environments ai ml automl - Azure/azureml-assets GitHub Wiki
An environment used by Azure ML AutoML for training models.
Version: 42
OS : Ubuntu20.04 Training Preview OpenMpi : 4.1.0 Python : 3.9
View in Studio: https://ml.azure.com/registries/azureml/environments/ai-ml-automl/version/42
Docker image: mcr.microsoft.com/azureml/curated/ai-ml-automl:42
FROM mcr.microsoft.com/azureml/openmpi5.0-ubuntu24.04:20260315.v1
ENV AZUREML_CONDA_ENVIRONMENT_PATH=/azureml-envs/azureml-automl
ENV PATH=$AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH
COPY --from=mcr.microsoft.com/azureml/mlflow-ubuntu20.04-py38-cpu-inference:20250506.v1 /var/mlflow_resources/ /var/mlflow_resources/
ENV MLFLOW_MODEL_FOLDER="mlflow-model"
# ENV AML_APP_ROOT="/var/mlflow_resources"
# ENV AZUREML_ENTRY_SCRIPT="mlflow_score_script.py"
ENV ENABLE_METADATA=true
# System package security upgrades
RUN apt-get update && \
apt-get install -y --only-upgrade \
libpam0g \
libpam-modules \
libpam-modules-bin \
libpam-runtime && \
apt-get clean && \
rm -rf /var/lib/apt/lists/*
# begin conda create
# Create conda environment (minimal — packages installed via pip to avoid solver OOM)
RUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \
python=3.10 \
-c conda-forge && \
conda clean -a -y
# Install packages via pip (avoids conda solver OOM)
RUN conda run -p $AZUREML_CONDA_ENVIRONMENT_PATH pip install --no-cache-dir \
'py-cpuinfo==5.0.0' \
'joblib==1.2.0' \
'setuptools-git' \
'setuptools==82.0.1' \
'psutil>5.0.0,<6.0.0' \
'torch==2.8.0' \
'pip>=26.0'
# end conda create
# begin pip install
# Install pip dependencies
RUN pip install \
# begin pypi dependencies
azureml-core==1.61.0.post3 \
azureml-mlflow==1.62.0.post1 \
azureml-pipeline-core==1.62.0 \
azureml-telemetry==1.62.0 \
azureml-interpret==1.62.0 \
azureml-responsibleai==1.62.0 \
azureml-automl-core==1.62.0 \
azureml-automl-runtime==1.62.0 \
azureml-train-automl-client==1.62.0 \
azureml-train-automl-runtime==1.62.0 \
azureml-train-automl==1.62.0 \
azureml-dataset-runtime==1.62.0 \
azureml-defaults==1.62.0 \
# TODO: replace the hard coded above by a referenceto azureml-train-automl[tabular]
'mlflow-skinny==2.16.0' \
'xgboost==1.5.2' \
'cmdstanpy==1.0.4' \
'prophet==1.1.4' \
'inference-schema' \
'mltable>=1.0.0' \
'pillow==12.1.1'
# end pypi dependencies
# end pip install
# begin pip ad-hoc
# Install pip ad-hoc dependencies for security updates
#
# pip>=26.0 (CVE-2025-8869): symlink path traversal during tar extraction
# Chain: direct conda dep (pip=26.0), upgraded for security
#
# starlette>=0.49.1 (CVE-2025-62727): DoS via quadratic HTTP Range header parsing
# Chain: mlflow-skinny -> fastapi -> starlette
#
# pyarrow==14.0.2 (CVE-2023-47248): arbitrary code execution via IPC/Parquet deserialization
# Chain: mltable -> azureml-dataprep[parquet] -> pyarrow
# Also: azureml-train-automl-runtime -> pyarrow (direct)
#
# urllib3==2.6.3 (CVE-2025-66418, CVE-2025-66471, CVE-2026-21441): DoS via decompression chain/bomb, redirect decompression bomb
# Chain: mlflow-skinny -> requests -> urllib3
# Also: azureml-core -> requests -> urllib3
# Also: azureml-automl-runtime -> urllib3 (direct)
# Also: azureml-train-automl-runtime -> urllib3 (direct)
#
# bokeh==3.8.2 (CVE-2026-21883): WebSocket origin validation bypass (CSWSH)
# Chain: azureml-train-automl-runtime -> bokeh (direct dep)
# Also: azureml-train-automl-runtime -> dask[complete] -> bokeh
#
# wheel>=0.46.2 (CVE-2026-24049): path traversal & privilege escalation via malicious wheel unpack
# Chain: setuptools[core] -> wheel (build dependency)
#
# cryptography>=46.0.5 (CVE-2026-26007): EC subgroup validation flaw in ECDH/ECDSA on SECT curves
# Chain: mltable -> cryptography (direct dep)
# Also: mltable -> azureml-dataprep -> azure-identity -> cryptography
#
# jaraco.context>=6.1.0: CVE-2026-23949 Zip Slip path traversal fix. Direct install required because
# setuptools 82.0.1 (latest) vendors jaraco.context with vulnerable code despite dist-info labeling 6.1.0
#
# protobuf>=5.29.6 (CVE-2025-4565): DoS via uncontrolled recursion in pure-Python decoder
# Chain: mlflow-skinny -> protobuf (direct dep)
# Also: mlflow-skinny -> databricks-sdk -> protobuf
#
# distributed>=2026.1.0 (CVE-2026-23528): XSS leading to RCE via Dask dashboard
# Chain: azureml-train-automl-runtime -> dask[complete] -> distributed
RUN pip install --upgrade \
'pip>=26.0' \
'starlette>=0.49.1' \
'pyarrow==14.0.2' \
'urllib3==2.6.3' \
'bokeh==3.8.2' \
'wheel>=0.46.2' \
'cryptography>=46.0.5' \
'jaraco.context>=6.1.0' \
'protobuf>=5.29.6' \
'distributed>=2026.1.0'
# end pip ad-hoc