The Pain of DL Empiricism—and How RapidFire AI Fixes It

Upayan Mathkari Arun Kumar · 2025-02-11

We stared at progress bars inching forward on our screens, watching days slip away. As data scientists across various sectors–automotive, tech, semiconductors, public health, social science, and precision medicine–we've spent countless hours watching models train sequentially. Over years of building CNNs, LSTMs, LLMs, etc. and working with diverse data modalities from time series to multimodal data, we've seen these challenges intensify as datasets and models grow larger and  complex. While off-the-shelf DL models can jumpstart applications, we've learned that truly impactful solutions require careful tailoring to each task's specific data - a process that forces us into a painful cycle of wrestling with lower level systems issues and watching promising experiments wait in line while less optimal ones waste precious GPU resources.

This frustration echoes across the industry. Consider two scenarios playing out in AI teams worldwide:

A medical imaging team has spent years accumulating a large private dataset of pathology slides to develop a model for identifying cellular abnormalities. But as they begin experimenting, the challenges mount: Should they try different learning rates? Test various vision transformer architectures? Modify their data augmentation strategy? Each change requires another full training run taking days.

Meanwhile, a background screening team is working to automate their adjudication process by fine-tuning open source language models on their specialized data. With millions of background checks processed monthly, they need to accurately classify complex records into hundreds of distinct categories. Each configuration experiment - testing different model architectures like Llama-3.2 or DeepSeek, various LoRA parameters, or learning rates - takes days to train, even with model parallelism across multiple GPUs.

This scenario illustrates a universal truth in deep learning: success depends heavily on high-throughput experimentation, but current tools make this process painfully slow. The problem becomes 10x worse as data scale increases or with larger models like LLMs. While pre-trained models have democratized AI, relying solely on off-the-shelf solutions means organizations are underutilizing their valuable data and foregoing immense competitive differentiation. Teams that can experiment effectively with DL customization hold a significant advantage.

The Inevitability of Experimentation in AI

The Empiricism Bottleneck

Deep learning accuracy depends critically on three classes of experimental choices, each requiring careful exploration and tuning if one wants to maximize accuracy for their use case:

Thorough experimentation in accuracy-critical applications can lead to an overwhelming number of configurations. Consider the following (simplified) config space for our medical imaging example:

configurations = [
    {
        "architecture": "ViT-base-patch16-224",
        "batch_size": 32,
        "optimizer": "Adam",
        "learning_rate": 1e-3,
        "augmentation": "standard"
    },
    {
        "architecture": "Resnet50",
        "batch_size": 64,
        "learning_rate": 1e-4,
        "optimizer": "AdamW",
        "augmentation": "aggressive"
    },
    # ... many more configurations
]

With just 8 hyperparameter combinations, 2 model architectures, and 2 data preprocessing strategies, we have 32 different configs. At, say, 24 hours per training run, that adds up to almost a month sequentially!

Alternatively, the team could pay for a lot more resources to increase the number of configs that can be run, but this often comes at prohibitively large costs. This challenge isn't unique to the above radiology application example or healthcare. Consider:

  • Media Analytics: Video processing pipelines require extensive tuning across different frame representations and task specific architectures.

  • Manufacturing: Vision QA requires careful adaption of hyperparameters across different lighting conditions and architecture size for edge deployment.

  • E-commerce: Product taxonomy models need adapter rank fine-tuning for achieving higher classification accuracy.

  • Precision Medicine: Thorough architecture exploration is required for processing omics sequence data to improve drug selection and treatments.

Scale Compounds the Challenge

The challenge of DL experimentation becomes exponentially harder as datasets and models grow. Consider our medical imaging team: their whole-slide imaging dataset spans multiple TBs, with each image requiring sophisticated tiling and preprocessing. Traditional data loading approaches break down at this scale – you can't simply load the entire dataset into memory on a single machine. End users are forced  to grapple with the painstaking task of manually  sharding and caching the data to run in parallel on a cluster.

Moreover, the dataset scale is only half the equation. Modern LLMs have grown enormously–even a modest 7B Llama model requires sophisticated techniques like LoRA and careful GPU memory management for fine-tuning. Large model sizes like this necessitate end users using complex tools like FSDP or DeepSpeed with unintuitive knob configurations to partition models across GPUs.

Often, teams are forced to face both challenges at once, creating a very complex resource planning challenge: How to efficiently distribute both the massive training dataset and the large model across your GPU cluster?

This challenge resonates across industries. Financial institutions processing compliance documents encounter similar model distribution challenges. Autonomous vehicle teams working with large-scale road video data face similar dataset scaling issues. The common thread? Existing solutions force teams to optimize for either data scale OR model scale, but not both.

Why Current Solutions Fall Short

Limited Experimentation & Control

The traditional approach to DL experimentation is fundamentally sequential. Consider this typical training loop:

# Sequential fine-tuning pattern 
configurations = [
    {
        "model": "meta-llama/Llama-3.2-3B",
        "learning_rate": 1e-5,
        "lora_r": 8,
        "lora_alpha": 16,
        "batch_size": 32,
        "gradient_checkpointing": True
    },
    {
        "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", 
        "learning_rate": 1e-4,
        "lora_r": 16, 
        "lora_alpha": 32,
        "batch_size": 16,
        "gradient_checkpointing": True
    },
    # ... many more configurations
]

for config in configurations:
    # GPUs idle during model loading and sharding
    initialize_distributed_setup()
    load_base_model(config["model"])
    
    # Must run entire fine-tuning even if metrics are poor
    model = finetune_with_lora(
        config,
        num_epochs=3,
        max_steps=10000
    )
    
    # More idle time during model cleanup and GPU memory clearing
    cleanup_distributed()
    clear_gpu_caches()

This creates two critical problems:

The Sequential Bottleneck

  • Each config must wait its turn in the queue

  • No opportunity to learn from running experiments

  • Comprehensive exploration becomes practically impossible, meaning a lot of accuracy is left on the table, hurting the application

Fixed Configuration Prison 

  • Each config must run to completion once started

  • No way to adjust across configs based on interim results because there is no way to dynamically reallocate compute

  • Promising new config ideas cannot be explored immediately because resources are locked

In our medical imaging example, this means the team might waste days training a clearly suboptimal model config simply because they can't dynamically reallocate those GPUs to a more promising experiment.

The False Choice of Scaling Strategies

Today's landscape forces teams into an impossible choice between different scaling approaches. More generally, a team faces a false trichotomy of three incomplete options:

A blue and white sign displaying information about a model parallel.

Teams end up needing to master multiple complex systems, each with their own configuration headaches and failure modes. The cognitive overhead just to run their experiments effectively is immense: practitioners need to understand distributed systems concepts, GPU memory management, network communication patterns, and more!.

This “complexity tax” is particularly painful for domain experts like our medical imaging team. Their expertise lies in pathology and computer vision, not in distributed systems configuration. Yet they're forced to become impromptu systems engineers just to run their experiments effectively.

RapidFire AI: A Paradigm Shift

RapidFire AI fundamentally reimagines how we approach DL experimentation. The three pillars of this new approach are as follows.

1. Hyperparallel Exploration

Instead of training configs one after another, hyperparallel exploration enables true parallel experimentation across your entire configuration space, spanning data representation, model architecture, and hyperparameters. The system automatically optimizes resource allocation, ensuring maximum GPU utilization while managing memory constraints.

Key benefits:

  • Train multiple model configurations simultaneously on the same cluster

  • System automatically handles resource optimization and load balancing

  • Efficient handling of both large datasets and/or large models

Applied to Medical Imaging: For our radiology team, this means they can simultaneously explore different vision transformer architectures, data augmentation strategies, and learning rates. Instead of waiting weeks for sequential experiments, they can get comprehensive results in hours while maximizing their GPU utilization.

2. Real-Time Control

RapidFire enables dynamic control over running experiments from our ML metrics dashboard without needing to do low level cluster management. This transforms deep learning from a rigid, sequential process into an agile, interactive one.

Key benefits:

  • Stop underperforming models in early epochs proactively. RapidFire AI automatically reallocates resources to the remaining models.

  • Clone and modify promising configurations on the fly. RapidFire AI automatically reapportions cluster resources to the clones.

  • Continually monitor and adapt models based on real-time results to get higher accuracy, either with a human in the loop or customizable automation or full AutoML.

Applied to Background Screening: The team can now monitor validation accuracy and training loss across different model configurations and quickly redirect resources when a model is underperforming. When certain combinations of LoRA parameters and learning rates show better classification accuracy, they can instantly clone those configs while adjusting key hyperparameters, e.g., increasing LoRA rank or fine-tuning additional layers. This is particularly valuable when fine-tuning foundational models like Llama-3.2-3B or DeepSeek-R1-Distill-Llama-8B, where each experiment consumes significant GPU resources.

3. Scale Automatically

RapidFire AI eliminates the need for teams to engage in any sort of manual setup for data parallelism, model parallelism, or task parallelism by providing an industry-first unified scaling approach without any complex configurations.

Key benefits:

  • Simple API for teams to write code as if it is for small data and small models on their laptop. RapidFire AI automatically scales it to larger data and/or models on multi-GPU machines or multi-machine clusters.

  • Work with datasets or clusters of any size with built-in fault tolerance.

  • Automatic system execution optimizations that maximize GPU utilization, rooted in years of award-winning research out of UC San Diego.

Applied to Medical Imaging: For our radiology team, this means they can start with a small subset of their pathology slide dataset on local GPUs during initial experimentation, then seamlessly scale to their full multi-TB dataset across the hospital's compute cluster with no code changes. They can focus on improving their abnormality detection models without wrestling with distributed training configurations or worrying about how to efficiently process their large-scale whole-slide images.

Conclusion

Let us recap how RapidFire fundamentally transforms the DL experimentation process:

Building successful AI applications should not depend on being a distributed systems expert. RapidFire AI transforms today’s painful process of DL experimentation into an efficient, interactive (and possibly even fun!) experience, representing a paradigm shift in how teams can leverage DL. Through our unified scaling approach, RapidFire AI supports any data modality (image, video, text, audio, time series, multimodal), any DL model architecture (CNNs, ViTs, LLMs, etc), and provides both an easy-to-use API that can run on any Kubernetes cluster–on your cloud or ours. Our API is a plug-and-play solution that allows AI data scientists, engineers, and researchers  to continue using their favorite tools–PyTorch, Jupyter Notebooks, MLflow, Hugging Face, etc.  This versatility, combined with its powerful parallelization and real-time control capabilities, lets AI teams focus on what truly matters – developing high-quality AI models to deliver high value to their use case.