IBM WML CE on Triton User Menu


To release the power of the advanced hardware, IBM provides Watson Machine Learning Community Edition (WML CE) which is a set of software packages for deep learning and machine learning development and applications on the state-of-the-art POWER system equiped with the most advanced NVIDIA GPUs. WML CE contains the popular open source deep learning frameworks such as TensorFlow and PyTorch, IBM-optimized Caffe, IBM’s machine learning library (Snap ML) and software for distributed training (DDL) and large model support (LMS).

Using Anaconda

The WML CE packages are distributed as conda packages in an online conda repository. You can use Anaconda or Miniconda to install and manage the packages.

On Triton, we have pre-installed Anaconda that is configured to point to the repository containing the WML CE packages. After logging to the system with ssh <your caneid>, you can do ml wml_anaconda3 to load the default version of the WML-configured Anaconda module.

We recommend using the pre-installed Anaconda since it will be easier for us to track down the problem if you need assistance. However, you can also install Anaconda or Miniconda in your home directory following the WML CE system setup guide, and handle it by yourself.

Installing WML CE packages

  • $ ml wml_anaconda3
  • $ conda create -n <your environment> python=<version> powerai=<version>

For example, conda create -n my_wml_env python=3.7 powerai=1.7.0 will create an environment named my_wml_env at ~/.conda/envs with python 3.7 and all the WML CE packages installed.

  • $ conda activate <your environment>

Installing all the WML CE packages at the same time in your environment:

  • (your environment)$ conda install powerai=1.6.2

Or installing individual package:

  • (your environment)$ conda install <WML CE supported package>

Using WML CE packages


You should only do small testing on the login node using the command line interface, formal jobs need to be submitted via LSF.

Small testing using the command line interface

  • $ ml wml_anaconda3
  • $ conda activate <your environment>
  • (your environment)$ python
  • $ conda deactivate

Submitting jobs using LSF on Triton

Use #BSUB -q normal to submit job to queue normal for testing on Triton now (it will change in the future).

Add #BSUB -gpu "num=<number of GPUs>" if you need GPUs in your job. You can use up to 2 GPUs if Distributed Deep Learning (DDL) is not involved.

A job script example:

#BSUB -J "my_example"
#BSUB -o "my_example_%J.out"
#BSUB -e "my_example_%J.err"
#BSUB -n 4
#BSUB -gpu "num=1"
#BSUB -q "normal"
#BSUB -W 00:10

ml wml_anaconda3
conda activate <your_environment>
python path/to/

If the above file is named my_job_script.job, run the below command to submit the job:

$ bsub < my_job_script.job

The output will show in the my_example_<job id>.out file after the job is done.

Installing other packages not included in WML CE


Installing other packages could cause conflicts with the installed WML CE packages.

If you really need to install other packages, you can try the steps below in order until you find it.

  1. conda install <package> or conda install <package>=<version> in the activated environment will search the package in the IBM WML CE repo, then the official repo hosted by Anaconda as configured in the wml_anaconda3. The package will be installed if it is found in the repos.
  2. Search in Anaconda Cloud and choose Platform linux-ppc64le, then click on the name of the found package. The detail page will show you how to install the package with a specific channel, such as conda install -c <a specific channel> <package>
  3. Use pip install <package>


Issues may arise when using pip and conda together. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software.

Using DDL (Testing)


ddl-tensorflow operator and pytorch DDL are DEPRECATED and will be REMOVED in the next WML CE release. Please start using horovod with NCCL backend.

A job script example:

#BSUB -L /bin/bash
#BSUB -n 12
#BSUB -R "span[ptile=4]"
#BSUB -gpu "num=2"
#BSUB -q "normal"
#BSUB -W 00:10

module unload gcc
ml wml_anaconda3
ml xl
ml smpi
conda activate <your environment>

# Workaround for GPU selection issue
cat > << EoF_l
#! /bin/sh
exec \$*
chmod +x

# Run the program
ddlrun ./ python /path/to/

# Clean up
/bin/rm -f
  • #BSUB -n 12 requests 12 CPU cores
  • #BSUB -R "span[ptile=4]" asks for 4 cores per node, so 3 nodes (12 / 4) will be involved.
  • #BSUB -gpu "num=2" requests 2 GPUs per node, and therefore 6 GPUs in total (2 * 3) are requested for this job.

System Pre-installed WML CE packages

We recommend you set up your own environment and install WML CE packages so you have a total control. However, you can also use the different versions of WML CE that we have installed on the system.

You can do ml wml/<versions> to activate the environment including packages of the specific WML CE version. ml -wml will deactivate the environment.

Conda General Commands

  • $ conda create -n <environment name> python=<version> to create an environment
  • $ conda env list to list all available environments
  • $ conda activate <environment name> to activate an environment

Inside an environment (after activating the environment):

  • $ conda list to list installed packages
  • $ conda install <package name> to install a package
  • $ conda install <package name>=<version> to install a package with a specific version
  • $ conda install -c <url> <package name> to install a package from a specific channel (repository)
  • $ conda remove <package name> to uninstall a package
  • $ conda deactivate to deactivate the environment

Please check the official document for details.