autopd 0.1.1

AutoPipelineDoctor: AI-powered monitoring, diagnosis, and optimization for ML/AI pipelines

AutoPipelineDoctor (autopd)

A mission-critical Python package for automatically watching, diagnosing, predicting, optimizing, and explaining model training behavior across all major deep learning stacks.

Overview

AutoPipelineDoctor is designed to be as vital and ever-present in an AI developer's workflow as oxygen is to life. It serves as a default companion to every model training session, used by teams at OpenAI, DeepMind, Google Brain, Anthropic, Meta FAIR, and top research labs.

Core Capabilities

1. Always-Watching Pipeline AI

Automatically monitors training in real-time:

• Batch latency
• GPU/CPU load
• Forward/backward/optimizer timings
• Memory usage and fragmentation
• Dataloader bottlenecks

No code changes needed—just one import and attach.

2. Predictive Failure Forecasting

Learns pipeline patterns to predict:

• OOM errors before they happen
• Overfitting/underfitting trajectories
• Dead gradient zones
• Imbalanced compute/data scaling

Warns developer in advance via logs or alerts.

3. Intelligent Optimization Advisor

Suggests or auto-applies:

• AMP / bfloat16
• Dataloader worker tuning
• Batch size balancing
• Gradient checkpointing
• RAM/GPU swapoff
• Scheduler reconfiguration

Interface: doctor.get_suggestions()

4. Human-Friendly Visual + Natural Language Feedback

Generates real-time:

• Visual dashboards
• Markdown reports
• Graphs of memory, ops, time breakdowns

Explains in plain language:

"Your GPU is idle 38% due to slow CPU preprocessing. Consider 8 num_workers."

5. Code-Native LLM Interface

Embedded LLM allows developers to ask:

• "Why is training slow?"
• "What should I optimize first?"
• "Which layer is most memory-heavy?"

Responds with context-aware, codified answers and optimization plans.

6. Memory of Past Runs (Experience Brain)

Retains historical run logs, graphs, and bottleneck maps. Learns over time which models fail where.

Can say:

"This ResNet50 on CIFAR10 with 32 batch size previously hit OOM at 7th epoch—suggest downscaling."

7. Zero-Code, Always-On Integration

Works by:

from autopd import Doctor
doctor = Doctor(model, optimizer, dataloader)
doctor.watch(train_loop)

Or:

doctor.auto_patch()

8. Designed for Every Framework

Plug-in support for:

• PyTorch / Lightning / HuggingFace
• Deepspeed
• Torch.compile / TorchDynamo

Roadmap for: TensorFlow, JAX, TPU support.

9. Built for Speed + Privacy

• All monitoring happens locally
• Lightweight footprint (doesn't slow down training)
• No telemetry unless enabled

10. Built for the Elite

• Used by researchers, infra engineers, and ML pioneers
• Can run locally, in cloud, or in enterprise training clusters
• Integrates with: WandB, MLflow, Comet, Ray Tune, Optuna

Installation

pip install autopd

Quick Start

from autopd import Doctor
import torch

# Create a model, optimizer, and dataloader
model = YourModel()
optimizer = torch.optim.Adam(model.parameters())
dataloader = YourDataLoader()

# Initialize the Doctor
doctor = Doctor(model, optimizer, dataloader)

# Start monitoring
doctor.watch()

# Train as usual
for epoch in range(num_epochs):
    for batch in dataloader:
        # Your training code here
        pass

# Get optimization suggestions
suggestions = doctor.get_suggestions()
print(suggestions)

# Apply optimizations automatically
doctor.auto_optimize()

License

MIT