Python SDK for interacting with the QDX Tengu API and modules
Project description
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 newest versions modules that can be run
modules = client.latest_modules()
# or for all modules
modules = client.modules()
# get input arguments that need to be provided for module
print(modules[0]["ins"])
## 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/77e44748f1d1e20c463ef34cc40178d4f656ef0a#convert", [
Arg(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", [
Arg( id = res["outs"][0]["id"] ), Arg( value = 1 ) }
])
client.poll_module_instance(id)
// status, progress, logs, outs - out values will be null until module_instance is done
Local runner
We also provide a local executor, that will run modules locally, without making remote calls
First, you must have nix installed and configured with an access token for qdx projects.
Then you must install the tengu-runtime with nix run github:talo/tengu#tengu-runtime -- install
Finally, you can run locally with
from tengu import LocalProvider
client = LocalProvider()
## you should be able to use client.run / client.object / client.module_instance / client.poll_module instance as normal
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", 100), # map of amino acids of interest
],
),
"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
completed_instance = client.poll_module_instance(qp_collate_instance[2]["id"])
# the result will be an object, so fetch from object store
client.object(completed_instance["outs"][0]["id"]) # will return the json qp results
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