This repo contains all the code and projects developed by following the MLBookCamp-code Course offered by DataTalks.Club and Alexey Grigorev.
All homework questions are solved in Jupyter Notebook format at the root of the repository. Further custom code might be found inside a few packages, such as model
.
Choose one of the following environment instructions below to build all dependencies to the project.
All requiments needed to run the project might be found within the docker/resources/conda.yml
file, which might be used to build a conda environment easily through the command:
conda env create -f docker/resources/conda.yml
Obs: Conda or Miniconda are required for this setup.
It is also possible to run the project through a local Jupyter Notebook by using a Docker Container in a few steps.
-
Access the
.env
file to customise the environment variables.Note: Map the
USER_UID
variable to your User ID. -
Build the Docker Image by running the command:
docker-compose build
-
Run the Docker container in detached mode by running:
docker-compose up -d
-
Verify if your container is running through the command below:
docker ps
Note: You should see an output like this:
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 03ca4cdb7893 miniconda-docker:latest "jupyter notebook --…" 3 minutes ago Up 3 minutes 0.0.0.0:8000->8000/tcp, :::8000->8000/tcp mlbookcamp_project_myenv_1
After this last step you should be able to access a local instance of Jupyter Notebook in the following link: http://localhost:8000/
Important: The default authentication token is
DOCKERNOTEBOOK
, but it can be changed in the.env
file.
Obs: Docker and Docker-Compose are required to this setup.
- mlbookcamp-code: https://github.com/alexeygrigorev/mlbookcamp-code/
- mlbookcamp-code 2022 Cohort: https://github.com/alexeygrigorev/mlbookcamp-code/blob/master/course-zoomcamp/cohorts/2022