volcengine-qcclient 0.2.1

A Python client for volcengine quantum chemistry service.

volcengine-qcclient

A Python client for volcano engine quantum chemistry service.

Installation

pip install volcengine-qcclient

Usage

High-level SDK.

1. run GPU4PySCF tasks.

import os
import requests
import h5py
import tarfile

from volcengine_qcclient import QcBatchJob


# download_output download output of qc task to ./{task_id}/ dir.
def download_output(url, task_id):
    os.mkdir(f"./{task_id}")
    if ".h5" in url:
        output_h5 = f"./{task_id}/output.h5"
        open(output_h5, 'wb').write(requests.get(url).content)
    elif "tar.gz" in url:
        output_targz = f"./{task_id}/output.tar.gz"
        open(output_targz, 'wb').write(requests.get(url).content)
        with tarfile.open(output_targz, 'r:gz') as tar:
            tar.extractall(path=f"./{task_id}")
        os.remove(output_targz)
    else:
        print("unsupported file type")


if __name__ == '__main__':
    # init batch_job
    batch_job = QcBatchJob()

    # load molecules.
    molecules = [
        '''
3
Water molecule
O          0.00000        0.00000        0.11779
H          0.00000        0.75545       -0.47116
H          0.00000       -0.75545       -0.47116
        '''
    ]

    batch_job.load_molecules(from_list=molecules)

    # set task config.
    task_config = {
        "spin": 0,
        "charge": 0,
        "basis": "def2-tzvpp",
    }
    # submit task.
    task_type = "sp"
    task_ids = batch_job.submit(task_type=task_type, task_config=task_config)
    print(task_ids)

    # wait for task to finish.
    batch_job.wait()

    # get tasks when batch_job finished.
    tasks = batch_job.get_tasks()
    print(tasks)

    # download output to ./{task_id}/
    task = tasks[0]
    download_output(task["OutputUrl"], task["Id"])

    # operation with output.
    if task_type in ["sp", "opt"]:
        with h5py.File(f"./{task['Id']}/output.h5", 'r') as f:
            print(f.keys())
    elif task_type == "pysisyphus":
        # print keys in `final_hessian.h5`.
        with h5py.File(f"./{task['Id']}/final_hessian.h5", 'r') as f:
            print(f.keys())

        # print keys in `optimization.h5`
        with h5py.File(f"./{task['Id']}/final_hessian.h5", 'r') as f:
            print(f.keys())

        # print final_geometry.xyz
        with open(f"./{task['Id']}/final_geometry.xyz", 'r') as f:
            print(f.read())

To verify the correctness of the task_config, tasks can be run locally using the command:

python -m volcengine_qcclient.drivers.sp_driver config_example.json

The task_config is specified in a config file

{
  "molecule": "h2o.xyz"
  "spin": 0,
  "charge": 0,
  "basis": "def2-tzvpp",
}

The molecular geometry is saved in the file specified by the field molecule, which is h2o.xyz in this example:

3
Water molecule
O          0.00000        0.00000        0.11779
H          0.00000        0.75545       -0.47116
H          0.00000       -0.75545       -0.47116

When the local verification is passed, the same task_config specification can be used in the QcBatchJob, while the geometries should be specified in a list and passed to the QcBatchJob.

2. run pysisyphus tasks.

import os
import requests
import h5py
import tarfile

from volcengine_qcclient import QcBatchJob


# download_output download output of qc task to ./{task_id}/ dir.
def download_output(url, task_id):
    os.mkdir(f"./{task_id}")
    if ".h5" in url:
        output_h5 = f"./{task_id}/output.h5"
        open(output_h5, 'wb').write(requests.get(url).content)
    elif "tar.gz" in url:
        output_targz = f"./{task_id}/output.tar.gz"
        open(output_targz, 'wb').write(requests.get(url).content)
        with tarfile.open(output_targz, 'r:gz') as tar:
            tar.extractall(path=f"./{task_id}")
        os.remove(output_targz)
    else:
        print("unsupported file type")


if __name__ == '__main__':
    # init batch_job
    batch_job = QcBatchJob()

    # load molecules.
    molecules = [
        '''
3
Water molecule
O          0.00000        0.00000        0.11779
H          0.00000        0.75545       -0.47116
H          0.00000       -0.75545       -0.47116
        '''
    ]

    batch_job.load_molecules(from_list=molecules)

    # set task config.
    task_config = {}
    # submit task.
    task_type = "pysisyphus"
    task_ids = batch_job.submit(task_type=task_type, task_config=task_config)
    print(task_ids)

    # wait for task to finish.
    batch_job.wait()

    # get tasks when batch_job finished.
    tasks = batch_job.get_tasks()
    print(tasks)

    # download output to ./{task_id}/
    task = tasks[0]
    download_output(task["OutputUrl"], task["Id"])

    # operation with output.
    if task_type in ["sp", "opt"]:
        with h5py.File(f"./{task['Id']}/output.h5", 'r') as f:
            print(f.keys())
    elif task_type == "pysisyphus":
        # print keys in `final_hessian.h5`.
        with h5py.File(f"./{task['Id']}/final_hessian.h5", 'r') as f:
            print(f.keys())

        # print keys in `optimization.h5`
        with h5py.File(f"./{task['Id']}/final_hessian.h5", 'r') as f:
            print(f.keys())

        # print final_geometry.xyz
        with open(f"./{task['Id']}/final_geometry.xyz", 'r') as f:
            print(f.read())

3. run pygsm tasks.

import os
import requests
import tarfile

from volcengine_qcclient import QcBatchJob

def download_output(url, task_id):
    os.mkdir(f"./{task_id}")
    output_targz = f"./{task_id}/output.tar.gz"
    open(output_targz, 'wb').write(requests.get(url).content)
    with tarfile.open(output_targz, 'r:gz') as tar:
        tar.extractall(path=f"./{task_id}")
    os.remove(output_targz)


if __name__ == '__main__':
    batch_job = QcBatchJob()

    # For pygsm, the xyz file may contain multiple frames (reactant/product).
    molecules = [
        '''
3

   H          0.00000000      0.00000000     1.5       
   F          0.00000000      0.00000000    -0.6
   H          0.00000000      0.00000000    -1.5
3

   H          0.00000000      0.00000000     1.5       
   F          0.00000000      0.00000000     0.6
   H          0.00000000      0.00000000    -1.5 
        '''
    ]

    batch_job.load_molecules(from_list=molecules)

    task_type = 'pygsm'
    task_config = [ {"charge": 0, "multiplicity": 2}]
    task_ids = batch_job.submit(task_type=task_type, task_config=task_config)
    print(task_ids)
    
    batch_job.wait()
    tasks = batch_job.get_tasks()
    task = tasks[0]

    download_output(task['OutputUrl'], task['Id'])
    print(f"Downloaded output to ./{task['Id']}/")

4. run confgen tasks.

make sure that you have a cpu qcservice id. here we only show the difference between confgen tasks and other tasks.

# init batch_job
batch_job = QcBatchJob(is_cpu=True) # use cpu resources

# load smiles
smiles_list = ["[OH2+]C1C=CC=CC=C1"]
molecule_names = ["unipKa-11"]

batch_job.load_smiles(from_list=smiles_list, with_molecule_names=molecule_names)
print(batch_job.molecules)

task_type = "confgen"

task_ids = batch_job.submit(task_type=task_type, task_config=[{}]) # method1 
task_ids = batch_job.submit(task_type=task_type, task_config={}) # method2
print(task_ids)

Low level SDK:

see low-level.md

Credentials

Environment variables