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Commit 47af8be9 authored by yuhai's avatar yuhai
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examples/legacy/iter_nn_slurm/share/mol_files.raw 0 → 100644
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examples/legacy/iter_nn_slurm/share/test.sh 0 → 100644
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mkdir test
python /path/to/source/deepks/train/test.py -m model.pth -d `cat train_paths.raw` -o test/train
python /path/to/source/deepks/train/test.py -m model.pth -d `cat test_paths.raw` -o test/test
examples/legacy/train_active_learning/run.py 0 → 100644
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#!/usr/bin/env python
# coding: utf-8
import os
import sys
from glob import glob
import numpy as np
# sys.path.append('/path/to/source')
import deepks
from deepks.task.task import PythonTask
from deepks.task.task import ShellTask
from deepks.task.task import BatchTask
from deepks.task.task import GroupBatchTask
from deepks.task.workflow import Sequence
from deepks.task.workflow import Iteration
# define key parameters
nsel = 200
nmodel = 4
niter = 21
# define training task
train_res = {"time_limit": "24:00:00",
"mem_limit": 32,
"numb_gpu": 1}
disp = {"context_type": 'local',
"batch_type": 'slurm'}
train_cmd = "python -u /path/to/source/deepks/train/main.py input.yaml"
batch_train = [BatchTask(cmds=train_cmd,
workdir=f'model.{i:02}',
share_folder="share",
link_share_files=["input.yaml"])
for i in range(nmodel)]
task_train = GroupBatchTask(batch_train,
resources=train_res,
dispatcher=disp,
outlog="log.train",
errlog="err.train",
link_prev_files=[('new_train_paths.raw', 'train_paths.raw'),
('new_test_paths.raw', 'test_paths.raw')])
# define testing task
test_cmd = "srun -N 1 -t 1:00:00 --gres=gpu:1 bash test_model.sh 1> log.test 2> err.test"
task_test = ShellTask(test_cmd,
share_folder="share",
link_share_files=["test_model.sh"])
#define selecting task
def select_data(nsel):
paths = glob("model.*")
old_trn = np.loadtxt("train_paths.raw", dtype=str)
old_tst = np.loadtxt("test_paths.raw", dtype=str)
trn_res = np.stack([np.loadtxt(f"{m}/test/train.all.out")[:,1] for m in paths], -1)
tst_res = np.stack([np.loadtxt(f"{m}/test/test.all.out")[:,1] for m in paths], -1)
tst_std = np.std(tst_res, axis=-1)
order = np.argsort(tst_std)[::-1]
sel = order[:nsel]
rst = np.sort(order[nsel:])
new_trn = np.concatenate([old_trn, old_tst[sel]])
new_tst = old_tst[rst]
np.savetxt("new_train_paths.raw", new_trn, fmt="%s")
np.savetxt("new_test_paths.raw", new_tst, fmt="%s")
task_select = PythonTask(select_data, call_args=[nsel])
# combine them together
iterate = Iteration([task_train, task_test, task_select], niter, init_folder='share/init', record_file='RECORD')
if os.path.exists('RECORD'):
iterate.restart()
else:
iterate.run()
examples/legacy/train_active_learning/share/init/new_test_paths.raw 0 → 100644
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examples/legacy/train_active_learning/share/init/new_train_paths.raw 0 → 100644
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examples/legacy/train_active_learning/share/input.yaml 0 → 100644
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model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: gelu
data_args:
batch_size: 1
group_batch: 16
e_name: l_e_delta
d_name: [dm_eig, se_eig, fe_eig]
preprocess_args:
preshift: false
prescale: false
prefit_ridge: 1e0
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.96
decay_steps: 500
display_epoch: 100
n_epoch: 50000
start_lr: 0.0001
train_paths:
- ../train_paths.raw
test_paths:
- ../test_paths.raw
examples/legacy/train_active_learning/share/test_model.sh 0 → 100644
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for fd in model.*; do mkdir $fd/test; done
echo training set
python /path/to/source/deepks/train/test.py -m model*/model.pth -d `cat train_paths.raw` -o test/train -D dm_eig se_eig fe_eig
echo testing set
python /path/to/source/deepks/train/test.py -m model*/model.pth -d `cat test_paths.raw` -o test/test -D dm_eig se_eig fe_eig
examples/train_input/extended.yaml 0 → 100644
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model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: gelu
data_args:
batch_size: 1
group_batch: 16
e_name: l_e_delta
d_name: [dm_eig, se_eig, fe_eig]
preprocess_args:
preshift: false
prescale: false
prefit_ridge: 1e0
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.96
decay_steps: 500
display_epoch: 100
n_epoch: 50000
start_lr: 0.0001
train_paths:
- train_paths.raw
test_paths:
- test_paths.raw
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model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: mygelu
data_args:
batch_size: 16
group_batch: 1
e_name: l_e_delta
d_name: dm_eig
f_name: l_f_delta
# gvx_name: grad_vx # experimental dm training
extra_label: true
conv_name: conv
preprocess_args:
preshift: false
prescale: false
prefit_ridge: 1e1
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.5
decay_steps: 1000
display_epoch: 100
force_factor: 0.1
n_epoch: 5000
start_lr: 0.0001
train_paths:
- data_train/*
test_paths:
- data_test/*
examples/train_input/gelu.yaml 0 → 100644
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model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: gelu
data_args:
batch_size: 1
group_batch: 16
e_name: l_e_delta
d_name: [dm_eig]
preprocess_args:
preshift: false
prescale: false
prefit_ridge: 1e1
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.98
decay_steps: 500
display_epoch: 100
n_epoch: 100000
start_lr: 0.0001
train_paths:
- train_paths.raw
test_paths:
- test_paths.raw
examples/train_input/restart.yaml 0 → 100644
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model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: gelu
data_args:
batch_size: 1
group_batch: 16
e_name: l_e_delta
d_name: [dm_eig]
preprocess_args:
preshift: false
prescale: false
prefit_ridge: 1e0
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.5
decay_steps: 1000
display_epoch: 100
n_epoch: 5000
start_lr: 0.0001
train_paths:
- train_paths.raw
test_paths:
- test_paths.raw
examples/water_cluster/.gitignore 0 → 100644
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iter.*
share
log.*
err.*
RECORD
PID
test_results
examples/water_cluster/README.md 0 → 100644
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# Example of water cluster
We provide here a detailed example on generating a DeePHF or DeePKS functional for water clusters, and demonstrate its generalizability with a test on proton transfer of a water hexamer ring.
Here we take `args.yaml` as the configuration file. The iteration can be run directly by execute the [`./run.sh`](./run.sh) file, which contains the following lines:
```bash
nohup python -u -m deepks iterate args.yaml >> log.iter 2> err.iter &
echo $! > PID
```
that runs the iterative learning procedure in background and record its PID in the designated file.
Note that we use `python -u -m deepks` to turn off python's output buffer. You can also use `deepks` or `dks` directly if you have installed it properly.
Here we are using Slurm to schedule jobs. If Slurm is not available, please execute [`./run_shell.sh`](./run_shell.sh) to run on local machine. In the following section we provide a work through on how to write the arguments for deepks input in the [`args.yaml`](./args.yaml). You can also take a look at it for explanation on each specific parameters.
## System preparation
We use randomly generated water monomers, dimers and trimers as training datasets. Each dataset contains 100 near equilibrium configurations. We also include 50 tetramers as a validation dataset. We use energy and force as labels. The reference values are given by CCSD calculation with cc-pVDZ basis. The system configurations and corresponding labels are grouped into different folders by the number of atoms, follow the convention described in [another example](../water_single/README.md). Note that the default length unit in deepks is Bohr. The systems we provided here are in Angstrom, so we add a `unit.raw` file containing "Angstrom" in each system folder to specify the unit different from default. The path to the folders can be specified in the config file as follows:
```yaml
systems_train: # can also be files that containing system paths
- ./systems/train.n[1-3]
systems_test: # if empty, use the last system of training set
- ./systems/valid.n4
```
## Initialization (DeePHF model)
As a first step, we need to train an energy model as the starting point of the iterative learning procedure. This consists of two steps. First, we solve the systems using the baseline method such as HF or PBE and dump descriptors needed for training the energy model. Second, we conduct the training from scratch using the previously dumped descriptors. If there is already an existing model, this step can be skipped, by provide the path of the model to the `init_model` key.
The energy model generated in this step is also a ready-to-use DeePHF model, saved at `iter.init/01.train/model.pth`. If self-consistency is not needed, the rest iteration steps can be ignored. We do not use forces as labels when training the energy model in this example.
The parameters of the init SCF calculation is specified under the `init_scf` key. The same set of parameters is also accepted as a standalone file by the `deepks scf` command when running SCF calculations directly. We use cc-pVDZ as the calculation basis. The required fields to be dumped are `dm_eig` for descriptors and `l_e_delta` for reference correction energies as labels. In addition, we also include `e_tot` for total energy, `conv` for a record of convergence.
```yaml
dump_fields: [dm_eig, l_e_delta, conv, e_tot]
```
Additional parameters for molecule and SCF calculation can also be provided to `mol_args` and `scf_args` keys, and will be directly passed to corresponding interfaces in PySCF.
The parameters of the init training is specified under the `init_train` key. Similarly, the parameters can also be passed to `deepks train` command as a standalone file. In `model_args`, we set the construction of the neural network model with three hidden layers and 100 neurons per layer, using GELU activation function and skip connections. We also scale the output correction energies by a factor of 100 so that it is of order one and easier to learn. In `preprocess_args`, the descriptors are set to be preprocessed to have zero mean on the training set. A prefitted ridge regression with penalty strength 10 is also added to the model to speed up training. We set in `data_args` the batch size to be 16 and in `train_args` the total number of training epochs to be 50000. The learning rate starts at 3e-4 and decays by a factor of 0.96 for every 500 steps.
## Iterative learning (DeePKS model)
For self-consistency, we take the model acquired in last step and perform several additional iterations of SCF calculation and NN training. The number of iterations is set in the `n_iter` key to be 10. If it is set to 0, no iteration will be performed, which gives the DeePHF model. In the iterative learning procedure, we also include forces as labels to improve accuracy.
The SCF parameters are provided in the `scf_input` key, following the same rules as the `init_scf` key. In order to use forces as labels, we added additional `grad_vx` for the gradients of descriptors and `l_f_delta` for reference correction forces. `f_tot` is also included for the total force results.
```yaml
dump_fields: [conv, e_tot, dm_eig, l_e_delta, f_tot, grad_vx, l_f_delta]
```
Due to the complexity of the neural network functional, we use looser (but still accurate enough) convergence criteria in `scf_args`, with `conv_tol` to be 1e-6.
The training parameters are provided in the `train_input` key, similar to `init_train`. But since we are restarting from the existing model, no `model_args` is needed, and the preprocessing procedure can be turned off. In addition, we add `extra_label: true` in `data_args` and `force_factor: 1` in `train_args` to enable using forces in training. The total number of training epochs is also reduced to 5000. The learning rate starts as 1e-4 and decays by a factor of 0.5 for every 1000 steps.
## Machine settings
How the SCF and training tasks are executed is specified in `scf_machine` and `train_machine`, respectively. Currently, both the initial and the following iterations share the same machine settings. In this example, we run our tasks on local computing cluster with Slurm as the job scheduler. The platform to run the tasks is specified under the `dispatcher` key, and the computing resources assigned to each task is specified under `resources`. The setting of this part differs on every computing platform. We provide here our `training_machine` settings as an example:
```yaml
dispatcher:
context: local
batch: slurm # set to "shell" to run on local machine
remote_profile: null # unnecessary in local context
resources:
time_limit: '24:00:00'
cpus_per_task: 4
numb_gpu: 1
mem_limit: 8 # gigabyte
python: "python" # use python in path
```
where we assign four CPU cores and one GPU to the training task, and set its time limit to be 24 hours and memory limit to be 8GB. The detailed settings available for `dispatcher` and `resources` can be found in the document of DP-GEN software, with a slightly different interface.
In case there's no Slurm scheduler system, DeePKS-kit can also be run on a local machine with vanilla shell scripts, simply by setting `batch: shell`. Please check [`shell.yaml`](./shell.yaml) for an example. In that case, `resources` will be ignored and all available resources on the machine will be used. Support for more scheduler systems will also be implemented in the future.
## Testing the model
During each iteration of the learning procedure, a brief summary on the accuracy of the SCF calculation can be found in `iter.xx/00.scf/log.data`. Average energy and force (if applicable) errors are shown for both training and validation dataset. The results of the SCF calculations is also stored in `iter.xx/00.scf/data_train` and `iter.xx/00.scf/data_test` grouped by training and testing systems.
After we finished our 10 iterations, the resulted DeePKS model can be found at `iter.09/01.train/model.pth`. The model can be used in either a python script creating the extended PySCF class, or directly the `deepks scf` command. As a testing example, we run the SCF calculation using the learned DeePKS model on the simultaneous six proton transfer path of a water hexamer ring.
The command can be found in [`test.sh`](./test.sh).
The results of each configuration during the proton transfer are grouped in the `test_result` folder.
We can see that all the predicted energy falls within the chemical accuracy range of the reference value given by the CCSD calculation. We note that none of the training dataset includes dissociated configurations in the proton transfer case. The DeePKS model trained on up to three water molecules exhibits good transferability, even in the bond breaking case.
\ No newline at end of file
examples/water_cluster/args.yaml 0 → 100644
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# all arguments are flatten into this file
# they can also be splitted into separate files and referenced here
n_iter: 10
# training and testing systems
systems_train: # can also be files that containing system paths
- ./systems/train.n[1-3]
systems_test: # if empty, use the last system of training set
- ./systems/valid.n4
# directory setting
workdir: "."
share_folder: "share" # folder that stores all other settings
# scf settings
scf_input: # can also be specified by a separete file
basis: ccpvdz
# this is for force training
# the following properties will be dumped in data folder
# please refer to https://arxiv.org/abs/2012.14615 for detailed explaination of each fields
dump_fields: [atom, e_base, e_tot, dm_eig, conv, f_base, f_tot, grad_vx, l_f_delta, l_e_delta]
verbose: 1
# parameters that will be passed directly to pyscf Mol class
mol_args:
incore_anyway: True
# parameters that will be passed directly to pyscf SCF class
scf_args:
conv_tol: 1e-6
conv_tol_grad: 3e-2
level_shift: 0.1
diis_space: 20
conv_check: false # pyscf conv_check has a bug
scf_machine:
# # of systems that will be in one task, default is 1
# task corresponds to a set of commands, and is the smallest unit to be tracked
sub_size: 1
# 2 tasks will be gathered into one group and submitted together
# group correspond to a job submitted to schedule system
group_size: 2
# if larger than 1, run n tasks parallelly in one group (one job)
ingroup_parallel: 1
# the parameters determining the machine settings that the jobs are running on
dispatcher:
# "local" to run on local machine, or "ssh" to run on a remote machine
context: local
# "slurm" to use slurm scheduler system, or "shell" to just use shell
batch: slurm
# only needed when using "ssh" in context
# pass a dict like {username: USERNAME, password: PASSWORD, work_path: /path/to/tmp/folder}
remote_profile: null
# the parameters determining the resources allocated for each job (group of tasks)
# only needed when batch is set to "slurm"
# for shell users, will automatically use all resources available
resources:
# only set to larger than 1 if parallel in multiple nodes with `ingroup_parallel`
# otherwise please keep to 1 since pyscf does not support mpi and can only run on a single node
numb_node: 1
time_limit: '24:00:00'
cpus_per_task: 8
mem_limit: 8 #GB
# environment variables
envs:
PYSCF_MAX_MEMORY: 8000 #MB, increase from default 4G to 8G to match the mem_limit above
# resources for each sub task in jobs (groups of tasks)
# only needed when ingroup_parallel is larger than 1
# the resources are reallocated between parallel tasks
sub_res:
cpus_per_task: 8
python: "python" # use python in path
# training settings
train_input:
# model_args is ignored, since this is used as restart
# see init_train for potential model_args
data_args:
# training batch size, 16 is recommended
batch_size: 16
# if larger than 1, n batch will be grouped together to form a larger one
# final batch size would be group_bath * batch_size
# only needed when a lot of systems have only one datapoint hence the batch size can only be 1
group_batch: 1
# if set to true, will try to find force labels and use them in training
extra_label: true
# if set to true, will read the convergence data from conv_name
# and only use converged datapoints to train
conv_filter: true
conv_name: conv
# to speed up training, deepks support first normalize the data (preshift and prescale)
# and do a linear regression on the whole training set as prefitting
preprocess_args:
preshift: false # restarting model already shifted. Will not recompute shift value
prescale: false # same as above
# prefitting is by default enabled
prefit_ridge: 1e1 # the ridge factor used in linear regression
prefit_trainable: false # make the linear regression fixed during the training
train_args:
# the start learning rate, will decay later
start_lr: 0.0001
# lr will decay a factor of `decay_rate` every `decay_steps` epoches
decay_rate: 0.5
decay_steps: 1000
# show training results every n epoch
display_epoch: 100
# the prefactor multiplied infront of the force part of the loss
force_factor: 1
# total number of epoch needed in training
n_epoch: 5000
train_machine:
# for training, no tasks or groups are needed since there's only one task
# the dispatcher settings are same as above
dispatcher:
context: local
batch: slurm
remote_profile: null # use lazy local
# resources settings are also same as above
resources:
time_limit: '24:00:00'
cpus_per_task: 4
# using gpu in training, current only support 1
numb_gpu: 1
mem_limit: 8 #GB
python: "python" # use python in path
# init settings
init_model: false # do not use existing model in share_folder/init/model.pth
# the first scf iteration, needed if init_model is false
# possible settings are same as scf_input
init_scf:
basis: ccpvdz
dump_fields: [atom, e_base, e_tot, dm_eig, conv, f_base, f_tot, grad_vx, l_f_delta, l_e_delta]
verbose: 1
mol_args:
incore_anyway: True
scf_args:
conv_tol: 1e-8
conv_check: false # pyscf conv_check has a bug
# the first scf iteration, needed if init_model is false
# most settings are same as scf_input but model_args will be specified here
init_train:
# whether to fit element-wise energy constant from the training data
# will require `dump_fields` contain `atom` if set to true
fit_elem: false # this is the default
# necessary as this is init training
model_args:
# the number of *hidden* neurons
# note the first (n_descriptor) and last (1) layer is not included here
hidden_sizes: [100, 100, 100]
# the output will be devided by 100 before comparing with labels, to improve training
output_scale: 100
# use skip connection between layers if the sizes are same
use_resnet: true
# gelu generally performs better than others
actv_fn: gelu
# whether to use a predefined embedding function
# to further symmetrize the eigenvalues as descriptors
# add embedding can make the energy surface smooth, hence improve convergence
# but may slightly reduce the accuracy (especially in generalization)
# for water we do not use it, if you encounter convergence problem, set it to
# embedding: thermal
embedding: null
# if `fit_elem` is true, set this will use user defined
# element energy constant, instead of fitting from data.
# can be an absolute path to the file, or a length 2 list
# containing element charges and constants, like
# [[1, 8], [-0.08, -0.04]]
elem_table: null
# the rest are the same as abpve
data_args:
batch_size: 16
group_batch: 1
preprocess_args:
preshift: true # init model will shift the input descriptors to mean zero
prescale: false
prefit_ridge: 1e1
prefit_trainable: false
# following are suggested parameters for initial training
# note in the deepks-kit paper the training curve shown use a different set of parameters
# the paper parameters take an unnecessary length of time and is no longer suggested
train_args:
decay_rate: 0.95 # 0.96 in paper example training curve
decay_steps: 300 # 500 in paper example training curve
display_epoch: 100
n_epoch: 15000 # 50000 in paper example training curve
start_lr: 0.0003
# other settings
cleanup: false
strict: true
examples/water_cluster/run.sh 0 → 100755
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nohup python -u -m deepks iterate args.yaml >> log.iter 2> err.iter &
echo $! > PID
examples/water_cluster/run_shell.sh 0 → 100755
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nohup python -u -m deepks iterate args.yaml shell.yaml >> log.iter 2> err.iter &
echo $! > PID
examples/water_cluster/shell.yaml 0 → 100644
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# to use this file, simply add its name as another argument
# in the command line after the main args.yaml
# for example, `deepks iterate args.yaml shell.yaml`
# this overwrite the settings by those specified in this file
scf_machine:
# every system will be run as a separate command (a task)
sub_size: 1
# 2 tasks will be gathered into one group and submitted together as a shell script
# all shell scirpt will be executed at same time
# hence in parallel and share the whole machine's resources
# you may want to set this as a large number
# because the number of tasks run at same time would be nsystems / (sub_size * group_size)
group_size: 2
dispatcher:
context: local
batch: shell # set to shell to run on local machine
remote_profile: null # not needed in local case
# resources are no longer needed, other than the envs can still be set here
resources:
envs:
PYSCF_MAX_MEMORY: 8000 # increase from 4G to 8G
python: "python" # use python in path
train_machine:
dispatcher:
context: local
batch: shell # same as above, use shell to run on local machine
remote_profile: null # use lazy local
python: "python" # use python in path
# resources are no longer needed, and the task will use gpu automatically if there is one
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