Installing the MLDL frameworks

Find instructions for installing the machine learning and deep learning (MLDL) frameworks.

Setting up the software repository

The WML CE MLDL packages are distributed as conda packages in an online conda repository. conda must be configured to give priority to installing packages from this channel.

Add the WML CE channel to the conda configuration by running the following command:

conda config --prepend channels \
https://public.dhe.ibm.com/ibmdl/export/pub/software/server/ibm-ai/conda/

Creating conda environments (optional)

With conda, you can create environments that have different versions of Python and/or packages installed in them. Switching between environments is called activating the environment.

The syntax to create and activate a conda environment is:

conda create --name <environment name> python=<python version>
conda activate <environment name>

The only valid Python versions with WML CE are Python 2.7 and 3.6.

For example, to create an environment named wmlce_env with Python 3.6:

conda create --name wmlce_env python=3.6
conda activate wmlce_env

For more information on what you can do with conda environment see https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html

The conda environments are optional; if not used, packages are installed in the default environment called base.

Installing all frameworks at the same time

All the MLDL frameworks can be installed at the same time by using the powerai meta-package

With the conda environment you want to install in activated, run:

conda install powerai

Or, to install only CPU-only packages:

conda install powerai-cpu

Note: The Python 2.7 or 3.6 version of the package installed is determined by the python version of the active environment.

Installing frameworks individually

You can install the MLDL frameworks individually. The framework packages include the following versions.

Table 1. Framework packages
Package	Description	Version	Available on ppc64le	Available on x86_64
`caffe`	IBM-optimized version of Berkeley Vision and Learning Center Caffe	1.0.0	X	X
`caffe-cpu`	IBM-optimized Caffe CPU-only package	1.0.0	X	X
`cudf`	Rapids cuDF	0.7.2	X
`cuml`	Rapids cuML	0.7.0	X
`ddl`	Distributed Deep Learning	1.4.0	X	X
`pai4sk`	WML CE Snap ML	1.4.0	X
`pytorch`	PyTorch	1.1.0	X	X
`snapml-spark`	WML CE Snap ML Spark	1.3.0	X
`tensorflow`	TensorFlow CPU-only package	1.14	X	X
`tensorflow-gpu`	TensorFlow with GPU support	1.14	X	X
`tensorflow-serving`	TensorFlow Serving CPU-only package	1.14
`tensorflow-serving-gpu`	TensorFlow Serving with GPU support	1.14
`tensorflow2-gpu`	TensorFlow with GPU support	2.0	X	X
`xgboost`	xgboost with GPU support	18.04
`xgboost-cpu`	xgboost CPU-only package	18.04

With the conda environment activated, run the following command:

conda install <package name>

Accepting the WML CE license agreement

During the conda install, the packages are downloaded from the internet and after downloading, the license agreement is presented. Read the license agreement and accept the terms and conditions to complete the install. If you decline the license agreement the packages are not installed.

After you finish reading the license agreement, future installations can be automated to silently accept the license agreement by running the following command before running the conda install command:

export IBM_POWERAI_LICENSE_ACCEPT=yes

The license accept has to be done only once on a per user basis.