The model utilizes a CSV file as the training dataset containing various attributes of FIFA players. The dataset includes features such as age, height_cm, marking, body_type, skill_moves (1-5), and other football player characteristics. "overall_rating" has been designated as the target variable.
This model is a regression model used to predict the "Overall Rating," and it is built using an artificial neural network. The layers of the model are structured as follows:
Input Layer: 512 neurons, ReLU activation function Hidden Layer: 128 neurons, ReLU activation function Output Layer: 1 neuron, Linear activation function The model is compiled using the "adam" optimization algorithm and the "mean_squared_error" loss function.
apt update && apt install make# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl gnupg
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg
# Add the repository to Apt sources:
echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
$(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
# Manage Docker as a non-root user
```sh
sudo groupadd docker
sudo usermod -aG docker $USER
newgrp docker- Please check Docker documention Docker Linux Postinstall
curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
chmod +x ./kubectl
mv kubectl /usr/local/bin- Check installation
kubectl versionsudo snap install microk8s --classic
microk8s enable dns
microk8s enable dashboard
microk8s enable storage
microk8s enable cert-manager- Set config for kubernetes
microk8s config > .kube/config- Check microk8s installation
kubectl get nodescurl -s "https://raw.githubusercontent.com/kubernetes-sigs/kustomize/master/hack/install_kustomize.sh" | bash
chmod +x ./kustomize
sudo mv kustomize /usr/local/bingit clone https://github.com/kubeflow/manifests.git
git checkout v1.8.0 #Checkoput v1.8.0 version for kubeflow
while ! kustomize build example | kubectl apply -f -; do echo "Retrying to apply resources"; sleep 10; done- Please check installation kubeflow for documention Kubeflow Manifest
curl https://pyenv.run | bashAdd those commands below to your shell
export PYENV_ROOT="$HOME/.pyenv
command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"
Python version will be specified with ".python_env" file in current directory
pyenv install
Set up venv for Python in the current directory
python3 -m venv .venv
source .venv/bin/activatepip install -r dev_requirements.txt
pip install -r requirements.txtmake formatmake lintmake create-modelkubectl port-forward svc/istio-ingressgateway -n istio-system --address 0.0.0.0 8080:80 &Tag for model. Pass make KF_PIPELINES_ENDPOINT and MODEL_OUTPUT_PATH environment
make kfp-compile TAG=1.2.4 KF_PIPELINES_ENDPOINT="http://localhost:8080" MODEL_OUTPUT_PATH="/data"make deploy-functionmake call-model