environments automl dnn vision gpu

automl-dnn-vision-gpu

Overview

GPU based environment for finetuning AutoML legacy models for image tasks.

Version: 32

Tags

OS : Ubuntu20.04 Training Preview

View in Studio: https://ml.azure.com/registries/azureml/environments/automl-dnn-vision-gpu/version/32

Docker image: mcr.microsoft.com/azureml/curated/automl-dnn-vision-gpu:32

Docker build context

Dockerfile

FROM mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.6-cudnn8-ubuntu20.04:20240614.v1

ENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/azureml-automl-dnn-vision-gpu
# Prepend path to AzureML conda environment
ENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH

COPY --from=mcr.microsoft.com/azureml/mlflow-ubuntu20.04-py38-cpu-inference:20230306.v3 /var/mlflow_resources/mlflow_score_script.py /var/mlflow_resources/mlflow_score_script.py

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

# Create conda environment
COPY conda_dependencies.yaml .
RUN conda env create -p $AZUREML_CONDA_ENVIRONMENT_PATH -f conda_dependencies.yaml -q && \
    rm conda_dependencies.yaml && \
    conda run -p $AZUREML_CONDA_ENVIRONMENT_PATH && \
    conda clean -a -y

# vulnearbility fix
RUN pip install pyarrow==14.0.2

ENV LD_LIBRARY_PATH $AZUREML_CONDA_ENVIRONMENT_PATH/lib:$LD_LIBRARY_PATH
# dummy number to change when needing to force rebuild without changing the definition: 1