Difference between revisions of "Eosc/easybuild/modules"

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(List of modules)
("Regular" modules)
 
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The laboratory is a reproducible custom-built set of NLP software.  
 
The laboratory is a reproducible custom-built set of NLP software.  
It is currently installed on Saga and Puhti HPC clusters.
+
It is currently installed on ''Saga'', ''Fox'', and ''Puhti'' HPC clusters.
  
To use on Saga: run the following command on (can be put in the ''~/.bashrc'' file to be run automatically at login):
+
- To use on ''Saga'': run the following command (can be put in the ''~/.bashrc'' file to be run automatically at login):
  
 
''module use -a /cluster/shared/nlpl/software/eb/etc/all/''
 
''module use -a /cluster/shared/nlpl/software/eb/etc/all/''
 +
 +
- To use on ''Fox'': run the following command (can be put in the ''~/.bashrc'' file to be run automatically at login):
 +
 +
''module use -a /fp/projects01/ec30/software/easybuild/modules/all/''
  
 
After that, the "nlpl"-branded modules will be available via ''module avail'', ''module load'', etc.
 
After that, the "nlpl"-branded modules will be available via ''module avail'', ''module load'', etc.
 +
 +
It is highly recommended to use them, instead of installing a copy in one's own home directory.
  
 
== List of modules ==
 
== List of modules ==
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Note that the modules which have "gomkl" in their names are built using
 
Note that the modules which have "gomkl" in their names are built using
 
Intel Math Kernel Library, making them significantly faster in CPU tasks
 
Intel Math Kernel Library, making them significantly faster in CPU tasks
with Intel processors.  
+
with Intel processors (for example, on ''Saga'').  
Those with "foss" instead of "gomkl" are CPU-agnostic and will run OK on AMD machines.
+
 
 +
Those with "foss" instead of "gomkl" are CPU-agnostic and will run on machines with AMD CPUs (for example, on ''Fox'').
 +
 
 +
Further on, we just use the placeholder '''ARCH''', replace it with either "gomkl" or "foss", depending on which machine you are working on.
  
 
=== "Bundle" modules ===
 
=== "Bundle" modules ===
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Here are the details:
 
Here are the details:
  
* '''nlpl-python-candy/2021.01-gomkl-2019b-Python-3.7.4''': various utility packages not directly related to NLP
+
* '''nlpl-python-candy/2021.01-ARCH-2019b-Python-3.7.4''': various utility packages not directly related to NLP
 
** tqdm 4.62.3
 
** tqdm 4.62.3
 
** pydot 1.4.2
 
** pydot 1.4.2
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** mpi4py 3.1.1
 
** mpi4py 3.1.1
 
** jsonlines 2.0.0
 
** jsonlines 2.0.0
* '''nlpl-nlptools/2021.01-gomkl-2019b-Python-3.7.4''': various utility packages related to NLP
+
** typing_extensions 3.7.4.3
 +
* '''nlpl-nlptools/2021.01-ARCH-2019b-Python-3.7.4''': various utility packages related to NLP
 
** conllu 4.4.1
 
** conllu 4.4.1
 
** seqeval 1.2.2
 
** seqeval 1.2.2
 
** langdetect 1.0.9
 
** langdetect 1.0.9
 
** leven 1.0.4
 
** leven 1.0.4
* '''nlpl-scipy-ecosystem/2021.01-gomkl-2019b-Python-3.7.4''': everything that constitutes the [https://scipy.org/ SciPy ecosystem]. Too many packages to enumerate them all, but the most important are:
+
* '''nlpl-scipy-ecosystem/2021.01-ARCH-2019b-Python-3.7.4''': everything that constitutes the [https://scipy.org/ SciPy ecosystem]. Too many packages to enumerate them all, but the most important are:
 
** scipy 1.5.4
 
** scipy 1.5.4
 
** pandas 1.2.1
 
** pandas 1.2.1
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These are more obvious modules, each one gives you one software piece:
 
These are more obvious modules, each one gives you one software piece:
  
* '''nlpl-cython/0.29.21-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-cython/0.29.21-ARCH-2019b-Python-3.7.4''': [http://cython.org/ Cython] 0.29.21
* '''nlpl-dllogger/0.1.0-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-datasets/1.17-ARCH-2019b-Python-3.7.4''': [https://github.com/huggingface/datasets HuggingFace Datasets] 1.17
* '''nlpl-gensim/3.8.3-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-dllogger/0.1.0-ARCH-2019b-Python-3.7.4''': [https://github.com/NVIDIA/dllogger DLLogger] 0.1.0 ''(status unclear)''
* '''nlpl-horovod/0.20.3-gomkl-2019b-tensorflow-1.15.2-Python-3.7.4'''
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* '''nlpl-gensim/3.8.3-ARCH-2019b-Python-3.7.4''': [https://github.com/RaRe-Technologies/gensim Gensim] 3.8.3
* '''nlpl-nltk/3.5-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-horovod/0.20.3-ARCH-2019b-tensorflow-1.15.2-Python-3.7.4''': [https://github.com/horovod/horovod Horovod] 0.20.3
* '''nlpl-numpy/1.18.1-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-horovod/0.23.0-ARCH-2019b-tensorflow-2.6.2-Python-3.7.4''': [https://github.com/horovod/horovod Horovod] 0.23.0
* '''nlpl-nvidia-bert/20.06.8-gomkl-2019b-tensorflow-1.15.2-Python-3.7.4'''
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* '''nlpl-huggingface-hub/0.4.0-ARCH-2019b-Python-3.7.4''': [https://pypi.org/project/huggingface-hub/ HuggingFace Hub] 0.4.0
* '''nlpl-pytorch/1.6.0-gomkl-2019b-cuda-10.1.243-Python-3.7.4'''
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* '''nlpl-nltk/3.5-ARCH-2019b-Python-3.7.4''': [https://www.nltk.org/ NLTK] 3.5, together with '''all''' the corpora and datasets (no need to download them separately!)
* '''nlpl-scikit-bundle/0.22.2.post1-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-numpy/1.18.1-ARCH-2019b-Python-3.7.4''': [https://numpy.org/ NumPy] 1.18.1
* '''nlpl-simple_elmo/0.9.0-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-nvidia-bert/20.06.8-ARCH-2019b-tensorflow-1.15.2-Python-3.7.4''': [https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow/LanguageModeling/BERT NVIDIA's BERT implementation] for TensorFlow 1 ''(status unclear)''
* '''nlpl-stanza/1.1.1-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-pytorch/1.6.0-ARCH-2019b-cuda-10.1.243-Python-3.7.4''': [https://pytorch.org/ PyTorch] 1.6.0
* '''nlpl-tensorflow/1.15.2-gomkl-2019b-cuda-10.1.243-Python-3.7.4'''
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* '''nlpl-pytorch/1.7.1-ARCH-2019b-cuda-10.1.243-Python-3.7.4''': [https://pytorch.org/ PyTorch] 1.7.1
* '''nlpl-tensorflow/2.3.2-gomkl-2019b-cuda-10.1.243-Python-3.7.4'''
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* '''nlpl-pytorch/1.7.1-ARCH-2019b-cuda-11.1.1-Python-3.7.4''': [https://pytorch.org/ PyTorch] 1.7.1 (for CUDA 11)
* '''nlpl-tokenizers/0.10.2-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-scikit-bundle/0.22.2.post1-ARCH-2019b-Python-3.7.4''': [https://scikit-learn.org/ Scikit-Learn] 0.22.2
* '''nlpl-transformers/4.5.1-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-simple_elmo/0.9.0-ARCH-2019b-Python-3.7.4''': [https://pypi.org/project/simple-elmo/ Simple_elmo] 0.9.0
* '''nlpl-wandb/0.12.6-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-stanza/1.1.1-ARCH-2019b-Python-3.7.4''': [https://stanfordnlp.github.io/stanza/ Stanza] 1.1.1
* '''sentencepiece/0.1.94-gomkl-2019b-Python-3.7.4'''
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* '''nlpl-stanza/1.3.0-ARCH-2019b-Python-3.7.4''': [https://stanfordnlp.github.io/stanza/ Stanza] 1.3.0
 +
* '''nlpl-tensorflow/1.15.2-ARCH-2019b-cuda-10.1.243-Python-3.7.4''': [https://www.tensorflow.org/ TensorFlow] 1.15.2
 +
* '''nlpl-tensorflow/2.3.2-ARCH-2019b-cuda-10.1.243-Python-3.7.4''': [https://www.tensorflow.org/ TensorFlow] 2.3.2
 +
* '''nlpl-tensorflow/2.6.2-ARCH-2019b-cuda-11.1.1-Python-3.7.4''': [https://www.tensorflow.org/ TensorFlow] 2.6.2 (for CUDA 11)
 +
* '''nlpl-tokenizers/0.10.2-ARCH-2019b-Python-3.7.4''': [https://github.com/huggingface/tokenizers HuggingFace Tokenizers] 0.10.2
 +
* '''nlpl-torchmetrics/0.7.3-ARCH-2019b-Python-3.7.4''': [https://pypi.org/project/torchmetrics/ TorchMetrics] 0.7.3
 +
* '''nlpl-transformers/4.5.1-ARCH-2019b-Python-3.7.4''': [https://huggingface.co/transformers/ HuggingFace Transformers] 4.5.1
 +
* '''nlpl-transformers/4.14.1-ARCH-2019b-Python-3.7.4''': [https://huggingface.co/transformers/ HuggingFace Transformers] 4.14.1
 +
* '''nlpl-wandb/0.12.6-ARCH-2019b-Python-3.7.4''': [https://pypi.org/project/wandb/ Weights and Biases (wandb)] 0.12.6
 +
* '''sentencepiece/0.1.94-ARCH-2019b-Python-3.7.4''': [https://github.com/google/sentencepiece SentencePiece] 0.1.94
 +
* '''sentencepiece/0.1.96-ARCH-2019b-Python-3.7.4''': [https://github.com/google/sentencepiece SentencePiece] 0.1.96
  
 
= Source =
 
= Source =
  
 
Currently, the virtual laboratory is generated using EasyBuild, all the code and easyconfigs available [https://source.coderefinery.org/nlpl/easybuild here].
 
Currently, the virtual laboratory is generated using EasyBuild, all the code and easyconfigs available [https://source.coderefinery.org/nlpl/easybuild here].

Latest revision as of 01:57, 12 April 2022

NLPL virtual laboratory

The laboratory is a reproducible custom-built set of NLP software. It is currently installed on Saga, Fox, and Puhti HPC clusters.

- To use on Saga: run the following command (can be put in the ~/.bashrc file to be run automatically at login):

module use -a /cluster/shared/nlpl/software/eb/etc/all/

- To use on Fox: run the following command (can be put in the ~/.bashrc file to be run automatically at login):

module use -a /fp/projects01/ec30/software/easybuild/modules/all/

After that, the "nlpl"-branded modules will be available via module avail, module load, etc.

It is highly recommended to use them, instead of installing a copy in one's own home directory.

List of modules

From time to time, updated modules with newer software versions will be added, but the older modules will never be removed (for reproducibility).

Note that the modules which have "gomkl" in their names are built using Intel Math Kernel Library, making them significantly faster in CPU tasks with Intel processors (for example, on Saga).

Those with "foss" instead of "gomkl" are CPU-agnostic and will run on machines with AMD CPUs (for example, on Fox).

Further on, we just use the placeholder ARCH, replace it with either "gomkl" or "foss", depending on which machine you are working on.

"Bundle" modules

These are the modules with the most cryptic names. Each of them contains a bunch of software pieces (Python packages, as a rule). Many of these modules will be loaded automatically as dependencies of the regular modules, but sometimes they can be useful themselves. Here are the details:

  • nlpl-python-candy/2021.01-ARCH-2019b-Python-3.7.4: various utility packages not directly related to NLP
    • tqdm 4.62.3
    • pydot 1.4.2
    • smart_open 5.2.1
    • cached-property 1.5.2
    • filelock 3.0.12
    • regex 2021.10.23
    • sacremoses 0.0.46
    • mpi4py 3.1.1
    • jsonlines 2.0.0
    • typing_extensions 3.7.4.3
  • nlpl-nlptools/2021.01-ARCH-2019b-Python-3.7.4: various utility packages related to NLP
    • conllu 4.4.1
    • seqeval 1.2.2
    • langdetect 1.0.9
    • leven 1.0.4
  • nlpl-scipy-ecosystem/2021.01-ARCH-2019b-Python-3.7.4: everything that constitutes the SciPy ecosystem. Too many packages to enumerate them all, but the most important are:
    • scipy 1.5.4
    • pandas 1.2.1
    • matplotlib 3.1.2
    • ipython 7.11.1
    • jupyter_core 4.6.1
    • jupyter_client 5.3.4
    • networkx 2.5.1
    • sympy 1.7.1

"Regular" modules

These are more obvious modules, each one gives you one software piece:

  • nlpl-cython/0.29.21-ARCH-2019b-Python-3.7.4: Cython 0.29.21
  • nlpl-datasets/1.17-ARCH-2019b-Python-3.7.4: HuggingFace Datasets 1.17
  • nlpl-dllogger/0.1.0-ARCH-2019b-Python-3.7.4: DLLogger 0.1.0 (status unclear)
  • nlpl-gensim/3.8.3-ARCH-2019b-Python-3.7.4: Gensim 3.8.3
  • nlpl-horovod/0.20.3-ARCH-2019b-tensorflow-1.15.2-Python-3.7.4: Horovod 0.20.3
  • nlpl-horovod/0.23.0-ARCH-2019b-tensorflow-2.6.2-Python-3.7.4: Horovod 0.23.0
  • nlpl-huggingface-hub/0.4.0-ARCH-2019b-Python-3.7.4: HuggingFace Hub 0.4.0
  • nlpl-nltk/3.5-ARCH-2019b-Python-3.7.4: NLTK 3.5, together with all the corpora and datasets (no need to download them separately!)
  • nlpl-numpy/1.18.1-ARCH-2019b-Python-3.7.4: NumPy 1.18.1
  • nlpl-nvidia-bert/20.06.8-ARCH-2019b-tensorflow-1.15.2-Python-3.7.4: NVIDIA's BERT implementation for TensorFlow 1 (status unclear)
  • nlpl-pytorch/1.6.0-ARCH-2019b-cuda-10.1.243-Python-3.7.4: PyTorch 1.6.0
  • nlpl-pytorch/1.7.1-ARCH-2019b-cuda-10.1.243-Python-3.7.4: PyTorch 1.7.1
  • nlpl-pytorch/1.7.1-ARCH-2019b-cuda-11.1.1-Python-3.7.4: PyTorch 1.7.1 (for CUDA 11)
  • nlpl-scikit-bundle/0.22.2.post1-ARCH-2019b-Python-3.7.4: Scikit-Learn 0.22.2
  • nlpl-simple_elmo/0.9.0-ARCH-2019b-Python-3.7.4: Simple_elmo 0.9.0
  • nlpl-stanza/1.1.1-ARCH-2019b-Python-3.7.4: Stanza 1.1.1
  • nlpl-stanza/1.3.0-ARCH-2019b-Python-3.7.4: Stanza 1.3.0
  • nlpl-tensorflow/1.15.2-ARCH-2019b-cuda-10.1.243-Python-3.7.4: TensorFlow 1.15.2
  • nlpl-tensorflow/2.3.2-ARCH-2019b-cuda-10.1.243-Python-3.7.4: TensorFlow 2.3.2
  • nlpl-tensorflow/2.6.2-ARCH-2019b-cuda-11.1.1-Python-3.7.4: TensorFlow 2.6.2 (for CUDA 11)
  • nlpl-tokenizers/0.10.2-ARCH-2019b-Python-3.7.4: HuggingFace Tokenizers 0.10.2
  • nlpl-torchmetrics/0.7.3-ARCH-2019b-Python-3.7.4: TorchMetrics 0.7.3
  • nlpl-transformers/4.5.1-ARCH-2019b-Python-3.7.4: HuggingFace Transformers 4.5.1
  • nlpl-transformers/4.14.1-ARCH-2019b-Python-3.7.4: HuggingFace Transformers 4.14.1
  • nlpl-wandb/0.12.6-ARCH-2019b-Python-3.7.4: Weights and Biases (wandb) 0.12.6
  • sentencepiece/0.1.94-ARCH-2019b-Python-3.7.4: SentencePiece 0.1.94
  • sentencepiece/0.1.96-ARCH-2019b-Python-3.7.4: SentencePiece 0.1.96

Source

Currently, the virtual laboratory is generated using EasyBuild, all the code and easyconfigs available here.