Difference between revisions of "AI Examples"
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[[Category:Help]][[Category:Machine Learning]] | [[Category:Help]][[Category:Machine Learning]] | ||
− | The UFIT Research Computing AI Support Team maintains a suite of examples for | + | The UFIT Research Computing AI Support Team maintains a suite of examples for AI software stacks on [https://github.com/UFResearchComputing/ UF Research Computing Github] and on HiPerGator, located in /data/ai/examples. Users may copy these examples to their own space, add modifications, and follow the instructions to run these jobs on HiPerGator. Each example has a readme file with additional information in its directory. |
Use [https://support.rc.ufl.edu https://support.rc.ufl.edu] to submit ticket if you need help with the examples or have any AI questions. | Use [https://support.rc.ufl.edu https://support.rc.ufl.edu] to submit ticket if you need help with the examples or have any AI questions. |
Latest revision as of 17:06, 21 May 2024
The UFIT Research Computing AI Support Team maintains a suite of examples for AI software stacks on UF Research Computing Github and on HiPerGator, located in /data/ai/examples. Users may copy these examples to their own space, add modifications, and follow the instructions to run these jobs on HiPerGator. Each example has a readme file with additional information in its directory.
Use https://support.rc.ufl.edu to submit ticket if you need help with the examples or have any AI questions.
Research Computing Github
We have some AI examples available on the UF Research Computing Github.
Catalog of available examples
Name | Categories | Location on HiPerGator | Author | Date added | Description
|
---|---|---|---|---|---|
Modulus single-GPU and multi-GPU example | PINN | /data/ai/examples/pinn/modulus
|
Yang Hong | May, 2024 | Modulus single-GPU Jupyter-notebook example is for using PINN to approximate the solution of a given PDE and boundary conditions. The multi-GPU example is for using GraphNN accelerates MD simulations to predict the force of each atom in the system. |
RAPIDS | Computer Vision | /data/ai/examples/rapids
|
Dimitri Bourilkov | April, 2024 | Examples for accelerated data science with RAPIDS. |
Rapids_singlecell | Healthcare and Life Science | /data/ai/examples/rapids_singlecell
|
Huiwen Ju and Qian Zhao | May, 2024 | Rapids-singlecell offers enhanced single-cell data analysis as a near drop-in replacement predominantly for scanpy |
Llama | Healthcare and Life Science | /data/ai/examples/llms/llama
|
Qian Zhao | May, 2024 | The Llama Family from Meta includes Llama 2 |
NVIDIA Clara Parabricks | Healthcare and Life Science | /data/ai/examples/parabricks
|
Huiwen Ju and Qian Zhao | May, 2024 | NVIDIA Clara Parabricks is a powerful genomics analysis software suite that leverages accelerated computing to process data efficiently. |
TensorFlow+Keras Convolutional Neural Net | Computer Vision | /data/ai/examples/image/tensorflow-bootcamp
|
Dimitri Bourilkov | July, 2021 | TensorFlow+Keras Convolutional Neural Net with NGC container |
Object Detection Tutorial | Computer Vision | /data/ai/examples/image/4.Object_Detection_Tutorial
|
Yunchao Yang | Oct, 2022 | This example illstrates how to generate a object detection on a video using pretrained models. |
OpenCV Intro Tutorial | Computer Vision | /data/ai/examples/image/1.OpenCV_Intro_Tutorial
|
Yunchao Yang | Oct, 2022 | This example illustrates the fundamentals of image processing techniques in opencv. |
PyTorch Instance Segmentation Tutorial | Computer Vision | /data/ai/examples/image/5.PyTorch_Instance_Segmentation_Tutorial
|
Yunchao Yang | Oct, 2022 | This example illstrates how to train an instance segementation model using mask r-cnn model |
PyTorch Transfer Learning Tutorial | Computer Vision | /data/ai/examples/image/3.PyTorch_Transfer_Learning_Tutorial
|
Yunchao Yang | Oct, 2022 | This example illstrates how to train a convolutional neural network for image classification using transfer learning. |
TorchVision Intro Tutorial | Computer Vision | /data/ai/examples/image/2.TorchVision_Transforms_Tutorial
|
Yunchao Yang | Oct, 2022 | This example illustrates the fundamentals of TorchVision in image transformation and augmentation |
GraphCNN.ipynb | Graphs | /data/ai/examples/graphs
|
Eric Stubbs | August, 2021 | A torch version of an article on Understanding Graph Convolutional Networks. The example shows how convolution learns from graphs even without training and backpropagation. |
MONAI Medical Images Classification Tutorial | Medical Image Processing | /data/ai/examples/image/6.MONAI_Medical_Imaging_Tutorial
|
Yunchao Yang | Oct, 2022 | This example illstrates how to train an medical image classification model using MONAI |
AI News GPT | NLP | /data/ai/examples/nlp/ai_news_GPT
|
Eric Stubbs | March, 2023 | See how trained from scratch GPT models can enable knowledge exploration or brainstorming. This example also shows how transfer learning, amount of data, and finetuning affect GPT models. |
bertopic_topics.ipynb | NLP | /data/ai/examples/nlp
|
Eric Stubbs | May, 2021 | Apply BERTopic software to examine topics of AI news articles. |
embeddings-megatronbert345.ipynb | NLP | /data/ai/examples/nlp
|
Eric Stubbs | June, 2021 | Create vector representations using Nvidia Megatron-BERT-345m. |
embeddings-to-bertopics.ipynb | NLP | /data/ai/examples/nlp
|
Eric Stubbs | July 2021 | Use cosine similarity, a similarity matrix, and a centrality matrix to identify key sentences within an article that provide a summary of the main points. |
Example Multinode GPT Pretraining | NLP | /data/ai/examples/distributed-compute/pytorch_distributed_exampleGPT
|
Eric Stubbs | July, 2021 | Use the pytorch distributed launch utility to pretrain a GPT language model using multiple nodes. |
Improved Megatron GPT Text Generation | NLP | /data/ai/examples/nlp/megatronGPT_text_generation/megatronGPT_text_generation
|
Eric Stubbs | August, 2022 | Megatron code was updated to allow for bulk prompt completion and language generation using a 23 billion parameter Megatron GPT language model trained by UFIT Research Computing. |
inferences-megatronbert345.ipynb | NLP | /data/ai/examples/nlp
|
Eric Stubbs | July 2021 | |
Language Model Inference Apps | NLP | /data/ai/examples/nlp/inference_apps
|
Eric Stubbs | May, 2022 | Explore knowledge in Nvidia Megatron BERT and GPT models using notebooks by creating lists of the most likely predicted words. |
MNLI Benchmark with UF Model | NLP | /data/ai/examples/nlp/multinli_benchmark
|
Eric Stubbs | August, 2022 | Apply a 9 billion parameter Megatron BERT language model trained by UFIT Research Computing to an inference benchmark using improved code from Megatron. The model achieved a score of 87% after only 1 epoch of finetuning. State of the art score on NLI is 92%. |
NeMo Text Classification | NLP | /data/ai/examples/nlp/nemotextclassify
|
Eric Stubbs | November, 2021 | Train a language model to classify text with a set of labels. Requires labelled data. |
Race Benchmark with UF Model | NLP | /data/ai/examples/nlp/race_benchmark_megatron
|
Eric Stubbs | August, 2022 | Apply a 9 billion parameter Megatron BERT language model undertrained on only 400gb of data by UFIT Research Computing to a question answering benchmark using code from Nvidia Megatron-LM. The model achieved a score of 85% on middle school questions and 81% on high school questions after only 1 epoch on the QA training data. State of the art score on RACE is 92%. |
summary-extractive.ipynb | NLP | /data/ai/examples/nlp
|
Eric Stubbs | May 2021 |