Run Machine Learning Jobs
This guide provides some of our recommendations for success in running machine learning (specifically deep learning) jobs in CHTC.
This is a new how-to guide on the CHTC website. Recommendations and feedback are welcome via email (firstname.lastname@example.org) or by creating an issue on the CHTC website Github repository: Create an issue
It is important to understand the needs of a machine learning job before submitting it and have a plan for managing software. This guide covers:
1. Job Requirements
Before digging into the nuts and bolts of software installation in the next section, it is important to first consider a few other job requirements that might apply to your machine learning job.
A. Do you need GPUs?
CHTC has about 4 publicly available GPUs and thousands of CPUs. When possible, using CPUs will allow your jobs to start more quickly and to have many running at once. For certain calculations, GPUs may provide a different advantage as some machine learning algorithms are optimized to run significantly faster on GPUs. Consider whether you would benefit from running one or two long-running calculations on a GPU or if your work is better suited to running many jobs on CHTC’s available CPUs.
If you need GPUs for your jobs, you can see a summary of available GPUs in CHTC and how to access them here:
Note that you may need to use different versions of your software, depending on whether or not you are using GPUs, as shown in the software section of this guide.
B. How big is your data?
CHTC’s usual data recommendations apply for machine learning jobs. If your job is using an input data set larger than a few hundred MB or generating output files larger than a few GB, you will likely need to use our large data file share. Contact the CHTC Research Computing Facilitators to get access and read about the large data location here:
C. How long does your job run?
CHTC’s default job length is 72 hours. If your task is long enough that you will encounter this limit, contact the CHTC Research Computing Facilitators (email@example.com) for potential work arounds.
D. How many jobs do you want to submit?
Do you have the ability to break your work into many independent pieces? If so, you can take advantage of CHTC’s capability to run many independent jobs at once, especially when each job is using a CPU. See our guide for running multiple jobs here:
2. Software Options
Many of the tools used for machine learning, specifically deep learning and convolutional neural networks, have enough dependencies that our usual installation processes work less reliably. The following options are the best way to handle the complexity of these software tools.
Please be aware of which CUDA library version you are using to run your code.
A. Using Docker Containers
CHTC’s HTC system has the ability to run jobs using Docker containers, which package up a whole system (and software) environment in a consistent, reproducible, portable format. When possible, we recommend using standard, publicly available Docker containers to run machine learning jobs in CHTC.
To see how you can use Docker containers to run jobs in CHTC, see:
You can also test and examine containers on your own computer:
Some machine learning frameworks publish ready-to-go Docker images:
- Tensorflow on Docker Hub - the “Overview” on that page describes how to choose an image.
- Pytorch on Docker Hub - we recommend choosing the most recently published image that ends in
If you can not find a Docker container with exactly the tools you need, you can build your own, starting with one of the containers above. For instructions on how to build and test your own Docker container, see this guide:
B. Using Conda
The Python package manager conda is a popular tool for installing and managing machine learning tools. See this guide for information on how to use conda to provide dependencies for CHTC jobs.
Note that when installing TensorFlow using
conda, it is important to install
not the generic
tensorflow package, but
tensorflow-gpu. This ensures that
the installation will include the
required for TensorFlow ‘s GPU capability.