Multi-node/Multi-gpus Data analytics with RAPIDS

Dask-RAPIDS is a powerful integration between Dask and RAPIDS that enables scalable, GPU-accelerated data processing across multiple GPUs and nodes.

On one hand, RAPIDS provides GPU-accelerated versions of pandas, NumPy, and scikit-learn (e.g., cuDF, cuML, cuGraph). On the other hand, Dask provides a distributed computing framework that allows you to run these RAPIDS components in parallel across many GPUs.

Together, Dask-RAPIDS enables:

Processing terabyte-scale data on multi-GPU or multi-node clusters
Familiar pandas/scikit-learn-style APIs, backed by GPU speed
Real-time performance monitoring via Dask Dashboard

The tight integration with CuPy, dask_cudf, dask_cuda, and UCX (for fast GPU communication) isi ideal for interactive data science, big data processing, and building GPU-accelerated ML pipelines.

Starting a multi-node/multi-gpus Dask-RAPIDS cluster

submit-rapids-job.shdask-rapids.py

The following launcher script requests the allocation of 2 GPU nodes for a total of 8 GPUs
Once resources are allocated, we setup the different UCX transports to enable fast communication between all GPUs
We start the dask scheduler on the first allocated
We then spawn as many dask cuda workers as GPUs

#!/bin/bash -l
#SBATCH --job-name=Dask-Rapids-Cluster
#SBATCH --account=<ACCOUNT>
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=4
#SBATCH --cpus-per-task=16
#SBATCH --gpus-per-task=1
#SBATCH --time=01:00:00
#SBATCH --qos=default
#SBATCH -p gpu


module --force purge
module load env/release/2024.1
module load RAPIDS

export SCHEDULER_FILE="scheduler_${SLURM_JOB_ID}.json"
export NODES=$(scontrol show hostnames)

export DASK_LOGGING__DISTRIBUTED=info
export DASK_DISTRIBUTED__COMM__UCX__CREATE_CUDA_CONTEXT=True
export UCX_MEMTYPE_REG_WHOLE_ALLOC_TYPES=cuda
export DASK_DISTRIBUTED__COMM__UCX__CUDA_COPY=True
export DASK_DISTRIBUTED__COMM__UCX__TCP=True
export DASK_DISTRIBUTED__COMM__UCX__NVLINK=True
export DASK_DISTRIBUTED__COMM__UCX__INFINIBAND=True
export DASK_DISTRIBUTED__COMM__UCX__RDMACM=True

# Start controller on this first task
dask-scheduler --scheduler-file ${SCHEDULER_FILE} --interface "ib0" --protocol "ucx" &
sleep 5

for NODE in ${NODES}; do
        echo "Starting ${SLURM_NTASKS_PER_NODE} workers on ${NODE}"
        for (( GPU_ID=1; GPU_ID<=${SLURM_NTASKS_PER_NODE};GPU_ID++ )); do
            srun --export=ALL,UCX_MEMTYPE_REG_WHOLE_ALLOC_TYPES=cuda,CUDA_VISIBLE_DEVICES=$(( ${GPU_ID}-1 )) \
            --cpu-bind=core -N 1 -n 1 -c ${SLURM_CPUS_PER_TASK} -w ${NODE} dask cuda worker --scheduler-file ${SCHEDULER_FILE} \
            --nthreads=${SLURM_CPUS_PER_TASK} --scheduler-file ${SCHEDULER_FILE} --interface "ib0" --protocol "ucx" --enable-tcp-over-ucx \ 
            --enable-nvlink --enable-infiniband --enable-rdmacm --rmm-pool-size "35GB" --rmm-maximum-pool-size  "39GB" --death-timeout 3600  &
        done
done

python "$@"

This python script first connects the dask-rapids cluster using the environment variable SCHEDULER_FILE
Then, a cudf dataframe with 100 millions rows is generated
The cudf dataframe is partionned between all 8 GPUs
We perform some operations on it as it could be done with pandas except that we use 8 GPUS with a total of 320GB of device memory

import dask
import cupy as cp
import cudf
import dask_cudf
from dask_cuda import LocalCUDACluster
from dask.distributed import Client, wait
import matplotlib.pyplot as plt
import time
import os

client = Client(scheduler_file=os.environ['SCHEDULER_FILE'])

print("Creating synthetic distributed GPU data...")
n_rows = 100_000_000  # 100 million rows
n_parts = len(client.has_what())  # one partition per worker/GPU

# Create a Dask-cuDF DataFrame
ddf = dask_cudf.from_cudf(
    cudf.DataFrame({
        'x': cp.random.rand(n_rows),
        'y': cp.random.rand(n_rows),
        'label': cp.random.choice([10,100,1000], n_rows)
    }),
    npartitions=n_parts
)
ddf = ddf.persist()
wait(ddf)
print("DataFrame ready with", ddf.npartitions, "partitions")

print("Performing operations (mean, groupby)...")
start = time.time()
mean_x = ddf['x'].mean().compute()
grouped = ddf.groupby('label').y.mean().compute()
print("Mean of x:", mean_x)
print("Grouped y mean:\n", grouped)
print("Completed in", time.time() - start, "seconds")

print("Plotting a GPU-based scatter sample...")
sample = ddf.sample(frac=0.001).compute().to_pandas()

plt.scatter(sample['x'], sample['y'], alpha=0.3)
plt.title("Sampled GPU-parallel data")
plt.xlabel("x")
plt.ylabel("y")
plt.show()

print("Applying CuPy UDF per partition")

def cupy_sigmoid(col):
    return 1 / (1 + cp.exp(-col))

def apply_sigmoid(df):
    df['sigmoid_x'] = cupy_sigmoid(df['x'].values)
    return df

sig_ddf = ddf.map_partitions(apply_sigmoid)
sig_ddf = sig_ddf.persist()
wait(sig_ddf)
print(sig_ddf.head())

# Done
print("All tasks complete!")

To execute the previous code, just use sbatch submit-rapids-job.sh dask-rapids.py on a login node. Make sure to replace <ACCOUNT> by your account on the platform.

Dask Dashboard Overview

The Dask Dashboard is a web-based monitoring tool that provides real-time insights into the performance and status of a Dask cluster. It helps users understand how tasks are distributed, how resources are utilized, and where potential bottlenecks exist. The dashboard is particularly useful for debugging, performance tuning, and optimizing parallel workloads.

To access the dashboard, you will need to create a ssh tunnel
In your python script, add the following command print(client.dashboard_link)
Record the output of the previous command and open a new terminal on your local machine
Enter the following command: ssh -p 8822 -NL 8787:XX.XX.XX.XX:8787 <user>@login.lxp.lu by replacing XX.XX.XX.XX with the ip address obtained from dashboard link