print('Setting up dataset')
mnistFileDataset = Dataset.get_by_name(workspace=ws, name=datasetName)
print("Defining environment")
# to install required packages
env = Environment('sklearn')
#cd = CondaDependencies.create(pip_packages=['azureml-dataprep[pandas,fuse]>=1.1.14', 'azureml-defaults'], conda_packages = ['scikit-learn==0.22.1'])
cd = CondaDependencies.create(pip_packages=[
'azureml-sdk', 'scikit-learn==0.22.1',
'azureml-dataprep[pandas,fuse]>=1.1.14'
])
env.python.conda_dependencies = cd
# Register environment to re-use later
env.register(workspace=ws)
print("Creating estimator")
script_params = {
# to mount files referenced by mnist dataset
'--data-folder': mnistFileDataset.as_named_input(datasetName).as_mount(),
'--regularization': 0.5
}
est = Estimator(source_directory=scriptFolder,
script_params=script_params,
compute_target=compute_target,
environment_definition=env,
entry_script='train_logistic.py')
print("Running experiment")
def create_env(ws):
'''Creates an azureml enviornment'''
# Create enviornment object
env = Environment(name='birdsong-env-gpu')
# define packages for image
cd = CondaDependencies.create(
pip_packages=[
'azureml-dataset-runtime[pandas,fuse]',
'azureml-defaults',
'tensorflow==2.4.0', #'tensorflow==2.5.0',
'tensorflow-io==0.17.1', # 'tensorflow-io==0.18.0',
'tensorflow-addons==0.13.0',
'Pillow',
'sklearn',
'kapre',
'sndfile',
'librosa',
'psutil'
],
conda_packages=['SciPy'])
env.python.conda_dependencies = cd
#Docker file
dockerfile = r'''
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
FROM mcr.microsoft.com/azureml/o16n-base/python-assets:20210210.31228572 AS inferencing-assets
# Tag: cuda:11.0.3-devel-ubuntu18.04
# Env: CUDA_VERSION=11.0.3
# Env: NCCL_VERSION=2.8.3
# Env: CUDNN_VERSION=8.0.5.39
FROM nvidia/cuda:11.0.3-cudnn8-devel-ubuntu18.04
USER root:root
ENV com.nvidia.cuda.version $CUDA_VERSION
ENV com.nvidia.volumes.needed nvidia_driver
ENV LANG=C.UTF-8 LC_ALL=C.UTF-8
ENV DEBIAN_FRONTEND noninteractive
ENV LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64
ENV NCCL_DEBUG=INFO
ENV HOROVOD_GPU_ALLREDUCE=NCCL
# Install Common Dependencies
RUN apt-get update && \
apt-get install -y --no-install-recommends \
# SSH and RDMA
libmlx4-1 \
libmlx5-1 \
librdmacm1 \
libibverbs1 \
libmthca1 \
libdapl2 \
dapl2-utils \
openssh-client \
openssh-server \
iproute2 && \
# Others
apt-get install -y \
build-essential \
bzip2 \
libbz2-1.0 \
systemd \
git \
wget \
cpio \
pciutils \
libnuma-dev \
ibutils \
ibverbs-utils \
rdmacm-utils \
infiniband-diags \
perftest \
librdmacm-dev \
libibverbs-dev \
libsm6 \
libxext6 \
libxrender-dev \
libssl1.0.0 \
linux-image-aws \
linux-image-azure \
linux-image-generic \
linux-image-kvm \
linux-image-lowlatency \
linux-image-virtual \
linux-image-gke \
linux-image-oem \
slapd \
perl \
ca-certificates \
apt \
p11-kit \
libp11-kit0 \
tar \
libsndfile-dev \
fuse && \
apt-get clean -y && \
rm -rf /var/lib/apt/lists/*
# Inference
# Copy logging utilities, nginx and rsyslog configuration files, IOT server binary, etc.
COPY --from=inferencing-assets /artifacts /var/
RUN /var/requirements/install_system_requirements.sh && \
cp /var/configuration/rsyslog.conf /etc/rsyslog.conf && \
cp /var/configuration/nginx.conf /etc/nginx/sites-available/app && \
ln -s /etc/nginx/sites-available/app /etc/nginx/sites-enabled/app && \
rm -f /etc/nginx/sites-enabled/default
ENV SVDIR=/var/runit
ENV WORKER_TIMEOUT=300
EXPOSE 5001 8883 8888
# Conda Environment
ENV MINICONDA_VERSION py37_4.9.2
ENV PATH /opt/miniconda/bin:$PATH
RUN wget -qO /tmp/miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-${MINICONDA_VERSION}-Linux-x86_64.sh && \
bash /tmp/miniconda.sh -bf -p /opt/miniconda && \
conda clean -ay && \
rm -rf /opt/miniconda/pkgs && \
rm /tmp/miniconda.sh && \
find / -type d -name __pycache__ | xargs rm -rf
# Open-MPI-UCX installation
RUN mkdir /tmp/ucx && \
cd /tmp/ucx && \
wget -q https://github.com/openucx/ucx/releases/download/v1.6.1-rc2/ucx-1.6.1.tar.gz && \
tar zxf ucx-1.6.1.tar.gz && \
cd ucx-1.6.1 && \
./configure --prefix=/usr/local --enable-optimizations --disable-assertions --disable-params-check --enable-mt && \
make -j $(nproc --all) && \
make install && \
rm -rf /tmp/ucx
# Open-MPI installation
ENV OPENMPI_VERSION 4.1.0
RUN mkdir /tmp/openmpi && \
cd /tmp/openmpi && \
wget https://download.open-mpi.org/release/open-mpi/v4.1/openmpi-${OPENMPI_VERSION}.tar.gz && \
tar zxf openmpi-${OPENMPI_VERSION}.tar.gz && \
cd openmpi-${OPENMPI_VERSION} && \
./configure --with-ucx=/usr/local/ --enable-mca-no-build=btl-uct --enable-orterun-prefix-by-default && \
make -j $(nproc) all && \
make install && \
ldconfig && \
rm -rf /tmp/openmpi
# Msodbcsql17 installation
RUN apt-get update && \
apt-get install -y curl && \
curl https://packages.microsoft.com/keys/microsoft.asc | apt-key add - && \
curl https://packages.microsoft.com/config/ubuntu/18.04/prod.list > /etc/apt/sources.list.d/mssql-release.list && \
apt-get update && \
ACCEPT_EULA=Y apt-get install -y msodbcsql17
#Cmake Installation
RUN apt-get update && \
apt-get install -y cmake
'''
env.docker.base_image = None
env.docker.base_dockerfile = dockerfile
# Register environment to re-use later
env = env.register(workspace=ws)
def deploy(ws_name,model_name,path_to_model,
environment_name,register_environment,pip_packages,conda_packages,
cpu_cores , memory_gb, path_to_entry_script,service_name):
'''
Get Workspace
'''
ws = Workspace.from_config()
print("Got Workspace {}".format(ws_name))
'''
Register Model
'''
model = Model.register(workspace = ws,
model_path =path_to_model,
model_name = model_name,
)
print("Registered Model {}".format(model_name))
'''
Register Environment
'''
# to install required packages
if register_environment:
env = Environment(environment_name)
cd = CondaDependencies.create(pip_packages=pip_packages, conda_packages = conda_packages)
env.python.conda_dependencies = cd
# Register environment to re-use later
env.register(workspace = ws)
print("Registered Environment")
myenv = Environment.get(workspace=ws, name=environment_name)
# Uncomment to save environment
# myenv.save_to_directory('./environ', overwrite=True)
'''
Config Objects
'''
aciconfig = AciWebservice.deploy_configuration(
cpu_cores=cpu_cores,
memory_gb=memory_gb,
)
inference_config = InferenceConfig(entry_script=path_to_entry_script, environment=myenv)
'''
Deploying
'''
print("Deploying....... This may take a few mins, check the status in MLS after the function finishes executing")
service = Model.deploy(workspace=ws,
name=ws_name,
models=[model],
inference_config=inference_config,
deployment_config=aciconfig, overwrite = True)
service.wait_for_deployment(show_output=True)
url = service.scoring_uri
print(url)
service = Webservice(ws,ws_name)
print(service.get_logs())
return url