Upgrade Dockerfile

Author: krystianfranus

Dockerfile

@@ -1,20 +1,48 @@
-FROM nvidia/cuda:10.1-base-ubuntu18.04
+FROM ubuntu:24.04
 
-# Install necessary system packages and Python 3.10
-RUN apt-get update && apt-get upgrade -y && \
-    apt-get install -y software-properties-common && \
-    add-apt-repository ppa:deadsnakes/ppa && \
-    apt-get install -y python3.10 python3.10-distutils python3.10-dev python3.10-venv vim
+# Install dependencies for building Python and basic tools
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    wget \
+    curl \
+    git \
+    libssl-dev \
+    zlib1g-dev \
+    libncurses5-dev \
+    libncursesw5-dev \
+    libreadline-dev \
+    libsqlite3-dev \
+    libgdbm-dev \
+    libbz2-dev \
+    libexpat1-dev \
+    liblzma-dev \
+    tk-dev \
+    libffi-dev \
+    uuid-dev \
+    vim \
+    ca-certificates
 
-# Set Python 3.10 as the default Python version
-RUN update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 1
+# Install Python 3.13.2 from source
+ENV PYTHON_VERSION=3.13.2
+WORKDIR /tmp
 
-## Create a virtual environment and activate it
+RUN wget https://www.python.org/ftp/python/${PYTHON_VERSION}/Python-${PYTHON_VERSION}.tgz && \
+    tar -xzf Python-${PYTHON_VERSION}.tgz && \
+    cd Python-${PYTHON_VERSION} && \
+    ./configure --enable-optimizations && \
+    make -j$(nproc) && \
+    make altinstall && \
+    ln -s /usr/local/bin/python3.13 /usr/bin/python3 && \
+    ln -s /usr/local/bin/pip3.13 /usr/bin/pip3
+
+# Create and activate virtual environment
 ENV VIRTUAL_ENV=/opt/venv
 RUN python3 -m venv $VIRTUAL_ENV
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
 
+# Set working directory and install project
 WORKDIR /project
 COPY . .
+
+# Install dependencies and Jupyter extensions
 RUN pip install --upgrade pip && pip install -e .
-RUN jupyter contrib nbextension install

README.md

@@ -1,8 +1,8 @@
 <div align="center">
 
-# 🚀⚡🔥 data-science-template 🚀⚡🔥
+# 🚀⚡🔥 Recommender System Template (Implicit Data) 🚀⚡🔥
 
-[![python](https://img.shields.io/badge/-Python_3.10-blue?logo=python&logoColor=white)](https://github.com/pre-commit/pre-commit)
+[![python](https://img.shields.io/badge/-Python_3.13.2-blue?logo=python&logoColor=white)](https://github.com/pre-commit/pre-commit)
 [![pytorch](https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org)
 [![hydra](https://img.shields.io/badge/Config-Hydra-89b8cd)](https://hydra.cc)
 [![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io)
@@ -11,17 +11,41 @@
 
 </div>
 
-Hi there! This is recommender system project prepared for data [ContentWise Impressions](https://github.com/ContentWise/contentwise-impressions) using:
-- `lightning` for model training
-- `hydra` for configuration
-- `clearml` for ML cycle control
-- (optionally) aws s3 as remote storage
+This repository provides a modular template for building recommender systems in Python using **implicit feedback data**. It is designed to streamline experimentation of recommendation models with a modern ML stack.
+
+### 🔧 Tech Stack
+- **PyTorch Lightning** – for scalable and structured model training
+- **Hydra** – for flexible configuration management
+- **ClearML** – for experiment tracking and ML workflow orchestration
+- **(Optional) AWS S3** – for storing datasets and models remotely
+
+### 📦 Dataset
+
+As an example, this template uses the [ContentWise Impressions](https://github.com/ContentWise/contentwise-impressions) dataset, which contains real-world implicit feedback data.
+
+### 🚀 Use Cases
+
+- Rapid prototyping of recommender systems
+- Benchmarking implicit models
+- Educational purposes (learning modern ML tools in practice)
+
+---
+
+More details about setup, usage, and customization can be found in the sections below.
+
 
 ## Prerequisites
 
-To make use of this repository, you have to:
-- [download](https://github.com/ContentWise/contentwise-impressions) ContentWise data, concretally `CW10M` directory, and place it at `cache/CW10M/` (cache directory is included in .gitignore)
-- set up a ClearML server and, optionally, AWS S3 storage
+To make use of this repository, follow these steps:
+
+1. **Download the dataset**  
+   Download the [ContentWise Impressions dataset](https://github.com/ContentWise/contentwise-impressions), specifically the `CW10M` directory.  
+   Place it in the following path: `cache/data-cw10m/`
+
+2. **Set up external services**  
+- Configure your connection to a ClearML server for experiment tracking.
+- (Optional) Set up access to AWS S3 if you want to use remote storage for data or models.
+
 
 ## Configuration and installation
 
@@ -32,6 +56,12 @@ CLEARML_WEB_HOST=<your-clearml-web-host>
 ...
 ```
 
+Create and activate virtual environment with conda:
+```
+conda create --name <env_name> python=3.13.2
+conda activate <env_name>
+```
+
 Install with pip:
 ```bash
 pip install .  # Add flag -e to install in editable mode
@@ -63,17 +93,4 @@ python steps/infer.py
 docker exec -it ds-container bash  # Execute bash in a running container
 docker compose start/stop/down
 docker builder prune  # Remove build cache
-bash linter.sh
-```
-
-## References
-
-* https://drivendata.github.io/cookiecutter-data-science
-* https://github.com/khuyentran1401/data-science-template
-
-## Trash
-(Obsolete) Remember about clearml agent based on docker image:
-
-```bash
-clearml-agent daemon --queue default --docker my-docker-image
 ```