How to Run Multi-GPU AI Training Sessions with H100

Running AI training sessions on multiple GPUs, especially using NVIDIA’s H100, can significantly accelerate deep learning workflows. H100 GPUs are designed for high-performance computing, offering advanced features such as NVLink, Transformer Engine, and enhanced memory bandwidth. 

Leveraging these capabilities ensures optimal performance and efficiency for large-scale AI models.

Prerequisites

Before setting up a multi-GPU AI training session with H100, ensure you have:

A system equipped with multiple H100 GPUs

A supported deep learning framework such as PyTorch or TensorFlow

NVIDIA GPU drivers and CUDA installed

A high-speed interconnect like NVLink for efficient communication

Sufficient storage and memory to handle large datasets

Step 1: Install and Configure Required Software

To begin, install the necessary drivers and software to enable multi-GPU training.

Install NVIDIA Drivers and CUDA

Download and install the latest NVIDIA drivers from the official website.

Verify installation using:

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nvidia-smi

Install CUDA and cuDNN by following the official documentation.

2. Install a Deep Learning Framework
   
   

Install PyTorch with CUDA support:

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pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118  

Install TensorFlow with GPU support:

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pip install tensorflow[and-cuda]  

Verify GPU Availability

Check if GPUs are detected in PyTorch:

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import torch

print(torch.cuda.device_count())

Check in TensorFlow:

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import tensorflow as tf

print(tf.config.list_physical_devices('GPU'))

Step 2: Enable Multi-GPU Training

To efficiently distribute training across multiple H100 GPUs, enable data parallelism or model parallelism based on the model size and computational needs.

Using Data Parallelism in PyTorch

Use DataParallel for Simple Multi-GPU Execution

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import torch

import torch.nn as nn  

model = nn.Linear(512, 10)  

model = torch.nn.DataParallel(model)  

model.to("cuda")  

Use DistributedDataParallel for Efficient Scaling

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from torch.nn.parallel import DistributedDataParallel as DDP  

torch.distributed.init_process_group(backend="nccl")  

model = DDP(model)  

Using Multi-GPU Training in TensorFlow

Use MirroredStrategy for Synchronous Training

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import tensorflow as tf  

strategy = tf.distribute.MirroredStrategy()  

with strategy.scope():  

    model = tf.keras.models.Sequential([...])  

Use MultiWorkerMirroredStrategy for Distributed Training

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strategy = tf.distribute.MultiWorkerMirroredStrategy()  

Step 3: Optimize Performance

To maximize efficiency when training on multiple H100 GPUs, consider the following optimizations:

Enable Mixed Precision Training

H100 GPUs support mixed precision training, reducing memory usage and speeding up computation.

In PyTorch:

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from torch.cuda.amp import autocast  

with autocast():  

    output = model(input)  

In TensorFlow:

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policy = tf.keras.mixed_precision.Policy("mixed_float16")  

tf.keras.mixed_precision.set_global_policy(policy)  

Use Gradient Accumulation

When memory is a constraint, use gradient accumulation to reduce batch size requirements.

python

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accumulation_steps = 4  

for step, batch in enumerate(dataloader):  

    loss = model(batch) / accumulation_steps  

    loss.backward()  

    if step % accumulation_steps == 0:  

        optimizer.step()  

        optimizer.zero_grad()  

Leverage NVLink for Faster Communication

Ensure that H100 GPUs are connected using NVLink to minimize inter-GPU communication latency.

Check NVLink status:

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nvidia-smi nvlink -s  

Step 4: Monitor and Debug Training

Use monitoring tools to track GPU utilization and optimize training:

NVIDIA-SMI to check memory usage:

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watch -n 1 nvidia-smi  

TensorBoard for logging training metrics:

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import tensorflow as tf  

log_dir = "logs/"  

writer = tf.summary.create_file_writer(log_dir)  

Step 5: Save and Resume Training

Saving and loading models properly is crucial for long training sessions.

In PyTorch:

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torch.save(model.state_dict(), "model.pth")  

model.load_state_dict(torch.load("model.pth"))  

In TensorFlow:

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model.save("model.h5")  

model = tf.keras.models.load_model("model.h5")  

Conclusion

Scaling AI training workloads with multiple H100 GPUs enhances computational efficiency and accelerates deep learning tasks. By setting up the environment correctly, utilizing data parallelism, and optimizing performance, users can leverage the full potential of H100 GPUs for large-scale AI models.

For a seamless cloud-based AI training experience, Cyfuture Cloud provides high-performance GPU instances optimized for machine learning workloads. With scalable infrastructure and enterprise-grade support, Cyfuture Cloud ensures that your AI models train faster and more efficiently. Start your AI journey today with Cyfuture Cloud’s powerful GPU solutions.