# Jupyter ML Training

Set up JupyterLab with GPU support for machine learning experiments and model training.

{% hint style="success" %}
All examples can be run on GPU servers rented through [CLORE.AI Marketplace](https://clore.ai/marketplace).
{% endhint %}

## Server Requirements

| Parameter    | Minimum     | Recommended |
| ------------ | ----------- | ----------- |
| RAM          | 16GB        | 32GB+       |
| VRAM         | 8GB         | 16GB+       |
| Network      | 200Mbps     | 500Mbps+    |
| Startup Time | 2-3 minutes | -           |

{% hint style="info" %}
JupyterLab itself is lightweight. Choose GPU and RAM based on your training workload requirements.
{% endhint %}

## Quick Deploy

**Docker Image:**

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

**Ports:**

```
22/tcp
8888/http
6006/http
```

**Environment:**

```
JUPYTER_TOKEN=your_secure_token_here
```

**Command:**

```bash
pip install jupyterlab tensorboard && \
jupyter lab --ip=0.0.0.0 --port=8888 --allow-root --NotebookApp.token='your_secure_token_here'
```

## Accessing Your Service

After deployment, find your `http_pub` URL in **My Orders**:

1. Go to **My Orders** page
2. Click on your order
3. Find the `http_pub` URL (e.g., `abc123.clorecloud.net`)

Use `https://YOUR_HTTP_PUB_URL` instead of `localhost` in examples below.

### Verify It's Working

```bash
# Check if JupyterLab is accessible
curl https://your-http-pub.clorecloud.net/

# Access with token
# https://your-http-pub.clorecloud.net/?token=your_secure_token_here
```

{% hint style="warning" %}
If you get HTTP 502, wait 2-3 minutes - the service is installing dependencies.
{% endhint %}

## Renting on CLORE.AI

1. Visit [CLORE.AI Marketplace](https://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>`

## Access Jupyter

1. Wait for deployment
2. Find port 8888 mapping
3. Open: `http://<proxy>:<port>?token=your_secure_token_here`

## Pre-configured ML Image

For full ML environment:

**Image:**

```
jupyter/pytorch-notebook:cuda12-pytorch-2.1.0
```

Or build custom:

```dockerfile
FROM pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime

RUN pip install --no-cache-dir \
    jupyterlab \
    numpy pandas matplotlib seaborn \
    scikit-learn \
    transformers datasets accelerate \
    tensorboard wandb \
    opencv-python pillow \
    tqdm rich

EXPOSE 8888 6006

CMD ["jupyter", "lab", "--ip=0.0.0.0", "--allow-root"]
```

## Essential Libraries

### Install in Jupyter

```python
!pip install transformers datasets accelerate bitsandbytes
!pip install wandb tensorboard
!pip install scikit-learn xgboost lightgbm
!pip install opencv-python albumentations
```

### Create requirements.txt

```

# ML Frameworks
torch>=2.1.0
torchvision
torchaudio

# NLP
transformers>=4.36.0
datasets
tokenizers
sentencepiece

# Training
accelerate
bitsandbytes
peft
trl

# Monitoring
wandb
tensorboard

# Data
numpy
pandas
matplotlib
seaborn
scikit-learn

# Computer Vision
opencv-python
pillow
albumentations
```

## Training Examples

### PyTorch Image Classification

```python
import torch
import torch.nn as nn
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader

# Check GPU
print(f"GPU: {torch.cuda.get_device_name(0)}")
print(f"Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB")

# Load data
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

train_data = torchvision.datasets.CIFAR10(
    root='./data', train=True, download=True, transform=transform
)
train_loader = DataLoader(train_data, batch_size=64, shuffle=True, num_workers=4)

# Model
model = torchvision.models.resnet18(pretrained=True)
model.fc = nn.Linear(512, 10)
model = model.cuda()

# Training
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss()

for epoch in range(10):
    model.train()
    for images, labels in train_loader:
        images, labels = images.cuda(), labels.cuda()

        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

    print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

# Save model
torch.save(model.state_dict(), 'model.pth')
```

### HuggingFace Text Classification

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from transformers import TrainingArguments, Trainer
from datasets import load_dataset
import numpy as np

# Load dataset
dataset = load_dataset("imdb")

# Load model
model_name = "distilbert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)

# Tokenize
def tokenize(examples):
    return tokenizer(examples["text"], padding="max_length", truncation=True)

tokenized = dataset.map(tokenize, batched=True)

# Training
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=64,
    warmup_steps=500,
    weight_decay=0.01,
    logging_dir="./logs",
    logging_steps=100,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized["train"],
    eval_dataset=tokenized["test"],
)

trainer.train()
trainer.save_model("./best_model")
```

### LLM Fine-tuning with LoRA

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from datasets import load_dataset
from trl import SFTTrainer
import torch

# Load model with 4-bit quantization
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

model = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.1",
    quantization_config=bnb_config,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
tokenizer.pad_token = tokenizer.eos_token

# Configure LoRA
lora_config = LoraConfig(
    r=16,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

model = prepare_model_for_kbit_training(model)
model = get_peft_model(model, lora_config)

# Load dataset
dataset = load_dataset("timdettmers/openassistant-guanaco")

# Train
trainer = SFTTrainer(
    model=model,
    train_dataset=dataset["train"],
    dataset_text_field="text",
    max_seq_length=512,
    tokenizer=tokenizer,
    args=TrainingArguments(
        output_dir="./lora_output",
        num_train_epochs=1,
        per_device_train_batch_size=4,
        gradient_accumulation_steps=4,
        learning_rate=2e-4,
        fp16=True,
        logging_steps=10,
        save_steps=100,
    ),
)

trainer.train()
trainer.save_model("./final_lora")
```

## TensorBoard Integration

### Start TensorBoard

```python
%load_ext tensorboard
%tensorboard --logdir ./logs --port 6006 --bind_all
```

Or via terminal:

```bash
tensorboard --logdir ./logs --port 6006 --bind_all &
```

### Log Training Metrics

```python
from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter('./logs')

for epoch in range(epochs):
    # ... training loop ...
    writer.add_scalar('Loss/train', train_loss, epoch)
    writer.add_scalar('Loss/val', val_loss, epoch)
    writer.add_scalar('Accuracy/val', accuracy, epoch)

writer.close()
```

## Weights & Biases Integration

```python
import wandb

wandb.init(project="my-project", name="experiment-1")

# Log metrics
wandb.log({"loss": loss, "accuracy": acc})

# Log model
wandb.save("model.pth")

# Finish
wandb.finish()
```

## Data Management

### Download Datasets

```python

# HuggingFace datasets
from datasets import load_dataset
dataset = load_dataset("squad")

# Kaggle datasets
!pip install kaggle
!kaggle datasets download -d username/dataset-name

# Direct download
!wget https://example.com/data.zip
!unzip data.zip
```

### Mount Cloud Storage

```python

# S3
!pip install boto3
import boto3
s3 = boto3.client('s3')
s3.download_file('bucket', 'key', 'local_path')

# Google Cloud
!pip install google-cloud-storage
from google.cloud import storage
client = storage.Client()
bucket = client.bucket('my-bucket')
blob = bucket.blob('data.zip')
blob.download_to_filename('data.zip')
```

## Saving Work

### Save to External Storage

```python

# Save model to S3
import boto3
s3 = boto3.client('s3',
    aws_access_key_id='YOUR_KEY',
    aws_secret_access_key='YOUR_SECRET'
)
s3.upload_file('model.pth', 'my-bucket', 'models/model.pth')
```

### Before Ending Session

```bash

# Download important files
scp -P <port> root@<host>:/workspace/model.pth ./
scp -P <port> -r root@<host>:/workspace/results/ ./results/
```

## Multi-GPU Training

```python
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel

# Check GPUs
print(f"Available GPUs: {torch.cuda.device_count()}")

# DataParallel (simple)
model = nn.DataParallel(model)

# DistributedDataParallel (better)

# Launch with: torchrun --nproc_per_node=4 train.py
dist.init_process_group("nccl")
model = DistributedDataParallel(model)
```

## Performance Tips

### Memory Optimization

```python

# Gradient checkpointing
model.gradient_checkpointing_enable()

# Mixed precision
from torch.cuda.amp import autocast, GradScaler
scaler = GradScaler()

with autocast():
    output = model(input)
    loss = criterion(output, target)

scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
```

### Data Loading

```python

# Faster data loading
loader = DataLoader(
    dataset,
    batch_size=64,
    num_workers=8,      # Use multiple workers
    pin_memory=True,    # Faster GPU transfer
    prefetch_factor=2   # Prefetch batches
)
```

## 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*](https://clore.ai/marketplace) *for current rates.*

**Save money:**

* Use **Spot** market for flexible workloads (often 30-50% cheaper)
* Pay with **CLORE** tokens
* Compare prices across different providers