https://github.com/alleninstitute/all-active-workflow
Creating the code base for All-active Model generation written on top of Bluepyopt
https://github.com/alleninstitute/all-active-workflow
Last synced: about 1 year ago
JSON representation
Creating the code base for All-active Model generation written on top of Bluepyopt
- Host: GitHub
- URL: https://github.com/alleninstitute/all-active-workflow
- Owner: AllenInstitute
- License: other
- Created: 2018-07-27T16:44:48.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2020-12-01T01:07:35.000Z (over 5 years ago)
- Last Synced: 2025-03-25T16:08:06.724Z (over 1 year ago)
- Language: Python
- Size: 146 MB
- Stars: 16
- Watchers: 3
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
[](https://travis-ci.com/anirban6908/All-active-Workflow)
[](https://alleninstitute.org/legal/terms-use/)
# Workflow for the generation and evaluation of bio-realistic, conductance-based single-neuron models
Code base for all-active model generation, evaluation and analysis (main engine: bluepyopt).
* Method developed and present in Nandi et al. (2020): https://www.biorxiv.org/content/10.1101/2020.04.09.030239v1
* Main developers: Ani Nandi, Werner van Geit, Tom Chartrand, Anatoly Buchin and Costas A. Anastassiou
* Genetic algorithm in action: **selection** + **evaluation** + **evolution**
## Dependencies
* Python 3.5+
* Neuron 7.5 (compiled with Python support)
* BluePyOpt
* eFEL
* AllenSDK
* NeuroM
* Pandas
* Seaborn
## Installation
```bash
pip install git+https://github.com/AllenInstitute/All-active-Workflow # install directly from the repository
```
or
```bash
git clone https://github.com/AllenInstitute/All-active-Workflow # clone repository from github
cd All-active-Workflow
pip install -e . # install in editable mode
```
## Quickstart: Launching optimization jobs
In a Unix environment :
```bash
$ source activate conda-env # conda environment with all dependencies
$ launch_optimjob --help # Look at the options
$ launch_optimjob --input_json job_config.json
```
* Configurable staged optimization
job_config.json
```json
{
"cty_config": {
"cell_id": "483101699"
},
"job_config": {
"highlevel_jobconfig": {
"conda_env": "ateam_opt",
"axon_type": "stub_axon",
"data_source": "web",
"ephys_dir": "ephys_data",
"non_standard_nwb": false,
"feature_stimtypes": [
"Long Square"
],
"feature_names_path": "feature_set_all.json",
"compiled_modfiles_dir": "x86_64",
"job_dir": "483101699_benchmark_timeout"
},
"stage_jobconfig": [
{
"stage_name": "Stage0",
"stage_stimtypes": [
"Long Square"
],
"stage_features": "feature_set_stage0.json",
"stage_parameters": "param_bounds_stage0.json",
"filter_rule": "filter_feat_proto_passive",
"offspring_size": 512,
"max_ngen": 50,
"optim_config":{
"nengines": 256,
"nnodes": 16,
"qos": "celltypes",
"nprocs": 16,
"error_stream": "job.err",
"output_stream": "job.out",
"jobmem": "100g",
"jobtime": "5:00:00",
"ipyparallel": true,
"ipyparallel_db": "sqlitedb",
"main_script": "Optim_Main.py"
},
"analysis_config":{
"main_script": "analyze_stagejob.py"
},
"seed": [
1
]
},
{
"stage_name": "Stage1",
"stage_stimtypes": [
"Long Square"
],
"stage_features": "feature_set_stage1.json",
"stage_parameters": "param_bounds_stage1.json",
"filter_rule": "filter_feat_proto_passive",
"offspring_size": 512,
"max_ngen": 50,
"optim_config":{
"nengines": 256,
"nnodes": 16,
"qos": "celltypes",
"nprocs": 16,
"error_stream": "job.err",
"output_stream": "job.out",
"jobmem": "100g",
"jobtime": "5:00:00",
"ipyparallel": true,
"ipyparallel_db": "sqlitedb",
"main_script": "Optim_Main.py"
},
"analysis_config":{
"main_script": "analyze_stagejob.py"
},
"seed": [
1
]
},
{
"stage_name": "Stage2",
"stage_stimtypes": [
"Long Square"
],
"stage_features": "feature_set_stage2.json",
"stage_parameters": "param_bounds_stage2_mouse_spiny.json",
"filter_rule": "filter_feat_proto_active",
"AP_initiation_zone": "axon",
"offspring_size": 512,
"cp_backup_dir": "checkpoints_backup",
"max_ngen": 200,
"optim_config":{
"nengines": 256,
"nnodes": 16,
"qos": "celltypes",
"nprocs": 16,
"error_stream": "job.err",
"output_stream": "job.out",
"jobmem": "150g",
"jobtime": "12:00:00",
"ipyparallel": true,
"ipyparallel_db": "sqlitedb",
"main_script": "Optim_Main.py"
},
"analysis_config":{
"main_script": "analyze_stagejob.py",
"ipyparallel": true,
"ipyparallel_db": "nodb",
"error_stream": "analysis.err",
"output_stream": "analysis.out",
"nengines": 40,
"nnodes": 4,
"nprocs": 10,
"jobtime": "10:00:00",
"jobmem": "100g",
"qos": "celltypes"
},
"seed": [
1,
2,
3,
4
],
"run_hof_analysis": true,
"run_peri_comparison": false,
"depol_block_check": true,
"add_fi_kink": true,
"calc_model_perf": true,
"model_postprocess": true,
"calc_time_statistics": true,
"timeout": 300,
"hoc_export": true
}
]
}
}
```
## HPC usage support
Instructions are included to run these compute intensive jobs on 4 different architectures (Allen Institute HPC cluster, NERSC, BBP5 and AWS).
## Level of support
We are planning on occasional updating this tool with no fixed schedule. Community involvement is encouraged through both issues and pull requests.