Index of /examples/machine_learning

Name	Last modified	Size

Parent Directory		-
BERT/	2021-04-23 13:43	-
basic/	2021-03-23 13:24	-
bootcamp/	2023-05-30 09:05	-
classic_ML/	2021-07-02 17:59	-
multiple_gpus_tensor..>	2021-03-23 14:14	-
pytorch/	2024-04-03 13:08	-
singularity/	2021-06-29 20:12	-
sklearn/	2023-10-16 14:12	-
tensorflow/	2022-03-11 10:03	-
torch/	2020-05-28 09:39	-
tutorials/	2024-09-10 09:22	-

Machine Learning/Deep Learning Examples

RCS examples are provided to assist you in learning the software and the development of your applications on the Shared Computing Cluster (SCC). The instructions provided along with the code assume that the underlying OS is Linux. If these examples are run on a different architecture/OS, you might need to make some changes to the code and/or the way the program is built and executed.

Directory Structure

basic: Basic example of how to run Tensorflow Python code on the SCC cluster.
multiple_gpus_tensorflow: Basic example of how to request and run Tensorflow Python code on the SCC cluster with multiple GPUs.
pytorch: Basic examples of how to run Pytorch code on the SCC cluster with CPUs and GPUs.
tensorflow: Basic examples of how to run Tensorflow Python code on the SCC cluster with CPUs and GPUs, for versions 1.x and 2.x of Tensorflow.
torch: Basic example of how to run Torch code on the SCC cluster.
tutorials: Files of Machine Learning tutorials.

Q & A

Question: How do I run Tensorflow or PyTorch on the SCC?

Answer: Check the examples we have in their respective folders. It's important to do so because we have some specific instructions and recommendations on how to do that.

Question: Can I use BERT and other NLP models on the SCC?

Answer: Yes, of course! You can load the Hugging Face Transformers module (Transformers), which comes with state-of-the-art pre-trained models ready for use. Check the example directory to see how to use it.

Question: Should I create a virtual environment for my work?

Answer: It depends! If you need to have separate Python environments where you can install and use specific/custom libraries, then it's probably a good idea to have a virtual environment. Otherwise, if your use case is standard, it's probably sufficient to use Python directly.

Question: Which virtual environment should I create if I decide I want to use one?

Answer: This is totally up to you! In some cases you may find that a Conda virtual environment is more useful; for example, if a specific package is only available as a conda package. In other cases you may prefer to use a virtualenv virtual environment ; for example, if you worry that the conda environment solver can be slow and sometimes buggy. Click on the links in this answer for more details.

Question: I need to run Tensorflow version 1.X, what do I do?

Answer: Luckily, we have examples just for that! Please check the Tensorflow directory here .

Question: Can I install a custom PIP package and use it on the SCC?

Answer: Yes, absolutely! Just make sure you install it correctly though, and to your project disk space not your home directory. We have a whole guide on this here.

Contact Information

Help: help@scv.bu.edu

Note: RCS example programs are provided "as is" without any warranty of any kind. The user assumes the entire risk of quality, performance, and repair of any defect. You are welcome to copy and modify any of the given examples for your own use.

Note: Research Computing Services (RCS) example programs are provided "as is" without any warranty of any kind. The user assumes the entire risk of quality, performance, and repair of any defects. You are encouraged to copy and modify any of the given examples for your own use.