DeepSpeed Training

Train large models efficiently with DeepSpeed on Clore.ai GPUs

Train large models efficiently with Microsoft DeepSpeed.

All examples can be run on GPU servers rented through CLORE.AI Marketplace.

Renting on CLORE.AI

Multi-GPU 80GB-class rigs are not listed on the Clore.ai marketplace. The biggest boxes listed today are 4× RTX PRO 6000 Blackwell (96GB each, 380GB total) and 8–11× RTX 5090 (32GB each). A100 / H200 / B200 capacity is sold as bare metal on request. Check GPU Pricing & Availability before sizing a deployment.

1. Visit CLORE.AI Marketplace

2. Filter by GPU type, VRAM, and price

3. Choose On-Demand (fixed rate) or Spot (bid price)

4. Configure your order:

   • Select Docker image

   • Set ports (TCP for SSH, HTTP for web UIs)

   • Add environment variables if needed

   • Enter startup command

5. Select payment: CLORE, BTC, or USDT/USDC

6. Create order and wait for deployment

Access Your Server

• Find connection details in My Orders

• Web interfaces: Use the HTTP port URL

• SSH: ssh -p <port> root@<proxy-address>

What is DeepSpeed?

DeepSpeed enables:

• Training models that don't fit in GPU memory

• Multi-GPU and multi-node training

• ZeRO optimization (memory efficiency)

• Mixed precision training

ZeRO Stages

Stage	Memory Saving	Speed
ZeRO-1	Optimizer states partitioned	Fast
ZeRO-2	+ Gradients partitioned	Balanced
ZeRO-3	+ Parameters partitioned	Maximum savings
ZeRO-Infinity	CPU/NVMe offload	Largest models

Quick Deploy

Docker Image:

pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel

Ports:

22/tcp

Command:

pip install deepspeed transformers datasets accelerate

Installation

pip install deepspeed

# Verify installation
ds_report

Basic Training

DeepSpeed Config

ds_config.json:

{
    "train_batch_size": 32,
    "gradient_accumulation_steps": 4,
    "optimizer": {
        "type": "AdamW",
        "params": {
            "lr": 1e-4,
            "betas": [0.9, 0.999],
            "eps": 1e-8,
            "weight_decay": 0.01
        }
    },
    "scheduler": {
        "type": "WarmupLR",
        "params": {
            "warmup_min_lr": 0,
            "warmup_max_lr": 1e-4,
            "warmup_num_steps": 100
        }
    },
    "fp16": {
        "enabled": true,
        "loss_scale": 0,
        "initial_scale_power": 16
    },
    "zero_optimization": {
        "stage": 2,
        "contiguous_gradients": true,
        "overlap_comm": true
    }
}

Training Script

import torch
import deepspeed
from transformers import AutoModelForCausalLM, AutoTokenizer

# Initialize
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# DeepSpeed initialization
model_engine, optimizer, _, _ = deepspeed.initialize(
    model=model,
    model_parameters=model.parameters(),
    config="ds_config.json"
)

# Training loop
for epoch in range(num_epochs):
    for batch in dataloader:
        inputs = tokenizer(batch["text"], return_tensors="pt", padding=True, truncation=True)
        inputs = {k: v.to(model_engine.device) for k, v in inputs.items()}

        outputs = model_engine(**inputs, labels=inputs["input_ids"])
        loss = outputs.loss

        model_engine.backward(loss)
        model_engine.step()

ZeRO Stage 2 Config

{
    "train_batch_size": "auto",
    "gradient_accumulation_steps": "auto",
    "gradient_clipping": 1.0,
    "fp16": {
        "enabled": true
    },
    "zero_optimization": {
        "stage": 2,
        "allgather_partitions": true,
        "allgather_bucket_size": 2e8,
        "reduce_scatter": true,
        "reduce_bucket_size": 2e8,
        "overlap_comm": true
    }
}

ZeRO Stage 3 Config

For large models:

{
    "train_batch_size": "auto",
    "gradient_accumulation_steps": "auto",
    "fp16": {
        "enabled": true
    },
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": true
        },
        "offload_param": {
            "device": "cpu",
            "pin_memory": true
        },
        "overlap_comm": true,
        "contiguous_gradients": true,
        "sub_group_size": 1e9,
        "reduce_bucket_size": "auto",
        "stage3_prefetch_bucket_size": "auto",
        "stage3_param_persistence_threshold": "auto",
        "stage3_max_live_parameters": 1e9,
        "stage3_max_reuse_distance": 1e9,
        "stage3_gather_16bit_weights_on_model_save": true
    }
}

With Hugging Face Transformers

Trainer Integration

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="./output",
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=1e-4,
    num_train_epochs=3,
    fp16=True,
    deepspeed="ds_config.json",
    logging_steps=10,
    save_steps=500,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    tokenizer=tokenizer,
)

trainer.train()

Multi-GPU Training

Launch Command

# Single node, 4 GPUs
deepspeed --num_gpus=4 train.py --deepspeed ds_config.json

# Specific GPUs
deepspeed --include="localhost:0,1,2,3" train.py --deepspeed ds_config.json

With torchrun

torchrun --nproc_per_node=4 train.py --deepspeed ds_config.json

Multi-Node Training

Hostfile

hostfile:

node1 slots=4
node2 slots=4

Launch

deepspeed --hostfile=hostfile train.py --deepspeed ds_config.json

SSH Setup

# Ensure passwordless SSH between nodes
ssh-keygen -t rsa
ssh-copy-id user@node2

Memory-Efficient Configs

7B Model on 24GB GPU

{
    "bf16": {"enabled": true},
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {"device": "cpu"},
        "offload_param": {"device": "cpu"}
    },
    "gradient_checkpointing": true,
    "train_micro_batch_size_per_gpu": 1,
    "gradient_accumulation_steps": 16
}

13B Model on 24GB GPU

{
    "bf16": {"enabled": true},
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {"device": "cpu"},
        "offload_param": {"device": "cpu"},
        "stage3_param_persistence_threshold": 0
    },
    "gradient_checkpointing": true,
    "train_micro_batch_size_per_gpu": 1,
    "gradient_accumulation_steps": 32
}

Gradient Checkpointing

Save memory by recomputing activations:

from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")
model.gradient_checkpointing_enable()

Save and Load Checkpoints

Save

# DeepSpeed handles checkpointing
model_engine.save_checkpoint("./checkpoints", tag="step_1000")

Load

model_engine.load_checkpoint("./checkpoints", tag="step_1000")

Save HuggingFace Format

# Convert DeepSpeed checkpoint to HF format
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint

state_dict = get_fp32_state_dict_from_zero_checkpoint("./checkpoints/step_1000")
model.load_state_dict(state_dict)
model.save_pretrained("./hf_model")

Monitoring

TensorBoard

{
    "tensorboard": {
        "enabled": true,
        "output_path": "./logs",
        "job_name": "training_run"
    }
}

Weights & Biases

{
    "wandb": {
        "enabled": true,
        "project": "my_project"
    }
}

Common Issues

Out of Memory

// Try:
{
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {"device": "cpu"},
        "offload_param": {"device": "cpu"}
    },
    "train_micro_batch_size_per_gpu": 1
}

Slow Training

• Reduce CPU offloading

• Increase batch size

• Use ZeRO Stage 2 instead of 3

NCCL Errors

# Set environment variables
export NCCL_DEBUG=INFO
export NCCL_IB_DISABLE=1

Performance Tips

Tip	Effect
Use bf16 over fp16	Better stability
Enable gradient checkpointing	Less memory
Tune batch size	Better throughput
Use NVMe offload	Larger models

Performance Comparison

Model	GPUs	ZeRO Stage	Training Speed
7B	1x A100	ZeRO-3	~1000 tokens/s
7B	4x A100	ZeRO-2	~4000 tokens/s
13B	4x A100	ZeRO-3	~2000 tokens/s
70B	8x A100	ZeRO-3	~800 tokens/s

Troubleshooting

Cost Estimate

Typical CLORE.AI marketplace rates (as of 2024):

GPU	Hourly Rate	Daily Rate	4-Hour Session
RTX 3060	~$0.03	~$0.70	~$0.12
RTX 3090	~$0.06	~$1.50	~$0.25
RTX 4090	~$0.10	~$2.30	~$0.40
A100 40GB	~$0.17	~$4.00	~$0.70
A100 80GB	~$0.25	~$6.00	~$1.00

Prices vary by provider and demand. Check CLORE.AI Marketplace for current rates.

Save money:

• Use the Spot market for interruptible work — about a third of servers price spot below on-demand (median ~13% off), the rest match it

• Pay with CLORE tokens

• Compare prices across different providers

Next Steps

• Fine-tune LLMs - LoRA training

• vLLM Inference - Deploy trained model

• Hugging Face Guide - Transformers library