From d50b8ebced1a5676a7017de118b7cf1ce314588c Mon Sep 17 00:00:00 2001 From: Baris Can Durak Date: Thu, 11 Jan 2024 17:42:37 +0100 Subject: [PATCH] removed gitflow completely --- gitflow/README.md | 439 ------------------ gitflow/_assets/1-1/digits_pipeline.png | Bin 11858 -> 0 bytes gitflow/_assets/1-1/view_run_in_dashboard.gif | Bin 7615031 -> 0 bytes .../1-2/dashboard_artifact_store_run_list.png | Bin 322196 -> 0 bytes gitflow/_assets/1-2/dashboard_cached.png | Bin 123296 -> 0 bytes gitflow/_assets/1-2/dashboard_initial.png | Bin 125056 -> 0 bytes .../_assets/1-2/dashboard_partly_cached.png | Bin 122619 -> 0 bytes gitflow/_assets/1-2/dashboard_stack_list.png | Bin 233421 -> 0 bytes .../_assets/1-2/local_stack_redesigned.png | Bin 48802 -> 0 bytes gitflow/_assets/2-1/mlflow_ui.png | Bin 167015 -> 0 bytes gitflow/_assets/2-1/wandb_ui.png | Bin 198341 -> 0 bytes gitflow/_assets/pipeline_architecture.png | Bin 1076910 -> 0 bytes gitflow/_assets/pipeline_local.png | Bin 267507 -> 0 bytes gitflow/_assets/pipeline_prod.png | Bin 466174 -> 0 bytes gitflow/_assets/pipeline_staging.png | Bin 440760 -> 0 bytes gitflow/_assets/zenml_logo.png | Bin 96801 -> 0 bytes 16 files changed, 439 deletions(-) delete mode 100644 gitflow/README.md delete mode 100644 gitflow/_assets/1-1/digits_pipeline.png delete mode 100644 gitflow/_assets/1-1/view_run_in_dashboard.gif delete mode 100644 gitflow/_assets/1-2/dashboard_artifact_store_run_list.png delete mode 100644 gitflow/_assets/1-2/dashboard_cached.png delete mode 100644 gitflow/_assets/1-2/dashboard_initial.png delete mode 100644 gitflow/_assets/1-2/dashboard_partly_cached.png delete mode 100644 gitflow/_assets/1-2/dashboard_stack_list.png delete mode 100644 gitflow/_assets/1-2/local_stack_redesigned.png delete mode 100644 gitflow/_assets/2-1/mlflow_ui.png delete mode 100644 gitflow/_assets/2-1/wandb_ui.png delete mode 100644 gitflow/_assets/pipeline_architecture.png delete mode 100644 gitflow/_assets/pipeline_local.png delete mode 100644 gitflow/_assets/pipeline_prod.png delete mode 100644 gitflow/_assets/pipeline_staging.png delete mode 100644 gitflow/_assets/zenml_logo.png diff --git a/gitflow/README.md b/gitflow/README.md deleted file mode 100644 index 7ae0c1d3..00000000 --- a/gitflow/README.md +++ /dev/null @@ -1,439 +0,0 @@ - - -# ZenML - GitHub Action Workflow - -> [!IMPORTANT] -> The actual code for this project lies in a separate GitHub repository -> [here](https://github.com/zenml-io/zenml-gitflow). - -## 🖼️ Overview - -This repository showcases how ZenML can be used for machine learning with a -GitHub workflow that automates CI/CD with continuous model training and -continuous model deployment to production. This allows data scientists to -experiment with data processing and model training locally and then have code -changes automatically tested and validated through the standard GitHub PR peer -review process. Changes that pass the CI and code-review are then deployed -automatically to production. - -This repository is also meant to be used as a template: you can fork it and -easily adapt it to your own MLOps stack, infrastructure, code and data. - -Here's an architecture diagram of what this can look like: - -Pipeline being run on staging/production stack through ci/cd - - -The workflow works as follows: - -A data scientist wants to make improvements to the ML pipeline. They clone the -repository, create a new branch, and experiment with new models or data -processing steps on their local machine. - -Pipeline with local stack - - -Once the data scientist thinks they have improved the pipeline, they create a -pull request for his branch on GitHub. This automatically triggers a GitHub Action -that will run the same pipeline in the staging environment (e.g. a pipeline -running on a cloud stack in GCP), potentially with different test data. As long -as the pipeline does not run successfully in the staging environment, the PR -cannot be merged. The pipeline also generates a set of metrics and test results -that are automatically published to the PR, where they can be peer-reviewed to -decide if the changes should be merged. - -Pipeline with staging stack - -Once the PR has been reviewed and passes all checks, the branch is merged into -main. This automatically triggers another GitHub Action that now runs a -pipeline in the production environment, which trains the same model on -production data, runs some checks to compare its performance with the model -currently served in production and then, if all checks pass, automatically -deploys the new model. - -Pipeline with production stack - -The pipeline implementations follow a set of best practices for MLOps summarized -below: - -* **Experiment Tracking**: All experiments are logged with an experiment tracker -(MLflow), which allows for easy comparison of different runs and models and -provides quick access to visualization and validation reports. -* **Data and Model validation**: The pipelines include a set of Deepchecks-powered -steps that verify integrity of the data and evaluate the model after -training. The results are gathered, analyzed and then a report is generated with -a summary of the findings and a suggested course of action. This provides useful -insights into the quality of the data and the performance of the model and helps to -catch potential issues early on, before the model is deployed to production. -* **Pipeline Tracking**: All pipeline runs and their artifacts are of course -versioned and logged with ZenML. This enables features such as lineage tracking, -provenance, caching and reproducibility. -* **Continuous Integration**: All changes to the code are tested and validated -automatically using GitHub Actions. Only changes that pass all tests are merged -into the main branch. This applies not only to the code itself, but also to -the ML artifacts, such as the data and the model. -* **Continuous Deployment**: When a change is merged into the main branch, it is -automatically deployed to production using ZenML and GitHub Actions. There are also -additional checks in place to ensure that the model is not deployed if it is -not fit for production or performs worse than the model currently deployed. -* **Software Dependency Management**: All software dependencies are managed -in a way that guarantees full reproducibility and are automatically installed -by ZenML in the pipeline runtime environments. Python package versions are frozen -and pinned to ensure that the pipeline runs are fully reproducible. -* **Reproducible Randomness**: All randomness is controlled and seeded to ensure -reproducibility and caching of the pipeline runs. - -## 📦 Local workflow for development and experimentation - -This workflow is meant to be run on a local machine by someone who wants to make -local code changes e.g. to build a better model or to use a different -data source. Aside from training the model, the training pipeline also includes -a set of Deepchecks steps that check the data integrity and evaluate the model. -The results are gathered, analyzed and then a report with the results is -generated and printed out. - -### 🏇 How to run - -1. Clone the repository and create a development branch - -``` -git clone git@github.com:zenml-io/zenml-gitflow.git -git checkout -b -``` - -2. Install local requirements in a virtual environment - -``` -virtualenv .venv -source .venv/bin/activate -pip install -r requirements.txt -``` - -3. Set up your local stack - -``` -stacks/setup_local_stack.sh -``` - -NOTE: this script registers a stack that consists of the following components: - -* the default local orchestrator and artifact store -* a local MLflow tracking server -* a local MLflow model deployer -* Deepchecks as a data/model validator - -4. Iterate on making local code changes and run the training pipeline locally to -test your changes: - -``` -python run.py -``` - -A report will be printed to the console and the model as well as the metrics -and check results generated by Deepchecks will be saved in the MLflow experiment -tracker. You can view the results in the MLflow UI by starting the MLFlow UI -server on your local machine, as instructed in the run output. - -You can optionally exercise the end-to-end workflow locally by running the -pipeline involved in the GitHub automated CI/CD workflow. The end-to-end -pipeline adds model deployment to model training. It also includes steps that -compare the performance of the recently trained model with the performance of -the model currently deployed: - -``` -python run.py --pipeline=end-to-end -``` - -5. When you're ready, push a PR with your changes to `main`. This will trigger -the GitHub CI workflow that will check your code changes using a different -stack running on Vertex AI in GCP! - -## 📦 GitHub staging workflow for automated CI - -This workflow is meant to be run as part of a automated gating CI/CD job that is -triggered to test code changes before they are incorporated into the main -repository branch on GitHub. - -### 🏇 How to trigger - -1. publish a PR with your code changes to the GitHub repository, or push -additional to an existing PR. - -2. this next part is automated with the help of GitHub Actions. A GitHub -workflow is triggered automatically when PR code changes are detected. The -workflow will run the same training pipeline as in the local setup, but this -time it will use a different dataset as input, a remote ZenML server and a cloud -ZenML stack consisting of the following: - -* a Vertex AI orchestrator -* a GCS artifact store -* a remote MLflow tracking server -* Deepchecks as a data/model validator - -The training results will be saved in the remote MLflow tracking server and the -report will be attached to the PR as a comment, where it can be analyzed by -other team members as part of the PR review process. - -3. When the PR is merged to `main`, the production workflow is triggered and -will deploy a new model to production with AWS and Kubeflow! - -### 🏇 How to run locally - -If you need to run the GitHub staging workflow locally e.g. to debug it or to -update it, you can do so by replicating [the steps in the staging GitHub workflow](./.github/workflows/staging.yaml). -However, you need to be granted write access to the remote ZenML server and -credentials to access cloud resources used in the stack, which is why you should -fork the repository, deploy ZenML and run the workflow on your own stack. - -Assuming you have already done so, this is how you can run the workflow locally: - -1. Install cloud requirements in a virtual environment (these are different -from the local requirements because you're using a GCP cloud stack): - -``` -virtualenv .venv-staging -source .venv-staging/bin/activate -pip install -r requirements-gcp.txt -``` - -2. Connect to the remote ZenML server (NOTE: the default local ZenML deployment -or a local ZenML server will not work because there are remote components in the -stack that also need to access the remote ZenML server): - -``` -zenml connect --url --username --password -``` -3. Authenticate to GCP: - -``` -gcloud auth configure-docker --project zenml-demos -``` - -4. Register and/or activate your GCP stack: - -``` -stacks/setup_staging_stack.sh - -# or - -zenml stack set gcp_gitflow_stack -``` - -5. Run the staging workflow: - -``` -python run.py --requirements=requirements-gcp.txt --dataset staging -``` - - -## 📦 GitHub production workflow for automated CD - -This workflow is meant to be run as part of a automated continuous deployment -job that is used to deploy a new model to production whenever a change is merged -into the main repository branch on GitHub. - -### 🏇 How to trigger - -1. merge a PR to the main branch in the GitHub repository, or push -changes directly to the main branch. - -2. this next part is automated with the help of GitHub Actions. A GitHub -workflow is triggered automatically when code changes land in the main branch. -The workflow will run an end-to-end pipeline that goes through model training, -same as the training pipeline, compares the model to the one already deployed -in production, then deploys the new model if it passes tests. This workflow -uses a remote ZenML server and a cloud ZenML stack consisting of the -following: - -* a Kubeflow orchestrator deployed in an AWS EKS cluster -* an S3 artifact store -* a remote MLflow tracking server -* Deepchecks as a data/model validator -* KServe as a model deployer, running on the same EKS cluster - -### 🏇 How to run locally - -If you need to run the GitHub staging workflow locally e.g. to debug it or to -update it, you can do so by replicating [the steps in the GitHub production workflow](./.github/workflows/production.yaml). -However, you need to be granted write access to the remote ZenML server and -credentials to access cloud resources used in the stack, which is why you should -fork the repository, deploy ZenML and run the workflow on your own stack. - -Assuming you have already done so, this is how you can run the workflow locally: - -1. Install cloud requirements in a virtual environment (these are different -from the local requirements because you're using a GCP cloud stack): - -``` -virtualenv .venv-prod -source .venv-prod/bin/activate -pip install -r requirements-aws.txt -``` - -2. Connect to the remote ZenML server (NOTE: the default local ZenML deployment -or a local ZenML server will not work because there are remote components in the -stack that also need to access the remote ZenML server): - -``` -zenml connect --url --username --password -``` - -3. Authenticate to AWS: - -``` -aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 715803424590.dkr.ecr.us-east-1.amazonaws.com -aws eks --region eu-central-1 update-kubeconfig --name kubeflowmultitenant --alias zenml-eks -``` - -4. Set up environment variables to access the multi-user Kubeflow orchestrator: - -``` -export KUBEFLOW_USERNAME= -export KUBEFLOW_PASSWORD= -export KUBEFLOW_NAMESPACE= -``` - -5. Register and/or activate your AWS stack: - -``` -stacks/setup_prod_stack.sh - -# or - -zenml stack set aws_gitflow_stack -``` - -6. Run the staging workflow: - -``` -python run.py \ - --pipeline end-to-end \ - --requirements requirements-aws.txt \ - --model iris-classifier \ - --dataset production -``` - -## 📦 Software requirements management - -In building ML pipelines and their dependencies for production, you want to make -sure that your builds are predictable and deterministic. This requires -Python packages to be pinned to very specific versions, including ZenML itself. - -A series of `requirements.in` files are maintained to contain loosely defined -package dependencies that are either required by the project or derived from the -ZenML integrations. A tool like `pip-compile` is then used to generate frozen -versions of those requirements that are used in the CI/CD pipelines to guarantee -deterministic builds and pipeline runs. - -* `requirements-base.in` contains the base requirements for the project, -including ZenML itself. Note that these are not requirements that are otherwise -managed by ZenML as integrations (e.g. deepchecks, scikit-learn, etc.). -* `requirements-local.in` contains the requirements needed by the local stack. -* `requirements-gcp.in` contains the requirements needed by the GCP stack. -* `requirements-aws.in` contains the requirements needed by the AWS stack. -* `requirements.txt` are frozen package requirements that you use in your -local development workflow. -* `requirements-gcp.txt` are frozen package requirements that you use in your -GCP staging workflow. -* `requirements-aws.txt` are frozen package requirements that you use in your -AWS production workflow. - -### 🏇 How to update requirements - -The frozen requirements files need to be updated whenever you make a change to -the project dependencies. This includes the following: - -* when you add, update or remove a new package to the project that is not -covered by ZenML or a ZenML integration -* when you update ZenML itself to a new version (which may also include updates -to the ZenML integrations and their requirements) -* when you add or remove a ZenML integration to/from the project (also reflected -in the code changes or stacks that you use for the various workflows) - -Whenever one of these happens, you need to update the `requirements.in` files to -reflect the changes. - -Example: you update ZenML to version 0.50.0: - -1. Update the ZenML version in `requirements-base.in`: - -``` -zenml==0.50.0 -``` - -2. Run `zenml integration export-requirements` commands to extract the -dependencies required by all the integrations used for each workflow and update -the corresponding `requirements.in` files: - -``` -# for the local workflow; these should go in requirements-local.in -zenml integration export-requirements sklearn mlflow deepchecks -# for the staging (GCP) workflow; these should go in requirements-gcp.in -zenml integration export-requirements sklearn mlflow deepchecks gcp -# for the staging (AWS) workflow; these should go in requirements-aws.in -zenml integration export-requirements sklearn mlflow deepchecks kubeflow s3 aws kserve -``` - -3. Run `pip-compile` to update the frozen requirements files: - -``` -pip-compile -v -o requirements.txt requirements-base.in requirements-local.in --resolver=backtracking -pip-compile -v -o requirements-gcp.txt requirements-base.in requirements-gcp.in --resolver=backtracking -pip-compile -v -o requirements-aws.txt requirements-base.in requirements-aws.in --resolver=backtracking -``` - -4. Commit the changes to the `requirements.in` and `requirements.txt` files. - -5. (Optional) Update the local python virtual environment that you are using for -development: - -``` -pip install -r requirements.txt -``` - -## 📦 How to fork and use this repository template - -Create a new repository from this template by clicking the "Use this template" -button on the top right of the GitHub repository page. This will create a new -repository in your GitHub account that is a copy of this one. - -You'll need to set up a few more things to connect your new repository to a -ZenML server and a cloud stack: - -* [deploy a ZenML server using a method of your choice](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-zenml) -somewhere where the GitHub Actions workflows can access it (e.g. in the cloud, on a server or local -machine with a public IP, etc.) -* set up the resources needed by the ZenML stacks that you'll be using for the -staging and production workflows. You can use one of the -[ZenML MLOps recipes](https://docs.zenml.io/platform-guide/set-up-your-mlops-platform/deploy-and-set-up-a-cloud-stack/deploy-a-stack-using-stack-recipes) -to speed this up. You'll need the following components to be part of your stack: - * a remote [ZenML artifact store](https://docs.zenml.io/user-guide/component-guide/artifact-stores) (e.g. GCS, S3, etc.) - * a remote [ZenML orchestrator](https://docs.zenml.io/user-guide/component-guide/orchestrators) (e.g. Kubeflow, Airflow, Vertex AI or a basic Kubernetes orchestrator) - * (optional) a remote [MLFlow experiment tracker](https://docs.zenml.io/user-guide/component-guide/experiment-trackers). - Alternatively, you can use a different experiment tracker if you make changes - to the code to use it instead of MLFlow. - * a Kubernetes cluster with the [KServe model deployer](https://docs.zenml.io/user-guide/component-guide/model-deployers) - installed. NOTE: due to a current ZenML limitation, KServe needs to be - installed in the same Kubernetes cluster as the orchestrator. Alternatively, - you can use a different model deployer if you make changes to the code to use - a different model deployment step. - * (optional) [a secrets manager](https://docs.zenml.io/user-guide/component-guide/secrets-managers) - is recommended to store the credentials for the other stack components. The - secrets manager service also needs to be accessible from the runtime used - to run pipelines. - -Once you have the ZenML server and the cloud stack set up, you'll need to do the -following to update the repository with the information about your own -environments: - -1. Register the stack(s) that you'll be using for the local, staging and -production workflows. Take a look at the `stacks` directory to see an example -of how the stacks are defined. - -2. Update the software dependencies in the `requirements.in` files to match -your own project requirements. - -3. Update the GitHub workflow files to reference the correct secrets. - -4. 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