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Merge branch 'master' into asalikhov/py_codestyle

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This commit is contained in:
Ayaz Salikhov
2020-09-28 06:37:23 +03:00
parent 3f189efd18 74b0a01aeb
commit e3085ca89e
16 changed files with 840 additions and 602 deletions
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2
.pre-commit-config.yaml
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@@ -25,6 +25,8 @@ repos:
name: Hadolint linter
description: Runs Hadolint to check for Dockerfile best practices
language: system
# Exclude ppc64le Dockerfile since cannot check their build easily
exclude: ppc64
types:
- dockerfile
entry: hadolint

34
base-notebook/Dockerfile
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@@ -4,7 +4,7 @@
# Ubuntu 20.04 (focal)
# https://hub.docker.com/_/ubuntu/?tab=tags&name=focal
# OS/ARCH: linux/amd64
ARG ROOT_CONTAINER=ubuntu:focal-20200703@sha256:d5a6519d9f048100123c568eb83f7ef5bfcad69b01424f420f17c932b00dea76
ARG ROOT_CONTAINER=ubuntu:focal-20200916@sha256:028d7303257c7f36c721b40099bf5004a41f666a54c0896d5f229f1c0fd99993
ARG BASE_CONTAINER=$ROOT_CONTAINER
FROM $BASE_CONTAINER
@@ -19,6 +19,17 @@ SHELL ["/bin/bash", "-o", "pipefail", "-c"]
USER root
# Miniconda installation
# Default values can be overridden at build time
# (ARGS are in lower case to distinguish them from ENV)
# Check https://repo.anaconda.com/miniconda/
# Miniconda archive to install
ARG miniconda_version="4.8.3"
# Archive MD5 checksum
ARG miniconda_checksum="d63adf39f2c220950a063e0529d4ff74"
# Conda version that can be different from the archive
ARG conda_version="4.8.5"
# Install all OS dependencies for notebook server that starts but lacks all
# features (e.g., download as all possible file formats)
ENV DEBIAN_FRONTEND noninteractive
@@ -53,9 +64,12 @@ COPY fix-permissions /usr/local/bin/fix-permissions
RUN chmod a+rx /usr/local/bin/fix-permissions
# Enable prompt color in the skeleton .bashrc before creating the default NB_USER
RUN sed -i 's/^#force_color_prompt=yes/force_color_prompt=yes/' /etc/skel/.bashrc
# hadolint ignore=SC2016
RUN sed -i 's/^#force_color_prompt=yes/force_color_prompt=yes/' /etc/skel/.bashrc && \
# Add call to conda init script see https://stackoverflow.com/a/58081608/4413446
echo 'eval "$(command conda shell.bash hook 2> /dev/null)"' >> /etc/skel/.bashrc
# Create NB_USER wtih name jovyan user with UID=1000 and in the 'users' group
# Create NB_USER with name jovyan user with UID=1000 and in the 'users' group
# and make sure these dirs are writable by the `users` group.
RUN echo "auth requisite pam_deny.so" >> /etc/pam.d/su && \
sed -i.bak -e 's/^%admin/#%admin/' /etc/sudoers && \
@@ -76,15 +90,15 @@ RUN mkdir /home/$NB_USER/work && \
fix-permissions /home/$NB_USER
# Install conda as jovyan and check the md5 sum provided on the download site
ENV MINICONDA_VERSION=4.8.3 \
MINICONDA_MD5=d63adf39f2c220950a063e0529d4ff74 \
CONDA_VERSION=4.8.3
ENV MINICONDA_VERSION="${miniconda_version}" \
CONDA_VERSION="${conda_version}"
WORKDIR /tmp
RUN wget --quiet https://repo.continuum.io/miniconda/Miniconda3-py38_${MINICONDA_VERSION}-Linux-x86_64.sh && \
echo "${MINICONDA_MD5} *Miniconda3-py38_${MINICONDA_VERSION}-Linux-x86_64.sh" | md5sum -c - && \
echo "${miniconda_checksum} *Miniconda3-py38_${MINICONDA_VERSION}-Linux-x86_64.sh" | md5sum -c - && \
/bin/bash Miniconda3-py38_${MINICONDA_VERSION}-Linux-x86_64.sh -f -b -p $CONDA_DIR && \
rm Miniconda3-py38_${MINICONDA_VERSION}-Linux-x86_64.sh && \
# Conda configuration see https://conda.io/projects/conda/en/latest/configuration.html
echo "conda ${CONDA_VERSION}" >> $CONDA_DIR/conda-meta/pinned && \
conda config --system --prepend channels conda-forge && \
conda config --system --set auto_update_conda false && \
@@ -92,7 +106,7 @@ RUN wget --quiet https://repo.continuum.io/miniconda/Miniconda3-py38_${MINICONDA
conda config --system --set channel_priority strict && \
if [ ! $PYTHON_VERSION = 'default' ]; then conda install --yes python=$PYTHON_VERSION; fi && \
conda list python | grep '^python ' | tr -s ' ' | cut -d '.' -f 1,2 | sed 's/$/.*/' >> $CONDA_DIR/conda-meta/pinned && \
conda install --quiet --yes conda && \
conda install --quiet --yes "conda=${CONDA_VERSION}" && \
conda install --quiet --yes pip && \
conda update --all --quiet --yes && \
conda clean --all -f -y && \
@@ -114,9 +128,9 @@ RUN conda install --quiet --yes 'tini=0.18.0' && \
# Do all this in a single RUN command to avoid duplicating all of the
# files across image layers when the permissions change
RUN conda install --quiet --yes \
'notebook=6.0.3' \
'notebook=6.1.4' \
'jupyterhub=1.1.0' \
'jupyterlab=2.1.5' && \
'jupyterlab=2.2.8' && \
conda clean --all -f -y && \
npm cache clean --force && \
jupyter notebook --generate-config && \

4
base-notebook/Dockerfile.ppc64le
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@@ -107,9 +107,9 @@ RUN conda install --quiet --yes 'tini=0.18.0' && \
# Do all this in a single RUN command to avoid duplicating all of the
# files across image layers when the permissions change
RUN conda install --quiet --yes \
'notebook=6.0.3' \
'notebook=6.1.3' \
'jupyterhub=1.1.0' \
'jupyterlab=2.1.1' && \
'jupyterlab=2.2.5' && \
conda clean --all -f -y && \
npm cache clean --force && \
jupyter notebook --generate-config && \

2
base-notebook/start.sh
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@@ -80,7 +80,7 @@ if [ $(id -u) == 0 ] ; then
if [ "$NB_UID" != $(id -u $NB_USER) ] || [ "$NB_GID" != $(id -g $NB_USER) ]; then
echo "Set user $NB_USER UID:GID to: $NB_UID:$NB_GID"
if [ "$NB_GID" != $(id -g $NB_USER) ]; then
groupadd -g $NB_GID -o ${NB_GROUP:-${NB_USER}}
groupadd -f -g $NB_GID -o ${NB_GROUP:-${NB_USER}}
fi
userdel $NB_USER
useradd --home /home/$NB_USER -u $NB_UID -g $NB_GID -G 100 -l $NB_USER

6
base-notebook/test/test_container_options.py
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@@ -11,11 +11,13 @@ LOGGER = logging.getLogger(__name__)
def test_cli_args(container, http_client):
"""Container should respect notebook server command line args
(e.g., disabling token security)"""
container.run(
command=['start-notebook.sh', '--NotebookApp.token=""']
c = container.run(
command=["start-notebook.sh", "--NotebookApp.token=''"]
)
resp = http_client.get('http://localhost:8888')
resp.raise_for_status()
logs = c.logs(stdout=True).decode('utf-8')
LOGGER.debug(logs)
assert 'login_submit' not in resp.text

26
datascience-notebook/Dockerfile
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@@ -14,6 +14,14 @@ SHELL ["/bin/bash", "-o", "pipefail", "-c"]
USER root
# Julia installation
# Default values can be overridden at build time
# (ARGS are in lower case to distinguish them from ENV)
# Check https://julialang.org/downloads/
ARG julia_version="1.5.1"
# SHA256 checksum
ARG julia_checksum="f5d37cb7fe40e3a730f721da8f7be40310f133220220949939d8f892ce2e86e3"
# R pre-requisites
RUN apt-get update && \
apt-get install -y --no-install-recommends \
@@ -24,16 +32,16 @@ RUN apt-get update && \
# Julia dependencies
# install Julia packages in /opt/julia instead of $HOME
ENV JULIA_DEPOT_PATH=/opt/julia
ENV JULIA_PKGDIR=/opt/julia
ENV JULIA_VERSION=1.4.1
ENV JULIA_DEPOT_PATH=/opt/julia \
JULIA_PKGDIR=/opt/julia \
JULIA_VERSION="${julia_version}"
WORKDIR /tmp
# hadolint ignore=SC2046
RUN mkdir "/opt/julia-${JULIA_VERSION}" && \
wget -q https://julialang-s3.julialang.org/bin/linux/x64/$(echo "${JULIA_VERSION}" | cut -d. -f 1,2)"/julia-${JULIA_VERSION}-linux-x86_64.tar.gz" && \
echo "fd6d8cadaed678174c3caefb92207a3b0e8da9f926af6703fb4d1e4e4f50610a *julia-${JULIA_VERSION}-linux-x86_64.tar.gz" | sha256sum -c - && \
echo "${julia_checksum} *julia-${JULIA_VERSION}-linux-x86_64.tar.gz" | sha256sum -c - && \
tar xzf "julia-${JULIA_VERSION}-linux-x86_64.tar.gz" -C "/opt/julia-${JULIA_VERSION}" --strip-components=1 && \
rm "/tmp/julia-${JULIA_VERSION}-linux-x86_64.tar.gz"
RUN ln -fs /opt/julia-*/bin/julia /usr/local/bin/julia
@@ -54,9 +62,9 @@ RUN conda install --quiet --yes \
'r-caret=6.0*' \
'r-crayon=1.3*' \
'r-devtools=2.3*' \
'r-forecast=8.12*' \
'r-forecast=8.13*' \
'r-hexbin=1.28*' \
'r-htmltools=0.4*' \
'r-htmltools=0.5*' \
'r-htmlwidgets=1.5*' \
'r-irkernel=1.1*' \
'r-nycflights13=1.0*' \
@@ -64,11 +72,11 @@ RUN conda install --quiet --yes \
'r-randomforest=4.6*' \
'r-rcurl=1.98*' \
'r-reshape2=1.4*' \
'r-rmarkdown=2.1*' \
'r-rmarkdown=2.3*' \
'r-rsqlite=2.2*' \
'r-shiny=1.4*' \
'r-shiny=1.5*' \
'r-tidyverse=1.3*' \
'rpy2=3.1*' \
'rpy2=3.3*' \
&& \
conda clean --all -f -y && \
fix-permissions "${CONDA_DIR}" && \

1121
docs/locale/en/LC_MESSAGES/using.po
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120
docs/using/specifics.md
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@@ -2,21 +2,81 @@
This page provides details about features specific to one or more images.
## Apache Spark
## Apache Spark™
**Specific Docker Image Options**
### Specific Docker Image Options
* `-p 4040:4040` - The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images open [SparkUI (Spark Monitoring and Instrumentation UI)](http://spark.apache.org/docs/latest/monitoring.html) at default port `4040`, this option map `4040` port inside docker container to `4040` port on host machine . Note every new spark context that is created is put onto an incrementing port (ie. 4040, 4041, 4042, etc.), and it might be necessary to open multiple ports. For example: `docker run -d -p 8888:8888 -p 4040:4040 -p 4041:4041 jupyter/pyspark-notebook`.
**Usage Examples**
### Build an Image with a Different Version of Spark
You can build a `pyspark-notebook` image (and also the downstream `all-spark-notebook` image) with a different version of Spark by overriding the default value of the following arguments at build time.
* Spark distribution is defined by the combination of the Spark and the Hadoop version and verified by the package checksum, see [Download Apache Spark](https://spark.apache.org/downloads.html) for more information. At this time the build will only work with the set of versions available on the Apache Spark download page, so it will not work with the archived versions.
* `spark_version`: The Spark version to install (`3.0.0`).
* `hadoop_version`: The Hadoop version (`3.2`).
* `spark_checksum`: The package checksum (`BFE4540...`).
* Spark is shipped with a version of Py4J that has to be referenced in the `PYTHONPATH`.
* `py4j_version`: The Py4J version (`0.10.9`), see the tip below.
* Spark can run with different OpenJDK versions.
* `openjdk_version`: The version of (JRE headless) the OpenJDK distribution (`11`), see [Ubuntu packages](https://packages.ubuntu.com/search?keywords=openjdk).
For example here is how to build a `pyspark-notebook` image with Spark `2.4.6`, Hadoop `2.7` and OpenJDK `8`.
```bash
# From the root of the project
# Build the image with different arguments
docker build --rm --force-rm \
-t jupyter/pyspark-notebook:spark-2.4.6 ./pyspark-notebook \
--build-arg spark_version=2.4.6 \
--build-arg hadoop_version=2.7 \
--build-arg spark_checksum=3A9F401EDA9B5749CDAFD246B1D14219229C26387017791C345A23A65782FB8B25A302BF4AC1ED7C16A1FE83108E94E55DAD9639A51C751D81C8C0534A4A9641 \
--build-arg openjdk_version=8 \
--build-arg py4j_version=0.10.7
# Check the newly built image
docker images jupyter/pyspark-notebook:spark-2.4.6
# REPOSITORY TAG IMAGE ID CREATED SIZE
# jupyter/pyspark-notebook spark-2.4.6 7ad7b5a9dbcd 4 minutes ago 3.44GB
# Check the Spark version
docker run -it --rm jupyter/pyspark-notebook:spark-2.4.6 pyspark --version
# Welcome to
# ____ __
# / __/__ ___ _____/ /__
# _\ \/ _ \/ _ `/ __/ '_/
# /___/ .__/\_,_/_/ /_/\_\ version 2.4.6
# /_/
#
# Using Scala version 2.11.12, OpenJDK 64-Bit Server VM, 1.8.0_265
```
**Tip**: to get the version of Py4J shipped with Spark:
* Build a first image without changing `py4j_version` (it will not prevent the image to build it will just prevent Python to find the `pyspark` module),
* get the version (`ls /usr/local/spark/python/lib/`),
* set the version `--build-arg py4j_version=0.10.7`.
```bash
docker run -it --rm jupyter/pyspark-notebook:spark-2.4.6 ls /usr/local/spark/python/lib/
# py4j-0.10.7-src.zip PY4J_LICENSE.txt pyspark.zip
# You can now set the build-arg
# --build-arg py4j_version=
```
*Note: At the time of writing there is an issue preventing to use Spark `2.4.6` with Python `3.8`, see [this answer on SO](https://stackoverflow.com/a/62173969/4413446) for more information.*
### Usage Examples
The `jupyter/pyspark-notebook` and `jupyter/all-spark-notebook` images support the use of [Apache Spark](https://spark.apache.org/) in Python, R, and Scala notebooks. The following sections provide some examples of how to get started using them.
### Using Spark Local Mode
#### Using Spark Local Mode
Spark **local mode** is useful for experimentation on small data when you do not have a Spark cluster available.
#### In Python
##### In Python
In a Python notebook.
@@ -33,7 +93,7 @@ rdd.sum()
# 5050
```
#### In R
##### In R
In a R notebook with [SparkR][sparkr].
@@ -71,9 +131,7 @@ sdf_len(sc, 100, repartition = 1) %>%
# 5050
```
#### In Scala
##### In a Spylon Kernel
##### In Scala
Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
options in a `%%init_spark` magic cell.
@@ -91,18 +149,7 @@ rdd.sum()
// 5050
```
##### In an Apache Toree Kernel
Apache Toree instantiates a local `SparkContext` for you in variable `sc` when the kernel starts.
```scala
// Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 100)
rdd.sum()
// 5050
```
### Connecting to a Spark Cluster in Standalone Mode
#### Connecting to a Spark Cluster in Standalone Mode
Connection to Spark Cluster on **[Standalone Mode](https://spark.apache.org/docs/latest/spark-standalone.html)** requires the following set of steps:
@@ -117,7 +164,7 @@ Connection to Spark Cluster on **[Standalone Mode](https://spark.apache.org/docs
**Note**: In the following examples we are using the Spark master URL `spark://master:7077` that shall be replaced by the URL of the Spark master.
#### In Python
##### In Python
The **same Python version** need to be used on the notebook (where the driver is located) and on the Spark workers.
The python version used at driver and worker side can be adjusted by setting the environment variables `PYSPARK_PYTHON` and / or `PYSPARK_DRIVER_PYTHON`, see [Spark Configuration][spark-conf] for more information.
@@ -135,7 +182,7 @@ rdd.sum()
# 5050
```
#### In R
##### In R
In a R notebook with [SparkR][sparkr].
@@ -172,9 +219,7 @@ sdf_len(sc, 100, repartition = 1) %>%
# 5050
```
#### In Scala
##### In a Spylon Kernel
##### In Scala
Spylon kernel instantiates a `SparkContext` for you in variable `sc` after you configure Spark
options in a `%%init_spark` magic cell.
@@ -192,29 +237,6 @@ rdd.sum()
// 5050
```
##### In an Apache Toree Scala Notebook
The Apache Toree kernel automatically creates a `SparkContext` when it starts based on configuration information from its command line arguments and environment variables. You can pass information about your cluster via the `SPARK_OPTS` environment variable when you spawn a container.
For instance, to pass information about a standalone Spark master, you could start the container like so:
```bash
docker run -d -p 8888:8888 -e SPARK_OPTS='--master=spark://master:7077' \
jupyter/all-spark-notebook
```
Note that this is the same information expressed in a notebook in the Python case above. Once the kernel spec has your cluster information, you can test your cluster in an Apache Toree notebook like so:
```scala
// should print the value of --master in the kernel spec
println(sc.master)
// Sum of the first 100 whole numbers
val rdd = sc.parallelize(0 to 100)
rdd.sum()
// 5050
```
## Tensorflow
The `jupyter/tensorflow-notebook` image supports the use of

11
examples/docker-compose/notebook/Dockerfile
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@@ -4,12 +4,13 @@
# Pick your favorite docker-stacks image
FROM jupyter/minimal-notebook:55d5ca6be183
USER jovyan
# Add permanent pip/conda installs, data files, other user libs here
# e.g., RUN pip install jupyter_dashboards
USER root
# Add permanent apt-get installs and other root commands here
# e.g., RUN apt-get install npm nodejs
USER $NB_UID
# Switch back to jovyan to avoid accidental container runs as root
# Add permanent pip/conda installs, data files, other user libs here
# e.g., RUN pip install jupyter_dashboards

11
examples/make-deploy/Dockerfile
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@@ -4,12 +4,13 @@
# Pick your favorite docker-stacks image
FROM jupyter/minimal-notebook:2d125a7161b5
USER jovyan
# Add permanent pip/conda installs, data files, other user libs here
# e.g., RUN pip install jupyter_dashboards
USER root
# Add permanent apt-get installs and other root commands here
# e.g., RUN apt-get install npm nodejs
USER $NB_UID
# Switch back to jovyan to avoid accidental container runs as root
# Add permanent pip/conda installs, data files, other user libs here
# e.g., RUN pip install jupyter_dashboards

27
pyspark-notebook/Dockerfile
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@@ -11,20 +11,30 @@ SHELL ["/bin/bash", "-o", "pipefail", "-c"]
USER root
# Spark dependencies
ENV APACHE_SPARK_VERSION=3.0.0 \
HADOOP_VERSION=3.2
# Default values can be overridden at build time
# (ARGS are in lower case to distinguish them from ENV)
ARG spark_version="3.0.1"
ARG hadoop_version="3.2"
ARG spark_checksum="E8B47C5B658E0FBC1E57EEA06262649D8418AE2B2765E44DA53AAF50094877D17297CC5F0B9B35DF2CEEF830F19AA31D7E56EAD950BBE7F8830D6874F88CFC3C"
ARG py4j_version="0.10.9"
ARG openjdk_version="11"
ENV APACHE_SPARK_VERSION="${spark_version}" \
HADOOP_VERSION="${hadoop_version}"
RUN apt-get -y update && \
apt-get install --no-install-recommends -y openjdk-11-jre-headless ca-certificates-java && \
apt-get install --no-install-recommends -y \
"openjdk-${openjdk_version}-jre-headless" \
ca-certificates-java && \
rm -rf /var/lib/apt/lists/*
# Using the preferred mirror to download Spark
# Spark installation
WORKDIR /tmp
# Using the preferred mirror to download Spark
# hadolint ignore=SC2046
RUN wget -q $(wget -qO- https://www.apache.org/dyn/closer.lua/spark/spark-${APACHE_SPARK_VERSION}/spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz\?as_json | \
python -c "import sys, json; content=json.load(sys.stdin); print(content['preferred']+content['path_info'])") && \
echo "BFE45406C67CC4AE00411AD18CC438F51E7D4B6F14EB61E7BF6B5450897C2E8D3AB020152657C0239F253735C263512FFABF538AC5B9FFFA38B8295736A9C387 *spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz" | sha512sum -c - && \
echo "${spark_checksum} *spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz" | sha512sum -c - && \
tar xzf "spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz" -C /usr/local --owner root --group root --no-same-owner && \
rm "spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}.tgz"
@@ -33,14 +43,15 @@ RUN ln -s "spark-${APACHE_SPARK_VERSION}-bin-hadoop${HADOOP_VERSION}" spark
# Configure Spark
ENV SPARK_HOME=/usr/local/spark
ENV PYTHONPATH=$SPARK_HOME/python:$SPARK_HOME/python/lib/py4j-0.10.9-src.zip \
ENV PYTHONPATH="${SPARK_HOME}/python:${SPARK_HOME}/python/lib/py4j-${py4j_version}-src.zip" \
SPARK_OPTS="--driver-java-options=-Xms1024M --driver-java-options=-Xmx4096M --driver-java-options=-Dlog4j.logLevel=info" \
PATH=$PATH:$SPARK_HOME/bin
USER $NB_UID
# Install pyarrow
RUN conda install --quiet -y 'pyarrow' && \
RUN conda install --quiet --yes --satisfied-skip-solve \
'pyarrow=1.0.*' && \
conda clean --all -f -y && \
fix-permissions "${CONDA_DIR}" && \
fix-permissions "/home/${NB_USER}"

10
r-notebook/Dockerfile
View File

@@ -25,22 +25,22 @@ USER $NB_UID
# R packages
RUN conda install --quiet --yes \
'r-base=4.0.0' \
'r-base=4.0.2' \
'r-caret=6.*' \
'r-crayon=1.3*' \
'r-devtools=2.3*' \
'r-forecast=8.12*' \
'r-forecast=8.13*' \
'r-hexbin=1.28*' \
'r-htmltools=0.4*' \
'r-htmltools=0.5*' \
'r-htmlwidgets=1.5*' \
'r-irkernel=1.1*' \
'r-nycflights13=1.0*' \
'r-randomforest=4.6*' \
'r-rcurl=1.98*' \
'r-rmarkdown=2.2*' \
'r-rmarkdown=2.3*' \
'r-rodbc=1.3*' \
'r-rsqlite=2.2*' \
'r-shiny=1.4*' \
'r-shiny=1.5*' \
'r-tidyverse=1.3*' \
'unixodbc=2.3.*' \
'r-tidymodels=0.1*' \

20
scipy-notebook/Dockerfile
View File

@@ -7,9 +7,9 @@ LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"
USER root
# ffmpeg for matplotlib anim & dvipng for latex labels
# ffmpeg for matplotlib anim & dvipng+cm-super for latex labels
RUN apt-get update && \
apt-get install -y --no-install-recommends ffmpeg dvipng && \
apt-get install -y --no-install-recommends ffmpeg dvipng cm-super && \
rm -rf /var/lib/apt/lists/*
USER $NB_UID
@@ -18,19 +18,17 @@ USER $NB_UID
RUN conda install --quiet --yes \
'beautifulsoup4=4.9.*' \
'conda-forge::blas=*=openblas' \
'bokeh=2.1.*' \
'bokeh=2.2.*' \
'bottleneck=1.3.*' \
'cloudpickle=1.5.*' \
'cloudpickle=1.6.*' \
'cython=0.29.*' \
'dask=2.20.*' \
'dask=2.25.*' \
'dill=0.3.*' \
'h5py=2.10.*' \
'hdf5=1.10.*' \
'ipywidgets=7.5.*' \
'ipympl=0.5.*'\
'matplotlib-base=3.2.*' \
# numba update to 0.49 fails resolving deps.
'numba=0.48.*' \
'matplotlib-base=3.3.*' \
'numba=0.51.*' \
'numexpr=2.7.*' \
'pandas=1.1.*' \
'patsy=0.5.*' \
@@ -39,9 +37,9 @@ RUN conda install --quiet --yes \
'scikit-image=0.17.*' \
'scikit-learn=0.23.*' \
'scipy=1.5.*' \
'seaborn=0.10.*' \
'seaborn=0.11.*' \
'sqlalchemy=1.3.*' \
'statsmodels=0.11.*' \
'statsmodels=0.12.*' \
'sympy=1.6.*' \
'vincent=0.4.*' \
'widgetsnbextension=3.5.*'\

20
scipy-notebook/test/data/matplotlib_fonts_1.py Normal file
View File

@@ -0,0 +1,20 @@
# Matplotlit: Test tex fonts
import matplotlib
import matplotlib.pyplot as plt
import os
matplotlib.rcParams['pgf.texsystem'] = 'pdflatex'
matplotlib.rcParams.update({'font.family': 'serif', 'font.size': 18,
'axes.labelsize': 20, 'axes.titlesize': 24,
'figure.titlesize': 28})
matplotlib.rcParams['text.usetex'] = True
fig, ax = plt.subplots(1, 1)
x = [1, 2]
y = [1, 2]
ax.plot(x, y, label='a label')
ax.legend(fontsize=15)
file_path = os.path.join("/tmp", "test_fonts.png")
fig.savefig(file_path)
print(f"File {file_path} saved")

24
scipy-notebook/test/test_matplotlib.py
View File

@@ -8,13 +8,24 @@ import os
LOGGER = logging.getLogger(__name__)
def test_matplotlib(container):
"""Test that matplotlib is able to plot a graph and write it as an image"""
host_data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data")
@pytest.mark.parametrize("test_file,expected_file,description",
[
("matplotlib_1.py", "test.png",
"Test that matplotlib is able to plot a graph and write it as an image ..."),
("matplotlib_fonts_1.py", "test_fonts.png",
"Test cm-super latex labels in matplotlib ...")
])
def test_matplotlib(container, test_file, expected_file, description):
"""Various tests performed on matplotlib
- Test that matplotlib is able to plot a graph and write it as an image
- Test matplotlib latex fonts, which depend on the cm-super package
"""
host_data_dir = os.path.join(os.path.dirname(
os.path.realpath(__file__)), "data")
cont_data_dir = "/home/jovyan/data"
test_file = "matplotlib_1.py"
output_dir = "/tmp"
LOGGER.info("Test that matplotlib is able to plot a graph and write it as an image ...")
LOGGER.info(description)
command = "sleep infinity"
running_container = container.run(
volumes={host_data_dir: {"bind": cont_data_dir, "mode": "ro"}},
@@ -27,8 +38,7 @@ def test_matplotlib(container):
LOGGER.debug(cmd.output.decode("utf-8"))
# Checking if the file is generated
# https://stackoverflow.com/a/15895594/4413446
expected_file = f"{output_dir}/test.png"
command = f"test -s {expected_file}"
command = f"test -s {output_dir}/{expected_file}"
cmd = running_container.exec_run(command)
assert cmd.exit_code == 0, f"Command {command} failed"
LOGGER.debug(cmd.output.decode("utf-8"))

2
tensorflow-notebook/Dockerfile
View File

@@ -7,6 +7,6 @@ LABEL maintainer="Jupyter Project <jupyter@googlegroups.com>"
# Install Tensorflow
RUN pip install --quiet --no-cache-dir \
'tensorflow==2.2.0' && \
'tensorflow==2.3.0' && \
fix-permissions "${CONDA_DIR}" && \
fix-permissions "/home/${NB_USER}"