Fast Prototyping of Deep Learning Multimodal Experiments with Hydra, PyTorch Lightning, and W&B

Introduction

As a computer vision and deep learning researcher, I often want to quickly prototype models and pipelines, including vision, language, and multimodal systems, without rewriting boilerplate every time. In this post, I’ll walk through building a flexible Python library for rapid prototyping using Hydra, PyTorch Lightning, and Weights & Biases (W&B).

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Overview

The goal of this library is to:

1. Easily switch between datasets, models, and loggers using Hydra configs.
2. Support vision models (like ResNet) and multimodal pipelines (CLIP + LLM).
3. Track experiments and outputs in W&B, including images, labels, prompts, and generated text.
4. Batch process images for research experiments.

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1. Project Structure

We started with a clean Python package:

myproto/
├── README.md
├── requirements.txt
├── src/
│   └── myproto/
│       ├── __init__.py
│       ├── datasets/
│       ├── models/
│       ├── trainers/
│       ├── vlm/
│       ├── llm/
│       ├── pipelines/
│       └── utils/
├── configs/
│   ├── config.yaml
│   ├── dataset/
│   ├── model/
│   ├── trainer/
│   └── logger/
├── scripts/
│   └── train.py
└── tests/

The design principle is config-driven modularity: datasets, models, and loggers are all swappable via Hydra.

2. Setting Up Dependencies

We used pip for simplicity. Core packages:

pip install torch torchvision torchaudio
pip install pytorch-lightning hydra-core omegaconf
pip install wandb timm einops albumentations opencv-python-headless
pip install transformers datasets

Optional for dev: pytest, black, isort.

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3. Config-Driven Design

Example Hydra configs:

configs/config.yaml

defaults:
  - dataset: fake
  - model: resnet18
  - trainer: default
  - logger: wandb
  - input: input

seed: 42

You can override any option from the CLI:

python scripts/train.py model=resnet18 dataset=fake
python scripts/train.py model=multimodal input.image_path=example.jpg

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4. Building a Vision Model

We added a model factory for ResNet18:

# src/myproto/models/resnet.py
import torchvision.models as tv
import torch.nn as nn

def build_resnet18(num_classes=10, pretrained=False):
    model = tv.resnet18(weights="IMAGENET1K_V1" if pretrained else None)
    in_features = model.fc.in_features
    model.fc = nn.Linear(in_features, num_classes)
    return model

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5. PyTorch Lightning Trainer

A LightningModule for training classifiers:

import torch
from torch import nn
import torchmetrics
import pytorch_lightning as pl

class ImageClassifier(pl.LightningModule):
    def __init__(self, model, lr=1e-3):
        super().__init__()
        self.model = model
        self.criterion = nn.CrossEntropyLoss()
        self.train_acc = torchmetrics.Accuracy(task="multiclass", num_classes=model.fc.out_features)
        self.lr = lr

    def forward(self, x):
        return self.model(x)

    def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = self.criterion(logits, y)
        self.log("train_loss", loss)
        return loss

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=self.lr)

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6. Multimodal Pipeline (CLIP + LLM)

We created a reusable pipeline to:

1. Run CLIP zero-shot on an image to get top-k labels.
2. Build a prompt using the labels.
3. Generate text using an LLM.

from transformers import CLIPProcessor, CLIPModel, pipeline
from PIL import Image
import torch

class MultiModalPipeline:
    def __init__(self, clip_model_name="openai/clip-vit-base-patch32",
                 clip_device=-1, llm_model_name="gpt2", llm_device=-1):
        self.clip_device = "cpu" if clip_device == -1 else f"cuda:{clip_device}"
        self.clip_model = CLIPModel.from_pretrained(clip_model_name).to(self.clip_device)
        self.clip_processor = CLIPProcessor.from_pretrained(clip_model_name)
        self.llm = pipeline("text-generation", model=llm_model_name, device=llm_device)

    def generate_from_image(self, image_path, candidate_labels, top_k=3, prompt_template=None):
        image = Image.open(image_path).convert("RGB")
        inputs = self.clip_processor(text=candidate_labels, images=image, return_tensors="pt", padding=True).to(self.clip_device)
        with torch.no_grad():
            logits = self.clip_model(**inputs).logits_per_image[0]
            probs = logits.softmax(dim=0)
        label_probs = sorted(zip(candidate_labels, probs.tolist()), key=lambda x: x[1], reverse=True)[:top_k]
        prompt = prompt_template.format(labels=", ".join([f"{l} ({p:.2f})" for l, p in label_probs]))
        generated_text = self.llm(prompt)[0]["generated_text"]
        return {"label_probs": label_probs, "prompt": prompt, "generated_text": generated_text, "image": image}

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7. W&B Logging

We integrated W&B logging for both training and multimodal runs:

from pytorch_lightning.loggers import WandbLogger
import wandb

logger = WandbLogger(project="myproto", log_model=True)

# Example: logging a multimodal run
logger.experiment.log({
    "input_image": wandb.Image(out["image"]),
    "top_labels": {l: p for l, p in out["label_probs"]},
    "prompt": out["prompt"],
    "generated_text": out["generated_text"],
})

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8. Batch Processing Images

We added a batch runner to process a folder of images:

import os
from myproto.models import build_model

pipeline = build_model(cfg.model)
image_files = [os.path.join(cfg.input_batch.image_folder, f)
               for f in os.listdir(cfg.input_batch.image_folder)
               if f.lower().endswith((".png", ".jpg"))]

for img_path in image_files:
    out = pipeline.generate_from_image(img_path, cfg.input_batch.candidate_labels,
                                       top_k=cfg.input_batch.top_k,
                                       prompt_template=cfg.input_batch.prompt_template)
    logger.experiment.log({
        "input_image": wandb.Image(out["image"], caption=os.path.basename(img_path)),
        "top_labels": {l: p for l, p in out["label_probs"]},
        "prompt": out["prompt"],
        "generated_text": out["generated_text"],
    })

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9. Running Experiments

Train CV model:

python scripts/train.py model=resnet18 dataset=fake

Run multimodal single image:

python scripts/train.py model=multimodal input.image_path=example.jpg

Run multimodal batch:

python scripts/multimodal_batch.py model=multimodal input_batch.image_folder=./images

All outputs are logged to W&B, so you can track experiments, generated texts, and images in one dashboard.

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10. Key Takeaways

• Hydra configs allow fully modular, swappable components.
• PyTorch Lightning simplifies training loops and logging.
• W&B integration captures both training metrics and multimodal outputs.
• Multimodal pipelines are fully reusable and batchable.
• This setup is perfect for rapid prototyping of CV and multimodal research ideas.

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This library now serves as a single entrypoint for experiments, letting you easily switch between CV training, LLM generation, and multimodal pipelines, all while keeping everything reproducible and tracked.

GitHub repo Link