| Name | platform-gen-ai JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | This is pipeline code for accelerating solution accelerators |
| upload_time | 2024-05-05 22:21:05 |
| maintainer | None |
| docs_url | None |
| author | Google LLC |
| requires_python | None |
| license | None |
| keywords |
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| VCS |
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| requirements |
No requirements were recorded.
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No Travis.
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# Solution Accelerators for GenAI
This repository contains platform code for accelerating development of GenAI solutions in Applied AI Engineering team
# Structure
- **docs**: This directory contains documentation, user guides, and any other resources that help you understand and use the GenAI solution accelerators effectively.
- **src**: The source code for the GenAI solution accelerators is located here. This is where you'll find the core codebase for the tools and frameworks provided in this repository.
- **data**: This directory may contain sample data or data-related resources that can be used for testing and development.
- **tests**: Test cases and resources related to testing the GenAI solution accelerators are stored in this directory.
- **scripts**: Utility scripts or automation scripts that can assist in various tasks related to GenAI development and deployment.
- **examples**: This directory may contain example projects, code snippets, or reference implementations that showcase how to use the provided solution accelerators effectively.
## Getting Started
To get started with the GenAI solution accelerators, follow the instructions in the documentation located in the `docs` directory. It will provide you with step-by-step guidance on how to set up your development environment and use the tools and frameworks provided in this repository.
## Contribution Guidelines
We welcome contributions from the GenAI community! If you'd like to contribute to this repository, please follow our [Contribution Guidelines](CONTRIBUTING.md) to ensure a smooth collaboration process.
## License
This repository is licensed under the [Apaache License](LICENSE). See the [LICENSE](LICENSE) file for details.
## Contact
If you have any questions or need assistance, feel free to reach out to the project maintainers or create an issue in this repository.
Happy GenAI development!
## Setting up
To begin development you can use 2 different approaches: using Python Environment or using Docker. Below are instructions for each approach.
### Setting up Python Environment
Make sure to install miniconda environment:
```
cd ~/
mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
~/miniconda3/bin/conda init bash
```
After that just install the package in editable mode:
```
pip install -e .
```
### Setting up Docker
If this is your first time, you probably don't have Docker installed on VM. Execute the following commands:
```
sudo apt update && sudo apt upgrade
sudo apt install make
sudo apt install docker.io
sudo groupadd docker
sudo usermod -aG docker $USER
sudo chmod 777 /var/run/docker.sock
```
### Setting up environment
```
make build && make container
```
If you want to remove it, execute:
```
make clean
```
### Copying resources
Make sure `gcs_source_bucket` field in `llm.yaml` is up to date with the latest extraction in use. Then run the copying python script:
```
python gen_ai/copy_resources.py
```
### Updating BigQuery table
It is currently set up that all the runs are logged into "uhg" dataset in "chertushkin-genai-sa" project. To change the project id - change `bq_project_id` field of `llm.yaml` file. If you receive an error in logging, check if the service account is added to BigQuery IAM of "chertushkin-genai-sa" project. Or whatever the project you specified in the config.
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"description": "# Solution Accelerators for GenAI\nThis repository contains platform code for accelerating development of GenAI solutions in Applied AI Engineering team\n\n\n\n# Structure\n\n- **docs**: This directory contains documentation, user guides, and any other resources that help you understand and use the GenAI solution accelerators effectively.\n\n- **src**: The source code for the GenAI solution accelerators is located here. This is where you'll find the core codebase for the tools and frameworks provided in this repository.\n\n- **data**: This directory may contain sample data or data-related resources that can be used for testing and development.\n\n- **tests**: Test cases and resources related to testing the GenAI solution accelerators are stored in this directory.\n\n- **scripts**: Utility scripts or automation scripts that can assist in various tasks related to GenAI development and deployment.\n\n- **examples**: This directory may contain example projects, code snippets, or reference implementations that showcase how to use the provided solution accelerators effectively.\n\n## Getting Started\n\nTo get started with the GenAI solution accelerators, follow the instructions in the documentation located in the `docs` directory. It will provide you with step-by-step guidance on how to set up your development environment and use the tools and frameworks provided in this repository.\n\n## Contribution Guidelines\n\nWe welcome contributions from the GenAI community! If you'd like to contribute to this repository, please follow our [Contribution Guidelines](CONTRIBUTING.md) to ensure a smooth collaboration process.\n\n## License\n\nThis repository is licensed under the [Apaache License](LICENSE). See the [LICENSE](LICENSE) file for details.\n\n## Contact\n\nIf you have any questions or need assistance, feel free to reach out to the project maintainers or create an issue in this repository.\n\nHappy GenAI development!\n\n\n## Setting up\nTo begin development you can use 2 different approaches: using Python Environment or using Docker. Below are instructions for each approach.\n\n### Setting up Python Environment\nMake sure to install miniconda environment:\n```\ncd ~/\nmkdir -p ~/miniconda3\nwget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh\nbash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3\n~/miniconda3/bin/conda init bash\n```\nAfter that just install the package in editable mode:\n\n```\npip install -e .\n```\n\n### Setting up Docker\nIf this is your first time, you probably don't have Docker installed on VM. Execute the following commands:\n```\nsudo apt update && sudo apt upgrade\nsudo apt install make\nsudo apt install docker.io\nsudo groupadd docker\nsudo usermod -aG docker $USER\nsudo chmod 777 /var/run/docker.sock\n```\n\n### Setting up environment\n\n```\nmake build && make container\n```\n\nIf you want to remove it, execute:\n\n```\nmake clean\n```\n\n\n### Copying resources\n\nMake sure `gcs_source_bucket` field in `llm.yaml` is up to date with the latest extraction in use. Then run the copying python script:\n```\npython gen_ai/copy_resources.py\n```\n\n\n### Updating BigQuery table\n\nIt is currently set up that all the runs are logged into \"uhg\" dataset in \"chertushkin-genai-sa\" project. To change the project id - change `bq_project_id` field of `llm.yaml` file. If you receive an error in logging, check if the service account is added to BigQuery IAM of \"chertushkin-genai-sa\" project. Or whatever the project you specified in the config.\n",
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