├── .DS_Store ├── LICENSE ├── README.md ├── __pycache__ └── paradigm.cpython-310.pyc ├── argo-darwin-amd64 ├── examples └── basic │ ├── p1.py │ ├── p2.ipynb │ ├── p3.py │ └── requirements.p3 ├── install-aws.sh ├── install.sh ├── paradigm.egg-info ├── PKG-INFO ├── SOURCES.txt ├── dependency_links.txt ├── entry_points.txt ├── requires.txt └── top_level.txt ├── paradigm.py ├── paradigm_aws.py ├── rbac.yaml ├── requirements.txt ├── setup-aws.py ├── setup.py └── utils ├── .DS_Store └── images ├── .DS_Store ├── logo_slogan_2.png ├── prdm-demo-1.gif ├── prdm-demo-2.gif └── tutorial_thumbnail.png /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ParadigmAI/paradigm/c89047c1ec85c9726f959d00e45366d79c9a100c/.DS_Store -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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2 | 3 |

4 | 5 |

6 | version 7 | discord 8 | email 9 | website 10 |

11 | 12 | 🌟 **With Paradigm, you ML code is production-ready from the beginning** 13 | 14 | Paradigm is a light-weight, lightning-fast, supremely adaptable tool, effortlessly packaging your ML code into robust pipelines for seamless deployment on Kubernetes. Bypass the need for code refactoring as Paradigm intelligently interprets your Python notebooks and scripts, priming them for scalable production. Paradigm is your ultimate ally in ML deployment, merging unparalleled speed, adaptability, and simplicity into one package. 15 | 16 | Official website - [paradigmai.net](https://www.paradigmai.net) 17 | 18 | # Paradigm in Action 19 | ## Terminal View 20 | ![](utils/images/prdm-demo-2.gif) 21 | 22 | ## DAG View 23 | ![](utils/images/prdm-demo-1.gif) 24 | 25 | # Deploy an ML pipeline in just 2 steps 26 | 27 | ```console 28 | $ paradigm launch --step 29 | $ paradigm deploy 30 | ``` 31 | 32 | # Quickstart 33 | 34 | ## Video 35 | [![paradigm](utils/images/tutorial_thumbnail.png)](https://youtu.be/mihz5iaJ3FY) 36 | 37 |

38 | 39 | ## Try Locally 💻 40 | 41 | You need a Kubernetes cluster and `kubectl` set up to be able to access that cluster. For this to run locally, we recommend using `minikube`. 42 | - Please refer to the [minikube documentation](https://minikube.sigs.k8s.io/docs/) 43 | 44 | ### Set up Paradigm 45 | 46 | - (Recommended) Create a new Python environment with your preferred environment manager 47 | - Clone this repo 48 | - `git clone https://github.com/ParadigmAI/paradigm.git` 49 | - Go into the directory 50 | - `cd paradigm` 51 | - Make the installation script executable 52 | - `chmod +x install.sh` 53 | - Run the intallation script 54 | - `./install.sh` 55 | - Validate if paradigm was properly installed 56 | - `paradigm --help` 57 | 58 | ### Now let's move into your ML project folder 59 | 60 | Your folder can contain one or more scripts or Python notebooks that you want to execute as steps in an ML pipeline. 61 | 62 | - First, let's configure your current terminal session to use the Docker daemon inside the Minikube environment instead of the default Docker daemon on your host machine. This eliminated the need for an image registry when working locally. 63 | - `eval $(minikube docker-env)` 64 | 65 | **From here we follow a basic example project just to make it easier to exaplin the commands. Please change the necessary parameters according to your project** 66 | - The preferred directory structure should be as follows. In the below example, `p1, p2 and p3` represent the names of the python scripts or notebooks you have. (Refer the [examples/basic](./examples/basic)) 67 | - IMPORTANT - Note the `requirements.` files. You have to create a txt with that specific naming **only for the scripts or notebooks that have additional dependencies**. It becomes the `requirements.txt` for that step. We promise this is the only file addition before taking your ML code to prodution. 68 | - Example: 69 | ``` 70 | - 📁 project_root 71 | - 📄 p1.py 72 | - 📄 p2.ipynb 73 | - 📄 p3.py 74 | - 📄 requirements.p1 75 | - 📄 requirements.p3 76 | ``` 77 | 78 | - Now we are ready to let Paradigm get things ready before deploying to Kubernetes. Include the scripts/notebook you want as steps in the below command. This command basically containerizes your code. 79 | ``` 80 | paradigm launch --steps p1 p2 p3 81 | ``` 82 | - As the final step, deploy the pipeline with the below command. 83 | 84 | ``` 85 | paradigm deploy --steps p1 p2 --dependencies "p2:p1,p3:p2|p1" --deployment p3 --deployment_port 8000 --output workflow.yaml --name pipeline1 86 | ``` 87 | - In the above command: 88 | - `--steps` should speicify all steps, except any step that should be run as a service, e.g., an API endpoint. 89 | - `--dependencies "p2:p1,p3:p2|p1"` defines the graph stucture (DAG) on how the steps should be run. In this example, we are stating that step `p2` is dependent on `p1` and step `p3` is dependent on both `p2` and `p1`. 90 | - `--deployment p3` defines a service that needs to be run at the end of the pipeline. Hence, we don't mention is under `--steps`. 91 | - `--deployment_port` is defined if the above service is exposed via a specific port internally. 92 | - `--name` can be any name that you want to give this particualr pipeline 93 | 94 | - (OPTIONAL) You can use Argo UI to observe all pipelines and get logs. For that, first make it accessible via your browser by running the below command. 95 | - `kubectl -n paradigm port-forward deployment/argo-server 2746:2746` 96 | - Now in your local browser, go to `http://localhost:2746` 97 | 98 | - (OPTIONAL) To access the service that is deployed in the previous set (for example an API endpoint), run the following code since we're working inside minikube. 99 | 100 | - `minikube service deploy-p3 -n paradigm` 101 | 102 | - (OPTIONAL) In case you want to delete the running service and deployment, use the following commands. `` is the name of the file that has the deolyment code. 103 | - `kubectl delete deployment deploy- -n paradigm` 104 | - `kubectl delete service deploy- -n paradigm` 105 | 106 |

107 | 108 | ## To Deploy in AWS ☁️ 109 | 110 | You need a Kubernetes cluster and `kubectl` set up to be able to access that cluster. On AWS, we use Amazon Elastic Kubernetes Service (Amazon EKS) for this. 111 | - Please refer to the [Amazon EKS](https://docs.aws.amazon.com/eks/latest/userguide/getting-started.html) on how to set things up 112 | - Make sure you can [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html) installed and configured as well 113 | 114 | Also, make sure [Docker](https://docs.docker.com/engine/install/) is installed and running in your environment 115 | 116 | ### Set up Paradigm 117 | 118 | In a terminal with the above kubectl access, follow the below steps. 119 | 120 | - (Recommended) Create a new Python environment with your preferred environment manager 121 | - Clone this repo 122 | - `git clone https://github.com/ParadigmAI/paradigm.git` 123 | - Go into the directory 124 | - `cd paradigm` 125 | - Make the installation script executable 126 | - `chmod +x install-aws.sh` 127 | - Run the intallation script 128 | - `./install-aws.sh` 129 | - Validate if paradigm was properly installed 130 | - `paradigm --help` 131 | 132 | ### Now let's move into your ML project folder 133 | 134 | Your folder can contain one or more scripts/notebooks that you want to execute as steps in an ML pipeline. 135 | 136 | **From here we follow a basic example project just to make it easier to exaplin the commands. Please change the necessary parameters according to your project** 137 | - The preferred directory structure should be as follows. In the below example, `p1, p2 and p3` represent the names of the python scripts or notebooks you have. (Refer the [examples/basic](./examples/basic)) 138 | - IMPORTANT - Note the `requirements.` files. You have to create a txt with that specific naming **only for the scripts or notebooks that have additional dependencies**. It becomes the `requirements.txt` for that step. We promise this is the only file addition before taking your ML code to prodution. 139 | - Example: 140 | ``` 141 | - 📁 project_root 142 | - 📄 p1.py 143 | - 📄 p2.ipynb 144 | - 📄 p3.py 145 | - 📄 requirements.p1 146 | - 📄 requirements.p3 147 | ``` 148 | 149 | - Now we are ready to let Paradigm get things ready before deploying to Kubernetes. Include the scripts/notebook you want as steps in the below command. This command basically containerizes your code. 150 | ``` 151 | paradigm launch --steps p1 p2 p3 --region_name us-east-1 152 | ``` 153 | - As the final step, deploy the pipeline with the below command. 154 | 155 | ``` 156 | paradigm deploy --steps p1 p2 --dependencies "p2:p1,p3:p2|p1" --deployment p3 --deployment_port 8000 --output workflow.yaml --name pipe1 --region_name us-east-1 157 | ``` 158 | - In the above command: 159 | - `--steps` should speicify all steps, except any step that should be run as a service, e.g., an API endpoint. 160 | - `--dependencies "p2:p1,p3:p2|p1"` defines the graph stucture (DAG) on how the steps should be run. In this example, we are stating that step `p2` is dependent on `p1` and step `p3` is dependent on both `p2` and `p1`. 161 | - `--deployment p3` defines a service that needs to be run at the end of the pipeline. Hence, we don't mention is under `--steps`. 162 | - `--deployment_port` is defined if the above service is exposed via a specific port internally. 163 | - `--name` can be any name that you want to give this particualr pipeline 164 | - `--region_name` is the aws region that you want to use 165 | 166 | - (OPTIONAL) You can use Argo UI to observe all pipelines and get logs. For that, first make it accessible via your browser by running the below command. 167 | - `kubectl -n paradigm port-forward deployment/argo-server 2746:2746` 168 | - Now I your local browser, go to `http://localhost:2746` 169 | 170 | - (OPTIONAL) In case you want to delete the running service and deployment, use the following commands. `` is the make of the file that has the deolyment code. 171 | - `kubectl delete deployment deploy- -n paradigm` 172 | - `kubectl delete service deploy- -n paradigm` 173 | 174 | 175 | 176 | 177 |

178 | 179 | ## Learn more 180 | 181 | | Section | Description | 182 | |-|-| 183 | | [Documentation](https://paradigmai.github.io/paradigm-docs/) | Full documentation and tutorials | 184 | | [Basic Tutorial](https://paradigmai.github.io/paradigm-docs/Tutorials/basic_example/) | The simplest example with Paradigm | 185 | 186 | 187 | ## Contributing 188 | 189 | Suggestions on additional features and functionality are highly appreciated. General instructions on how to contribute are mentioned in [CONTRIBUTING](CONTRIBUTING.md) 190 | 191 | ## Getting help 192 | 193 | Please use the issues tracker of this repository to report on any bugs or questions you have. 194 | 195 | Also you can join the [DISCORD](https://discord.gg/KxHPMsBdyp) -------------------------------------------------------------------------------- /__pycache__/paradigm.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ParadigmAI/paradigm/c89047c1ec85c9726f959d00e45366d79c9a100c/__pycache__/paradigm.cpython-310.pyc -------------------------------------------------------------------------------- /argo-darwin-amd64: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ParadigmAI/paradigm/c89047c1ec85c9726f959d00e45366d79c9a100c/argo-darwin-amd64 -------------------------------------------------------------------------------- /examples/basic/p1.py: -------------------------------------------------------------------------------- 1 | print('p1 woot') -------------------------------------------------------------------------------- /examples/basic/p2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "print(\"notebook woot\")" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": null, 15 | "metadata": {}, 16 | "outputs": [], 17 | "source": [ 18 | "print(\"notebook second cell\")" 19 | ] 20 | } 21 | ], 22 | "metadata": { 23 | "kernelspec": { 24 | "display_name": "Python 3", 25 | "language": "python", 26 | "name": "python3" 27 | }, 28 | "language_info": { 29 | "name": "python", 30 | "version": "3.9.16" 31 | }, 32 | "orig_nbformat": 4, 33 | "vscode": { 34 | "interpreter": { 35 | "hash": "a665b5d41d17b532ea9890333293a1b812fa0b73c9c25c950b3cedf1bebd0438" 36 | } 37 | } 38 | }, 39 | "nbformat": 4, 40 | "nbformat_minor": 2 41 | } 42 | -------------------------------------------------------------------------------- /examples/basic/p3.py: -------------------------------------------------------------------------------- 1 | from fastapi import FastAPI 2 | import uvicorn 3 | 4 | app = FastAPI() 5 | 6 | @app.get("/") 7 | def hello_world(): 8 | return {"message": "Hello, World!"} 9 | 10 | if __name__ == "__main__": 11 | uvicorn.run(app, host="0.0.0.0", port=8000) 12 | 13 | -------------------------------------------------------------------------------- /examples/basic/requirements.p3: -------------------------------------------------------------------------------- 1 | fastapi 2 | uvicorn -------------------------------------------------------------------------------- /install-aws.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Install Argo Workflows CLI 4 | echo "Installing..." 5 | mv setup-aws.py setup.py 6 | kubectl create namespace paradigm 7 | kubectl create namespace argo 8 | kubectl apply -f rbac.yaml 9 | kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.7/install.yaml 10 | kubectl patch deployment \ 11 | argo-server \ 12 | --namespace argo \ 13 | --type='json' \ 14 | -p='[{"op": "replace", "path": "/spec/template/spec/containers/0/args", "value": [ 15 | "server", 16 | "--auth-mode=server" 17 | ]}]' 18 | # kubectl -n paradigm port-forward deployment/argo-server 2746:2746 19 | 20 | # Detect the running operating system 21 | os_name="$(uname)" 22 | 23 | # Execute commands according to the detected OS 24 | case "${os_name}" in 25 | Darwin) # MacOS 26 | echo "Detected MacOS" 27 | # Download the binary 28 | curl -sLO https://github.com/argoproj/argo-workflows/releases/download/v3.4.7/argo-darwin-amd64.gz 29 | 30 | # Unzip 31 | gunzip argo-darwin-amd64.gz 32 | 33 | # Make binary executable 34 | chmod +x argo-darwin-amd64 35 | 36 | # Move binary to path 37 | mv ./argo-darwin-amd64 /usr/local/bin/argo 38 | ;; 39 | Linux) 40 | echo "Detected Linux" 41 | # Download the binary 42 | curl -sLO https://github.com/argoproj/argo-workflows/releases/download/v3.4.7/argo-linux-amd64.gz 43 | 44 | # Unzip 45 | gunzip argo-linux-amd64.gz 46 | 47 | # Make binary executable 48 | chmod +x argo-linux-amd64 49 | 50 | # Move binary to path 51 | sudo mv ./argo-linux-amd64 /usr/local/bin/argo 52 | ;; 53 | *) 54 | echo "Unsupported operating system: ${os_name}" 55 | exit 1 56 | ;; 57 | esac 58 | 59 | #Installing Paradigm Utility 60 | 61 | # Install requirements 62 | pip install -r requirements.txt 63 | 64 | # Install command-line utility 65 | pip install -e . 66 | 67 | echo "Installation complete! You can now use the 'paradigm' commands." 68 | -------------------------------------------------------------------------------- /install.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Install Argo Workflows CLI 4 | echo "Installing..." 5 | mv setup-local.py setup.py 6 | kubectl create namespace paradigm 7 | kubectl create namespace argo 8 | kubectl apply -f rbac.yaml 9 | kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.7/install.yaml 10 | kubectl patch deployment \ 11 | argo-server \ 12 | --namespace argo \ 13 | --type='json' \ 14 | -p='[{"op": "replace", "path": "/spec/template/spec/containers/0/args", "value": [ 15 | "server", 16 | "--auth-mode=server" 17 | ]}]' 18 | # kubectl -n paradigm port-forward deployment/argo-server 2746:2746 19 | 20 | # Detect the running operating system 21 | os_name="$(uname)" 22 | 23 | # Execute commands according to the detected OS 24 | case "${os_name}" in 25 | Darwin) # MacOS 26 | echo "Detected MacOS" 27 | # Download the binary 28 | curl -sLO https://github.com/argoproj/argo-workflows/releases/download/v3.4.7/argo-darwin-amd64.gz 29 | 30 | # Unzip 31 | gunzip argo-darwin-amd64.gz 32 | 33 | # Make binary executable 34 | chmod +x argo-darwin-amd64 35 | 36 | # Move binary to path 37 | mv ./argo-darwin-amd64 /usr/local/bin/argo 38 | ;; 39 | Linux) 40 | echo "Detected Linux" 41 | # Download the binary 42 | curl -sLO https://github.com/argoproj/argo-workflows/releases/download/v3.4.7/argo-linux-amd64.gz 43 | 44 | # Unzip 45 | gunzip argo-linux-amd64.gz 46 | 47 | # Make binary executable 48 | chmod +x argo-linux-amd64 49 | 50 | # Move binary to path 51 | sudo mv ./argo-linux-amd64 /usr/local/bin/argo 52 | ;; 53 | *) 54 | echo "Unsupported operating system: ${os_name}" 55 | exit 1 56 | ;; 57 | esac 58 | 59 | #Installing Paradigm Utility 60 | 61 | # Install requirements 62 | pip install -r requirements.txt 63 | 64 | # Install command-line utility 65 | pip install -e . 66 | 67 | echo "Installation complete! You can now use the 'paradigm' commands." 68 | -------------------------------------------------------------------------------- /paradigm.egg-info/PKG-INFO: -------------------------------------------------------------------------------- 1 | Metadata-Version: 2.1 2 | Name: paradigm 3 | Version: 0.1.0 4 | License-File: LICENSE 5 | -------------------------------------------------------------------------------- /paradigm.egg-info/SOURCES.txt: -------------------------------------------------------------------------------- 1 | LICENSE 2 | README.md 3 | setup.py 4 | paradigm.egg-info/PKG-INFO 5 | paradigm.egg-info/SOURCES.txt 6 | paradigm.egg-info/dependency_links.txt 7 | paradigm.egg-info/entry_points.txt 8 | paradigm.egg-info/requires.txt 9 | paradigm.egg-info/top_level.txt -------------------------------------------------------------------------------- /paradigm.egg-info/dependency_links.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /paradigm.egg-info/entry_points.txt: -------------------------------------------------------------------------------- 1 | [console_scripts] 2 | paradigm = paradigm:main 3 | -------------------------------------------------------------------------------- /paradigm.egg-info/requires.txt: -------------------------------------------------------------------------------- 1 | PyYAML 2 | docker 3 | -------------------------------------------------------------------------------- /paradigm.egg-info/top_level.txt: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /paradigm.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import yaml 3 | import docker 4 | import os 5 | import shutil 6 | import sys 7 | import subprocess 8 | import json 9 | from halo import Halo 10 | 11 | 12 | spinner = Halo(text='⚡ Processing...', spinner='dots12') 13 | 14 | def convert_ipynb_to_py(input_file, step): 15 | output_file = f"./{step}/" + os.path.splitext(input_file)[0] + ".py" 16 | with open(input_file, "r", encoding="utf-8") as file: 17 | notebook = json.load(file) 18 | 19 | with open(output_file, "w", encoding="utf-8") as output: 20 | for cell in notebook["cells"]: 21 | if cell["cell_type"] == "code": 22 | code = "".join(cell["source"]) 23 | output.write(code) 24 | output.write("\n\n") 25 | 26 | def build_and_push_docker_image(step): 27 | client = docker.from_env() 28 | image_tag = f"{step}:latest" 29 | 30 | # Create step directory if not exists 31 | step_dir = f"./{step}" 32 | if not os.path.exists(step_dir): 33 | os.mkdir(step_dir) 34 | 35 | # Copy Python file and requirements.txt to step directory 36 | # also convert any notebook to a python script 37 | if os.path.exists(f"{step}.ipynb"): 38 | convert_ipynb_to_py(f"{step}.ipynb", step) 39 | else: 40 | shutil.copy(f"{step}.py", step_dir) 41 | 42 | # if no requirements for a file, create an empty requirements file 43 | if os.path.exists(f"requirements.{step}"): 44 | shutil.copy(f"requirements.{step}", step_dir) 45 | else: 46 | with open(f"{step_dir}/" + f"requirements.{step}", "w") as file: 47 | file.write("") 48 | 49 | # Rename requirements.txt in step directory 50 | os.rename(f"{step_dir}/requirements.{step}", f"{step_dir}/requirements.txt") 51 | 52 | # Generate Dockerfile 53 | dockerfile_content = f"""\ 54 | FROM python:3.9 55 | 56 | WORKDIR /app 57 | 58 | COPY ./{step}.py /app 59 | COPY ./requirements.txt /app 60 | 61 | RUN pip3 install --no-cache-dir -r requirements.txt 62 | 63 | CMD ["python", "./{step}.py"] 64 | """ 65 | 66 | dockerfile_path = f"{step_dir}/Dockerfile" 67 | with open(dockerfile_path, "w") as dockerfile: 68 | dockerfile.write(dockerfile_content) 69 | 70 | print(f"Building Docker image: {image_tag}") 71 | image, _ = client.images.build(path=step_dir, tag=image_tag) 72 | 73 | def containerize_steps(steps): 74 | for step in steps: 75 | if not os.path.exists(step): 76 | os.mkdir(step) 77 | build_and_push_docker_image(step) 78 | 79 | def create_workflow_yaml(steps=None, dependencies=None, deployment_step=None, deployment_port=None, name=None): 80 | dag_tasks = [] 81 | container_templates = [] 82 | 83 | if steps: 84 | for step in steps: 85 | task_name = f"step-{step}" 86 | dag_task = { 87 | "name": task_name, 88 | "template": step 89 | } 90 | 91 | if step in dependencies: 92 | dag_task["dependencies"] = [f"step-{d}" for d in dependencies[step]] 93 | 94 | dag_tasks.append(dag_task) 95 | 96 | container_templates.append({ 97 | "name": step, 98 | "container": { 99 | "image": f"{step}:latest", 100 | "command": ["python", f"{step}.py"], 101 | "imagePullPolicy": "Always" 102 | } 103 | }) 104 | 105 | templates = [ 106 | { 107 | "name": "dag-steps", 108 | "dag": { 109 | "tasks": dag_tasks 110 | } 111 | }, 112 | *container_templates 113 | ] 114 | 115 | if deployment_step: 116 | 117 | if deployment_step in dependencies: 118 | deploy_task = { 119 | "name": f"step-{deployment_step}", 120 | "dependencies": [f"step-{d}" for d in dependencies[deployment_step]], 121 | "template": f"deploy-{deployment_step}" 122 | } 123 | else: 124 | deploy_task = { 125 | "name": f"step-{deployment_step}", 126 | "template": f"deploy-{deployment_step}" 127 | } 128 | dag_tasks.append(deploy_task) 129 | 130 | deployment_template = { 131 | "name": f"deploy-{deployment_step}", 132 | "script": { 133 | "image": "bitnami/kubectl:latest", 134 | "command": ["/bin/bash"], 135 | "source": f"""kubectl apply -f - <