tengu-py 0.4.0

Python SDK for interacting with the QDX Tengu API

Tengu-py: Python SDK for the QDX Tengu API

This package exposes a simple provider and CLI for the different tools exposed by the QDX Tengu GraphQL API.

Usage

As a library

import json
from pathlib import Path

import tengu

TOKEN = "your qdx access token"

# get our client to talk with the API
client = tengu.Provider(access_token=TOKEN)

# get modules that can be run
client.modules()

## running convert

# path to protein pdb with correct charges and protonation
protein_pdb = Path("./examples/4w9f_prepared_protein.pdb")

# get base64 encoded data

file_arg = provider.upload_arg(protein_pdb)

res = client.run("github:talo/tengu-prelude/f8e2e55d9bd428aa7f2bbe3f87c24775fa592b10#convert", [ 
{ "value": "PDB" }, file_arg
])

// res contains "id" - the instance id; and "outs" - the ids of the return values 

// we can pass arguments by "id" reference or by value literal

client.run("github:talo/tengu-prelude/f8e2e55d9bd428aa7f2bbe3f87c24775fa592b10#pick_conformer", [ 
{ "id": res["outs"][0]["id"] }, { "value": 1 }
])

client.poll_module_instance(id) 
// status, progress, logs, outs - out values will be null until module_instance is done

Sample QP Run

frag_keywords = {
    "dimer_cutoff": 25,
    "dimer_mp2_cutoff": 25,
    "fragmentation_level": 2,
    "method": "MBE",
    "monomer_cutoff": 30,
    "monomer_mp2_cutoff": 30,
    "ngpus_per_node": 4,
    "reference_fragment": 293,
    "trimer_cutoff": 10,
    "trimer_mp2_cutoff": 10,
    "lattice_energy_calc": True,
}

scf_keywords = {
    "convergence_metric": "diis",
    "dynamic_screening_threshold_exp": 10,
    "ndiis": 8,
    "niter": 40,
    "scf_conv": 0.000001,
}

default_model = {"method": "RIMP2", "basis": "cc-pVDZ", "aux_basis": "cc-pVDZ-RIFIT", "frag_enabled": True}

qp_instances = client.qp_run(
    "github:talo/tengu-prelude/0986e4b23780d5e976e7938dc02a949185090fa1#qp_gen_inputs",
    "github:talo/tengu-prelude/0986e4b23780d5e976e7938dc02a949185090fa1#hermes_energy",
    "github:talo/tengu-prelude/0986e4b23780d5e976e7938dc02a949185090fa1#qp_collate",
    provider.upload_arg(Path("some.pdb")),
    provider.upload_arg(Path("some.gro")),
    provider.upload_arg(Path("some.sdf")),
    Arg(None, "sdf"),
    Arg(None, "MOL"),
    Arg(
        None,
        default_model,
    ),
    Arg(None, {"frag": frag_keywords, "scf": scf_keywords}),
    Arg(
        None,
        [
            ("GLY", 993),
            ("ASP", 994),
            ("VAL", 863),
            ("LYS", 882),
            ("TYR", 931),
            ("GLY", 935),
            ("VAL", 911),
            ("GLU", 930),
            ("ALA", 880),
            ("LEU", 983),
            ("PRO", 933),
            ("LEU", 855),
            ("MET", 929),
            ("SER", 936),
            ("LEU", 932),
        ],
    ),
    "GADI",
    {"walltime": 420},
    autopoll = (10, 100) # optionally configure polling to wait on the final instance, 
                         # and clean up if any of the prior instances fails
)

# if you set autpoll, you will get the results of the qp_collate instance,
# otherwise you will get an array with all the spawned instances, and have to poll manually
client.poll_module_instance(qp_collate_instance[2]["id"])