https://github.com/agnostiqhq/blog_covalent_aws_2023
https://github.com/agnostiqhq/blog_covalent_aws_2023
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/agnostiqhq/blog_covalent_aws_2023
- Owner: AgnostiqHQ
- Created: 2023-01-18T17:59:58.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-01-19T18:31:53.000Z (over 2 years ago)
- Last Synced: 2025-01-18T03:26:09.363Z (9 months ago)
- Language: Python
- Size: 12.7 KB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Covalent Machine Learning Study
## Supplementary Code
This repository contains the complete workflow script (`workflow.py`) corresponding to this post on AWS Blogs (*link to be included upon publication*).
The solution here is adapted from [this script](https://www.kaggle.com/code/mateuszbuda/brain-segmentation-pytorch/script), originally written by Mateusz Buda. The complete input data can be downloaded [here](https://www.kaggle.com/datasets/mateuszbuda/lgg-mri-segmentation).
## Instructions
Before running the workflow, ensure that you have valid AWS credentials and that AWS Batch is correctly configured. The input data should be uploaded to an S3 Bucket as a ZIP file, say `data_full.zip`.
After the above, proceed with the following:
* Install the required packages (includes Covalent): `pip install -r requirements.txt`.
* Run the shell command `covalent start` to start Covalent.
We recommend running the experiment through the `argeparse` CLI included in `workflow.py`.
* Run `python workflow.py --help` to see CLI options that specify the scope of the experiment.
For example, we used the following command to run the experiment in the blog post linked above:
```
python workflow.py -B 16 -E 20 -Z 64 128 192 256 -L 0.000075 0.0001 0.000125 -d data_full
```
Alternatively, just call the workflow function (`workflow`) normally by passing an arbitrary list of parameters.