Messages

1

General Questions and Answers / Re: TorchX Potentials

« on: September 24, 2024, 16:18 »

Thank you!

2

General Questions and Answers / QuantumATK-W-2024.09 CUDA requirements

« on: September 23, 2024, 14:52 »

Hi,

I would like to ask about specific hardware/software requirements for installation of QuantumATK-W-2024.09 so the TorchX potentials can be used.

I was trying to use TorchX interatomic potentials implemented in the version W-2024.09 but QuantumATK. On my laptop, it has crashed quantumATK with following message:

Code

terminate called after throwing an instance of 'c10::Error'
  what():  CUDA error: no kernel image is available for execution on the device
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Exception raised from c10_cuda_check_implementation at /slowfs/qatkdev2/users/qatktest/de02vlbamboo17/exlibs/.conan/data/torch/2.3.0.dev2/quantumatk/qatk2024.09/build/98ec6f6a829f6910030c9440491421bd8b3c4e2e/c10/cuda/CUDAException.cpp:43 (most recent call first):
frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >) + 0xa9 (0x7f2003d112c9 in /home/karolcia/QuantumATK/QuantumATK-W-2024.09/lib/libc10.so)
frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0xc2 (0x7f2003cc0280 in /home/karolcia/QuantumATK/QuantumATK-W-2024.09/lib/libc10.so)
frame #2: c10::cuda::c10_cuda_check_implementation(int, char const*, char const*, int, bool) + 0x3c6 (0x7f2003c3e996 in /home/karolcia/QuantumATK/QuantumATK-W-2024.09/lib/libc10_cuda.so)
frame #3: <unknown function> + 0x17a5b71 (0x7f1fdc7d4b71 in /home/karolcia/QuantumATK/QuantumATK-W-2024.09/lib/libtorch_cuda.so)
frame #4: at::native::copy_device_to_device(at::TensorIterator&, bool, bool) + 0xce5 (0x7f1fdc7f6b55 in /home/karolcia/QuantumATK/QuantumATK-W-2024.09/lib/libtorch_cuda.so)
...

It seems that there is some software incompatibility. I have tested it again with atkpython to receive more info:

Code

>: ./atkpython
                            QuantumATK®                             

    Version: W-2024.09 for Windows and Linux [Build fe68a9810a2]    

                Copyright © 2004-2024 Synopsys, Inc.                

This software and the associated documentation are proprietary to Synopsys,
Inc. This software may only be used in accordance with the terms and
conditions of a written license agreement with Synopsys Inc. All other use,
reproduction, modification, or distribution of this software is strictly
prohibited. Licensed Products communicate with Synopsys servers for the
purpose of providing software updates, detecting software piracy and
verifying that customers are using Licensed products in conformity with the
applicable License Key for such Licensed Products. Synopsys will use
information gathered in connection with this process to deliver software
        updates and pursue software pirates and infringers.         

Inclusivity & Diversity - Visit SolvNetPlus to read the "Synopsys Statement
  on Inclusivity and Diversity" (Refer to the article 000036315 at  
                 https://solvnetplus.synopsys.com)                  

In [1]: import torch

In [2]: torch.cuda.is_available()
Out[2]: True

In [3]: device=torch.device("cuda:0")

In [4]: torch.randn(64,100,device=device)
/home/karolcia/QuantumATK/QuantumATK-W-2024.09/atkpython/lib/python3.11/site-packages/torch/cuda/__init__.py:184: UserWarning: 
    Found GPU0 Quadro P2000 which is of cuda capability 6.1.
    PyTorch no longer supports this GPU because it is too old.
    The minimum cuda capability supported by this library is 7.0.
    
  warnings.warn(
/home/karolcia/QuantumATK/QuantumATK-W-2024.09/atkpython/lib/python3.11/site-packages/torch/cuda/__init__.py:209: UserWarning: 
Quadro P2000 with CUDA capability sm_61 is not compatible with the current PyTorch installation.
The current PyTorch install supports CUDA capabilities sm_70 sm_80 sm_90.
If you want to use the Quadro P2000 GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

  warnings.warn(
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-4-ba9783c479eb> in <module>
----> 1 torch.randn(64,100,device=device)

RuntimeError: CUDA error: no kernel image is available for execution on the device
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

I have the following configuration:
OS: debian bookworm
kernel: 6.1.0-22-amd64 #1 SMP PREEMPT_DYNAMIC Debian 6.1.94-1 (2024-06-21) x86_64 GNU/Linux

GPU: NVIDIA Corporation GP107GLM [Quadro P2000 Mobile] (rev a1)
system GPU driver: nvidia-driver/stable-backports,now 535.183.06-1~bpo12+1 amd64 [installed]
system CUDA toolkit: nvidia-cuda-dev/stable,now 11.8.89~11.8.0-5~deb12u1 amd64 [installed]


I have compiled a local version of torch (2.6.0a0+gitea737e4) in conda enviroment and I can run following commands without issues:

Code

>: python3
Python 3.12.4 | packaged by Anaconda, Inc. | (main, Jun 18 2024, 15:12:24) [GCC 11.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> torch.cuda.is_available()
True
>>> device=torch.device("cuda:0")
>>> torch.randn(64,100,device=device)
tensor([[-0.2413, -0.2472,  1.6616,  ...,  0.9969, -2.3384, -0.2548],
        [ 0.2143,  0.3940, -1.8022,  ...,  0.4503,  0.1832,  0.1646],
        [ 0.0362,  0.2684, -0.5221,  ...,  0.2252,  0.5317,  1.2204],
        ...,
        [ 0.9273,  0.5185, -1.5020,  ...,  0.3420, -0.7115,  1.6105],
        [-1.3727, -1.3622, -0.9894,  ...,  0.6896,  0.4132, -0.8442],
        [-0.1157,  0.1780,  1.8506,  ...,  0.2973, -1.2583,  0.2122]],
       device='cuda:0')
>>> 

Is it possible to use custom versions of torch library in quantumatk/atkpython?
If so, where could I find some informaton how to set it up?
If not, would it be possible to get a patch for quantumATK that also allows to use older CUDA capabilities?

3

General Questions and Answers / TorchX Potentials

« on: September 23, 2024, 13:16 »

Hi,

What are the differences between: TorchX_MACE_MP_0_L0_2023, TorchX_MACE_MP_0_L1_2023 and TorchX_MACE_MP_0_L2_2024? According to the QuantumATK website they are all based on one paper by Gábor Csányi's group and co-workers: 'A foundation model for atomistic materials chemistry', where only MACE-MP-0 name is used. Could you provide some details (training sets, levels of theory - whether it was PBE, PBE-D3 or revPBE-D3) about TorchX potentials implemented in ATK?

Kind regards,
Karolina

4

Plugin Development / not working DOS Analyzer in Version W-2024.09

« on: September 15, 2024, 18:32 »

Hi,

I am wondering if there is a way to use DOS analyser in the newest version to open DeviceDensityOfStates block generated in version T. Version V open it without any problem but version W allows to chose only between two options: 'Open with Editor' or 'Open with Text Representation'.

Kind regards,
Karolia

5

General Questions and Answers / Re: class STM

« on: September 20, 2022, 18:07 »

Dear Anders,

Thank you. As far as I see, in the TAMR case study the STM tip structure is not considered. However,  class STM for which there is no usage example in the 2022 manual seems to have tip_configuration parameter that allows to read bulk configuration.

So l am wondering whether tip and sample configuration should be stored in separate
 files and then somehow read.

6

General Questions and Answers / class STM

« on: September 5, 2022, 18:59 »

Dear All,

I am wondering how to use STM class. There is no usage example in the manual.

The STM image can be simply calculated using LDOS of bulk configuration in Tersoff-Hamann approximation but it seems that STM class enables also to take into account tip  structure.

Could you kindly explain how to use STM class?

7

General Questions and Answers / DREIDING Calculator error

« on: August 14, 2022, 03:51 »

I encountered the following error while trying to run Time-Stamped Force-Bias Monte Carlo calculations using DREIDING Calculator:

Traceback (most recent call last):
  File "n73_p3_175toluene_175tetralin_NPT_MC.py", line 23441, in <module>
    calculator = potential_builder.createCalculator(bulk_configuration)
  File "zipdir/NL/Calculators/ExternalCalculators/ClassicalCalculators/DreidingPotentialBuilder.py", line 1240, in createCalculator
  File "zipdir/NL/Calculators/ExternalCalculators/ClassicalCalculators/EEMAtomicCharges.py", line 594, in charges
  File "zipdir/NL/CommonConcepts/PhysicalQuantity.py", line 1915, in __setitem__
ValueError: shape mismatch: value array of shape (316,) could not be broadcast to indexing result of shape (1,)

I am wondering what is wrong and how to fix it.

The system contains cnt, few units of polymer, and solvent. The initial configuration was created using packmol and optimized and equilibrated using ReaxFF. I defined the static bonds in the system.

This is how my input looks like below static bonds definitions:


bond_constraint = BondConstraint([[ Nitrogen,  Sulfur],
                     [ Nitrogen,  Carbon]],  inversion_order=8, rotation_order=5)

com_constraint = FixCenterOfMass()

# -------------------------------------------------------------
# DREIDING Calculator
# -------------------------------------------------------------
potential_builder = DreidingPotentialBuilder()
potential_builder.assignAtomTypes(bulk_configuration)
calculator = potential_builder.createCalculator(bulk_configuration)
bulk_configuration.setCalculator(calculator)

bulk_configuration.update()
nlsave('test6.hdf5', bulk_configuration)

# -------------------------------------------------------------
# Time-Stamped Force-Bias Monte Carlo
# -------------------------------------------------------------

method = ForceBiasMonteCarloNPTBerendsen(
    temperature=300*Kelvin,
    max_atom_displacement=0.05*Angstrom,
    heating_rate=0*Kelvin,
    pressure=1*bar,
    barostat_factor=500,
    compressibility=0.0001*bar**-1,
)

tfmc_trajectory = TimeStampedForceBiasMonteCarlo(
    bulk_configuration,
    constraints=[com_constraint],
    trajectory_filename='test6.hdf5',
    steps=100000,
    log_interval=5000,
    method=method,
)

bulk_configuration = tfmc_trajectory.lastImage()
nlsave('test6.hdf5', tfmc_trajectory)

The MC calculation runs when I delete solvent molecules from system.

8

General Questions and Answers / Re: 'Compare Data' Analyzer in T-2022.03 version

« on: June 7, 2022, 11:40 »

Yes, it would be fantastic if you could add step-by-step instructions!

9

General Questions and Answers / Re: 'Compare Data' Analyzer in T-2022.03 version

« on: June 7, 2022, 02:14 »

I have already tried to select two transmission spectra and click on the 'Open all with Transmission Analyzer (Ctrl+O)'. In result I am getting two new windows. In each I see only one spectrum. But I would like to have both plots on one graph - as the old 'Compare Data' worked. How may I do that (except for using 'Compare Data' in the previous version of ATK?)?

10

General Questions and Answers / 'Compare Data' Analyzer in T-2022.03 version

« on: April 16, 2022, 12:24 »

Where can I find the 'Compare Data' Analyzer in the T-2022.03 version?

I would like to plot on one graph the transmission spectra from few files.

In previous ATK NanoLab versions, the 'Compare Data' Analyzer was on the right panel under Compare category (together with 'Compare Configurations'). But in the new version of ATK, the right panel does not contain the complete list of Analyzers; only the supplementary tools list.