AutoML-GPT: Automatic Machine Learning with GPT
AI tasks encompass a wide range of domains and fields. While numerous AI
models have been designed for specific tasks and applications, they often
require considerable human efforts in finding the right model architecture,
optimization algorithm, and hyperparameters. Recent advances in large language
models (LLMs) like ChatGPT show remarkable capabilities in various aspects of
reasoning, comprehension, and interaction. Consequently, we propose developing
task-oriented prompts and automatically utilizing LLMs to automate the training
pipeline. To implement this concept, we present the AutoML-GPT, which employs
GPT as the bridge to diverse AI models and dynamically trains models with
optimized hyperparameters. AutoML-GPT dynamically takes user requests from the
model and data cards and composes the corresponding prompt paragraph.
Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct
the experiments from data processing to model architecture, hyperparameter
tuning, and predicted training log. By leveraging {\ours}'s robust language
capabilities and the available AI models, AutoML-GPT can tackle numerous
intricate AI tasks across various tasks and datasets. This approach achieves
remarkable results in computer vision, natural language processing, and other
challenging areas. Extensive experiments and ablation studies demonstrate that
our method can be general, effective, and beneficial for many AI tasks.
64546102363bb3aaf9ca5de2
Shujian Zhang
64546102363bb3aaf9ca5de3
Chengyue Gong
64546102363bb3aaf9ca5de4
Lemeng Wu
admin_assigned
2023-06-02T13:35:00.764Z
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/avatars/f642a7dcc098f50fef865f30f93a25c3.svg
Lemeng Wu
user
klight
64546102363bb3aaf9ca5de5
Xingchao Liu
64546102363bb3aaf9ca5de6
Mingyuan Zhou
AutoML-GPT: Automatic Machine Learning with GPT
AI tasks encompass a wide range of domains and fields. While numerous AI
models have been designed for specific tasks and applications, they often
require considerable human efforts in finding the right model architecture,
optimization algorithm, and hyperparameters. Recent advances in large language
models (LLMs) like ChatGPT show remarkable capabilities in various aspects of
reasoning, comprehension, and interaction. Consequently, we propose developing
task-oriented prompts and automatically utilizing LLMs to automate the training
pipeline. To implement this concept, we present the AutoML-GPT, which employs
GPT as the bridge to diverse AI models and dynamically trains models with
optimized hyperparameters. AutoML-GPT dynamically takes user requests from the
model and data cards and composes the corresponding prompt paragraph.
Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct
the experiments from data processing to model architecture, hyperparameter
tuning, and predicted training log. By leveraging {\ours}'s robust language
capabilities and the available AI models, AutoML-GPT can tackle numerous
intricate AI tasks across various tasks and datasets. This approach achieves
remarkable results in computer vision, natural language processing, and other
challenging areas. Extensive experiments and ablation studies demonstrate that
our method can be general, effective, and beneficial for many AI tasks.
AutoML-GPT uses LLMs to automate the entire AI training pipeline from data processing to hyperparameter tuning, achieving high performance across various domains.
large language models LLMs ChatGPT AutoML-GPT model architecture optimization algorithm hyperparameters data cards prompt paragraph data processing hyperparameter tuning computer vision natural language processing