178.8 0.00 0.00 0
512 128 float sum -1 177.2 0.00 0.01 0 177.1 0.00 0.01 0
1024 256 float sum -1 179.2 0.01 0.01 0 179.3 0.01 0.01 0
2048 512 float sum -1 181.3 0.01 0.02 0 181.2 0.01 0.02 0
4096 1024 float sum -1 184.2 0.02 0.04 0 183.9 0.02 0.04 0
8192 2048 float sum -1 191.2 0.04 0.08 0 190.6 0.04 0.08 0
16384 4096 float sum -1 202.5 0.08 0.15 0 202.3 0.08 0.15 0
32768 8192 float sum -1 233.0 0.14 0.26 0 232.1 0.14 0.26 0
65536 16384 float sum -1 238.6 0.27 0.51 0 235.1 0.28 0.52 0
131072 32768 float sum -1 237.2 0.55 1.04 0 236.8 0.55 1.04 0
262144 65536 float sum -1 248.3 1.06 1.98 0 247.0 1.06 1.99 0
524288 131072 float sum -1 309.2 1.70 3.18 0 307.7 1.70 3.20 0
1048576 262144 float sum -1 408.7 2.57 4.81 0 404.3 2.59 4.86 0
2097152 524288 float sum -1 613.5 3.42 6.41 0 607.9 3.45 6.47 0
4194304 1048576 float sum -1 924.5 4.54 8.51 0 914.8 4.58 8.60 0
8388608 2097152 float sum -1 1059.5 7.92 14.85 0 1054.3 7.96 14.92 0
16777216 4194304 float sum -1 1269.9 13.21 24.77 0 1272.0 13.19 24.73 0
33554432 8388608 float sum -1 1642.7 20.43 38.30 0 1636.7 20.50 38.44 0
67108864 16777216 float sum -1 2446.7 27.43 51.43 0 2445.8 27.44 51.45 0
134217728 33554432 float sum -1 4143.6 32.39 60.73 0 4142.4 32.40 60.75 0
268435456 67108864 float sum -1 7351.9 36.51 68.46 0 7346.7 36.54 68.51 0
536870912 134217728 float sum -1 13717 39.14 73.39 0 13703 39.18 73.46 0
1073741824 268435456 float sum -1 26416 40.65 76.21 0 26420 40.64 76.20 0
...
# Out of bounds values : 0 OK
# Avg bus bandwidth : 15.5514 To Validate that the EFA tests returned a valid result, please use the following tests to confirm:
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Get the instance type using EC2 Instance Metadata:
TOKEN=$(curl -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 21600") INSTANCE_TYPE=$(curl -H "X-aws-ec2-metadata-token: $TOKEN" -v http://169.254.169.254/latest/meta-data/instance-type) -
Run the Performance Tests
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Set the Following Parameters
CUDA_VERSION CUDA_RUNTIME_VERSION NCCL_VERSION -
Validate the Results as shown:
RETURN_VAL=`echo $?` if [ ${RETURN_VAL} -eq 0 ]; then # [0] NCCL INFO NET/OFI Initializing aws-ofi-nccl 1.13.2-aws # [0] NCCL INFO NET/OFI Using CUDA driver version 12060 with runtime 12010 # cudaDriverVersion 12060 --> This is max supported cuda version by nvidia driver # NCCL version 2.23.4+cuda12.5 --> This is NCCL version compiled with cuda version # Validation of logs grep "NET/OFI Configuring AWS-specific options" ${TRAINING_LOG} || { echo "AWS-specific options text not found"; exit 1; } grep "busbw" ${TRAINING_LOG} || { echo "busbw text not found"; exit 1; } grep "Avg bus bandwidth " ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } grep "NCCL version $NCCL_VERSION" ${TRAINING_LOG} || { echo "Text not found: NCCL version $NCCL_VERSION"; exit 1; } if [[ ${INSTANCE_TYPE} == "p4d.24xlarge" ]]; then grep "NET/Libfabric/0/GDRDMA" ${TRAINING_LOG} || { echo "Text not found: NET/Libfabric/0/GDRDMA"; exit 1; } grep "NET/OFI Selected Provider is efa (found 4 nics)" ${TRAINING_LOG} || { echo "Selected Provider is efa text not found"; exit 1; } elif [[ ${INSTANCE_TYPE} == "p4de.24xlarge" ]]; then grep "NET/Libfabric/0/GDRDMA" ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } grep "NET/OFI Selected Provider is efa (found 4 nics)" ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } elif [[ ${INSTANCE_TYPE} == "p5.48xlarge" ]]; then grep "NET/Libfabric/0/GDRDMA" ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } grep "NET/OFI Selected Provider is efa (found 32 nics)" ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } elif [[ ${INSTANCE_TYPE} == "p5e.48xlarge" ]]; then grep "NET/Libfabric/0/GDRDMA" ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } grep "NET/OFI Selected Provider is efa (found 32 nics)" ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } elif [[ ${INSTANCE_TYPE} == "p5en.48xlarge" ]]; then grep "NET/Libfabric/0/GDRDMA" ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } grep "NET/OFI Selected Provider is efa (found 16 nics)" ${TRAINING_LOG} || { echo "Avg bus bandwidth text not found"; exit 1; } elif [[ ${INSTANCE_TYPE} == "p3dn.24xlarge" ]]; then grep "NET/OFI Selected Provider is efa (found 4 nics)" ${TRAINING_LOG} || { echo "Selected Provider is efa text not found"; exit 1; } fi echo "***************************** check_efa_nccl_all_reduce passed for cuda version ${CUDA_VERSION} *****************************" else echo "***************************** check_efa_nccl_all_reduce failed for cuda version ${CUDA_VERSION} *****************************" fi -
To access the benchmark data, we can parse the final row of table output from the Multi Node all_reduce test:
benchmark=$(sudo cat ${TRAINING_LOG} | grep '1073741824' | tail -n1 | awk -F " " '{{print $12}}' | sed 's/ //' | sed 's/ 5e-07//') if [[ -z "${benchmark}" ]]; then echo "benchmark variable is empty" exit 1 fi echo "Benchmark throughput: ${benchmark}"
