Esm2 on Sagemaker Hyperpod #387
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Signed-off-by: Ankur Srivastava <[email protected]>
Signed-off-by: Ankur Srivastava <[email protected]>
Signed-off-by: Ankur Srivastava <[email protected]>
Signed-off-by: Ankur Srivastava <[email protected]>
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Do we have any SMHP specific feature in this test case? see also #381 |
KeitaW
reviewed
Jul 30, 2024
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| | Model | device_batch_size | num_nodes | torch.compile | Instance | Throughput | | ||
| |:------:|:-----------------:|:---------:|:-------------:| :------------: | :------------: | | ||
| | ESM2 | 8 | 2 | No | g5.12xlarge | 160 samples/s | |
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The set up instruction advise to use 24xl but actually 12xl was used?
KeitaW
reviewed
Jul 30, 2024
| ## What is ESM-2? | ||
| [ESM-2](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1) is a pLM trained using unsupervied masked language modelling on 250 Million protein sequences by researchers at [Facebook AI Research (FAIR)](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v1). It is available in several sizes, ranging from 8 Million to 15 Billion parameters. The smaller models are suitable for various sequence and token classification tasks. The FAIR team also adapted the 3 Billion parameter version into the ESMFold protein structure prediction algorithm. They have since used ESMFold to predict the struture of [more than 700 Million metagenomic proteins](https://esmatlas.com/about). | ||
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| ESM-2 is a powerful pLM. We will demonstrate how to use QLoRA to fine-tune ESM-2 on g5.24xlarge instances. We will use ESM-2 to predict [subcellular localization](https://academic.oup.com/nar/article/50/W1/W228/6576357?login=false). Understanding where proteins appear in cells can help us understand their role in disease and find new drug targets. |
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Is this test case demonstrating pretraining? or finetuning? I believe latter but the title states former.
KeitaW
reviewed
Jul 30, 2024
KeitaW
reviewed
Jul 30, 2024
KeitaW
reviewed
Jul 30, 2024
KeitaW
reviewed
Jul 30, 2024
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