feat: remove python 3.9

.circleci/config.yml

@@ -77,7 +77,7 @@ commands:
     steps:
       - run:
           name: Retag image and push SOCI v1 index
-          no_output_timeout: 30m
+          no_output_timeout: 60m
           command: |
             set -euxo pipefail
 
@@ -109,7 +109,7 @@ commands:
     steps:
       - run:
           name: Convert and push SOCI v2 image
-          no_output_timeout: 30m
+          no_output_timeout: 60m
           command: |
             set -euxo pipefail
 
@@ -228,7 +228,7 @@ jobs:
             } >> "$BASH_ENV"
       - run:
           name: Build and push python-datascience image
-          no_output_timeout: 30m
+          no_output_timeout: 60m
           command: |
             docker buildx build \
               --file=./python/datascience/Dockerfile.datascience \
@@ -297,7 +297,7 @@ jobs:
         type: string
       python-version:
         type: string
-        default: "3.9"
+        default: "3.10"
     environment:
       R_VERSION: << parameters.r-version >>
       PYTHON_VERSION: << parameters.python-version >>
@@ -317,7 +317,7 @@ jobs:
             } >> "$BASH_ENV"
       - run:
           name: Build and push ir image
-          no_output_timeout: 60m
+          no_output_timeout: 90m
           command: |
             docker buildx build \
               --file=./ir/ir-base/Dockerfile.ir \
@@ -365,7 +365,7 @@ jobs:
             } >> "$BASH_ENV"
       - run:
           name: Build and push ir with datascience libraries
-          no_output_timeout: 60m
+          no_output_timeout: 90m
           command: |
             docker buildx build \
               --file=./ir/ir-with-libs/Dockerfile.ir \
@@ -418,7 +418,7 @@ jobs:
             } >> "$BASH_ENV"
       - run:
           name: Build and push gpu legacy image
-          no_output_timeout: 30m
+          no_output_timeout: 60m
           command: |
             docker buildx build \
               --file=./gpu/Dockerfile.legacy \
@@ -465,7 +465,7 @@ jobs:
             } >> "$BASH_ENV"
       - run:
           name: Build and push gpu image
-          no_output_timeout: 30m
+          no_output_timeout: 60m
           command: |
             docker buildx build \
               --file=./gpu/Dockerfile \
@@ -484,7 +484,6 @@ jobs:
       - convert_and_push_soci_v2
 
 python-versions: &python-versions
-  - "3.9"
   - "3.10"
   - "3.11"
   - "3.12"
@@ -529,7 +528,7 @@ workflows:
             parameters:
               r-version: *r-versions
           requires:
-            - Python 3.9
+            - Python 3.10
       - build-and-push-ir:
           name: R << matrix.r-version >>
           matrix:

ir/ir-base/Dockerfile.ir

@@ -1,5 +1,5 @@
 # Define build arguments for Python and R versions
-ARG PYTHON_VERSION=3.9
+ARG PYTHON_VERSION=3.10
 # Use the specified Python version as the base image
 FROM deepnote/python:${PYTHON_VERSION}
 

python/conda/Dockerfile.conda

@@ -1,4 +1,4 @@
-ARG PYTHON_VERSION=3.9
+ARG PYTHON_VERSION=3.10
 ARG CIRCLE_PULL_REQUEST
 FROM deepnote/python:base
 

python/readme.md

@@ -2,8 +2,8 @@
 -  **Maintained by**: [Deepnote](https://deepnote.com/)
 
 # Supported Tags and Respective Dockerfile Links
--  [`3.9`, `3.10`, `3.11`, `3.12`, `3.13`](https://github.com/deepnote/environments/tree/main/python/python)
--  [`3.9-datascience`, `3.10-datascience`, `3.11-datascience`, `3.12-datascience`, `3.13-datascience`](https://github.com/deepnote/environments/blob/main/python/datascience/Dockerfile.datascience)
+-  [`3.10`, `3.11`, `3.12`, `3.13`](https://github.com/deepnote/environments/tree/main/python/python)
+-  [`3.10-datascience`, `3.11-datascience`, `3.12-datascience`, `3.13-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.
@@ -14,7 +14,7 @@ Deepnote Python is a set of Docker images tailored for use in the Deepnote platf
 To create a custom Dockerfile using Deepnote's Python image, you can start with the following template:
 
 ```dockerfile
-ARG PYTHON_VERSION=3.9
+ARG PYTHON_VERSION=3.10
 FROM deepnote/python:$PYTHON_VERSION
 
 #Determine the Python version and set the version-specifications file
@@ -24,7 +24,7 @@ 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.
+This Dockerfile uses the `deepnote/python:3.10` 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.