mirror of https://github.com/kubeflow/examples.git
379 lines
13 KiB
Markdown
379 lines
13 KiB
Markdown
# Objective
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This example is based on the Titanic OpenVaccine competition (https://www.kaggle.com/c/stanford-covid-vaccine). The objective of this exercise is to develop models and design rules for RNA degradation.
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## Environment
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This pipeline was tested using Kubeflow 1.4 and kfp 1.1.2 and x86-64 and ARM based system which includes all Intel and AMD based CPU's and M1/M2 series Macbooks.
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## Step 1: Setup Kubeflow as a Service
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- If you haven’t already, sign up (https://www.arrikto.com/kubeflow-as-a-service/)
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- Deploy Kubeflow
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## Step 2: Launch a Notebook Server
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- Bump memory to 2GB and vCPUs to 2
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## Step 3: Clone the Project Repo to Your Notebook
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- (Kubeflow as a Service) Open up a terminal in the Notebook Server and git clone the `kubeflow/examples` repository
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```
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git clone https://github.com/kubeflow/examples
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```
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## Step 4: Setup DockerHub and Docker
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- If you haven’t already, sign up (https://hub.docker.com/) for DockerHub
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- If you haven’t already, install Docker Desktop locally (https://www.docker.com/products/docker-desktop/) OR install the Docker command line utility (https://docs.docker.com/get-docker/) and enter `sudo docker login` command in your terminal and log into Docker with your your DockerHub username and password
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## Step 5: Setup Kaggle
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- If you haven’t already done so, sign up (https://www.kaggle.com/) for Kaggle
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- (On Kaggle) Generate an API token (https://www.kaggle.com/docs/api)
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- (Kubeflow as a Service) Create a Kubernetes secret
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```
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kubectl create secret generic kaggle-secret --from-literal=KAGGLE_USERNAME=<username> --from-literal=KAGGLE_KEY=<api_token>
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```
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## Step 6: Install Git
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- (Locally) If you don’t have it already, install Git (https://github.com/git-guides/install-git)
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## Step 7: Clone the Project Repo Locally
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- (Locally) Git clone the `kubeflow/examples` repository
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```
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git clone https://github.com/kubeflow/examples
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```
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## Step 8: Create a PodDefault Resource
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- (Kubeflow as a Service) Navigate to the `openvaccine-kaggle-competition` directory
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- Create a `resource.yaml` file
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resource.yaml:
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```
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apiVersion: "kubeflow.org/v1alpha1"
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kind: PodDefault
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metadata:
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name: kaggle-access
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spec:
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selector:
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matchLabels:
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kaggle-secret: "true"
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desc: "kaggle-access"
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volumeMounts:
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- name: secret-volume
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mountPath: /secret/kaggle
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volumes:
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- name: secret-volume
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secret:
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secretName: kaggle-secret
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```
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- Apply created resource using: `kubectl apply -f resource.yaml`
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## Step 9: Explore the `load-data` directory
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/load-data` directory
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- Open up the `load.py` file
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- Note the code in this file that will perform the actions required in the “load-data” pipeline step
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<img width="209" alt="image7" src="https://user-images.githubusercontent.com/17012391/177001311-efbee116-5a7a-498e-9d3a-6b50f192db97.png">
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## Step 10: Build the `load-data` Docker Image
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/load-data` directory
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- Build the Docker image if locally you are using arm64 (Apple M1)
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```
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docker build --platform=linux/amd64 -t <docker_username>/<docker_imagename>:<tag>-amd64 .
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```
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- OR build the Docker image if locally you are using amd64
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```
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docker build -t <docker_username>/<docker_imagename>:<tag> .
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```
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## Step 11: Push the `load-data` Docker Image to DockerHub
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/load-data` directory
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- Push the Docker image if locally you are using arm64 (Apple M1)
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```
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docker push <docker_username>/<docker_imagename>:<tag>-amd64
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```
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- OR build the Docker image if locally you are using amd64
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```
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docker push <docker_username>/<docker_imagename>:<tag>
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```
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## Step 12: Explore the `preprocess-data` directory
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/preprocess-data` directory
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- Open up the `preprocess.py` file
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- Note the code in this file that will perform the actions required in the “preprocess” pipeline step
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<img width="209" alt="image5" src="https://user-images.githubusercontent.com/17012391/177001608-bc82e3ea-7581-4de9-bd29-2d0fc410b817.png">
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## Step 13: Explore the `preprocess-data` directory
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/preprocess-data` directory
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- Build the Docker image if locally you are using arm64 (Apple M1)
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```
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docker build --platform=linux/amd64 -t <docker_username>/<docker_imagename>:<tag>-amd64 .
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```
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- OR build the Docker image if locally you are using amd64
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```
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docker build -t <docker_username>/<docker_imagename>:<tag> .
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```
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## Step 14: Push the `preprocess-data` Docker Image to DockerHub
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/preprocess-data` directory
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- Push the Docker image if locally you are using arm64 (Apple M1)
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```
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docker push <docker_username>/<docker_imagename>:<tag>-amd64
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```
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- OR build the Docker image if locally you are using amd64
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```
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docker push <docker_username>/<docker_imagename>:<tag>
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```
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## Step 15: Explore the `model-training` directory
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/model-training` directory
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- Open up the `model.py` file
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- Note the code in this file that will perform the actions required in the “train” pipeline step
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## Step 16: Build the `model-training` Docker Image
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/model-training` directory
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- Build the Docker image if locally you are using arm64 (Apple M1)
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```
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docker build --platform=linux/amd64 -t <docker_username>/<docker_imagename>:<tag>-amd64 .
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```
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- OR build the Docker image if locally you are using amd64
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```
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docker build -t <docker_username>/<docker_imagename>:<tag> .
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```
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## Step 17: Push the `model-training` Docker Image to DockerHub
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/model-training` directory
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- Push the Docker image if locally you are using arm64 (Apple M1)
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```
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docker push <docker_username>/<docker_imagename>:<tag>-amd64
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```
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- OR build the Docker image if locally you are using amd64
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```
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docker push <docker_username>/<docker_imagename>:<tag>
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```
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## Step 18: Explore the `model-evaluation` directory
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/model-evaluation` directory
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- Open up the `eval.py` file
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- Note the code in this file that will perform the actions required in the “test” pipeline step
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## Step 19: Build the `model-evaluation` Docker Image
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/model-evaluation` directory
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- Build the Docker image if locally you are using arm64 (Apple M1)
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```
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docker build --platform=linux/amd64 -t <docker_username>/<docker_imagename>:<tag>-amd64 .
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```
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- OR build the Docker image if locally you are using amd64
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```
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docker build -t <docker_username>/<docker_imagename>:<tag> .
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```
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## Step 20: Push the `model-evaluation` Docker Image to DockerHub
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- (Locally) Navigate to the `openvaccine-kaggle-competition/pipeline-components/model-evaluation` directory
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- Push the Docker image if locally you are using arm64 (Apple M1)
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```
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docker push <docker_username>/<docker_imagename>:<tag>-amd64
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```
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- OR build the Docker image if locally you are using amd64
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```
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docker push <docker_username>/<docker_imagename>:<tag>
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```
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## Step 21: Modify the openvaccine-kaggle-competiton-kfp.py file
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- (Kubeflow as a Service) Navigate to the `openvaccine-kaggle-competition` directory
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- Update the `openvaccine-kaggle-competiton-kfp.py` with accurate Docker Image inputs
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```
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return dsl.ContainerOp(
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name = 'load-data',
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image = '<dockerhub username>/<image name>:<tag>',
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—-----
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def GetMsg(comp1):
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return dsl.ContainerOp(
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name = 'preprocess',
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image = '<dockerhub username>/<image name>:<tag>',
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—-----
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def Train(comp2, trial, epoch, batchsize, embeddim, hiddendim, dropout, spdropout, trainsequencelength):
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return dsl.ContainerOp(
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name = 'train',
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image = '<dockerhub username>/<image name>:<tag>',
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—-----
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def Eval(comp1, trial, epoch, batchsize, embeddim, hiddendim, dropout, spdropout, trainsequencelength):
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return dsl.ContainerOp(
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name = 'Evaluate',
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image = '<dockerhub username>/<image name>:<tag>',
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```
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## Step 22: Generate a KFP Pipeline yaml File
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- (Locally) Navigate to the `openvaccine-kaggle-competition` directory and delete the existing `openvaccine-kaggle-competition-kfp.yaml` file
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- (Kubeflow as a Service) Navigate to the openvaccine-kaggle-competition directory
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Build a python virtual environment :
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Step a) Update pip
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```
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python3 -m pip install --upgrade pip
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```
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Step b) Install virtualenv
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```
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sudo pip3 install virtualenv
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```
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Step c) Check the installed version of venv
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```
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virtualenv --version
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```
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Step d) Name your virtual enviornment as kfp
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```
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virtualenv kfp
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```
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Step e) Activate your venv.
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```
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source kfp/bin/activate
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```
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After this virtual environment will get activated. Now in our activated venv we need to install following packages:
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```
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sudo apt-get update
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sudo apt-get upgrade
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sudo apt-get install -y git python3-pip
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python3 -m pip install kfp==1.1.2
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```
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After installing packages create the yaml file
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Inside venv point your terminal to a path which contains our kfp file to build pipeline (openvaccine-kaggle-competition-kfp.py) and run these commands to generate a `yaml` file for the Pipeline:
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```
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python3 openvaccine-kaggle-competition-kfp.py
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```
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<img width="492" alt="image3" src="https://user-images.githubusercontent.com/17012391/177002110-5c0bdfc3-f9cc-453d-84b4-0107f506d54d.png">
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Download the `openvaccine-kaggle-competition-kfp.yaml` file that was created to your local `openvaccine-kaggle-competition` directory
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## Step 23: Create an Experiment
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- (Kubeflow as a Service) Within the Kubeflow Central Dashboard, navigate to the Experiments (KFP) > Create Experiment view
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- Name the experiment and click Next
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- Click on Experiments (KFP) to view the experiment you just created
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## Step 24: Create a Pipeline
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- (Kubeflow as a Service) Within the Kubeflow Central Dashboard, navigate to the Pipelines > +Upload Pipeline view
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- Name the pipeline
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- Click on Upload a file
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- Upload the local `openvaccine-kaggle-competition-kfp.yaml` file
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- Click Create
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## Step 25: Create a Run
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- (Kubeflow as a Service) Click on Create Run in the view from the previous step
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- Choose the experiment we created in Step 23
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- Input your desired run parameters. For example:
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```
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TRIAL = 1
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EPOCHS = 2
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BATCH_SIZE = 64
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EMBED_DIM = 100
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HIDDEN_DIM = 128
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DROPOUT = .2
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SP_DROPOUT = .3
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TRAIN_SEQUENCE_LENGTH = 107
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```
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- Click Start
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- Click on the run name to view the runtime execution graph
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## Troubleshooting Tips:
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While running the pipeline as mentioned above you may come across this error:
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errorlog:
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```
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kaggle.rest.ApiException: (403)
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Reason: Forbidden
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HTTP response headers: HTTPHeaderDict({'Content-Type': 'application/json', 'Date': 'Thu, 23 Jun 2022 11:31:18 GMT', 'Access-Control-Allow-Credentials': 'true', 'Set-Cookie': 'ka_sessionid=6817a347c75399a531148e19cad0aaeb; max-age=2626560; path=/, GCLB=CIGths3--ebbUg; path=/; HttpOnly', 'Transfer-Encoding': 'chunked', 'Vary':
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HTTP response body: b'{"code":403,"message":"You must accept this competition\\u0027s rules before you\\u0027ll be able to download files."}'
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```
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This error occours for two reasons:
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- Your Kaggle account is not verified with your phone number.
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- Rules for this specific competitions are not accepted.
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Lets accept Rules of competition
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Click on "I Understand and Accept". After this you will be prompted to verify your account using your phone number:
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Add your phone number and Kaggle will send the code to your number, enter this code and verify your account. ( Note: pipeline wont run if your Kaggle account is not verified )
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## Success
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After the kaggle account is verified pipeline run is successful we will get the following:
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<img width="1244" alt="Screenshot 2022-06-06 at 3 00 51 PM" src="https://user-images.githubusercontent.com/17012391/172135040-50d6e9f3-d156-4e95-9b9f-492e99108d82.png">
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