https://github.com/matthewfeickert/distributed-inference-with-pyhf-and-funcX

Example code for vCHEP 2021 paper "Distributed statistical inference with pyhf enabled through funcX"
https://github.com/matthewfeickert/distributed-inference-with-pyhf-and-funcX

chep2021 funcx pyhf

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Example code for vCHEP 2021 paper "Distributed statistical inference with pyhf enabled through funcX"

• Host: GitHub
• URL: https://github.com/matthewfeickert/distributed-inference-with-pyhf-and-funcX
• Owner: matthewfeickert
• License: mit
• Created: 2021-02-27T21:26:23.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2024-10-07T22:36:39.000Z (about 1 month ago)
• Last Synced: 2024-11-08T09:53:19.682Z (5 days ago)
• Topics: chep2021, funcx, pyhf
• Language: Python
• Homepage:
• Size: 68.4 KB
• Stars: 0
• Watchers: 4
• Forks: 2
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Distributed Inference with `pyhf` and `funcX`

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Example code for vCHEP 2021 paper "Distributed statistical inference with pyhf enabled through funcX"

## Setup

Create a Python 3 virtual environment and then install the `pyhf` and `funcX` dependencies in `requirements.txt`.

```

(distributed-inference) $ python -m pip install --upgrade pip setuptools wheel

(distributed-inference) $ python -m pip install -r requirements.txt

```

### Reproducible environment

To install a reproducible environment that is consistent down to the hash level, use `pip-compile` to compile a lock file from `requirements.txt` and install it following the [`pip-secure-install`][pip-secure-install] recommendations.

```

(distributed-inference) $ bash compile_dependencies.sh

(distributed-inference) $ bash secure_install.sh

```

[pip-secure-install]: https://github.com/brettcannon/pip-secure-install

### On XSEDE's EXPANSE

On EXPANSE, to use a Python 3.7+ runtime Conda must be used, so create a Conda environment from the `expanse-environment.yml` provided, which uses the different `requirements.txt` files to provide the dependencies.

```console

$ conda env create -f expanse-environment.yml

$ conda activate distributed-inference

```

Once a GPU session has been entered, source the `setup_expanse_funcx_test_env.sh` shell script to activate the environment and load all required modules

```

(distributed-inference) $ . setup_expanse_funcx_test_env.sh

```

#### Machine Configuration

EXPANSE has the following Nvidia drivers and GPUs:

```console

$ nvcc --version

nvcc: NVIDIA (R) Cuda compiler driver

Copyright (c) 2005-2020 NVIDIA Corporation

Built on Thu_Jun_11_22:26:38_PDT_2020

Cuda compilation tools, release 11.0, V11.0.194

Build cuda_11.0_bu.TC445_37.28540450_0

$ nvidia-smi --list-gpus

GPU 0: Tesla V100-SXM2-32GB (UUID: GPU-XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX)

```

## Run

Create a file named `endpoint_id.txt` in the top level of this repository and save your funcX endpoint ID into the file.

```

(distributed-inference) $ touch endpoint_id.txt

```

This will be read in during the run.

Pass the config JSON file for the analysis you want to run to `fit_analysis.py`

```

(distributed-inference) $ python fit_analysis.py -c config/1Lbb.json -b numpy

```

```console

$ python fit_analysis.py --help

usage: fit_analysis.py [-h] [-c CONFIG_FILE] [-b BACKEND]

configuration arguments provided at run time from the CLI

optional arguments:

  -h, --help            show this help message and exit

  -c CONFIG_FILE, --config-file CONFIG_FILE

                        config file

  -b BACKEND, --backend BACKEND

                        pyhf backend str alias

```