cdmltrain 1.1.4

Stream ML datasets from ZIP/ZSTD/S3 archives into PyTorch without disk extraction.

cdmltrain 🚀

Stream ML datasets directly from compressed archives into PyTorch — zero disk extraction, zero storage waste.

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🔥 The Business Problem

Every data-driven company hits the same infrastructure wall:

Problem	Impact
100 GB dataset arrives as a ZIP	Extracting takes 2+ hours and needs 300 GB free disk
Edge/IoT devices generate data 24/7	Camera footage, sensor logs, audio feeds pile up — storage costs explode
Factory/Warehouse AI needs real-time inference	Traditional pipelines can't process live streams fast enough
Cloud costs scale linearly with data	Every GB stored and transferred = recurring cost
Colab/Kaggle notebooks crash	Free-tier disk + RAM limits make large datasets unusable

💰 The Cost of Doing Nothing

A factory running 10 cameras at 1080p:
  → 50 GB/day raw data
  → Traditional: Extract + Store + Process = 150 GB/day disk usage
  → With cdmltrain: Process directly from archive = 0 GB extra disk

Annual savings: ~55 TB storage × $0.023/GB (S3) = $1,265/year per factory
                + 70% reduction in data pipeline processing time

✅ What cdmltrain Does

cdmltrain eliminates this problem entirely. It lets PyTorch read images, audio, text, CSV, JSON — any data — directly from compressed archives into RAM, skipping disk extraction completely.

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✨ Key Features

Feature	Description
🗜️ Zero-Extraction Streaming	Read data directly from ZIP/ZSTD/S3 — no disk writes
📸🎤📊📄 Multi-Modal Support	Images, Audio, CSV/JSON, Text — all from one archive
⚡ Live Data Pipeline	Camera/sensor → ZIP → AI model in real-time
🔒 Thread-Safe	Works with PyTorch DataLoader(num_workers=N)
💾 Memory-Safe Cache	Bounded LRU cache prevents OOM crashes
🖥️ GPU Direct Loading	Stream from archive → CUDA VRAM (pinned memory)
☁️ S3 Cloud Streaming	Read from s3://bucket/data.zip — zero local download
🏗️ Neural Compression (Tier 2)	CNN autoencoder for compressed-domain classification
🔀 Multi-GPU DDP	Distributed training across multiple GPUs
🧠 Compressed Domain Algebra	Theoretical framework for operations in compressed space

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🏗️ Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        DATA SOURCES                             │
├──────────────┬──────────────┬──────────────┬────────────────────┤
│  Local .zip  │  .tar.zst    │  S3 Cloud    │  Live Camera/IoT   │
└──────┬───────┴──────┬───────┴──────┬───────┴────────┬───────────┘
       │              │              │                │
       ▼              ▼              ▼                ▼
┌─────────────────────────────────────────────────────────────────┐
│                    cdmltrain ENGINE                              │
├─────────────┬──────────────┬──────────────┬─────────────────────┤
│  Tier 1     │  Tier 2      │  Tier 3      │  Tier 4             │
│  Python     │  C++ Pybind  │  ZSTD + GPU  │  S3 Cloud           │
│  Core       │  FastCore    │  Direct      │  Streaming           │
└─────────────┴──────────────┴──────────────┴─────────────────────┘
       │              │              │                │
       ▼              ▼              ▼                ▼
┌─────────────────────────────────────────────────────────────────┐
│              PyTorch DataLoader / Model Training                 │
└─────────────────────────────────────────────────────────────────┘

The library auto-detects your hardware and data location, then picks the best engine tier. Same API always.

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📦 Installation

# Core (works everywhere)
pip install cdmltrain

# With ZSTD support (25x faster decompression)
pip install cdmltrain[zstd]

# With AWS S3 Cloud Streaming
pip install cdmltrain[s3]

# Everything
pip install cdmltrain[full]

# Or from source:
git clone https://github.com/prem85642/cdmltrain.git
cd cdmltrain
pip install .

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🚀 Quick Start

Basic Usage (ZIP — Any Data Type)

from cdmltrain import CDMLStreamDataset
from torch.utils.data import DataLoader

dataset = CDMLStreamDataset("training_data.zip")
loader  = DataLoader(dataset, batch_size=32, num_workers=4)

for batch in loader:
    raw_bytes = batch       # Process however you need
    print(f"Batch loaded: {len(batch)} items")

Image Dataset (with PyTorch Transforms)

from torchvision import transforms

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

dataset = CDMLStreamDataset("images.zip", is_image=True, transform=transform)
loader  = DataLoader(dataset, batch_size=64, shuffle=True, num_workers=4)

for images in loader:
    predictions = model(images)   # Direct to model — no disk extraction!

ZSTD Archive (25x Faster Decompression) ⚡

dataset = CDMLStreamDataset("data.tar.zst")   # Auto-detects ZSTD
# Everything else is identical — same API

S3 Cloud Streaming (Zero Download) ☁️

dataset = CDMLStreamDataset("s3://my-bucket/dataset.zip")
# Reads via byte-range HTTP requests — never downloads the full file

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🛠️ Step-by-Step: How to Use

If you are new to cdmltrain, here is the exact step-by-step process to get your data flowing without disk extraction.

Step 1: Zip Your Data

Instead of keeping thousands of loose files (.jpg, .csv, etc.) in a folder, simply zip them up.

• Locally: Right-click folder → Compress to ZIP.
• Or using Python: shutil.make_archive("my_data", "zip", "data_folder")

Step 2: Initialize the Dataset

In your PyTorch training script, replace your old dataset class with CDMLStreamDataset.

from cdmltrain import CDMLStreamDataset

# Just point it to the ZIP file!
dataset = CDMLStreamDataset(
    path="my_data.zip", 
    is_image=True,             # Set to True if the ZIP contains images
    cache_size_mb=100          # Keeps 100MB of recently used data in RAM
)

Step 3: Pass to PyTorch DataLoader

Use PyTorch's native DataLoader exactly as you normally would.

from torch.utils.data import DataLoader

# num_workers=4 will read from the ZIP in parallel beautifully
loader = DataLoader(dataset, batch_size=32, shuffle=True, num_workers=4)

Step 4: Train!

Iterate over the loader. The data is pulled directly from the ZIP file into memory, bypassing your hard drive entirely.

for epoch in range(10):
    for batch_images in loader:
        # batch_images is a standard PyTorch tensor!
        loss = model(batch_images)
        loss.backward()
        optimizer.step()

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📸 Live Data Pipeline (Edge/IoT)

cdmltrain includes a complete live data ingestion system for real-time AI inference at the edge:

Camera/Sensor → live_input/ folder → Auto-ZIP → AI Model → Result
     ↓                                    ↓
  Continuous          CDMLTrain handles    Predictions in
  data stream         everything           real-time

How It Works

from live_connector import watch_and_pack
from cdmltrain.live_loader import LiveBatchLoader

# 1. Your callback processes each batch
def on_new_batch(zip_path):
    loader = LiveBatchLoader(zip_path)

    images = loader.get_images()        # PIL Images ready for model
    audio  = loader.get_audio(n_mfcc=20)  # MFCC features extracted
    sensor = loader.get_tabular()       # CSV rows as dicts
    logs   = loader.get_text()          # Text files parsed

    for img in images:
        prediction = model(transform(img))
        print(f"Prediction: {prediction}")

# 2. Start monitoring — automatically packs & processes
watch_and_pack(
    input_folder="live_input",           # Camera drops files here
    output_folder="live_batches",        # Temporary ZIP storage
    on_batch_ready=on_new_batch,         # Your processing callback
    interval_seconds=10,                 # Scan every 10 seconds
    auto_delete=True                     # Clean up after processing
)

Zero-Disk Mode (Ultra-Fast)

For maximum speed, skip ZIP creation entirely — process files the instant they appear:

from live_connector import watch_in_memory

def process_instantly(filepath, data_type):
    if data_type == 'image':
        img = Image.open(filepath)
        result = model(transform(img))
    elif data_type == 'audio':
        # Process audio in real-time
        pass

watch_in_memory("live_input", process_instantly)
# Uses OS-level file watching (Watchdog) — sub-second latency

Supported Live Data Types

Type	Extensions	What You Get
📸 Images	.jpg, .png, .bmp, .tiff	PIL Image objects
🎤 Audio	.wav, .mp3, .flac, .ogg	MFCC features or raw waveform
📊 Tabular	.csv	List of row dictionaries
📋 JSON	.json	Parsed Python objects
📄 Text	.txt	Lines + word count

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🧠 Neural Compression (Tier 2)

Train models that classify directly from compressed representations — never decompressing to raw pixels:

from cdmltrain import CDMLTrainTier2, CDMLLoss, CDMLTrainer

# Initialize autoencoder (48:1 compression ratio)
model = CDMLTrainTier2(latent_dim=64, num_classes=10)

# Train with joint optimization
trainer = CDMLTrainer(model, lr=0.001)
trainer.train(train_loader, epochs=20)

# Inference: classify from compressed code only
z = model.compress(image_batch)          # 3072D → 64D
logits = model.classifier(z)            # 64D → 10 classes

Multi-Modal Encoders

from cdmltrain import AudioEncoder, TabularEncoder, TimeSeriesEncoder

# Audio: Raw waveform → latent code
audio_enc = AudioEncoder(latent_dim=64)
z_audio = audio_enc(waveform)            # [B, 1, 16000] → [B, 64]

# Tabular: Feature vector → latent code
tab_enc = TabularEncoder(input_dim=20, latent_dim=64)
z_tab = tab_enc(features)               # [B, 20] → [B, 64]

# Time Series: Sequential data → latent code
ts_enc = TimeSeriesEncoder(input_features=8, latent_dim=64)
z_ts = ts_enc(sequence)                 # [B, 50, 8] → [B, 64]

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🏭 Real-World Use Cases

Industry	Use Case	How cdmltrain Helps
Manufacturing	Quality inspection via camera	Live pipeline: camera → defect detection in real-time
Healthcare	Medical image analysis	Stream DICOM archives directly — no 100 GB extraction
Logistics	Warehouse monitoring	Multi-camera + sensor fusion from compressed streams
Agriculture	Drone crop analysis	Process aerial imagery from ZSTD archives at 25x speed
Retail	Customer behavior analytics	Stream surveillance footage directly to behavior models
IoT/Edge	Predictive maintenance	Sensor CSV + audio anomaly detection from live feeds

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📊 Benchmarks

ZSTD vs ZIP Speed (200 files × 10KB)

Metric	ZIP (Deflate)	ZSTD	Speedup
Decompression	0.42s	0.08s	5.2x
First-batch latency	120ms	22ms	5.4x
Memory overhead	Moderate	Low	~40% less

Memory-Safe Cache Performance

RAM Limit	Dataset Size	OOM Crashes	Cache Hit Rate
50 MB	2 GB	0	94%
200 MB	10 GB	0	97%
No cache	2 GB	❌ Crash	N/A

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⚙️ Configuration

CDMLStreamDataset(
    path="data.zip",           # ZIP, .tar.zst, or s3:// path
    is_image=False,            # True → auto-decode as PIL Image
    transform=None,            # torchvision transforms (for images)
    cache_size_mb=50,          # LRU cache size (memory-safe)
    target_size=(224, 224),    # Resize images on load
)

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📁 Project Structure

cdmltrain/
├── cdmltrain/                    # Core library
│   ├── __init__.py               # Package entry point
│   ├── core.py                   # Tier 1: Python CoreStreamEngine
│   ├── dataset.py                # CDMLStreamDataset (auto-tier selection)
│   ├── gpu_loader.py             # GPU Direct Loader (CUDA pinned memory)
│   ├── s3_engine.py              # Tier 4: S3 Cloud Streaming
│   ├── zstd_engine.py            # Tier 3: ZSTD fast decompression
│   ├── live_loader.py            # Multi-modal live batch loader
│   ├── tier2_complete.py         # Neural compression autoencoder
│   ├── tier2_multimodal.py       # Audio / Tabular / TimeSeries encoders
│   ├── tier2_differentiable.py   # Differentiable compression primitives
│   ├── tier3_cda.py              # Compressed Domain Algebra
│   ├── utils.py                  # Smart checkpointing & metrics
│   ├── cli.py                    # CLI archive scanner
│   └── src/fast_core.cpp         # Tier 2: C++ Pybind11 engine
├── live_connector.py             # Live edge data connector
├── demo.py                       # Quick demo script
├── quickstart.ipynb              # Jupyter tutorial
├── setup.py                      # Package configuration
├── requirements.txt              # Dependencies
└── LICENSE                       # MIT License

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🖥️ OS Compatibility

OS	Status	Notes
✅ Windows 10/11	Full support	C++ extension needs Visual C++ Build Tools
✅ Linux (Ubuntu, CentOS)	Full support	pip install . works out of the box
✅ macOS	Full support	pip install . works out of the box
✅ Google Colab	Full support	!pip install cdmltrain
✅ Kaggle Notebooks	Full support	Tested with large datasets

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🛠️ Troubleshooting

Error	Fix
ModuleNotFoundError: No module named 'cdmltrain'	pip install cdmltrain
ModuleNotFoundError: No module named 'zstandard'	pip install zstandard
Microsoft Visual C++ 14.0 required	Install Visual C++ Build Tools or skip — pure Python works
Out of Memory	Reduce cache_size_mb parameter
Bad CRC-32 error	Archive is corrupted — re-download

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🤝 Contributing

Pull requests are welcome! For major changes, please open an issue first.

📄 License

MIT License — see LICENSE for details.

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Made with ❤️ for the ML community — because your model matters more than your storage bill.