Cleanup documentation & datascience image

ir/ir-base/readme.md

@@ -0,0 +1,25 @@
+# Quick Reference
+-  **Maintained by**: [Deepnote](https://deepnote.com/)
+
+## IR Tags
+-  [`4.0.3`, `4.2.0`]
+
+## What is Deepnote IR?
+Deepnote IR provides official Docker images that are pre-configured for use on [Deepnote](https://deepnote.com). These images include the R programming language installed in specific versions, along with the [IRkernel](https://github.com/IRkernel/IRkernel), which is an R kernel for Jupyter.
+
+These images are designed to facilitate seamless integration with Deepnote's platform, allowing users to leverage R for data analysis and visualization within Jupyter notebooks. If you find that the provided images do not meet all your requirements, you are encouraged to extend them by building your own custom images on top of these bases.
+
+## How to Customize Your Image
+To create a custom Docker image based on Deepnote's IR images, you can use the following example Dockerfile:
+
+```dockerfile
+FROM deepnote/ir:4.2.0
+
+RUN R -e "install.packages('tidyverse', repos='https://cloud.r-project.org', dependencies=TRUE)"
+```
+
+This example demonstrates how to build upon the `deepnote/ir:4.2.0` image by installing the tidyverse package, a collection of R packages designed for data science. You can modify this Dockerfile to include other R packages or configurations as needed.
+
+# Additional Information
+These images serve as a robust foundation for R-based data science projects within Deepnote, providing the necessary tools and libraries to get started quickly. For more detailed information about Deepnote and how these images can enhance your data science workflows, please visit Deepnote's website.
+

python/datascience/Dockerfile.datascience

@@ -2,31 +2,20 @@ ARG PYTHON_VERSION=3.8
 ARG CIRCLE_PULL_REQUEST
 FROM deepnote/python:${PYTHON_VERSION}${CIRCLE_PULL_REQUEST:+-ra-${CIRCLE_PULL_REQUEST##*/}}
 
-RUN pip install --upgrade pip
-
-
 # Add the requirements files
 ADD requirements-3.11+.txt /requirements-3.11+.txt
 ADD requirements-below-3.11.txt /requirements-below-3.11.txt
-ADD constraints.txt /constraints.txt
 
 #Determine the Python version and set the version-specifications file
 ARG PYTHON_VERSION
 
-RUN python -m pip install --upgrade setuptools pip
-
 RUN if [ "$(printf '%s\n' "$PYTHON_VERSION" "3.11" | sort -V | head -n1)" = "3.11" ]; then \
   mv "requirements-3.11+.txt" "requirements.txt" \
   ; else \
   mv "requirements-below-3.11.txt" "requirements.txt" \
   ; fi
 
-# Install dependencies
-# Combine all RUN commands into one to minimize the number of layers
-RUN apt-get update \
-  && apt-get install -y --no-install-recommends gcc \
-  && pip install --no-cache-dir -r requirements.txt -c constraints.txt \
-  && apt-get purge -y gcc \
-  && apt-get autoremove -y \
-  && apt-get clean \
-  && rm -rf /var/lib/apt/lists/* /root/.cache
+
+RUN python -m venv --system-site-packages ~/venv
+RUN . ~/venv/bin/activate \ 
+  && pip install --no-cache-dir -r requirements.txt -c https://tk.deepnote.com/constraints${PYTHON_VERSION}.txt

python/python/Dockerfile.python3.10

@@ -111,7 +111,3 @@ RUN set -eux; \
 		ln -svT "$src" "/usr/local/bin/$dst"; \
 	done
 
-
-# We create the virtual environment in the home directory in the Dockerfile
-# for performance improvement.
-RUN python -m venv --system-site-packages ~/venv

python/python/Dockerfile.python3.11

@@ -110,7 +110,3 @@ RUN set -eux; \
 		ln -svT "$src" "/usr/local/bin/$dst"; \
 	done
 
-
-# We create the virtual environment in the home directory in the Dockerfile
-# for performance improvement.
-RUN python -m venv --system-site-packages ~/venv

python/python/Dockerfile.python3.8

@@ -109,9 +109,3 @@ RUN set -eux; \
 		[ ! -e "/usr/local/bin/$dst" ]; \
 		ln -svT "$src" "/usr/local/bin/$dst"; \
 	done
-
-
-
-# We create the virtual environment in the home directory in the Dockerfile
-# for performance improvement.
-RUN python -m venv --system-site-packages ~/venv

python/python/Dockerfile.python3.9

@@ -109,8 +109,3 @@ RUN set -eux; \
 		[ ! -e "/usr/local/bin/$dst" ]; \
 		ln -svT "$src" "/usr/local/bin/$dst"; \
 	done
-
-
-# We create the virtual environment in the home directory in the Dockerfile
-# for performance improvement.
-RUN python -m venv --system-site-packages ~/venv

python/readme.md

@@ -0,0 +1,31 @@
+# Quick Reference
+-  **Maintained by**: [Deepnote](https://deepnote.com/)
+
+# Supported Tags and Respective Dockerfile Links
+-  [`3.8`, `3.9`, `3.10`, `3.11`](https://github.com/deepnote/environments/tree/main/python/python)
+-  [`3.8-datascience`, `3.9-datascience`, `3.10-datascience`, `3.11-datascience`](https://github.com/deepnote/environments/blob/main/python/datascience/Dockerfile.datascience)
+
+# What is Deepnote Python?
+Deepnote Python is a set of Docker images tailored for use in the Deepnote platform. These images are based on the official [Python Docker images](https://hub.docker.com/_/python) and include additional binaries and configurations to enhance the user experience within Deepnote. They are designed to simplify development workflows, especially for data science projects, by providing pre-configured environments that are ready to use.
+
+# How to Use This Image
+
+## Create Custom Dockerfile for Deepnote Use
+To create a custom Dockerfile using Deepnote's Python image, you can start with the following template:
+
+```dockerfile
+ARG PYTHON_VERSION=3.9
+FROM deepnote/python:$PYTHON_VERSION
+
+#Determine the Python version and set the version-specifications file
+ARG PYTHON_VERSION
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt -c https://tk.deepnote.com/constraints${PYTHON_VERSION}.txt
+```
+
+This Dockerfile uses the `deepnote/python:3.9` image as a base and installs Python packages specified in requirements.txt. The use of constraints from `https://tk.deepnote.com/constraints<python_version>.txt` ensures compatibility and stability of package installations.
+
+# Additional Information
+These images are intended to serve as a starting point for creating reproducible and scalable data science environments in Deepnote. They include various utilities and binaries that streamline workflows, such as Jupyter support, data visualization tools, and more.
+For more information about Deepnote and how these images can be used to enhance your data science projects, visit Deepnote's website.