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learningmatter-mit / surface-sampling

MCMC-based algorithm for sampling surface reconstructions
https://github.com/learningmatter-mit/surface-sampling
MIT License
21 stars 4 forks source link

Where can I get the NeuralFF python module and others that are missing from NFF? #1

Closed wanfuse123 closed 8 months ago

wanfuse123 commented 11 months ago

Where can I get the NeuralFF python module and others that are missing from NFF?

xiaochendu commented 11 months ago

Hi, does the link in the README (https://github.com/learningmatter-mit/NeuralForceField) work for you?

wanfuse123 commented 11 months ago

Exception reporting mode: Verbose ['AttentionPool', 'BatchedPreActivation', 'BondPrior', 'ChemPropConv', 'ChemPropInit', 'ChemPropMsgToNode', 'ConfAttention', 'CosineEnvelope', 'CpSchNetConv', 'DEFAULT_BONDPRIOR_PARAM', 'DEFAULT_DROPOUT_RATE', 'DataLoader', 'Dense', 'DiabaticReadout', 'Diagonalize', 'DimeNet', 'DimeNetDelta', 'DimeNetDiabat', 'DimeNetDiabatDelta', 'DimeNetRadialBasis', 'DimeNetSphericalBasis', 'DirectedMessage', 'DirectedMessagePP', 'EPS', 'EPSILON', 'EdgeEmbedding', 'EdgeUpdateModule', 'EmbeddingBlock', 'Envelope', 'ExpNormalBasis', 'F', 'Gaussian', 'GaussianSmearing', 'GraphAttention', 'InteractionBlock', 'LeakyReLU', 'Linear', 'LinearConfAttention', 'MeanPool', 'MessagePassingModule', 'MixedSchNetConv', 'ModuleDict', 'MolFpPool', 'NodeEmbedding', 'NodeMultiTaskReadOut', 'OutputBlock', 'PainnRadialBasis', 'PreActivation', 'RadialBasis', 'ReLU', 'ResidualBlock', 'ScaleShift', 'SchNet', 'SchNetConv', 'SchNetDiabat', 'SchNetEdgeFilter', 'SchNetEdgeUpdate', 'Sequential', 'Softmax', 'SphericalBasis', 'StochasticIncrease', 'SumPool', 'TestModules', 'builtins', 'cached', 'doc', 'file', 'loader', 'name', 'package', 'path', 'spec', 'activations', 'add_stress', 'adj_nbrs_and_z', 'att_readout_probs', 'batch_and_sum', 'bessel_basis', 'chemprop_msg_to_node', 'chemprop_msg_update', 'clean_matrix', 'collections', 'compute_angle', 'compute_grad', 'compute_hess', 'computejacobian', 'constant', 'construct_module_dict', 'construct_sequential', 'copy', 'diabat', 'dimenet', 'gaussian_smearing', 'general_batched_hessian', 'get_act', 'get_default_readout', 'get_dense', 'get_offsets', 'get_painn_hessians', 'get_rij', 'get_schnet_hessians', 'grad', 'graphconv', 'graphop', 'hess_from_atoms', 'hess_from_pad', 'hess_from_results', 'inspect', 'lattice_points_in_supercell', 'layer_types', 'layers', 'models', 'modules', 'nn', 'np', 'pad', 'partial', 'real_sph_harm', 'remove_bias', 'results_from_stack', 'scatter_add', 'schnet', 'schnet_batched_hessians', 'shifted_softplus', 'single_spec_nbrs', 'softmax', 'sum_and_grad', 'sym', 'tensorgrad', 'to_module', 'torch', 'torch_nbr_list', 'unittest', 'utils', 'xavieruniform', 'zeros_initializer'] 3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) [GCC 12.3.0]

CondaError: Run 'conda init' before 'conda activate'

no change /home/top/miniconda3/condabin/conda no change /home/top/miniconda3/bin/conda no change /home/top/miniconda3/bin/conda-env no change /home/top/miniconda3/bin/activate no change /home/top/miniconda3/bin/deactivate no change /home/top/miniconda3/etc/profile.d/conda.sh no change /home/top/miniconda3/etc/fish/conf.d/conda.fish no change /home/top/miniconda3/shell/condabin/Conda.psm1 no change /home/top/miniconda3/shell/condabin/conda-hook.ps1 no change /home/top/miniconda3/lib/python3.11/site-packages/xontrib/conda.xsh no change /home/top/miniconda3/etc/profile.d/conda.csh no change /home/top/.bashrc No action taken.

packages in environment at /home/top/VENV/conda_surface_sampling:

#

Name Version Build Channel

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1.8.0 pyhd8ed1ab_0 conda-forge pre-commit 3.5.0 pyha770c72_0 conda-forge prometheus_client 0.19.0 pyhd8ed1ab_0 conda-forge prompt-toolkit 3.0.42 pyha770c72_0 conda-forge prompt_toolkit 3.0.42 hd8ed1ab_0 conda-forge psutil 5.9.5 py38h01eb140_1 conda-forge pthread-stubs 0.4 h36c2ea0_1001 conda-forge ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge pulseaudio-client 16.1 hb77b528_5 conda-forge pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge pybtex 0.24.0 pyhd8ed1ab_2 conda-forge pycairo 1.25.1 py38h1a1917b_0 conda-forge pycodestyle 2.11.1 pyhd8ed1ab_0 conda-forge pycparser 2.21 pyhd8ed1ab_0 conda-forge pydantic 2.5.2 pyhd8ed1ab_0 conda-forge pydantic-core 2.14.5 py38h0cc4f7c_0 conda-forge pydantic-settings 2.1.0 pyhd8ed1ab_1 conda-forge pydash 7.0.6 pyh1a96a4e_0 conda-forge pyflakes 3.1.0 pyhd8ed1ab_0 conda-forge pygments 2.17.2 pyhd8ed1ab_0 conda-forge pylint 3.0.3 pyhd8ed1ab_0 conda-forge pymatgen 2023.5.10 py38h905acbe_0 conda-forge pymc 5.6.1 hd8ed1ab_0 conda-forge pymc-base 5.6.1 pyhd8ed1ab_0 conda-forge pymongo 4.6.1 py38h17151c0_0 conda-forge pynacl 1.5.0 py38h01eb140_3 conda-forge pynng 0.7.2 py38h01eb140_1 conda-forge pyopenssl 23.3.0 pyhd8ed1ab_0 conda-forge pyparsing 3.1.1 pyhd8ed1ab_0 conda-forge pyqt 5.15.9 py38hffdaa6c_5 conda-forge pyqt5-sip 12.12.2 py38h17151c0_5 conda-forge pysocks 1.7.1 pyha2e5f31_6 conda-forge pytensor 2.13.1 py38h1128e8f_0
pytensor-base 2.12.3 py38h01efb38_3 conda-forge python 3.8.18 hd12c33a_0_cpython conda-forge python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge python-dotenv 1.0.0 pyhd8ed1ab_1 conda-forge python-fastjsonschema 2.19.0 pyhd8ed1ab_0 conda-forge python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge python_abi 3.8 4_cp38 conda-forge pytorch 2.0.0 cuda118py38hde7dd16_304 conda-forge pytorch-cuda 11.7 h778d358_5 pytorch pytz 2023.3.post1 pyhd8ed1ab_0 conda-forge pyyaml 6.0.1 py38h01eb140_1 conda-forge pyzmq 25.1.2 py38h34c975a_0 conda-forge qt-main 5.15.8 h82b777d_17 conda-forge quippy 0.9.12 py38h6939b76_1 conda-forge rdkit 2023.09.3 py38h6c71e64_1 conda-forge readline 8.2 h8228510_1 conda-forge referencing 0.32.0 pyhd8ed1ab_0 conda-forge reportlab 4.0.8 py38h01eb140_0 conda-forge requests 2.31.0 pyhd8ed1ab_0 conda-forge rfc3339-validator 0.1.4 pyhd8ed1ab_0 conda-forge rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge rhash 1.4.4 hd590300_0 conda-forge rlpycairo 0.2.0 pyhd8ed1ab_0 conda-forge rpds-py 0.13.2 py38h0cc4f7c_0 conda-forge ruamel.yaml 0.18.5 py38h01eb140_0 conda-forge ruamel.yaml.clib 0.2.7 py38h01eb140_2 conda-forge s3transfer 0.9.0 pyhd8ed1ab_0 conda-forge scikit-learn 1.3.2 py38ha25d942_2 conda-forge scipy 1.10.1 py38h59b608b_3 conda-forge sdaxen_python_utilities 0.1.5 pyhd8ed1ab_0 conda-forge send2trash 1.8.2 pyh41d4057_0 conda-forge sentinels 1.0.0 py_1 conda-forge setuptools 68.2.2 pyhd8ed1ab_0 conda-forge sip 6.7.12 py38h17151c0_0 conda-forge six 1.16.0 pyh6c4a22f_0 conda-forge sleef 3.5.1 h9b69904_2 conda-forge smart_open 6.4.0 pyhd8ed1ab_0 conda-forge snappy 1.1.10 h9fff704_0 conda-forge sniffio 1.3.0 pyhd8ed1ab_0 conda-forge soupsieve 2.5 pyhd8ed1ab_1 conda-forge spglib 2.2.0 py38hb851694_1 conda-forge sqlalchemy 2.0.23 py38h01eb140_0 conda-forge sshtunnel 0.4.0 pyhd8ed1ab_1 conda-forge stack_data 0.6.2 pyhd8ed1ab_0 conda-forge starlette 0.27.0 pyhd8ed1ab_0 conda-forge sympy 1.12 pypyh9d50eac_103 conda-forge sysroot_linux-64 2.12 he073ed8_16 conda-forge tabulate 0.9.0 pyhd8ed1ab_1 conda-forge tbb 2021.11.0 h00ab1b0_0 conda-forge tenacity 8.2.3 pyhd8ed1ab_0 conda-forge terminado 0.18.0 pyh0d859eb_0 conda-forge threadpoolctl 3.2.0 pyha21a80b_0 conda-forge tinycss2 1.2.1 pyhd8ed1ab_0 conda-forge tk 8.6.13 noxft_h4845f30_101 conda-forge toml 0.10.2 pyhd8ed1ab_0 conda-forge tomli 2.0.1 pyhd8ed1ab_0 conda-forge tomlkit 0.12.3 pyha770c72_0 conda-forge toolz 0.12.0 pyhd8ed1ab_0 conda-forge tornado 6.3.3 py38h01eb140_1 conda-forge tqdm 4.66.1 pyhd8ed1ab_0 conda-forge traitlets 5.14.0 pyhd8ed1ab_0 conda-forge types-python-dateutil 2.8.19.14 pyhd8ed1ab_0 conda-forge typing-extensions 4.9.0 hd8ed1ab_0 conda-forge typing_extensions 4.9.0 pyha770c72_0 conda-forge typing_utils 0.1.0 pyhd8ed1ab_0 conda-forge ukkonen 1.0.1 py38h7f3f72f_4 conda-forge uncertainties 3.1.7 pyhd8ed1ab_0 conda-forge unicodedata2 15.1.0 py38h01eb140_0 conda-forge uri-template 1.3.0 pyhd8ed1ab_0 conda-forge urllib3 1.26.18 pyhd8ed1ab_0 conda-forge virtualenv 20.25.0 pyhd8ed1ab_0 conda-forge voro 0.4.6 h00ab1b0_0 conda-forge wcwidth 0.2.12 pyhd8ed1ab_0 conda-forge webcolors 1.13 pyhd8ed1ab_0 conda-forge webencodings 0.5.1 pyhd8ed1ab_2 conda-forge websocket-client 1.7.0 pyhd8ed1ab_0 conda-forge werkzeug 3.0.1 pyhd8ed1ab_0 conda-forge wget 1.20.3 ha35d2d1_1 conda-forge wheel 0.42.0 pyhd8ed1ab_0 conda-forge xarray 2022.11.0 py38h06a4308_0
xarray-einstats 0.5.1 pyhd8ed1ab_0 conda-forge xcb-util 0.4.0 hd590300_1 conda-forge xcb-util-image 0.4.0 h8ee46fc_1 conda-forge xcb-util-keysyms 0.4.0 h8ee46fc_1 conda-forge xcb-util-renderutil 0.3.9 hd590300_1 conda-forge xcb-util-wm 0.4.1 h8ee46fc_1 conda-forge xkeyboard-config 2.40 hd590300_0 conda-forge xorg-kbproto 1.0.7 h7f98852_1002 conda-forge xorg-libice 1.1.1 hd590300_0 conda-forge xorg-libsm 1.2.4 h7391055_0 conda-forge xorg-libx11 1.8.7 h8ee46fc_0 conda-forge xorg-libxau 1.0.11 hd590300_0 conda-forge xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge xorg-libxext 1.3.4 h0b41bf4_2 conda-forge xorg-libxrender 0.9.11 hd590300_0 conda-forge xorg-renderproto 0.11.1 h7f98852_1002 conda-forge xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge xorg-xf86vidmodeproto 2.3.1 h7f98852_1002 conda-forge xorg-xproto 7.0.31 h7f98852_1007 conda-forge xz 5.2.6 h166bdaf_0 conda-forge yaml 0.2.5 h7f98852_2 conda-forge zeromq 4.3.5 h59595ed_0 conda-forge zipp 3.17.0 pyhd8ed1ab_0 conda-forge zlib 1.2.13 hd590300_5 conda-forge zstd 1.5.5 hfc55251_0 conda-forge ERROR: Directory '.' is not installable. Neither 'setup.py' nor 'pyproject.toml' found. /home/top/VENV/conda_surface_sampling/lib/python3.8/site-packages An error occurred: cannot import name 'NeuralFF' from 'nff' (/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/nff/init.py)

Traceback (most recent call last): File "/tmp/ipykernel_776040/2079156642.py", line 22, in from nff import NeuralFF, AtomsBatch, EnsembleNFF ImportError: cannot import name 'NeuralFF' from 'nff' (/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/nff/init.py)

do I have to run the training for these modules to appear?

xiaochendu commented 11 months ago

Hi, you won't have to train a model to use NFF. What command are you trying to run when this error popped up?

Is it pip install . in the NeuralForceField folder?

wanfuse123 commented 11 months ago

%xmode Verbose

import traceback import sys

Add the path to sys.path

sys.path.append('/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/') from nff import nn # Import the nn submodule

List the contents of the nn submodule

print(dir(nn))

print(sys.version) !conda activate nff !conda init !conda list !pip install . !echo $PYTHONPATH
try:

import torch
from nff import NeuralFF, AtomsBatch, EnsembleNFF
xiaochendu commented 11 months ago

I see. It seems like you're running a Jupyter notebook that you created. If possible, I would suggest you modify /.bashrc as documented in the Setup section of the README, specifically 

export NFFDIR="/path/to/NeuralForceField"
export PYTHONPATH=$NFFDIR:$PYTHONPATH

You might have to restart the terminal.

After that, try to run tutorials/SrTiO3_001.ipynb.

wanfuse123 commented 11 months ago

NFFDIR is the path to the install directory or the conda virtual environment? thanks

xiaochendu commented 11 months ago

It's the path to the install directory or the main folder.

Say if I clone the NeuralForceField repo on my home directory and my home directory is /home/usr, the path should be /home/usr/NeuralForceField.

wanfuse123 commented 11 months ago

ok here is more information, I just previously posted the diagnostics code from the notebook to show that those three custom python modules are not in NFF

Here is more, hope it isnt confusing

ON debian 12

MORE INFORMATION ~/.bashrc function set_default_paths() {

Set default system PATH

export PATH="/usr/bin:/usr/sbin:/usr/lib/cuda/bin:/home/top/.cargo/bin:/usr/local/bin:/bin:/usr/local/games:/usr/games"

# Clear PYTHONPATH
export PYTHONPATH="/usr/bin/python3"

# Deactivate conda if active and not in a legitimate path
if [[ -n "$CONDA_PREFIX" && "$PWD" != "/sda1/NeuralForceField"*  ]]; then
    conda deactivate
fi

# Deactivate venv if active and not in a legitimate path
if [[ -n "$VIRTUAL_ENV" && "$PWD" != "/sda1/lammps"* ]]; then
    deactivate
fi

    # Deactivate venv if active and not in a legitimate path
if [[ -n "$VIRTUAL_ENV" && "$PWD" != "/sda1/surface-sampling"* ]]; then
    deactivate
fi

}

function update_environment() { if [[ "$PWD" == "/sda1/NeuralForceField"* ]]; then set_default_paths export PATH="/sda1/NeuralForceField:$PATH"

source /home/top/miniconda3/bin/activate /home/top/miniconda3/envs/nff # commented out by conda initialize

    export PYTHONPATH="/home/top/miniconda3/envs/nff/lib/python3.9/site-packages:$NFFDIR"

elif [[ "$PWD" == "/sda1/surface-sampling"* ]]; then
    set_default_paths

source /home/top/miniconda3/bin/activate /home/top/VENV/conda_surface_sampling # commented out by conda initialize

    export PATH="/sda1/surface_sampling:$PATH"
    export PYTHONPATH="/home/top/VENV/conda_surface_sampling/lib/python3.8/site-packages"

elif [[ "$PWD" == "/sda1/lammps"* ]]; then
    set_default_paths

source /home/top/VENV/lammps/bin/activate # commented out by conda initialize

    export PATH="/sda1/lammps/src/:$PATH"
    export PYTHONPATH="/home/top/VENV/lammps/bin/python"

else
    set_default_paths
fi

}

Set default PATH and LD_LIBRARY_PATH

export LD_LIBRARY_PATH="/usr/local/ssl/lib:/usr/local/cuda/lib64" export EDITOR="kate"

MPI Configuration

export MPI_HOME="/usr/lib/x86_64-linux-gnu/openmpi" export PATH="$MPI_HOME/bin:$PATH" export LD_LIBRARY_PATH="$MPI_HOME/lib:$LD_LIBRARY_PATH" export C_INCLUDE_PATH="$MPI_HOME/include" export CPLUS_INCLUDE_PATH="$MPI_HOME/include"

export NFFDIR="/sda1/NeuralForceField"

Update environment based on current directory

PROMPT_COMMAND="update_environment"

Python wrapper function (unchanged)

python3() { REAL_PYTHON3="/usr/bin/python3" SCRIPT_PATH="~/add_required_lines.sh"

script_file="$1"
if [[ -f "$script_file" && -r "$script_file" ]]; then
    if head -n 5 "$script_file" | grep -q 'python'; then
        bash "$SCRIPT_PATH" "$script_file"
    fi
fi
exec "$REAL_PYTHON3" "$@"

}

>>> conda initialize >>>

!! Contents within this block are managed by 'conda init' !!

conda_setup="$('/home/top/miniconda3/bin/conda' 'shell.bash' 'hook' 2> /dev/null)" if [ $? -eq 0 ]; then eval "$__conda_setup" else if [ -f "/home/top/miniconda3/etc/profile.d/conda.sh" ]; then . "/home/top/miniconda3/etc/profile.d/conda.sh" else export PATH="/home/top/miniconda3/bin:$PATH" fi fi unset conda_setup

<<< conda initialize <<<

------------------------ NOTEBOOK AS TEXT------------------------ { "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import os\n", "import pickle\n", "import sys\n", "sys.path.append('/home/top/miniconda3/envs/nff/lib/python3.9/site-packages')\n", "import sys\n", "sys.path.append('/home/top/miniconda3/envs/nff/lib/python3.9')\n", "import nff\n", "from collections.abc import MutableMapping\n", "\n", "import pymc\n", "from time import perf_counter\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using GPU 0\n" ] } ], "source": [ "import torch\n", "\n", "#Check GPU availability\n", "if torch.cuda.is_available():\n", " # Get the index of the current GPU\n", " gpu_index = torch.cuda.current_device()\n", " print(f\"Using GPU {gpu_index}\")\n", "else:\n", " print(\"CUDA is not available.\")\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "import os\n", "import pickle\n", "import sys\n", "\n", "# uncomment and replace with correct path if there are import errors\n", "# sys.path.append(\"/sda1/surface-sampling/\")\n", "# sys.path.append(\"/sda1/NeuralForceField/\")\n", "# os.environ[\"LAMMPS_POTENTIALS\"] = \"/sda1/lammps/potentials/\"\n", "# os.environ[\"LAMMPS_COMMAND\"] =\"/sda1/lammps/src/lmp_mpi\"\n", "# os.environ[\"ASE_LAMMPSRUN_COMMAND\"] = os.environ[\"LAMMPS_COMMAND\"]\n", "\n", "from mcmc import MCMC\n", "from time import perf_counter" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Initialize test slab and parameters" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Load prepared pristine slab\n", "adsorbates = [\"Sr\", \"Ti\", \"O\"]\n", "chem_pot = [-2, 0, 0] # in eV, 1 entry for each chempot\n", "\n", "element = []\n", "slab_pkl = open(\"data/SrTiO3_001_2x2_pristine_slab.pkl\", \"rb\")\n", "slab = pickle.load(slab_pkl)\n", "\n", "surface_name = \"SrTiO3(001)\"\n", "alpha = 1 # no annealing\n", "temp = 1.0 # in terms of kbT\n", "num_sweeps = 10 # can adjust" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Obtain adsorption sites" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pymatgen.analysis.adsorption import AdsorbateSiteFinder\n", "from pymatgen.io.ase import AseAtomsAdaptor\n", "\n", "pristine_slab = slab.copy()\n", "pristine_pmg_slab = AseAtomsAdaptor.get_structure(pristine_slab)\n", "site_finder = AdsorbateSiteFinder(pristine_pmg_slab)\n", "sites = site_finder.find_adsorption_sites(\n", " distance=1.55, put_inside=False, symm_reduce=False, no_obtuse_hollow=False\n", ")\n", "ads_positions = sites[\"all\"]\n", "print(\"adsorption coordinates are\")\n", "print(ads_positions)\n", "\n", "sweep_size = len(ads_positions)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Set up NFF (calculator). We are using neural network weights from our Zenodo dataset (https://zenodo.org/record/7927039). The ensemble requires an offset_data.json file" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Exception reporting mode: Verbose\n", "['AttentionPool', 'BatchedPreActivation', 'BondPrior', 'ChemPropConv', 'ChemPropInit', 'ChemPropMsgToNode', 'ConfAttention', 'CosineEnvelope', 'CpSchNetConv', 'DEFAULT_BONDPRIOR_PARAM', 'DEFAULT_DROPOUT_RATE', 'DataLoader', 'Dense', 'DiabaticReadout', 'Diagonalize', 'DimeNet', 'DimeNetDelta', 'DimeNetDiabat', 'DimeNetDiabatDelta', 'DimeNetRadialBasis', 'DimeNetSphericalBasis', 'DirectedMessage', 'DirectedMessagePP', 'EPS', 'EPSILON', 'EdgeEmbedding', 'EdgeUpdateModule', 'EmbeddingBlock', 'Envelope', 'ExpNormalBasis', 'F', 'Gaussian', 'GaussianSmearing', 'GraphAttention', 'InteractionBlock', 'LeakyReLU', 'Linear', 'LinearConfAttention', 'MeanPool', 'MessagePassingModule', 'MixedSchNetConv', 'ModuleDict', 'MolFpPool', 'NodeEmbedding', 'NodeMultiTaskReadOut', 'OutputBlock', 'PainnRadialBasis', 'PreActivation', 'RadialBasis', 'ReLU', 'ResidualBlock', 'ScaleShift', 'SchNet', 'SchNetConv', 'SchNetDiabat', 'SchNetEdgeFilter', 'SchNetEdgeUpdate', 'Sequential', 'Softmax', 'SphericalBasis', 'StochasticIncrease', 'SumPool', 'TestModules', 'builtins', 'cached', 'doc', 'file', 'loader', 'name', 'package', 'path', 'spec', 'activations', 'add_stress', 'adj_nbrs_and_z', 'att_readout_probs', 'batch_and_sum', 'bessel_basis', 'chemprop_msg_to_node', 'chemprop_msg_update', 'clean_matrix', 'collections', 'compute_angle', 'compute_grad', 'compute_hess', 'computejacobian', 'constant', 'construct_module_dict', 'construct_sequential', 'copy', 'diabat', 'dimenet', 'gaussian_smearing', 'general_batched_hessian', 'get_act', 'get_default_readout', 'get_dense', 'get_offsets', 'get_painn_hessians', 'get_rij', 'get_schnet_hessians', 'grad', 'graphconv', 'graphop', 'hess_from_atoms', 'hess_from_pad', 'hess_from_results', 'inspect', 'lattice_points_in_supercell', 'layer_types', 'layers', 'models', 'modules', 'nn', 'np', 'pad', 'partial', 'real_sph_harm', 'remove_bias', 'results_from_stack', 'scatter_add', 'schnet', 'schnet_batched_hessians', 'shifted_softplus', 'single_spec_nbrs', 'softmax', 'sum_and_grad', 'sym', 'tensorgrad', 'to_module', 'torch', 'torch_nbr_list', 'unittest', 'utils', 'xavieruniform', 'zeros_initializer']\n", "3.8.18 | packaged by conda-forge | (default, Oct 10 2023, 15:44:36) \n", "[GCC 12.3.0]\n", "\n", "CondaError: Run 'conda init' before 'conda activate'\n", "\n", "no change /home/top/miniconda3/condabin/conda\n", "no change /home/top/miniconda3/bin/conda\n", "no change /home/top/miniconda3/bin/conda-env\n", "no change /home/top/miniconda3/bin/activate\n", "no change /home/top/miniconda3/bin/deactivate\n", "no change /home/top/miniconda3/etc/profile.d/conda.sh\n", "no change /home/top/miniconda3/etc/fish/conf.d/conda.fish\n", "no change /home/top/miniconda3/shell/condabin/Conda.psm1\n", "no change /home/top/miniconda3/shell/condabin/conda-hook.ps1\n", "no change /home/top/miniconda3/lib/python3.11/site-packages/xontrib/conda.xsh\n", "no change /home/top/miniconda3/etc/profile.d/conda.csh\n", "no change /home/top/.bashrc\n", "No action taken.\n", "# packages in environment at /home/top/VENV/conda_surface_sampling:\n", "#\n", "# Name Version Build Channel\n", "_libgcc_mutex 0.1 conda_forge conda-forge\n", "_openmp_mutex 4.5 2_kmp_llvm conda-forge\n", "aioitertools 0.11.0 pyhd8ed1ab_0 conda-forge\n", "alsa-lib 1.2.10 hd590300_0 conda-forge\n", "annotated-types 0.6.0 pyhd8ed1ab_0 conda-forge\n", "anyio 4.1.0 pyhd8ed1ab_0 conda-forge\n", "argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge\n", "argon2-cffi-bindings 21.2.0 py38h01eb140_4 conda-forge\n", "arrow 1.3.0 pyhd8ed1ab_0 conda-forge\n", "arviz 0.15.1 pyhd8ed1ab_0 conda-forge\n", "ase 3.22.1 pyhd8ed1ab_1 conda-forge\n", "astroid 3.0.2 py38h578d9bd_0 conda-forge\n", "asttokens 2.4.1 pyhd8ed1ab_0 conda-forge\n", "async-lru 2.0.4 pyhd8ed1ab_0 conda-forge\n", "attr 2.5.1 h166bdaf_1 conda-forge\n", "attrs 23.1.0 pyh71513ae_1 conda-forge\n", "babel 2.14.0 pyhd8ed1ab_0 conda-forge\n", "backcall 0.2.0 pyh9f0ad1d_0 conda-forge\n", "bcrypt 4.1.1 py38h0cc4f7c_0 conda-forge\n", "beautifulsoup4 4.12.2 pyha770c72_0 conda-forge\n", "binutils 2.40 hdd6e379_0 conda-forge\n", "binutils_impl_linux-64 2.40 hf600244_0 conda-forge\n", "binutils_linux-64 2.40 hbdbef99_2 conda-forge\n", "blas 2.120 openblas conda-forge\n", "blas-devel 3.9.0 20_linux64_openblas conda-forge\n", "bleach 6.1.0 pyhd8ed1ab_0 conda-forge\n", "blinker 1.7.0 pyhd8ed1ab_0 conda-forge\n", "blosc 1.21.5 h0f2a231_0 conda-forge\n", "boto3 1.34.0 pyhd8ed1ab_0 conda-forge\n", "botocore 1.34.0 pyhd8ed1ab_0 conda-forge\n", "brotli 1.1.0 hd590300_1 conda-forge\n", "brotli-bin 1.1.0 hd590300_1 conda-forge\n", "brotli-python 1.1.0 py38h17151c0_1 conda-forge\n", "bzip2 1.0.8 hd590300_5 conda-forge\n", "c-ares 1.23.0 hd590300_0 conda-forge\n", "c-compiler 1.5.2 h0b41bf4_0 conda-forge\n", "ca-certificates 2023.11.17 hbcca054_0 conda-forge\n", "cached-property 1.5.2 hd8ed1ab_1 conda-forge\n", "cached_property 1.5.2 pyha770c72_1 conda-forge\n", "cachetools 5.3.2 pyhd8ed1ab_0 conda-forge\n", "cairo 1.18.0 h3faef2a_0 conda-forge\n", "catkit 0.5.4 pypi_0 pypi\n", "certifi 2023.11.17 py38h06a4308_0 \n", "cffi 1.16.0 py38h6d47a40_0 conda-forge\n", "cfgv 3.3.1 pyhd8ed1ab_0 conda-forge\n", "chardet 5.2.0 py38h578d9bd_1 conda-forge\n", "charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge\n", "click 8.1.7 unix_pyh707e725_0 conda-forge\n", "cloudpickle 3.0.0 pyhd8ed1ab_0 conda-forge\n", "cmake 3.28.1 hcfe8598_0 conda-forge\n", "colorama 0.4.6 pyhd8ed1ab_0 conda-forge\n", "comm 0.1.4 pyhd8ed1ab_0 conda-forge\n", "cons 0.4.6 pyhd8ed1ab_0 conda-forge\n", "contourpy 1.1.1 py38h7f3f72f_1 conda-forge\n", "cryptography 41.0.7 py38he241bfc_1 conda-forge\n", "cuda-cudart 11.7.99 0 nvidia\n", "cuda-cupti 11.7.101 0 nvidia\n", "cuda-libraries 11.7.1 0 nvidia\n", "cuda-nvrtc 11.7.99 0 nvidia\n", "cuda-nvtx 11.7.91 0 nvidia\n", "cuda-runtime 11.7.1 0 nvidia\n", "cuda-version 11.8 h70ddcb2_2 conda-forge\n", "cudatoolkit 11.8.0 h4ba93d1_12 conda-forge\n", "cudnn 8.8.0.121 hcdd5f01_4 conda-forge\n", "cxx-compiler 1.5.2 hf52228f_0 conda-forge\n", "cycler 0.12.1 pyhd8ed1ab_0 conda-forge\n", "dbus 1.13.6 h5008d03_3 conda-forge\n", "debugpy 1.8.0 py38h17151c0_1 conda-forge\n", "decorator 5.1.1 pyhd8ed1ab_0 conda-forge\n", "defusedxml 0.7.1 pyhd8ed1ab_0 conda-forge\n", "dill 0.3.7 pyhd8ed1ab_0 conda-forge\n", "distlib 0.3.8 pyhd8ed1ab_0 conda-forge\n", "dnspython 2.4.2 pyhd8ed1ab_1 conda-forge\n", "e3fp 1.2.5 pyh56297ac_0 conda-forge\n", "emmet-core 0.69.5 pyhd8ed1ab_0 conda-forge\n", "entrypoints 0.4 pyhd8ed1ab_0 conda-forge\n", "etuples 0.3.9 pyhd8ed1ab_0 conda-forge\n", "exceptiongroup 1.2.0 pyhd8ed1ab_0 conda-forge\n", "executing 2.0.1 pyhd8ed1ab_0 conda-forge\n", "expat 2.5.0 hcb278e6_1 conda-forge\n", "f90wrap 0.2.9 py38h5856114_0 conda-forge\n", "fastapi 0.103.0 pyhd8ed1ab_0 conda-forge\n", "fastprogress 1.0.3 pyhd8ed1ab_0 conda-forge\n", "fftw 3.3.10 nompi_hc118613_108 conda-forge\n", "filelock 3.13.1 pyhd8ed1ab_0 conda-forge\n", "fireworks 2.0.3 pypi_0 pypi\n", "flake8 6.1.0 pyhd8ed1ab_0 conda-forge\n", "flask 3.0.0 pyhd8ed1ab_0 conda-forge\n", "flask-paginate 2023.10.24 pypi_0 pypi\n", "font-ttf-dejavu-sans-mono 2.37 hab24e00_0 conda-forge\n", "font-ttf-inconsolata 3.000 h77eed37_0 conda-forge\n", "font-ttf-source-code-pro 2.038 h77eed37_0 conda-forge\n", "font-ttf-ubuntu 0.83 h77eed37_1 conda-forge\n", "fontconfig 2.14.2 h14ed4e7_0 conda-forge\n", "fonts-conda-ecosystem 1 0 conda-forge\n", "fonts-conda-forge 1 0 conda-forge\n", "fonttools 4.46.0 py38h01eb140_0 conda-forge\n", "fqdn 1.5.1 pyhd8ed1ab_0 conda-forge\n", "freetype 2.12.1 h267a509_2 conda-forge\n", "freetype-py 2.3.0 pyhd8ed1ab_0 conda-forge\n", "future 0.18.3 pyhd8ed1ab_0 conda-forge\n", "gcc 11.4.0 h7baecda_2 conda-forge\n", "gcc_impl_linux-64 11.4.0 h7aa1c59_3 conda-forge\n", "gcc_linux-64 11.4.0 hfd045f2_2 conda-forge\n", "gettext 0.21.1 h27087fc_0 conda-forge\n", "gfortran_impl_linux-64 11.4.0 h86428dc_3 conda-forge\n", "gfortran_linux-64 11.4.0 h3047280_2 conda-forge\n", "glib 2.78.3 hfc55251_0 conda-forge\n", "glib-tools 2.78.3 hfc55251_0 conda-forge\n", "gmp 6.3.0 h59595ed_0 conda-forge\n", "gmpy2 2.1.2 py38h793c122_1 conda-forge\n", "graphite2 1.3.13 h58526e2_1001 conda-forge\n", "greenlet 3.0.2 py38h17151c0_0 conda-forge\n", "gsl 2.7 he838d99_0 conda-forge\n", "gst-plugins-base 1.22.7 h8e1006c_1 conda-forge\n", "gstreamer 1.22.7 h98fc4e7_1 conda-forge\n", "gunicorn 21.2.0 pypi_0 pypi\n", "gxx 11.4.0 h7baecda_2 conda-forge\n", "gxx_impl_linux-64 11.4.0 h7aa1c59_3 conda-forge\n", "gxx_linux-64 11.4.0 hfc1ae95_2 conda-forge\n", "h11 0.14.0 pyhd8ed1ab_0 conda-forge\n", "h2 4.1.0 pyhd8ed1ab_0 conda-forge\n", "h5netcdf 1.1.0 pyhd8ed1ab_1 conda-forge\n", "h5py 3.10.0 nompi_py38h2c1edd7_101 conda-forge\n", "harfbuzz 8.3.0 h3d44ed6_0 conda-forge\n", "hdf4 4.2.15 h2a13503_7 conda-forge\n", "hdf5 1.14.3 nompi_h4f84152_100 conda-forge\n", "hpack 4.0.0 pyh9f0ad1d_0 conda-forge\n", "httpcore 1.0.2 pyhd8ed1ab_0 conda-forge\n", "hyperframe 6.0.1 pyhd8ed1ab_0 conda-forge\n", "icu 73.2 h59595ed_0 conda-forge\n", "identify 2.5.33 pyhd8ed1ab_0 conda-forge\n", "idna 3.6 pyhd8ed1ab_0 conda-forge\n", "importlib-metadata 7.0.0 pyha770c72_0 conda-forge\n", "importlib-resources 6.1.1 pyhd8ed1ab_0 conda-forge\n", "importlib_metadata 7.0.0 hd8ed1ab_0 conda-forge\n", "importlib_resources 6.1.1 pyhd8ed1ab_0 conda-forge\n", "ipykernel 6.26.0 pyhf8b6a83_0 conda-forge\n", "ipython 8.12.2 pyh41d4057_0 conda-forge\n", "isoduration 20.11.0 pyhd8ed1ab_0 conda-forge\n", "isort 5.13.2 pyhd8ed1ab_0 conda-forge\n", "itsdangerous 2.1.2 pyhd8ed1ab_0 conda-forge\n", "jedi 0.19.1 pyhd8ed1ab_0 conda-forge\n", "jinja2 3.1.2 pyhd8ed1ab_1 conda-forge\n", "jmespath 1.0.1 pyhd8ed1ab_0 conda-forge\n", "joblib 1.3.2 pyhd8ed1ab_0 conda-forge\n", "json5 0.9.14 pyhd8ed1ab_0 conda-forge\n", "jsonpointer 2.4 py38h578d9bd_3 conda-forge\n", "jsonschema 4.20.0 pyhd8ed1ab_0 conda-forge\n", "jsonschema-specifications 2023.11.2 pyhd8ed1ab_0 conda-forge\n", "jsonschema-with-format-nongpl 4.20.0 pyhd8ed1ab_0 conda-forge\n", "jupyter-lsp 2.2.1 pyhd8ed1ab_0 conda-forge\n", "jupyter_client 8.6.0 pyhd8ed1ab_0 conda-forge\n", "jupyter_core 5.5.0 py38h578d9bd_0 conda-forge\n", "jupyter_events 0.9.0 pyhd8ed1ab_0 conda-forge\n", "jupyter_server 2.12.1 pyhd8ed1ab_0 conda-forge\n", "jupyter_server_terminals 0.5.0 pyhd8ed1ab_0 conda-forge\n", "jupyterlab 4.0.9 pyhd8ed1ab_0 conda-forge\n", "jupyterlab_pygments 0.3.0 pyhd8ed1ab_0 conda-forge\n", "jupyterlab_server 2.25.2 pyhd8ed1ab_0 conda-forge\n", "kernel-headers_linux-64 2.6.32 he073ed8_16 conda-forge\n", "keyutils 1.6.1 h166bdaf_0 conda-forge\n", "kim-api 2.3.0 h097297d_1 conda-forge\n", "kiwisolver 1.4.5 py38h7f3f72f_1 conda-forge\n", "krb5 1.21.2 h659d440_0 conda-forge\n", "lame 3.100 h166bdaf_1003 conda-forge\n", "lammps 2023.08.02 cpu_py38_hc50ecde_mpich_7 conda-forge\n", "latexcodec 2.0.1 pyh9f0ad1d_0 conda-forge\n", "lcms2 2.16 hb7c19ff_0 conda-forge\n", "ld_impl_linux-64 2.40 h41732ed_0 conda-forge\n", "lerc 4.0.0 h27087fc_0 conda-forge\n", "libabseil 20230802.1 cxx17_h59595ed_0 conda-forge\n", "libaec 1.1.2 h59595ed_1 conda-forge\n", "libblas 3.9.0 20_linux64_openblas conda-forge\n", "libboost 1.82.0 h6fcfa73_6 conda-forge\n", "libboost-python 1.82.0 py38hae673b5_6 conda-forge\n", "libbrotlicommon 1.1.0 hd590300_1 conda-forge\n", "libbrotlidec 1.1.0 hd590300_1 conda-forge\n", "libbrotlienc 1.1.0 hd590300_1 conda-forge\n", "libcap 2.69 h0f662aa_0 conda-forge\n", "libcblas 3.9.0 20_linux64_openblas conda-forge\n", "libclang 15.0.7 default_hb11cfb5_4 conda-forge\n", "libclang13 15.0.7 default_ha2b6cf4_4 conda-forge\n", "libcublas 11.10.3.66 0 nvidia\n", "libcufft 10.7.2.124 h4fbf590_0 nvidia\n", "libcufile 1.8.1.2 0 nvidia\n", "libcups 2.3.3 h4637d8d_4 conda-forge\n", "libcurand 10.3.4.101 0 nvidia\n", "libcurl 8.5.0 hca28451_0 conda-forge\n", "libcusolver 11.4.0.1 0 nvidia\n", "libcusparse 11.7.4.91 0 nvidia\n", "libdeflate 1.19 hd590300_0 conda-forge\n", "libedit 3.1.20191231 he28a2e2_2 conda-forge\n", "libev 4.33 hd590300_2 conda-forge\n", "libevent 2.1.12 hf998b51_1 conda-forge\n", "libexpat 2.5.0 hcb278e6_1 conda-forge\n", "libffi 3.4.2 h7f98852_5 conda-forge\n", "libflac 1.4.3 h59595ed_0 conda-forge\n", "libgcc-devel_linux-64 11.4.0 h922705a_103 conda-forge\n", "libgcc-ng 13.2.0 h807b86a_3 conda-forge\n", "libgcrypt 1.10.3 hd590300_0 conda-forge\n", "libgfortran-ng 13.2.0 h69a702a_3 conda-forge\n", "libgfortran5 13.2.0 ha4646dd_3 conda-forge\n", "libglib 2.78.3 h783c2da_0 conda-forge\n", "libgomp 13.2.0 h807b86a_3 conda-forge\n", "libgpg-error 1.47 h71f35ed_0 conda-forge\n", "libhwloc 2.9.3 default_h554bfaf_1009 conda-forge\n", "libiconv 1.17 hd590300_1 conda-forge\n", "libidn2 2.3.4 h166bdaf_0 conda-forge\n", "libjpeg-turbo 3.0.0 hd590300_1 conda-forge\n", "liblapack 3.9.0 20_linux64_openblas conda-forge\n", "liblapacke 3.9.0 20_linux64_openblas conda-forge\n", "libllvm15 15.0.7 h5cf9203_3 conda-forge\n", "libmagma 2.7.2 h09159a4_1 conda-forge\n", "libmagma_sparse 2.7.2 h09b5827_1 conda-forge\n", "libnetcdf 4.9.2 nompi_h80fb2b6_112 conda-forge\n", "libnghttp2 1.58.0 h47da74e_1 conda-forge\n", "libnpp 11.7.4.75 0 nvidia\n", "libnsl 2.0.1 hd590300_0 conda-forge\n", "libnvjpeg 11.8.0.2 0 nvidia\n", "libogg 1.3.4 h7f98852_1 conda-forge\n", "libopenblas 0.3.25 pthreads_h413a1c8_0 conda-forge\n", "libopus 1.3.1 h7f98852_1 conda-forge\n", "libpng 1.6.39 h753d276_0 conda-forge\n", "libpq 16.1 h33b98f1_7 conda-forge\n", "libprotobuf 4.24.4 hf27288f_0 conda-forge\n", "libsanitizer 11.4.0 h4dcbe23_3 conda-forge\n", "libsndfile 1.2.2 hc60ed4a_1 conda-forge\n", "libsodium 1.0.18 h36c2ea0_1 conda-forge\n", "libsqlite 3.44.2 h2797004_0 conda-forge\n", "libssh2 1.11.0 h0841786_0 conda-forge\n", "libstdcxx-devel_linux-64 11.4.0 h922705a_103 conda-forge\n", "libstdcxx-ng 13.2.0 h7e041cc_3 conda-forge\n", "libsystemd0 255 h3516f8a_0 conda-forge\n", "libtiff 4.6.0 ha9c0a0a_2 conda-forge\n", "libunistring 0.9.10 h7f98852_0 conda-forge\n", "libuuid 2.38.1 h0b41bf4_0 conda-forge\n", "libuv 1.46.0 hd590300_0 conda-forge\n", "libvorbis 1.3.7 h9c3ff4c_0 conda-forge\n", "libwebp-base 1.3.2 hd590300_0 conda-forge\n", "libxcb 1.15 h0b41bf4_0 conda-forge\n", "libxkbcommon 1.6.0 h5d7e998_0 conda-forge\n", "libxml2 2.11.6 h232c23b_0 conda-forge\n", "libzip 1.10.1 h2629f0a_3 conda-forge\n", "libzlib 1.2.13 hd590300_5 conda-forge\n", "llvm-openmp 17.0.6 h4dfa4b3_0 conda-forge\n", "logical-unification 0.4.6 pyhd8ed1ab_0 conda-forge\n", "lz4-c 1.9.4 hcb278e6_0 conda-forge\n", "maggma 0.46.1 pyhd8ed1ab_0 conda-forge\n", "magma 2.7.2 h4aca40b_1 conda-forge\n", "make 4.3 hd18ef5c_1 conda-forge\n", "markupsafe 2.1.3 py38h01eb140_1 conda-forge\n", "matplotlib 3.7.3 py38h578d9bd_0 conda-forge\n", "matplotlib-base 3.7.3 py38h58ed7fa_0 conda-forge\n", "matplotlib-inline 0.1.6 pyhd8ed1ab_0 conda-forge\n", "mccabe 0.7.0 pyhd8ed1ab_0 conda-forge\n", "minikanren 1.0.3 pyhd8ed1ab_0 conda-forge\n", "mistune 3.0.2 pyhd8ed1ab_0 conda-forge\n", "mkl 2022.2.1 h84fe81f_16997 conda-forge\n", "mkl-service 2.4.0 py38h80f09db_0 conda-forge\n", "mlip 2.0 hd0d41c4_0 conda-forge\n", "mmh3 4.0.1 py38h17151c0_2 conda-forge\n", "mongogrant 0.3.3 pyhd8ed1ab_0 conda-forge\n", "mongomock 4.0.0 pyhd8ed1ab_0 conda-forge\n", "monty 2023.11.3 pyhd8ed1ab_0 conda-forge\n", "mp-api 0.37.1 pyhd8ed1ab_1 conda-forge\n", "mpc 1.3.1 hfe3b2da_0 conda-forge\n", "mpfr 4.2.1 h9458935_0 conda-forge\n", "mpg123 1.32.3 h59595ed_0 conda-forge\n", "mpi 1.0 mpich conda-forge\n", "mpich 4.1.2 h846660c_100 conda-forge\n", "mpmath 1.3.0 pyhd8ed1ab_0 conda-forge\n", "msgpack-python 1.0.7 py38h7f3f72f_0 conda-forge\n", "multipledispatch 0.6.0 py_0 conda-forge\n", "munkres 1.1.4 pyh9f0ad1d_0 conda-forge\n", "mysql-common 8.0.33 hf1915f5_6 conda-forge\n", "mysql-libs 8.0.33 hca2cd23_6 conda-forge\n", "n2p2 2.2.0 py38h329ad1e_4 conda-forge\n", "nbclient 0.8.0 pyhd8ed1ab_0 conda-forge\n", "nbconvert-core 7.12.0 pyhd8ed1ab_0 conda-forge\n", "nbformat 5.9.2 pyhd8ed1ab_0 conda-forge\n", "nccl 2.19.4.1 h6103f9b_0 conda-forge\n", "ncurses 6.4 h59595ed_2 conda-forge\n", "nest-asyncio 1.5.8 pyhd8ed1ab_0 conda-forge\n", "networkx 3.1 pyhd8ed1ab_0 conda-forge\n", "nodeenv 1.8.0 pyhd8ed1ab_0 conda-forge\n", "notebook 7.0.6 pyhd8ed1ab_0 conda-forge\n", "notebook-shim 0.2.3 pyhd8ed1ab_0 conda-forge\n", "nspr 4.35 h27087fc_0 conda-forge\n", "nss 3.95 h1d7d5a4_0 conda-forge\n", "numpy 1.22.4 py38h99721a1_0 conda-forge\n", "openblas 0.3.25 pthreads_h7a3da1a_0 conda-forge\n", "openjpeg 2.5.0 h488ebb8_3 conda-forge\n", "openkim-models 2021.01.28 h2cc385e_1 conda-forge\n", "openssl 3.2.0 hd590300_1 conda-forge\n", "orjson 3.9.10 py38h0488081_0 conda-forge\n", "overrides 7.4.0 pyhd8ed1ab_0 conda-forge\n", "packaging 23.2 pyhd8ed1ab_0 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1.20.3 ha35d2d1_1 conda-forge\n", "wheel 0.42.0 pyhd8ed1ab_0 conda-forge\n", "xarray 2022.11.0 py38h06a4308_0 \n", "xarray-einstats 0.5.1 pyhd8ed1ab_0 conda-forge\n", "xcb-util 0.4.0 hd590300_1 conda-forge\n", "xcb-util-image 0.4.0 h8ee46fc_1 conda-forge\n", "xcb-util-keysyms 0.4.0 h8ee46fc_1 conda-forge\n", "xcb-util-renderutil 0.3.9 hd590300_1 conda-forge\n", "xcb-util-wm 0.4.1 h8ee46fc_1 conda-forge\n", "xkeyboard-config 2.40 hd590300_0 conda-forge\n", "xorg-kbproto 1.0.7 h7f98852_1002 conda-forge\n", "xorg-libice 1.1.1 hd590300_0 conda-forge\n", "xorg-libsm 1.2.4 h7391055_0 conda-forge\n", "xorg-libx11 1.8.7 h8ee46fc_0 conda-forge\n", "xorg-libxau 1.0.11 hd590300_0 conda-forge\n", "xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge\n", "xorg-libxext 1.3.4 h0b41bf4_2 conda-forge\n", "xorg-libxrender 0.9.11 hd590300_0 conda-forge\n", "xorg-renderproto 0.11.1 h7f98852_1002 conda-forge\n", "xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge\n", "xorg-xf86vidmodeproto 2.3.1 h7f98852_1002 conda-forge\n", "xorg-xproto 7.0.31 h7f98852_1007 conda-forge\n", "xz 5.2.6 h166bdaf_0 conda-forge\n", "yaml 0.2.5 h7f98852_2 conda-forge\n", "zeromq 4.3.5 h59595ed_0 conda-forge\n", "zipp 3.17.0 pyhd8ed1ab_0 conda-forge\n", "zlib 1.2.13 hd590300_5 conda-forge\n", "zstd 1.5.5 hfc55251_0 conda-forge\n", "\u001b[31mERROR: Directory '.' is not installable. Neither 'setup.py' nor 'pyproject.toml' found.\u001b[0m\u001b[31m\n", "\u001b[0m/home/top/VENV/conda_surface_sampling/lib/python3.8/site-packages\n", "An error occurred: cannot import name 'NeuralFF' from 'nff' (/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/nff/init.py)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Traceback (most recent call last):\n", " File \"/tmp/ipykernel_776040/2079156642.py\", line 22, in \n", " from nff import NeuralFF, AtomsBatch, EnsembleNFF\n", "ImportError: cannot import name 'NeuralFF' from 'nff' (/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/nff/init.py)\n" ] } ], "source": [ "%xmode Verbose\n", "\n", "import traceback\n", "import sys\n", "# Add the path to sys.path\n", "sys.path.append('/home/top/miniconda3/envs/nff/lib/python3.9/site-packages/')\n", "from nff import nn # Import the nn submodule\n", "\n", "# List the contents of the nn submodule\n", "print(dir(nn))\n", "\n", "\n", "print(sys.version)\n", "!conda activate nff\n", "!conda init\n", "!conda list\n", "!pip install .\n", "!echo $PYTHONPATH \n", "try:\n", " \n", " import torch\n", " from nff import NeuralFF, AtomsBatch, EnsembleNFF\n", " \n", " #if torch.cuda.is_available():\n", " # DEVICE = 0\n", " #else:\n", " DEVICE = \"cpu\"\n", "\n", " # requires an ensemble of models in this path and an offset_data.json file\n", " nnids = [\"model01\", \"model02\", \"model03\"]\n", " model_dirs = [\n", " os.path.join(\n", " os.getcwd(),\n", " \"data/nff\",\n", " str(x),\n", " \"best_model\",\n", " )\n", " for x in nnids\n", " ]\n", "\n", " models = []\n", " for modeldir in model_dirs:\n", " m = NeuralFF.from_file(modeldir, device=DEVICE).model\n", " models.append(m)\n", "\n", " nff_calc = EnsembleNFF(models, device=DEVICE)\n", "\n", " # set attributes\n", " CUTOFF = 5\n", " relax = True\n", " relax_steps = 5\n", " optimizer = \"BFGS\"\n", " offset = True\n", " offset_data = os.path.join(\n", " os.getcwd(),\n", " \"data/nff\",\n", " \"offset_data.json\",\n", " )\n", "\n", " slab_batch = AtomsBatch(\n", " positions=slab.positions,\n", " numbers=slab.numbers,\n", " cell=slab.cell,\n", " pbc=True,\n", " cutoff=CUTOFF,\n", " props={\"energy\": 0, \"energy_grad\": []},\n", " calculator=nff_calc,\n", " requires_large_offsets=True,\n", " directed=True,\n", " device=DEVICE,\n", " )\n", "\n", " # fix bulk atoms\n", " from ase.constraints import FixAtoms\n", "\n", " num_bulk_atoms = len(slab_batch)\n", " bulk_indices = list(range(num_bulk_atoms))\n", " print(f\"bulk indices {bulk_indices}\")\n", " surf_indices = slab.get_surface_atoms()\n", "\n", " fix_indices = list(set(bulk_indices) - set(surf_indices))\n", " print(f\"fix indices {fix_indices}\")\n", "\n", " c = FixAtoms(indices=fix_indices)\n", " slab_batch.set_constraint(c)\n", "except Exception as e:\n", " print(f\"An error occurred: {e}\")\n", " traceback.print_exc() " ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Perform MCMC and view results. Detailed results can be found in the corresponding run in the SrTiO3(001)/ folder." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import copy\n", "\n", "run_slab = copy.deepcopy(slab_batch)\n", "\n", "mcmc = MCMC(\n", " calc=nff_calc,\n", " canonical=False,\n", " testing=False,\n", " surface_name=surface_name,\n", " element=element,\n", " adsorbates=adsorbates,\n", " ads_coords=ads_positions,\n", " relax=relax,\n", " offset=offset,\n", " offset_data=offset_data,\n", " relax_steps=relax_steps,\n", " optimizer=optimizer,\n", ")\n", "\n", "start = perf_counter()\n", "# call the main function\n", "mcmc.mcmc_run(\n", " total_sweeps=num_sweeps,\n", " sweep_size=sweep_size,\n", " start_temp=temp,\n", " pot=chem_pot, alpha=alpha, slab=run_slab\n", ")\n", "\n", "stop = perf_counter()\n", "print(f\"Time taken = {stop - start} seconds\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Save structures for later use in latent space clustering or analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "structures = mcmc.history\n", "with open(\"data/SrTiO3_001_2x2_mcmc_structures.pkl\", \"wb\") as f:\n", " pickle.dump(structures, f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "surface_sampling_CPU", "language": "python", "name": "surface_sampling" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.18" }, "vscode": { "interpreter": { "hash": "e3e0723b7fd9866ee8f9ae4f62931968cf8456ef2195b337b8930ae9f61708cf" } } }, "nbformat": 4, "nbformat_minor": 4 }

xiaochendu commented 11 months ago

Hi, it seems like you're importing using this command from nff import NeuralFF, AtomsBatch, EnsembleNFF. If you look at the ase.py here (https://github.com/learningmatter-mit/NeuralForceField/blob/master/nff/io/ase.py), you will find the NeuralFF, AtomsBatch, EnsembleNFF classes. Can you try from nff.io.ase import NeuralFF, AtomsBatch, EnsembleNFF or from nff.io import NeuralFF, AtomsBatch, EnsembleNFF as in the tutorial notebook?

Also, if you want me to take a look at the notebook, can you upload the notebook as a file here (as a zip file)? Example: SrTiO3_001.ipynb.zip