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Running FlexFlow on Multiple Nodes

To build, install, and run FlexFlow on multiple nodes, follow the instructions below. We will use AWS as an example to present the instructions.

1. Spin up instances

Spin up multiple instances with GPU support. For AWS, we recommend using p3.2xlarge with Deep Learning AMI GPU PyTorch 1.13.1 (Ubuntu 20.04) to simplify the procedure.

Place the instances in a placement group that utilizes the cluster strategy to achieve low-latency network performance.

To enable communication between instances, attach the same security group to all instances and add an inbound rule in the security group to allow all incoming traffic from the same security group. An example inbound rule is as follows:

Type: Custom TCP
Port range: 1 - 65535
Source: Custom (use the security group ID)

You can also use your own GPU cluster, as long as all machines are interconnected with a low-latency network.

2. Configure and build UCX

Find the latest source code release for UCX at https://github.com/openucx/ucx/releases. As of writing this documentation, the latest UCX was 1.15.0 at https://github.com/openucx/ucx/releases/download/v1.15.0/ucx-1.15.0.tar.gz. Extract it and switch to the directory with UCX source code, and run:

CUDA_PATH=/usr/local/cuda
PREFIX=$PWD/install
./contrib/configure-release-mt --prefix="$PREFIX" --without-go --enable-mt --with-cuda="$CUDA_PATH"
make -j install
echo "$PREFIX"

Replace {{ CUDA_PATH }} with the path of your CUDA installation. If you don't know the path, try which nvcc. Take note of the path of UCX installation, echoed as part of the last command.

3. Configure and build FlexFlow

Follow steps 1 to 5 in INSTALL.md to download the source code, install system dependencies, install the Python dependencies, configure the FlexFlow build, and build FlexFlow on each instance at the same path. Or you can use NFS to mount home directory of each instance so that only a single build is necessary.

You can skip step 2 (Install system dependencies) if you have spun up instances with Deep Learning AMI, which comes preconfigured with CUDA. Otherwise, you need to install system dependencies on each instance.

For step 4 (Configuring the FlexFlow build), here are the parameters that need to be configured:

Other configuration options are optional.

4. Configure MPI

MPI is an easy way to launch FlexFlow across all instances simultaneously and set up communication between them.

To use MPI, enable non-interactive ssh logins between instances. This can be done by referring to the Open MPI documentation. Here are the detailed steps:

  1. Choose one of the nodes as the main instance and create a public/private key pair on the instance. This will be the instance from which you launch MPI commands. Run the following command:
ssh-keygen -t ed25519

This will create a public key at ~/.ssh/id_ed25519.pub and a private key at ~/.ssh/id_ed25519.

  1. Append the contents of the public key to ~/.ssh/authorized_keys on all machines (if the file does not exist, create one). Execute the following command on all instances:
mkdir -p ~/.ssh
echo '<public key>' >> ~/.ssh/authorized_keys

Replace <public key> with the public key from ~/.ssh/id_ed25519.pub on the main instance. It should be a single line containing a string like:

ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIOy5NKYdE8Cwgid59rx6xMqyj9vLaWuXIwy/BSRiK4su instance
  1. Create a hostfile at ~/hostfile, with one line for each instance (add more lines if you have more instances):
<host1_private_ip> slots=<slot1>
<host2_private_ip> slots=<slot2>

<slot1> and <slot2> refer to the number of slots available for each instance, respectively. Set it to one if you have a GPU on each instance.

  1. SSH into each host and make sure you can log into them. It may ask you to verify the public key. Make sure to trust the public key so that it doesn't ask you again.

  2. Test MPI by running mpirun -N 1 --hostfile ~/hostfile hostname. It should display the hostname of all your nodes. If you encounter any errors like WARNING: Open MPI accepted a TCP connection from what appears to be another Open MPI process but cannot find a corresponding process entry for that peer., add the parameter --mca btl_tcp_if_include in the mpirun command (refer to this Stack Overflow question).

5. Test FlexFlow

Follow step 6 in the Build from source guide to set the environment variables.

A script to run a Python example on multiple nodes is available at scripts/mnist_mlp_run.sh. Run the script to test FlexFlow on mnist mlp training. You can adjust the script to run any other program. Make sure to change the FLEXFLOW_DIR and UCX_DIR variables in it to appropriate paths.