How to assign a device for each worker correctly in a multi gpu multi node scenario

In a multi-GPU multi-node scenario, it is important to assign a device for each worker correctly to ensure efficient utilization of resources. There are several ways to achieve this in Julia.

Option 1: Using Distributed.jl

The Distributed.jl package provides a convenient way to distribute computations across multiple workers. To assign a device for each worker, you can use the `addprocs` function with the `device` argument. Here’s an example:

using Distributed

# Add workers with specific devices
addprocs(2, device=[:gpu, :gpu])

# Get the list of available workers
workers = workers()

# Assign a device for each worker
for (i, worker) in enumerate(workers)
    device = get_worker_attr(worker, :device)
    @show device
    # Assign the device to the worker
    @spawnat worker begin
        using CUDA
        CUDA.device!(device)
    end
end

This code snippet adds two workers with GPU devices and assigns a device for each worker using the `CUDA.device!` function from the CUDA.jl package. The `@spawnat` macro is used to execute the code on the specified worker.

Option 2: Using MPI.jl

If you are working in a distributed computing environment that supports the Message Passing Interface (MPI), you can use the MPI.jl package to assign a device for each worker. Here’s an example:

using MPI

# Initialize MPI
MPI.Init()

# Get the number of processes and the rank of the current process
nprocs = MPI.Comm_size(MPI.COMM_WORLD)
rank = MPI.Comm_rank(MPI.COMM_WORLD)

# Assign a device for each worker
device = rank % 2 == 0 ? :gpu1 : :gpu2

# Assign the device to the worker
using CUDA
CUDA.device!(device)

# Finalize MPI
MPI.Finalize()

This code snippet initializes MPI, gets the number of processes and the rank of the current process, and assigns a device based on the rank. The `CUDA.device!` function is used to assign the device to the worker.

Option 3: Using ClusterManagers.jl

If you are using a cluster manager to manage your distributed computations, you can use the ClusterManagers.jl package to assign a device for each worker. Here’s an example using the Slurm cluster manager:

using ClusterManagers

# Set up the cluster manager
addprocs(SlurmManager(2, partition="gpu", devices=[:gpu, :gpu]))

# Get the list of available workers
workers = workers()

# Assign a device for each worker
for (i, worker) in enumerate(workers)
    device = get_worker_attr(worker, :device)
    @show device
    # Assign the device to the worker
    @spawnat worker begin
        using CUDA
        CUDA.device!(device)
    end
end

This code snippet sets up the Slurm cluster manager with two workers and assigns a device for each worker using the `CUDA.device!` function. The `@spawnat` macro is used to execute the code on the specified worker.

Among the three options, the best choice depends on your specific requirements and the environment you are working in. If you are already using a specific distributed computing framework or cluster manager, it may be more convenient to use the corresponding package. Otherwise, Distributed.jl provides a flexible and easy-to-use solution for assigning devices to workers in a multi-GPU multi-node scenario.