> For the complete documentation index, see [llms.txt](https://docs.clore.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.clore.ai/guides/guides_v2-de/training/deepspeed-training.md).

# DeepSpeed Training

Trainiere große Modelle effizient mit Microsoft DeepSpeed.

{% hint style="success" %}
Alle Beispiele können auf GPU-Servern ausgeführt werden, die über [CLORE.AI Marketplace](https://clore.ai/marketplace).
{% endhint %}

## Mieten auf CLORE.AI

1. Besuchen Sie [CLORE.AI Marketplace](https://clore.ai/marketplace)
2. Nach GPU-Typ, VRAM und Preis filtern
3. Wählen **On-Demand** (Festpreis) oder **Spot** (Gebotspreis)
4. Konfigurieren Sie Ihre Bestellung:
   * Docker-Image auswählen
   * Ports festlegen (TCP für SSH, HTTP für Web-UIs)
   * Umgebungsvariablen bei Bedarf hinzufügen
   * Startbefehl eingeben
5. Zahlung auswählen: **CLORE**, **BTC**, oder **USDT/USDC**
6. Bestellung erstellen und auf Bereitstellung warten

### Zugriff auf Ihren Server

* Verbindungsdetails finden Sie in **Meine Bestellungen**
* Webschnittstellen: Verwenden Sie die HTTP-Port-URL
* SSH: `ssh -p <port> root@<proxy-address>`

## Was ist DeepSpeed?

DeepSpeed ermöglicht:

* Modelle zu trainieren, die nicht in den GPU-Speicher passen
* Training auf mehreren GPUs und mehreren Knoten
* ZeRO-Optimierung (Speichereffizienz)
* Training mit gemischter Genauigkeit

## ZeRO-Stufen

| Stufe         | Speichereinsparung               | Geschwindigkeit       |
| ------------- | -------------------------------- | --------------------- |
| ZeRO-1        | Optimiererzustände partitioniert | Schnell               |
| ZeRO-2        | + Gradienten partitioniert       | Ausgeglichen          |
| ZeRO-3        | + Parameter partitioniert        | Maximale Einsparungen |
| ZeRO-Infinity | CPU/NVMe-Auslagerung             | Größte Modelle        |

## Schnelle Bereitstellung

**Docker-Image:**

```
pytorch/pytorch:2.5.1-cuda12.4-cudnn9-devel
```

**Ports:**

```
22/tcp
```

**Befehl:**

```bash
pip install deepspeed transformers datasets accelerate
```

## Installation

```bash
pip install deepspeed

# Installation überprüfen
ds_report
```

## Grundlegendes Training

### DeepSpeed-Konfiguration

**ds\_config.json:**

```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
    }
}
```

### Trainingsskript

```python
import torch
import deepspeed
from transformers import AutoModelForCausalLM, AutoTokenizer

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

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

# Trainingsschleife
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 Stufe-2 Konfiguration

```json
{
    "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 Stufe-3 Konfiguration

Für große Modelle:

```json
{
    "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
    }
}
```

## Mit Hugging Face Transformers

### Trainer-Integration

```python
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

### Startbefehl

```bash

# Einzelner Knoten, 4 GPUs
deepspeed --num_gpus=4 train.py --deepspeed ds_config.json

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

### Mit torchrun

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

## Multi-Node-Training

### Hostdatei

**hostfile:**

```
node1 slots=4
node2 slots=4
```

### Starten

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

### SSH-Einrichtung

```bash

# Stelle passwortloses SSH zwischen den Knoten sicher
ssh-keygen -t rsa
ssh-copy-id user@node2
```

## Speichereffiziente Konfigurationen

### 7B-Modell auf 24GB GPU

```json
{
    "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-Modell auf 24GB GPU

```json
{
    "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

Sparen Sie Speicher, indem Aktivierungen neu berechnet werden:

```python
from transformers import AutoModelForCausalLM

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

## Checkpoints speichern und laden

### Speichern

```python

# DeepSpeed übernimmt das Checkpointing
model_engine.save_checkpoint("./checkpoints", tag="step_1000")
```

### Laden

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

### Im HuggingFace-Format speichern

```python

# DeepSpeed-Checkpoint in HF-Format konvertieren
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")
```

## Überwachung

### TensorBoard

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

### Weights & Biases

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

## Häufige Probleme

### Kein Speicher mehr

```json
// Versuchen:
{
    "zero_optimization": {
        "stage": 3,
        "offload_optimizer": {"device": "cpu"},
        "offload_param": {"device": "cpu"}
    },
    "train_micro_batch_size_per_gpu": 1
}
```

### Langsames Training

* Reduziere CPU-Auslagerung
* Erhöhe die Batch-Größe
* Verwende ZeRO Stufe 2 statt 3

### NCCL-Fehler

```bash

# Setze Umgebungsvariablen
export NCCL_DEBUG=INFO
export NCCL_IB_DISABLE=1
```

## Performance-Tipps

| Tipp                             | Effekt             |
| -------------------------------- | ------------------ |
| Verwende bf16 statt fp16         | Bessere Stabilität |
| Aktiviere Gradient Checkpointing | Weniger Speicher   |
| Tunte die Batch-Größe            | Besserer Durchsatz |
| Verwende NVMe-Auslagerung        | Größere Modelle    |

## Leistungsvergleich

| Modell | GPUs    | ZeRO-Stufe | Trainingstempo  |
| ------ | ------- | ---------- | --------------- |
| 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  |

## Fehlerbehebung

## Kostenabschätzung

Typische CLORE.AI-Marktplatztarife (Stand 2024):

| GPU       | Stundensatz | Tagessatz | 4-Stunden-Sitzung |
| --------- | ----------- | --------- | ----------------- |
| 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           |

*Preise variieren je nach Anbieter und Nachfrage. Prüfen Sie* [*CLORE.AI Marketplace*](https://clore.ai/marketplace) *auf aktuelle Preise.*

**Geld sparen:**

* Verwenden Sie **Spot** Markt für flexible Workloads (oft 30–50% günstiger)
* Bezahlen mit **CLORE** Token
* Preise bei verschiedenen Anbietern vergleichen

## Nächste Schritte

* [Feinabstimmung von LLMs](/guides/guides_v2-de/training/finetune-llm.md) - LoRA-Training
* vLLM Inferenz - Bereitstellung des trainierten Modells
* [Hugging Face Anleitung](/guides/guides_v2-de/training/huggingface-transformers.md) - Transformers-Bibliothek


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