$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources (2410.23261v1)

Abstract: Pre-training is notoriously compute-intensive and academic researchers are notoriously under-resourced. It is, therefore, commonly assumed that academics can't pre-train models. In this paper, we seek to clarify this assumption. We first survey academic researchers to learn about their available compute and then empirically measure the time to replicate models on such resources. We introduce a benchmark to measure the time to pre-train models on given GPUs and also identify ideal settings for maximizing training speed. We run our benchmark on a range of models and academic GPUs, spending 2,000 GPU-hours on our experiments. Our results reveal a brighter picture for academic pre-training: for example, although Pythia-1B was originally trained on 64 GPUs for 3 days, we find it is also possible to replicate this model (with the same hyper-parameters) in 3x fewer GPU-days: i.e. on 4 GPUs in 18 days. We conclude with a cost-benefit analysis to help clarify the trade-offs between price and pre-training time. We believe our benchmark will help academic researchers conduct experiments that require training larger models on more data. We fully release our codebase at: https://github.com/apoorvkh/academic-pretraining.

• The paper presents an empirical study quantifying GPU resource trade-offs in AI pre-training using typical academic setups.
• It benchmarks varied training configurations, demonstrating that optimized strategies can replicate high-end model training with fewer GPUs over extended durations.
• The study provides actionable cost-benefit analyses and technical optimizations that make cutting-edge AI research accessible to academia.

Overview of "$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources"

The paper "$100K or 100 Days: Trade-offs when Pre-Training with Academic Resources" addresses a pressing issue in AI research—specifically the accessibility and feasibility of pre-training large AI models using limited computational resources typically available in academic settings. The authors conduct an empirical analysis to shed light on the often-intimidating resource barriers academic researchers face when engaging in pre-training experiments, which are usually compute-intensive.

Main Contributions

1. Compute Resources Survey: The authors first conduct a survey to assess the typical compute resources available within academic institutions. They discover that academic researchers frequently have access to between 1 and 8 GPUs, usually for durations that span days to weeks. This survey highlights the disparity in computational resources between academia and industry, setting the stage for the subsequent empirical analysis.
2. Benchmarking Training Configurations: The paper introduces a benchmark designed to measure the time required to pre-train models on different academic GPU setups. The benchmark was applied to several models and GPU configurations, accumulating approximately 2,000 GPU-hours of experimental data.
3. Empirical Findings: Their empirical results provide an optimistic view compared to common pessimistic assumptions in academia. For example, the paper illustrates that replicating the Pythia-1B model, which originally required 64 GPUs over three days, can be achieved with 4 GPUs over 18 days using specific optimizations. This finding is critical as it dispels the notion that pre-training is entirely out of reach for under-resourced academic institutions.
4. Optimization Strategies: The paper evaluates various efficient training methods (e.g., activation checkpointing, model sharding) and their combinations to minimize training times without altering model architecture or compromising the training recipe. This approach focuses on technical optimizations that do not change the model nor its theoretical training regimen, thereby maintaining the fidelity of the pre-training process.
5. Cost-Benefit Analysis: The authors perform a pragmatic cost-benefit analysis to guide researchers on making informed decisions about hardware investments based on training time and cost. For example, they analyze situations where investing in high-end GPUs like 8 H100s can be justified by significant time savings, thus making them more cost-effective in the long run.

Implications and Future Directions

The findings of this paper have several implications for both the academic and industrial research communities. Practically, it provides academic researchers with actionable insights and a clear framework to approach the problem of pre-training large models on constrained budgets. It makes the case for re-thinking resource investments in university settings, suggesting that with careful planning, significant AI research endeavors are feasible.

Theoretically, the results encourage a shift in perspective within AI research, emphasizing that exploration of new models and architectures should not be monopolized by well-funded industry labs. Smaller, more principled investigations carried out by academia can contribute meaningfully to scientific progress, thereby ensuring a vibrant, diverse, and competitive research landscape.

In the future, similar studies could explore more recent hardware innovations and training methodologies. As AI models grow more complex, understanding the dynamic between available resources and the practicality of emerging techniques will be crucial. Furthermore, expanding the codebase to accommodate a wider array of models and sharing it as an extensible toolset invites both reproducibility and further experimentation within the community.

In conclusion, this paper demystifies the process of model pre-training under constrained resources, providing clarity and encouragement to pursue ambitious, controlled AI research within the academic ecosystem. Its contributions lay down a pathway for researchers aiming to maximize their limited resources while still engaging in cutting-edge AI model development.