45 lines
1.3 KiB
Docker
45 lines
1.3 KiB
Docker
# Obraz ubuntu latest
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FROM ubuntu:latest
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# Instalacja niezbędnych bibliotek
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RUN apt update
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RUN apt-get update
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RUN apt install -y figlet
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RUN export PATH=”$PATH:/usr/local/bin/python”
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RUN apt install python3-pip -y
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RUN apt install unzip -y
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RUN pip3 install kaggle
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RUN pip3 install pandas
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RUN pip3 install torch
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RUN pip3 install torchvision
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RUN pip3 install jovian
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RUN pip3 install matplotlib
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RUN pip3 install seaborn
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RUN pip3 install sklearn
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RUN pip3 install sacred
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RUN pip3 install numpy
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RUN pip3 install wheel --no-deps -U
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RUN pip3 install GitPython
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RUN pip3 install pymongo
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RUN pip3 install mlflow
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RUN pip3 install dvc
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RUN pip3 install dvc[ssh] paramiko
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RUN apt install -y sshpass openssh-client
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RUN useradd -r -u 111 jenkins
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# Przygotowanie kaggle
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RUN mkdir ~/.kaggle/
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RUN echo '{"username":"sebastianwalesa","key":"c879e3806be4cd6ade7aa10ea81d4cfe"}' > ~/.kaggle/kaggle.json
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# Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)
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WORKDIR /app
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# Skopiujmy nasz skrypt do katalogu /app w kontenerze
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COPY ./skrypt.sh ./
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COPY ./init.py ./
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COPY ./understat.csv ./
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COPY ./ml_pytorch_mlflow.py ./
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# Domyślne polecenie, które zostanie uruchomione w kontenerze po jego starcie
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CMD ./skrypt.sh |