diff --git a/doc/getting-started/quick_start.ipynb b/doc/getting-started/quick_start.ipynb index 1c53665b7d..67674c4654 100644 --- a/doc/getting-started/quick_start.ipynb +++ b/doc/getting-started/quick_start.ipynb @@ -1,31 +1,51 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "id": "b22f597d-ec17-4ab9-8933-28e92af2438d", "metadata": {}, "source": [ - "# DeePMD-kit Quick Start Tutorial\n", - "\n", - "\"Open" + "# DeePMD-kit Quick Start Tutorial" + ] + }, + { + "cell_type": "markdown", + "id": "7a41db5f", + "metadata": {}, + "source": [ + "\"Open" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "85b62e3d-dfae-402f-96a5-5672367d2d17", "metadata": {}, "source": [ - "**DeePMD-kit is a deep learning package for many-body potential energy representation and molecular dynamics.**\n", - "\n", - "> This tutorial can be directly run on **Bohrium Notebook**, you can click the `Open in Bohrium` button above to quickly run this document in Bohrium.\n", - ">\n", - "> After opening Bohrium Notebook, click the button `connect`, and choose `deepmd-kit:2.2.1-cuda11.6-notebook` as image and `c4_m8_cpu` as computing resources. Wait a minute and you can get started.\n" + "
\n", + "

\n", + " ©️ Copyright 2024 @ Authors
\n", + " πŸ“– Getting Started Guide
\n", + " Licensing Agreement: This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

\n", + " This document can be executed directly on the Bohrium Notebook. To begin,you can click the Open in Bohrium button above to quickly run this document in Bohrium.

\n", + " After opening Bohrium Notebook, click the button connect .We have already set up the recommended image and the recommended machine type for you.\n", + "

\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "dbab8963", + "metadata": {}, + "source": [ + "**This is a quick start guide for \"Deep Potential\" molecular dynamics using DeePMD-kit, through which you can quickly understand the paradigm cycle that DeePMD-kit operates in and apply it to your projects.**\n", + "

Deep Potential is the convergence of machine learning and physical principles, presenting a new computational paradigm as shown in the figure below.

\n", + "
\n", + " \"Fig2\"\n", + "

Figure | A new computational paradigm, composed of Molecular Modeling, Machine Learning, and High-Performance Computing (HPC).

\n", + "
\n" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "f4416190-479c-4b2f-9e08-33690bc4941d", "metadata": {}, @@ -38,7 +58,7 @@ "\n", "* Prepare the formataive dataset and running scripts for training with DeePMD-kit;\n", "* Train, freeze, and test DeePMD-kit models;\n", - "* Use DeePMD-kit in LAMMPS for calculations;\n", + "* Use DeePMD-kit in Lammps for calculations;\n", "\n", "Work through this tutorial. It will take you 20 minutes, max!" ] @@ -48,144 +68,198 @@ "id": "8e6587cb-ba6e-42ba-a139-0595fc7f79d7", "metadata": {}, "source": [ - "## Table of contents\n", "\n", - "* Get tutorial data via git\n", - "* General Introduction\n", - "* Data preparation\n", - "* Prepare input script\n", - "* Train a model\n", - "* Freeze a model\n", - "* Test a model\n", - "* Run MD with LAMMPS" + "```{contents} Table of Contents\n", + ":depth: 3\n", + "```" ] }, { "cell_type": "markdown", - "id": "adc6bbb1-a51b-421d-88f6-eb8eef79f432", + "id": "42afcb0e", "metadata": {}, "source": [ - "## Get tutorial data via git" + "## Background\n", + "\n", + "In this tutorial, we will take the gaseous methane molecule as an example to provide a detailed introduction to the training and application of the Deep Potential (DP) model.\n", + "\n", + "DeePMD-kit is a software tool that employs neural networks to fit potential energy models based on first-principles data for molecular dynamics simulations. Without manual intervention, it can end-to-end transform the data provided by users into a deep potential model in a matter of hours. This model can seamlessly integrate with common molecular dynamics simulation software (like LAMMPS, OpenMM, and GROMACS).\n", + "\n", + "DeePMD-kit significantly elevates the limits of molecular dynamics through high-performance computing and machine learning, achieving system scales of up to hundreds of millions of atoms while still maintaining the high accuracy of \"ab initio\" calculations. The simulation time scale is improved by at least 1000 times compared to traditional methods. Its achievements earned the 2020 ACM Gordon Bell Prize, one of the highest honors in the field of high-performance computing, and it has been used by over a thousand research groups in physics, chemistry, materials science, biology, and other fields globally.\n", + "\n", + "\"Fig1\"\n", + "\n", + "For more detailed usage, you can refer to the [DeePMD-kit’s documentation](https://docs.deepmodeling.org/projects/deepmd/en/master/index.html) as a comprehensive reference.\n", + "\n", + "In this case, the Deep Potential (DP) model was generated using the **DeePMD-kit package**.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "275d9d23", + "metadata": {}, + "source": [ + "## Practice" + ] + }, + { + "cell_type": "markdown", + "id": "a9be4db1", + "metadata": {}, + "source": [ + "### Data Preparation\n", + "\n", + "We have prepared the initial data for $CH_4$ required to run DeePMD-kit computations and placed it in the `DeePMD-kit_Tutorial` folder. You can view the corresponding files by clicking on the dataset on the left side:" ] }, { "cell_type": "code", - "execution_count": 1, - "id": "f493fdb7-7844-4152-8b8e-a4d9d1da58ff", - "metadata": { - "tags": [] - }, + "execution_count": 2, + "id": "125c96ef", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Cloning into 'colombo-academy-tutorials'...\n", - "remote: Enumerating objects: 7164, done.\u001b[K\n", - "remote: Counting objects: 100% (174/174), done.\u001b[K\n", - "remote: Compressing objects: 100% (138/138), done.\u001b[K\n", - "remote: Total 7164 (delta 78), reused 71 (delta 32), pack-reused 6990\u001b[K\n", - "Receiving objects: 100% (7164/7164), 45.31 MiB | 3.85 MiB/s, done.\n", - "Resolving deltas: 100% (3378/3378), done.\n", - "Updating files: 100% (185/185), done.\n" + "Dataset is already downloaded and extracted.\n", + "Current path is: /personal\n" ] } ], "source": [ - "! if ! [ -e colombo-academy-tutorials ];then git clone https://gitee.com/deepmodeling/colombo-academy-tutorials.git;fi;" + "import os\n", + "\n", + "# Define the dataset URL and the paths\n", + "dataset_url = \"https://bohrium-api.dp.tech/ds-dl/DeePMD-kit-Tutorial-a8z5-v1.zip\"\n", + "zip_file_name = \"DeePMD-kit-Tutorial-a8z5-v1.zip\"\n", + "dataset_directory = \"DeePMD-kit_Tutorial\"\n", + "local_zip_path = f\"/personal/{zip_file_name}\"\n", + "extract_path = \"/personal/\"\n", + "\n", + "# Check if the dataset directory exists to avoid re-downloading and re-extracting\n", + "if not os.path.isdir(f\"{extract_path}{dataset_directory}\"):\n", + " # Download and extract if not exists\n", + " if not os.path.isfile(local_zip_path):\n", + " print(\"Downloading dataset...\")\n", + " !wget -q -O {local_zip_path} {dataset_url}\n", + "\n", + " print(\"Extracting dataset...\")\n", + " !unzip -q -n {local_zip_path} -d {extract_path}\n", + "else:\n", + " print(\"Dataset is already downloaded and extracted.\")\n", + "\n", + "# Change the current working directory\n", + "os.chdir(f\"{extract_path}\")\n", + "print(f\"Current path is: {os.getcwd()}\")" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "f98ce5f8-fb36-4e00-962f-028011801a53", + "id": "a265a0d9", "metadata": {}, "source": [ - "## General Introduction\n", - "This tutorial will introduce you to the basic usage of the DeePMD-kit, taking a gas phase methane molecule as an example. [DeePMD-kit's documentation](../index.rst) is recommended as the complete reference.\n", - "\n", - "The DP model is generated using the DeePMD-kit package (v2.1.5). The training data is converted into the format of DeePMD-kit using a tool named dpdata (v0.2.14). \n", - "\n", - "Details of dpdata can be found in [the dpdata documentation](https://docs.deepmodeling.com/projects/dpdata). \n", - "\n", - "We've prepared initial data for $CH_4$ for you, and put them in the folder `colombo-academy-tutorials/DeePMD-kit/00.data`" + "Let's take a look at the downloaded DeePMD-kit_Tutorial folder." ] }, { "cell_type": "code", - "execution_count": 2, - "id": "cfbbb9cc-2d7a-40f6-b384-46c698b77162", + "execution_count": 12, + "id": "2ea12f86", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[01;34mDeePMD-kit_Tutorial\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34m00.data\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34m01.train\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34m01.train.finished\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34m02.lmp\u001b[0m\n", + "└── \u001b[01;34m02.lmp.finished\u001b[0m\n", + "\n", + "5 directories, 0 files\n" + ] + } + ], "source": [ - "import os\n", - "\n", - "prefix_path = os.getcwd()" + "! tree DeePMD-kit_Tutorial -L 1" ] }, { "cell_type": "markdown", - "id": "209c5dd7-983a-468e-9406-652ade04be91", + "id": "1da4b858", "metadata": {}, "source": [ - "Folder `abacus_md` is obtained by performing ab-initio molecular dynamics with ABACUS. Detailed instructions on ABACUS can be found in its [document](https://abacus.deepmodeling.com/). " + "There are 3 subfolders under the DeePMD-kit_Tutorial folder: 00.data, 01.train, and 02.lmp.\n", + "\n", + "- The 00.data folder is used to store training and testing data.\n", + "- The 01.train folder contains example scripts for training models using DeePMD-kit.\n", + "- The 01.train.finished folder includes the complete results of the training process.\n", + "- The 02.lmp folder contains example scripts for molecular dynamics simulations using LAMMPS.\n", + "\n", + "Let's first take a look at the DeePMD-kit_Tutorial/00.data folder." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "533a4436-803b-45dd-91a3-2813d118df18", + "execution_count": 13, + "id": "509f7efe", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "['C_ONCV_PBE-1.2.upf',\n", - " 'C_gga_6au_100Ry_2s2p1d.orb',\n", - " 'H_ONCV_PBE-1.2.upf',\n", - " 'H_gga_6au_100Ry_2s1p.orb',\n", - " 'INPUT',\n", - " 'KPT',\n", - " 'OUT.ABACUS',\n", - " 'STRU']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[01;34mDeePMD-kit_Tutorial/00.data\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34mabacus_md\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34mtraining_data\u001b[0m\n", + "└── \u001b[01;34mvalidation_data\u001b[0m\n", + "\n", + "3 directories, 0 files\n" + ] } ], "source": [ - "os.chdir(\n", - " os.path.join(prefix_path, \"colombo-academy-tutorials\", \"DeePMD-kit\", \"00.data\")\n", - ")\n", - "os.listdir(\"abacus_md\")" + "! tree DeePMD-kit_Tutorial/00.data -L 1" ] }, { "cell_type": "markdown", - "id": "f630966f-379f-4140-8ce9-4df19f7c1cc6", + "id": "8373a3a4", "metadata": {}, "source": [ - "## Data preparation\n", + "DeePMD-kit's training data originates from first-principles calculation data, including atomic types, simulation cells, atomic coordinates, atomic forces, system energies, and virials.\n", "\n", - "The training data utilized by DeePMD-kit comprises essential information such as atom type, simulation box, atom coordinate, atom force, system energy, and virial. A snapshot of a molecular system that includes this data is called a `frame`. Multiple frames with the same number of atoms and atom types make up a `system` of data. For instance, a molecular dynamics trajectory can be converted into a system of data, with each time step corresponding to a frame in the system.\n", + "
\"image-20230116161737203\"
\n", "\n", - "To simplify the process of converting data generated by popular simulation software like CP2K, Gaussian, Quantum-Espresso, ABACUS, and LAMMPS into the compressed format of DeePMD-kit, we offer a convenient tool called `dpdata`.\n" + "In the *00.data* folder, there is only the *abacus_md* folder, which contains data obtained through *ab initio* Molecular Dynamics (AIMD) simulations using ABACUS. In this tutorial, we have already completed the *ab initio* molecular dynamics calculations for the methane molecule for you.\n", + "\n", + "Detailed information about ABACUS can be found in its [documentation](https://abacus.deepmodeling.com/en/latest/). \n", + "\n", + "DeePMD-kit uses a compressed data format. All training data should first be converted into this format before they can be used in DeePMD-kit. This data format is explained in detail in the DeePMD-kit manual, which can be found on [DeePMD-kit's Github](http://www.github.com/deepmodeling/deepmd-kit).\n", + "\n", + "We provide a convenient tool **dpdata**, which can convert data generated by VASP, CP2K, Gaussian, Quantum Espresso, ABACUS, and LAMMPS into DeePMD-kit's compressed format.\n", + "\n", + "A snapshot of a molecular system that contains computational data information is called a frame. A data system comprises many frames sharing the same number of atoms and atom types.\n", + "\n", + "For example, a molecular dynamics trajectory can be converted into a data system, where each timestep corresponds to one frame in the system.\n" ] }, { "cell_type": "markdown", - "id": "f971d1d5-a90d-499f-92f2-b3c31f3e70ab", + "id": "e62f0115", "metadata": {}, "source": [ - "Next, the data from AIMD is splited randomly as training and validation data." + "Next, we use the dpdata tool to randomly split the data in abacus_md into training and validation data.\n" ] }, { "cell_type": "code", "execution_count": 4, - "id": "647bf5c0-4df1-4510-8444-51774295adc7", + "id": "b5fbc838", "metadata": {}, "outputs": [ { @@ -203,7 +277,7 @@ "import numpy as np\n", "\n", "# load data of abacus/md format\n", - "data = dpdata.LabeledSystem(\"abacus_md\", fmt=\"abacus/md\")\n", + "data = dpdata.LabeledSystem(\"DeePMD-kit_Tutorial/00.data/abacus_md\", fmt=\"abacus/md\")\n", "print(\"# the data contains %d frames\" % len(data))\n", "\n", "# random choose 40 index for validation_data\n", @@ -216,10 +290,10 @@ "data_validation = data.sub_system(index_validation)\n", "\n", "# all training data put into directory:\"training_data\"\n", - "data_training.to_deepmd_npy(\"training_data\")\n", + "data_training.to_deepmd_npy(\"DeePMD-kit_Tutorial/00.data/training_data\")\n", "\n", "# all validation data put into directory:\"validation_data\"\n", - "data_validation.to_deepmd_npy(\"validation_data\")\n", + "data_validation.to_deepmd_npy(\"DeePMD-kit_Tutorial/00.data/validation_data\")\n", "\n", "print(\"# the training data contains %d frames\" % len(data_training))\n", "print(\"# the validation data contains %d frames\" % len(data_validation))" @@ -230,44 +304,69 @@ "id": "98719865-ab3d-4440-8c14-78a1c253c3a6", "metadata": {}, "source": [ - "As you can see, 161 frames are picked as training data, and the other 40 frames are validation dat." + "As you can see, 161 frames are picked as training data, and the other 40 frames are validation dat.\n", + "\n", + "Let's take another look at the 00.data folder, where new files have been generated, which are the training and validation sets required for Deep Potential training with DeePMD-kit.\n" ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "a999f41b-e343-4dc2-8499-84fee6e52221", + "cell_type": "code", + "execution_count": 15, + "id": "e5befaf5-c464-4e8f-8544-a2634f5fd1d2", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[01;34mDeePMD-kit_Tutorial/00.data/\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34mabacus_md\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34mtraining_data\u001b[0m\n", + "└── \u001b[01;34mvalidation_data\u001b[0m\n", + "\n", + "3 directories, 0 files\n" + ] + } + ], "source": [ - "The DeePMD-kit adopts a compressed data format. All training data should first be converted into this format and can then be used by DeePMD-kit. The data format is explained in detail in the DeePMD-kit manual that can be found in [the DeePMD-kit Data Introduction](../data/system.md)." + "! tree DeePMD-kit_Tutorial/00.data/ -L 1" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "e5befaf5-c464-4e8f-8544-a2634f5fd1d2", + "execution_count": 16, + "id": "70cc9898", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[01;34mtraining_data\u001b[00m\n", - "β”œβ”€β”€ \u001b[01;34mset.000\u001b[00m\n", - "β”‚Β Β  β”œβ”€β”€ box.npy\n", - "β”‚Β Β  β”œβ”€β”€ coord.npy\n", - "β”‚Β Β  β”œβ”€β”€ energy.npy\n", - "β”‚Β Β  β”œβ”€β”€ force.npy\n", - "β”‚Β Β  └── virial.npy\n", - "β”œβ”€β”€ type.raw\n", - "└── type_map.raw\n", + "\u001b[01;34mDeePMD-kit_Tutorial/00.data/training_data\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;34mset.000\u001b[0m\n", + "β”œβ”€β”€ \u001b[00mtype.raw\u001b[0m\n", + "└── \u001b[00mtype_map.raw\u001b[0m\n", "\n", - "1 directory, 7 files\n" + "1 directory, 2 files\n" ] } ], "source": [ - "! tree training_data" + "! tree DeePMD-kit_Tutorial/00.data/training_data -L 1" + ] + }, + { + "cell_type": "markdown", + "id": "542b24fd", + "metadata": {}, + "source": [ + "The functions of these files are as follows:\n", + "\n", + "- set.000: It is a directory that contains compressed format data (NumPy compressed arrays).\n", + "- type.raw: It is a file that contains the types of atoms (represented as integers).\n", + "- type_map.raw: It is a file that contains the names of the types of atoms.\n", + "\n", + "Let's take a look at these files." ] }, { @@ -280,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 13, "id": "a686585c-3e5b-4a5c-9cc7-4db7759b74b2", "metadata": {}, "outputs": [ @@ -297,7 +396,7 @@ } ], "source": [ - "! cat training_data/type.raw" + "! cat DeePMD-kit_Tutorial/00.data/training_data/type.raw " ] }, { @@ -311,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 14, "id": "6216f02d-3a21-481f-99da-3f23b438a7c0", "metadata": {}, "outputs": [ @@ -325,7 +424,7 @@ } ], "source": [ - "! cat training_data/type_map.raw" + "! cat DeePMD-kit_Tutorial/00.data/training_data/type_map.raw" ] }, { @@ -335,7 +434,7 @@ "source": [ "This tells us the type \"0\" is named by \"H\", and the type \"1\" is named by \"C\".\n", "\n", - "More detailed doc about Data conversion can be found [here](../data/data-conv.md)." + "More detailed documentation on using dpdata for data conversion can be found [here](../data/data-conv.html)" ] }, { @@ -343,20 +442,104 @@ "id": "ac6c969b-10cb-49f0-9b84-7dc9ffa38c61", "metadata": {}, "source": [ - "## Prepare input script \n", - "Once the data preparation is done, we can go on with training. Now go to the training directory" + "### Prepare input script\n", + "Once the data preparation is done, we can go on with training. Now go to the training directory.\n", + "DeePMD-kit requires a `json` format file to specify parameters for training. " ] }, { "cell_type": "code", - "execution_count": 8, - "id": "f0b2fa6b-81c2-41ec-b29e-37ff1f6e3b31", + "execution_count": null, + "id": "749c24f4", "metadata": {}, "outputs": [], "source": [ - "os.chdir(\n", - " os.path.join(prefix_path, \"colombo-academy-tutorials\", \"DeePMD-kit\", \"01.train\")\n", - ")" + "# Checke dargs version and Install\n", + "!pip show dargs || pip install --upgrade dargs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "995c0b91", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
{
  \"_comment\": \"that's all\",
  \"model\"model:
type: dict: {
    \"type_map\"type_map:
type: typing.List[str], optional
A list of strings. Give the name to each type of atoms. It is noted that the number of atom type of training system must be less than 128 in a GPU environment. If not given, type.raw in each system should use the same type indexes, and type_map.raw will take no effect.: [
     \"H\",
     \"C\"
    ],
    \"descriptor\"descriptor:
type: dict
The descriptor of atomic environment.: {
      \"type\"type:
type: str
The type of the descritpor. See explanation below.
- loc_frame: Defines a local frame at each atom, and the compute the descriptor as local coordinates under this frame.
- se_e2_a: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor.
- se_e2_r: Used by the smooth edition of Deep Potential. Only the distance between atoms is used to construct the descriptor.
- se_e3: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Three-body embedding will be used by this descriptor.
- se_a_tpe: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Type embedding will be used by this descriptor.
- se_atten: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism will be used by this descriptor.
- se_atten_v2: Used by the smooth edition of Deep Potential. The full relative coordinates are used to construct the descriptor. Attention mechanism with new modifications will be used by this descriptor.
- se_a_mask: Used by the smooth edition of Deep Potential. It can accept a variable number of atoms in a frame (Non-PBC system). aparam are required as an indicator matrix for the real/virtual sign of input atoms.
- hybrid: Concatenate of a list of descriptors as a new descriptor.: \"se_e2_a\",
      \"sel\"sel:
type: str | typing.List[int], optional, default: auto
This parameter set the number of selected neighbors for each type of atom. It can be:
- List[int]. The length of the list should be the same as the number of atom types in the system. sel[i] gives the selected number of type-i neighbors. sel[i] is recommended to be larger than the maximally possible number of type-i neighbors in the cut-off radius. It is noted that the total sel value must be less than 4096 in a GPU environment.
- str. Can be \"auto:factor\" or \"auto\". \"factor\" is a float number larger than 1. This option will automatically determine the sel. In detail it counts the maximal number of neighbors with in the cutoff radius for each type of neighbor, then multiply the maximum by the \"factor\". Finally the number is wraped up to 4 divisible. The option \"auto\" is equivalent to \"auto:1.1\".: \"auto\",
      \"rcut_smth\"rcut_smth:
type: float, optional, default: 0.5
Where to start smoothing. For example the 1/r term is smoothed from rcut to rcut_smth: 0.5,
      \"rcut\"rcut:
type: float, optional, default: 6.0
The cut-off radius.: 6.0,
      \"neuron\"neuron:
type: typing.List[int], optional, default: [10, 20, 40]
Number of neurons in each hidden layers of the embedding net. When two layers are of the same size or one layer is twice as large as the previous layer, a skip connection is built.: [
       25,
       50,
       100
      ],
      \"resnet_dt\"resnet_dt:
type: bool, optional, default: False
Whether to use a \"Timestep\" in the skip connection: false,
      \"axis_neuron\"axis_neuron:
type: int, optional, default: 4, alias: n_axis_neuron
Size of the submatrix of G (embedding matrix).: 16,
      \"seed\"seed:
type: NoneType | int, optional
Random seed for parameter initialization: 1,
      \"_comment\": \" that's all\"
    },
    \"fitting_net\"fitting_net:
type: dict
The fitting of physical properties.: {
      \"neuron\"neuron:
type: typing.List[int], optional, default: [120, 120, 120], alias: n_neuron
The number of neurons in each hidden layers of the fitting net. When two hidden layers are of the same size, a skip connection is built.: [
       240,
       240,
       240
      ],
      \"resnet_dt\"resnet_dt:
type: bool, optional, default: True
Whether to use a \"Timestep\" in the skip connection: true,
      \"seed\"seed:
type: NoneType | int, optional
Random seed for parameter initialization of the fitting net: 1,
      \"_comment\": \" that's all\"
    },
    \"_comment\": \" that's all\"
  },
  \"learning_rate\"learning_rate:
type: dict, optional
The definitio of learning rate: {
    \"type\"type:
type: str, default: exp
The type of the learning rate.: \"exp\",
    \"decay_steps\"decay_steps:
type: int, optional, default: 5000
The learning rate is decaying every this number of training steps.: 50,
    \"start_lr\"start_lr:
type: float, optional, default: 0.001
The learning rate at the start of the training.: 0.001,
    \"stop_lr\"stop_lr:
type: float, optional, default: 1e-08
The desired learning rate at the end of the training.: 3.51e-08,
    \"_comment\": \"that's all\"
  },
  \"loss\"loss:
type: dict, optional
The definition of loss function. The loss type should be set to tensor, ener or left unset.: {
    \"type\"type:
type: str, default: ener
The type of the loss. When the fitting type is ener, the loss type should be set to ener or left unset. When the fitting type is dipole or polar, the loss type should be set to tensor.: \"ener\",
    \"start_pref_e\"start_pref_e:
type: float | int, optional, default: 0.02
The prefactor of energy loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the energy label should be provided by file energy.npy in each data system. If both start_pref_e and limit_pref_e are set to 0, then the energy will be ignored.: 0.02,
    \"limit_pref_e\"limit_pref_e:
type: float | int, optional, default: 1.0
The prefactor of energy loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.: 1,
    \"start_pref_f\"start_pref_f:
type: float | int, optional, default: 1000
The prefactor of force loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the force label should be provided by file force.npy in each data system. If both start_pref_f and limit_pref_f are set to 0, then the force will be ignored.: 1000,
    \"limit_pref_f\"limit_pref_f:
type: float | int, optional, default: 1.0
The prefactor of force loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.: 1,
    \"start_pref_v\"start_pref_v:
type: float | int, optional, default: 0.0
The prefactor of virial loss at the start of the training. Should be larger than or equal to 0. If set to none-zero value, the virial label should be provided by file virial.npy in each data system. If both start_pref_v and limit_pref_v are set to 0, then the virial will be ignored.: 0,
    \"limit_pref_v\"limit_pref_v:
type: float | int, optional, default: 0.0
The prefactor of virial loss at the limit of the training, Should be larger than or equal to 0. i.e. the training step goes to infinity.: 0,
    \"_comment\": \" that's all\"
  },
  \"training\"training:
type: dict
The training options.: {
    \"training_data\"training_data:
type: dict, optional
Configurations of training data.: {
      \"systems\"systems:
type: str | typing.List[str]
The data systems for training. This key can be provided with a list that specifies the systems, or be provided with a string by which the prefix of all systems are given and the list of the systems is automatically generated.: [
       \"../00.data/training_data\"
      ],
      \"batch_size\"batch_size:
type: str | typing.List[int] | int, optional, default: auto
This key can be
- list: the length of which is the same as the systems _. The batch size of each system is given by the elements of the list.
- int: all systems _ use the same batch size.
- string \"auto\": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.
- string \"auto:N\": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.
- string \"mixed:N\": the batch data will be sampled from all systems and merged into a mixed system with the batch size N. Only support the se_atten descriptor.
If MPI is used, the value should be considered as the batch size per task.: \"auto\",
      \"_comment\": \"that's all\"
    },
    \"validation_data\"validation_data:
type: NoneType | dict, optional, default: None
Configurations of validation data. Similar to that of training data, except that a numb_btch argument may be configured: {
      \"systems\"systems:
type: str | typing.List[str]
The data systems for validation. This key can be provided with a list that specifies the systems, or be provided with a string by which the prefix of all systems are given and the list of the systems is automatically generated.: [
       \"../00.data/validation_data\"
      ],
      \"batch_size\"batch_size:
type: str | typing.List[int] | int, optional, default: auto
This key can be
- list: the length of which is the same as the systems _. The batch size of each system is given by the elements of the list.
- int: all systems _ use the same batch size.
- string \"auto\": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.
- string \"auto:N\": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.: \"auto\",
      \"numb_btch\"numb_btch:
type: int, optional, default: 1, alias: numb_batch
An integer that specifies the number of batches to be sampled for each validation period.: 1,
      \"_comment\": \"that's all\"
    },
    \"numb_steps\"numb_steps:
type: int, alias: stop_batch
Number of training batch. Each training uses one batch of data.: 10000,
    \"seed\"seed:
type: NoneType | int, optional
The random seed for getting frames from the training data set.: 10,
    \"disp_file\"disp_file:
type: str, optional, default: lcurve.out
The file for printing learning curve.: \"lcurve.out\",
    \"disp_freq\"disp_freq:
type: int, optional, default: 1000
The frequency of printing learning curve.: 200,
    \"save_freq\"save_freq:
type: int, optional, default: 1000
The frequency of saving check point.: 1000,
    \"_comment\": \"that's all\"
  }
}
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show input.json\n", + "from deepmd.utils.argcheck import gen_args\n", + "from dargs.notebook import JSON\n", + "\n", + "with open(\"./DeePMD-kit_Tutorial/01.train/input.json\") as f:\n", + " JSON(f.read(), gen_args())" ] }, { @@ -455,7 +638,7 @@ "id": "7b0edb0f-df47-4e6c-8c37-5f32c4bd6b39", "metadata": {}, "source": [ - "More detailed docs about Data conversion can be found [here](../data/data-conv.md)." + "More detailed docs about Data conversion can be found [here](https://docs.deepmodeling.org/projects/deepmd/en/master/data/data-conv.html)" ] }, { @@ -463,13 +646,13 @@ "id": "bafe20b8-3bde-403c-ae42-b68ba5f29703", "metadata": {}, "source": [ - "## Train a model\n", + "### Train a model\n", "After the training script is prepared, we can start the training with DeePMD-kit by simply running" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "id": "e04a05a3-ccac-474b-bc24-58fbf20b9ffe", "metadata": { "scrolled": true, @@ -480,51 +663,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "non-resource variables are not supported in the long term\n", "WARNING:root:To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information.\n", - "WARNING:root:Environment variable KMP_BLOCKTIME is empty. Use the default value 0\n", - "WARNING:root:Environment variable KMP_AFFINITY is empty. Use the default value granularity=fine,verbose,compact,1,0\n", - "/opt/deepmd-kit-2.2.1/lib/python3.10/importlib/__init__.py:169: UserWarning: The NumPy module was reloaded (imported a second time). This can in some cases result in small but subtle issues and is discouraged.\n", - " _bootstrap._exec(spec, module)\n", "DEEPMD INFO Calculate neighbor statistics... (add --skip-neighbor-stat to skip this step)\n", - "2023-04-20 23:35:59.335932: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", - "2023-04-20 23:35:59.335979: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", - "OMP: Info #155: KMP_AFFINITY: Initial OS proc set respected: 0-7\n", - "OMP: Info #216: KMP_AFFINITY: decoding x2APIC ids.\n", - "OMP: Info #216: KMP_AFFINITY: cpuid leaf 11 not supported.\n", - "OMP: Info #216: KMP_AFFINITY: decoding legacy APIC ids.\n", - "OMP: Info #157: KMP_AFFINITY: 8 available OS procs\n", - "OMP: Info #158: KMP_AFFINITY: Uniform topology\n", - "OMP: Info #287: KMP_AFFINITY: topology layer \"LL cache\" is equivalent to \"socket\".\n", - "OMP: Info #192: KMP_AFFINITY: 1 socket x 4 cores/socket x 2 threads/core (4 total cores)\n", - "OMP: Info #218: KMP_AFFINITY: OS proc to physical thread map:\n", - "OMP: Info #172: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 6 maps to socket 0 core 3 thread 0 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 7 maps to socket 0 core 3 thread 1 \n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 140 thread 1 bound to OS proc set 2\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 142 thread 2 bound to OS proc set 4\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 144 thread 4 bound to OS proc set 1\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 143 thread 3 bound to OS proc set 6\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 145 thread 5 bound to OS proc set 3\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 146 thread 6 bound to OS proc set 5\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 147 thread 7 bound to OS proc set 7\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 148 thread 8 bound to OS proc set 0\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 139 thread 9 bound to OS proc set 2\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 149 thread 10 bound to OS proc set 4\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 150 thread 11 bound to OS proc set 6\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 151 thread 12 bound to OS proc set 1\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 152 thread 13 bound to OS proc set 3\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 153 thread 14 bound to OS proc set 5\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 154 thread 15 bound to OS proc set 7\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 155 thread 16 bound to OS proc set 0\n", - "DEEPMD INFO training data with min nbor dist: 1.045920568611028\n", + "DEEPMD INFO training data with min nbor dist: 1.0460506586976848\n", "DEEPMD INFO training data with max nbor size: [4 1]\n", "DEEPMD INFO _____ _____ __ __ _____ _ _ _ \n", "DEEPMD INFO | __ \\ | __ \\ | \\/ || __ \\ | | (_)| | \n", @@ -534,19 +678,20 @@ "DEEPMD INFO |_____/ \\___| \\___||_| |_| |_||_____/ |_|\\_\\|_| \\__|\n", "DEEPMD INFO Please read and cite:\n", "DEEPMD INFO Wang, Zhang, Han and E, Comput.Phys.Comm. 228, 178-184 (2018)\n", - "DEEPMD INFO installed to: /home/conda/feedstock_root/build_artifacts/deepmd-kit_1678943793317/work/_skbuild/linux-x86_64-3.10/cmake-install\n", - "DEEPMD INFO source : v2.2.1\n", + "DEEPMD INFO Zeng et al, J. Chem. Phys., 159, 054801 (2023)\n", + "DEEPMD INFO See https://deepmd.rtfd.io/credits/ for details.\n", + "DEEPMD INFO installed to: /root/miniconda3/envs/deepmd\n", + "DEEPMD INFO source : v2.2.7\n", "DEEPMD INFO source brach: HEAD\n", - "DEEPMD INFO source commit: 3ac8c4c7\n", - "DEEPMD INFO source commit at: 2023-03-16 12:33:24 +0800\n", + "DEEPMD INFO source commit: 839f4fe7\n", + "DEEPMD INFO source commit at: 2023-10-27 21:10:24 +0800\n", "DEEPMD INFO build float prec: double\n", - "DEEPMD INFO build variant: cuda\n", - "DEEPMD INFO build with tf inc: /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/include;/opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/../../../../include\n", + "DEEPMD INFO build variant: cpu\n", + "DEEPMD INFO build with tf inc: /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/include;/root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/../../../../include\n", "DEEPMD INFO build with tf lib: \n", "DEEPMD INFO ---Summary of the training---------------------------------------\n", - "DEEPMD INFO running on: bohrium-14076-1013950\n", + "DEEPMD INFO running on: bohrium-21213-1088639\n", "DEEPMD INFO computing device: cpu:0\n", - "DEEPMD INFO CUDA_VISIBLE_DEVICES: unset\n", "DEEPMD INFO Count of visible GPU: 0\n", "DEEPMD INFO num_intra_threads: 0\n", "DEEPMD INFO num_inter_threads: 0\n", @@ -563,87 +708,113 @@ "DEEPMD INFO --------------------------------------------------------------------------------------\n", "DEEPMD INFO training without frame parameter\n", "DEEPMD INFO data stating... (this step may take long time)\n", - "OMP: Info #254: KMP_AFFINITY: pid 118 tid 118 thread 0 bound to OS proc set 0\n", "DEEPMD INFO built lr\n", "DEEPMD INFO built network\n", "DEEPMD INFO built training\n", "WARNING:root:To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information.\n", "DEEPMD INFO initialize model from scratch\n", "DEEPMD INFO start training at lr 1.00e-03 (== 1.00e-03), decay_step 50, decay_rate 0.950006, final lr will be 3.51e-08\n", - "DEEPMD INFO batch 200 training time 6.10 s, testing time 0.02 s\n", - "DEEPMD INFO batch 400 training time 4.83 s, testing time 0.02 s\n", - "DEEPMD INFO batch 600 training time 4.84 s, testing time 0.02 s\n", - "DEEPMD INFO batch 800 training time 4.85 s, testing time 0.02 s\n", - "DEEPMD INFO batch 1000 training time 4.85 s, testing time 0.02 s\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/train/trainer.py:1197: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "tf.py_func is deprecated in TF V2. Instead, there are two\n", + " options available in V2.\n", + " - tf.py_function takes a python function which manipulates tf eager\n", + " tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n", + " an ndarray (just call tensor.numpy()) but having access to eager tensors\n", + " means `tf.py_function`s can use accelerators such as GPUs as well as\n", + " being differentiable using a gradient tape.\n", + " - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n", + " (it is not differentiable, and manipulates numpy arrays). It drops the\n", + " stateful argument making all functions stateful.\n", + " \n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/train/trainer.py:1197: py_func (from tensorflow.python.ops.script_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "tf.py_func is deprecated in TF V2. Instead, there are two\n", + " options available in V2.\n", + " - tf.py_function takes a python function which manipulates tf eager\n", + " tensors instead of numpy arrays. It's easy to convert a tf eager tensor to\n", + " an ndarray (just call tensor.numpy()) but having access to eager tensors\n", + " means `tf.py_function`s can use accelerators such as GPUs as well as\n", + " being differentiable using a gradient tape.\n", + " - tf.numpy_function maintains the semantics of the deprecated tf.py_func\n", + " (it is not differentiable, and manipulates numpy arrays). It drops the\n", + " stateful argument making all functions stateful.\n", + " \n", + "DEEPMD INFO batch 200 training time 17.53 s, testing time 0.05 s, total wall time 18.41 s\n", + "DEEPMD INFO batch 400 training time 14.96 s, testing time 0.05 s, total wall time 15.11 s\n", + "DEEPMD INFO batch 600 training time 15.47 s, testing time 0.05 s, total wall time 15.65 s\n", + "DEEPMD INFO batch 800 training time 14.25 s, testing time 0.04 s, total wall time 14.41 s\n", + "DEEPMD INFO batch 1000 training time 15.49 s, testing time 0.05 s, total wall time 15.65 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 1200 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 1400 training time 5.39 s, testing time 0.02 s\n", - "DEEPMD INFO batch 1600 training time 4.84 s, testing time 0.02 s\n", - "DEEPMD INFO batch 1800 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 2000 training time 4.84 s, testing time 0.02 s\n", + "DEEPMD INFO batch 1200 training time 16.33 s, testing time 0.08 s, total wall time 17.33 s\n", + "DEEPMD INFO batch 1400 training time 14.31 s, testing time 0.05 s, total wall time 14.47 s\n", + "DEEPMD INFO batch 1600 training time 16.54 s, testing time 0.05 s, total wall time 16.72 s\n", + "DEEPMD INFO batch 1800 training time 16.90 s, testing time 0.09 s, total wall time 17.09 s\n", + "DEEPMD INFO batch 2000 training time 17.20 s, testing time 0.06 s, total wall time 17.37 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 2200 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 2400 training time 4.90 s, testing time 0.02 s\n", - "DEEPMD INFO batch 2600 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 2800 training time 4.84 s, testing time 0.02 s\n", - "DEEPMD INFO batch 3000 training time 4.86 s, testing time 0.02 s\n", + "DEEPMD INFO batch 2200 training time 14.29 s, testing time 0.04 s, total wall time 14.83 s\n", + "DEEPMD INFO batch 2400 training time 13.11 s, testing time 0.04 s, total wall time 13.29 s\n", + "DEEPMD INFO batch 2600 training time 12.93 s, testing time 0.04 s, total wall time 13.08 s\n", + "DEEPMD INFO batch 2800 training time 14.58 s, testing time 0.04 s, total wall time 14.74 s\n", + "DEEPMD INFO batch 3000 training time 13.21 s, testing time 0.04 s, total wall time 13.35 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 3200 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 3400 training time 4.99 s, testing time 0.02 s\n", - "DEEPMD INFO batch 3600 training time 4.88 s, testing time 0.02 s\n", - "DEEPMD INFO batch 3800 training time 4.85 s, testing time 0.02 s\n", - "DEEPMD INFO batch 4000 training time 4.88 s, testing time 0.02 s\n", + "DEEPMD INFO batch 3200 training time 14.40 s, testing time 0.07 s, total wall time 15.14 s\n", + "DEEPMD INFO batch 3400 training time 13.08 s, testing time 0.04 s, total wall time 13.23 s\n", + "DEEPMD INFO batch 3600 training time 12.93 s, testing time 0.06 s, total wall time 13.13 s\n", + "DEEPMD INFO batch 3800 training time 15.23 s, testing time 0.05 s, total wall time 15.43 s\n", + "DEEPMD INFO batch 4000 training time 13.20 s, testing time 0.04 s, total wall time 13.35 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 4200 training time 4.88 s, testing time 0.02 s\n", - "DEEPMD INFO batch 4400 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 4600 training time 4.92 s, testing time 0.02 s\n", - "DEEPMD INFO batch 4800 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 5000 training time 4.86 s, testing time 0.02 s\n", + "DEEPMD INFO batch 4200 training time 14.82 s, testing time 0.05 s, total wall time 16.06 s\n", + "DEEPMD INFO batch 4400 training time 14.26 s, testing time 0.05 s, total wall time 14.42 s\n", + "DEEPMD INFO batch 4600 training time 15.50 s, testing time 0.05 s, total wall time 15.66 s\n", + "DEEPMD INFO batch 4800 training time 14.12 s, testing time 0.05 s, total wall time 14.29 s\n", + "DEEPMD INFO batch 5000 training time 15.71 s, testing time 0.05 s, total wall time 15.88 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 5200 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 5400 training time 4.88 s, testing time 0.02 s\n", - "DEEPMD INFO batch 5600 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 5800 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 6000 training time 4.90 s, testing time 0.02 s\n", - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/training/saver.py:1066: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n", + "DEEPMD INFO batch 5200 training time 14.36 s, testing time 0.07 s, total wall time 15.40 s\n", + "DEEPMD INFO batch 5400 training time 15.77 s, testing time 0.05 s, total wall time 15.93 s\n", + "DEEPMD INFO batch 5600 training time 14.12 s, testing time 0.05 s, total wall time 14.29 s\n", + "DEEPMD INFO batch 5800 training time 15.53 s, testing time 0.04 s, total wall time 15.70 s\n", + "DEEPMD INFO batch 6000 training time 15.39 s, testing time 0.09 s, total wall time 15.58 s\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/training/saver.py:1066: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use standard file APIs to delete files with this prefix.\n", - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/training/saver.py:1066: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/training/saver.py:1066: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use standard file APIs to delete files with this prefix.\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 6200 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 6400 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 6600 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 6800 training time 4.84 s, testing time 0.02 s\n", - "DEEPMD INFO batch 7000 training time 4.93 s, testing time 0.02 s\n", + "DEEPMD INFO batch 6200 training time 14.74 s, testing time 0.05 s, total wall time 15.64 s\n", + "DEEPMD INFO batch 6400 training time 15.24 s, testing time 0.09 s, total wall time 15.44 s\n", + "DEEPMD INFO batch 6600 training time 14.29 s, testing time 0.05 s, total wall time 14.48 s\n", + "DEEPMD INFO batch 6800 training time 15.46 s, testing time 0.09 s, total wall time 15.66 s\n", + "DEEPMD INFO batch 7000 training time 15.34 s, testing time 0.05 s, total wall time 15.54 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 7200 training time 4.89 s, testing time 0.02 s\n", - "DEEPMD INFO batch 7400 training time 4.88 s, testing time 0.02 s\n", - "DEEPMD INFO batch 7600 training time 4.88 s, testing time 0.02 s\n", - "DEEPMD INFO batch 7800 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 8000 training time 4.86 s, testing time 0.02 s\n", + "DEEPMD INFO batch 7200 training time 15.63 s, testing time 0.05 s, total wall time 16.19 s\n", + "DEEPMD INFO batch 7400 training time 14.71 s, testing time 0.06 s, total wall time 14.90 s\n", + "DEEPMD INFO batch 7600 training time 15.96 s, testing time 0.05 s, total wall time 16.12 s\n", + "DEEPMD INFO batch 7800 training time 19.68 s, testing time 0.06 s, total wall time 19.92 s\n", + "DEEPMD INFO batch 8000 training time 15.81 s, testing time 0.07 s, total wall time 16.00 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 8200 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 8400 training time 4.85 s, testing time 0.02 s\n", - "DEEPMD INFO batch 8600 training time 4.86 s, testing time 0.02 s\n", - "DEEPMD INFO batch 8800 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 9000 training time 4.83 s, testing time 0.02 s\n", + "DEEPMD INFO batch 8200 training time 13.62 s, testing time 0.04 s, total wall time 14.54 s\n", + "DEEPMD INFO batch 8400 training time 13.23 s, testing time 0.04 s, total wall time 13.38 s\n", + "DEEPMD INFO batch 8600 training time 14.90 s, testing time 0.04 s, total wall time 15.08 s\n", + "DEEPMD INFO batch 8800 training time 13.19 s, testing time 0.04 s, total wall time 13.34 s\n", + "DEEPMD INFO batch 9000 training time 13.78 s, testing time 0.09 s, total wall time 14.00 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO batch 9200 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 9400 training time 4.95 s, testing time 0.02 s\n", - "DEEPMD INFO batch 9600 training time 4.89 s, testing time 0.02 s\n", - "DEEPMD INFO batch 9800 training time 4.87 s, testing time 0.02 s\n", - "DEEPMD INFO batch 10000 training time 4.88 s, testing time 0.02 s\n", + "DEEPMD INFO batch 9200 training time 13.76 s, testing time 0.04 s, total wall time 14.41 s\n", + "DEEPMD INFO batch 9400 training time 13.06 s, testing time 0.04 s, total wall time 13.20 s\n", + "DEEPMD INFO batch 9600 training time 14.23 s, testing time 0.04 s, total wall time 14.42 s\n", + "DEEPMD INFO batch 9800 training time 13.72 s, testing time 0.05 s, total wall time 13.88 s\n", + "DEEPMD INFO batch 10000 training time 13.92 s, testing time 0.09 s, total wall time 14.12 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", - "DEEPMD INFO average training time: 0.0244 s/batch (exclude first 200 batches)\n", + "DEEPMD INFO average training time: 0.0737 s/batch (exclude first 200 batches)\n", "DEEPMD INFO finished training\n", - "DEEPMD INFO wall time: 256.669 s\n" + "DEEPMD INFO wall time: 756.650 s\n" ] } ], "source": [ - "! dp train input.json" + "# ########## Time Warning: 120 secs,C32_CPU ; 13 mins ,C2_CPU ##########\n", + "! cd DeePMD-kit_Tutorial/01.train/ && dp train input.json" ] }, { @@ -654,6 +825,7 @@ }, "source": [ "On the screen, you will see the information of the data system(s)\n", + "\n", "```\n", "DEEPMD INFO -----------------------------------------------------------------\n", "DEEPMD INFO ---Summary of DataSystem: training ----------------------------------\n", @@ -667,11 +839,15 @@ "DEEPMD INFO ../00.data/validation_data 5 7 5 1.000 T\n", "DEEPMD INFO -------------------------------------------------------------------------\n", "```\n", + "\n", "and the starting and final learning rate of this training\n", + "\n", "```\n", "DEEPMD INFO start training at lr 1.00e-03 (== 1.00e-03), decay_step 50, decay_rate 0.950006, final lr will be 3.51e-08\n", "```\n", + "\n", "If everything works fine, you will see, on the screen, information printed every 1000 steps, like\n", + "\n", "```\n", "DEEPMD INFO batch 200 training time 6.04 s, testing time 0.02 s\n", "DEEPMD INFO batch 400 training time 4.80 s, testing time 0.02 s\n", @@ -686,6 +862,7 @@ "DEEPMD INFO batch 2000 training time 4.46 s, testing time 0.02 s\n", "DEEPMD INFO saved checkpoint model.ckpt\n", "```\n", + "\n", "They present the training and testing time counts. At the end of the 1000th batch, the model is saved in Tensorflow's checkpoint file `model.ckpt`. At the same time, the training and testing errors are presented in file `lcurve.out`. \n", "\n", "The file contains 8 columns, form left to right, are the training step, the validation loss, training loss, root mean square (RMS) validation error of energy, RMS training error of energy, RMS validation error of force, RMS training error of force and the learning rate. The RMS error (RMSE) of the energy is normalized by number of atoms in the system. \n", @@ -705,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "id": "4aabcec8-9058-4582-863d-00550bb05187", "metadata": {}, "outputs": [ @@ -714,14 +891,14 @@ "output_type": "stream", "text": [ "# step rmse_val rmse_trn rmse_e_val rmse_e_trn rmse_f_val rmse_f_trn lr\n", - " 0 2.06e+01 1.94e+01 1.34e-01 1.35e-01 6.51e-01 6.14e-01 1.0e-03\n", - " 9800 5.49e-02 4.00e-02 7.55e-04 7.28e-04 5.37e-02 3.91e-02 4.3e-08\n", - " 10000 6.56e-02 6.37e-02 1.13e-03 1.54e-03 6.44e-02 6.25e-02 3.5e-08\n" + " 0 1.79e+01 2.26e+01 1.35e-01 1.33e-01 5.67e-01 7.15e-01 1.0e-03\n", + " 9800 3.53e-02 2.64e-02 5.75e-04 3.01e-04 3.46e-02 2.59e-02 4.3e-08\n", + " 10000 2.76e-02 2.25e-02 4.83e-04 1.62e-04 2.71e-02 2.21e-02 3.5e-08\n" ] } ], "source": [ - "! head -n 2 lcurve.out && tail -n 2 lcurve.out" + "! cd DeePMD-kit_Tutorial/01.train.finished/ && head -n 2 lcurve.out && tail -n 2 lcurve.out" ] }, { @@ -734,13 +911,13 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "id": "a7444fb4-a869-4b44-a1e3-2d74679189ed", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -754,9 +931,11 @@ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", - "with open(\"lcurve.out\") as f:\n", + "with open(\"./DeePMD-kit_Tutorial/01.train.finished/lcurve.out\") as f:\n", " headers = f.readline().split()[1:]\n", - "lcurve = pd.DataFrame(np.loadtxt(\"lcurve.out\"), columns=headers)\n", + "lcurve = pd.DataFrame(\n", + " np.loadtxt(\"./DeePMD-kit_Tutorial/01.train.finished/lcurve.out\"), columns=headers\n", + ")\n", "legends = [\"rmse_e_val\", \"rmse_e_trn\", \"rmse_f_val\", \"rmse_f_trn\"]\n", "for legend in legends:\n", " plt.loglog(lcurve[\"step\"], lcurve[legend], label=legend)\n", @@ -771,20 +950,14 @@ "id": "8009ad0a-9902-42d3-b280-caee92cbdf10", "metadata": {}, "source": [ - "## Freeze a model" - ] - }, - { - "cell_type": "markdown", - "id": "36792467-3a1b-4ee9-8cea-512f270071f4", - "metadata": {}, - "source": [ - "At the end of the training, the model parameters saved in TensorFlow's checkpoint file should be frozen as a model file that is usually ended with extension `.pb`. Simply execute" + "### Freeze a model\n", + "\n", + "At the end of the training, the model parameters saved in TensorFlow's checkpoint file should be frozen as a model file that is usually ended with extension .pb. Simply execute" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 33, "id": "b20d368a-135c-4314-ae4c-47bf1f28c4d7", "metadata": { "scrolled": true, @@ -795,35 +968,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "non-resource variables are not supported in the long term\n", "WARNING:root:To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information.\n", - "WARNING:root:Environment variable KMP_BLOCKTIME is empty. Use the default value 0\n", - "WARNING:root:Environment variable KMP_AFFINITY is empty. Use the default value granularity=fine,verbose,compact,1,0\n", - "/opt/deepmd-kit-2.2.1/lib/python3.10/importlib/__init__.py:169: UserWarning: The NumPy module was reloaded (imported a second time). This can in some cases result in small but subtle issues and is discouraged.\n", - " _bootstrap._exec(spec, module)\n", - "2023-04-20 23:40:25.666203: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", - "2023-04-20 23:40:25.666257: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", - "DEEPMD INFO The following nodes will be frozen: ['model_type', 'descrpt_attr/rcut', 'descrpt_attr/ntypes', 'model_attr/tmap', 'model_attr/model_type', 'model_attr/model_version', 'train_attr/min_nbor_dist', 'train_attr/training_script', 'o_energy', 'o_force', 'o_virial', 'o_atom_energy', 'o_atom_virial', 'fitting_attr/dfparam', 'fitting_attr/daparam']\n", - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/deepmd/entrypoints/freeze.py:354: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "DEEPMD WARNING The following nodes are not in the graph: {'fitting_attr/aparam_nall', 'spin_attr/ntypes_spin'}. Skip freezeing these nodes. You may be freezing a checkpoint generated by an old version.\n", + "DEEPMD INFO The following nodes will be frozen: ['descrpt_attr/rcut', 'model_attr/model_version', 'o_atom_virial', 'model_attr/tmap', 'model_attr/model_type', 'o_force', 'o_energy', 'train_attr/min_nbor_dist', 'model_type', 't_mesh', 'fitting_attr/daparam', 'train_attr/training_script', 'fitting_attr/dfparam', 'o_atom_energy', 'descrpt_attr/ntypes', 'o_virial']\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/entrypoints/freeze.py:370: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n", - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/deepmd/entrypoints/freeze.py:354: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/entrypoints/freeze.py:370: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n", - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/framework/convert_to_constants.py:925: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/framework/convert_to_constants.py:925: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/framework/convert_to_constants.py:925: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/framework/convert_to_constants.py:925: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", - "DEEPMD INFO 1211 ops in the final graph.\n" + "DEEPMD INFO 1222 ops in the final graph.\n" ] } ], "source": [ - "! dp freeze -o graph.pb" + "## Navigate to the DeePMD-kit_Tutorial/01.train/ Directory to Freeze the Model\n", + "! cd DeePMD-kit_Tutorial/01.train.finished/ && dp freeze -o graph.pb" ] }, { @@ -834,19 +1003,116 @@ "and it will output a model file named `graph.pb` in the current directory. " ] }, + { + "cell_type": "markdown", + "id": "3de9a2d0", + "metadata": {}, + "source": [ + "### Compress a model\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "8089fe4d", + "metadata": {}, + "source": [ + "To enhance computational efficiency with DP models, compression significantly accelerates DP-based calculations and reduces memory usage. We can compress the model by running:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "bb71bdd0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "non-resource variables are not supported in the long term\n", + "WARNING:root:To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information.\n", + "DEEPMD INFO \n", + "\n", + "\n", + "DEEPMD INFO stage 1: compress the model\n", + "DEEPMD INFO _____ _____ __ __ _____ _ _ _ \n", + "DEEPMD INFO | __ \\ | __ \\ | \\/ || __ \\ | | (_)| | \n", + "DEEPMD INFO | | | | ___ ___ | |__) || \\ / || | | | ______ | | __ _ | |_ \n", + "DEEPMD INFO | | | | / _ \\ / _ \\| ___/ | |\\/| || | | ||______|| |/ /| || __|\n", + "DEEPMD INFO | |__| || __/| __/| | | | | || |__| | | < | || |_ \n", + "DEEPMD INFO |_____/ \\___| \\___||_| |_| |_||_____/ |_|\\_\\|_| \\__|\n", + "DEEPMD INFO Please read and cite:\n", + "DEEPMD INFO Wang, Zhang, Han and E, Comput.Phys.Comm. 228, 178-184 (2018)\n", + "DEEPMD INFO Zeng et al, J. Chem. Phys., 159, 054801 (2023)\n", + "DEEPMD INFO See https://deepmd.rtfd.io/credits/ for details.\n", + "DEEPMD INFO installed to: /root/miniconda3/envs/deepmd\n", + "DEEPMD INFO source : v2.2.7\n", + "DEEPMD INFO source brach: HEAD\n", + "DEEPMD INFO source commit: 839f4fe7\n", + "DEEPMD INFO source commit at: 2023-10-27 21:10:24 +0800\n", + "DEEPMD INFO build float prec: double\n", + "DEEPMD INFO build variant: cpu\n", + "DEEPMD INFO build with tf inc: /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/include;/root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/../../../../include\n", + "DEEPMD INFO build with tf lib: \n", + "DEEPMD INFO ---Summary of the training---------------------------------------\n", + "DEEPMD INFO running on: bohrium-21213-1088639\n", + "DEEPMD INFO computing device: cpu:0\n", + "DEEPMD INFO Count of visible GPU: 0\n", + "DEEPMD INFO num_intra_threads: 0\n", + "DEEPMD INFO num_inter_threads: 0\n", + "DEEPMD INFO -----------------------------------------------------------------\n", + "DEEPMD INFO training without frame parameter\n", + "DEEPMD INFO training data with lower boundary: [-0.92929175 -0.99957951]\n", + "DEEPMD INFO training data with upper boundary: [1.97058099 1.10195361]\n", + "DEEPMD INFO built lr\n", + "DEEPMD INFO built network\n", + "DEEPMD INFO built training\n", + "WARNING:root:To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information.\n", + "DEEPMD INFO initialize model from scratch\n", + "DEEPMD INFO finished compressing\n", + "DEEPMD INFO \n", + "\n", + "\n", + "DEEPMD INFO stage 2: freeze the model\n", + "DEEPMD WARNING The following nodes are not in the graph: {'spin_attr/ntypes_spin', 'fitting_attr/aparam_nall'}. Skip freezeing these nodes. You may be freezing a checkpoint generated by an old version.\n", + "DEEPMD INFO The following nodes will be frozen: ['train_attr/min_nbor_dist', 'o_energy', 'descrpt_attr/rcut', 'o_force', 'model_type', 'fitting_attr/daparam', 'model_attr/tmap', 'o_atom_energy', 'descrpt_attr/ntypes', 'o_virial', 't_mesh', 'model_attr/model_type', 'fitting_attr/dfparam', 'o_atom_virial', 'train_attr/training_script', 'model_attr/model_version']\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/entrypoints/freeze.py:370: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/entrypoints/freeze.py:370: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.convert_variables_to_constants`\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/framework/convert_to_constants.py:925: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/framework/convert_to_constants.py:925: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use `tf.compat.v1.graph_util.extract_sub_graph`\n", + "DEEPMD INFO 858 ops in the final graph.\n" + ] + } + ], + "source": [ + "## Navigate to the DeePMD-kit_Tutorial/01.train/ Directory to Compress the Model\n", + "! cd DeePMD-kit_Tutorial/01.train.finished/ && dp compress -i graph.pb -o compress.pb" + ] + }, { "cell_type": "markdown", "id": "2882c201-0e85-46d0-94f8-e055a540b6fb", "metadata": {}, "source": [ - "## Test a model \n", + "### Test a model\n", "\n", "We can check the quality of the trained model by running\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 35, "id": "5d201246-b110-4e63-a09b-ad7308bc1367", "metadata": { "scrolled": true, @@ -857,67 +1123,36 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "non-resource variables are not supported in the long term\n", "WARNING:root:To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information.\n", - "WARNING:root:Environment variable KMP_BLOCKTIME is empty. Use the default value 0\n", - "WARNING:root:Environment variable KMP_AFFINITY is empty. Use the default value granularity=fine,verbose,compact,1,0\n", - "/opt/deepmd-kit-2.2.1/lib/python3.10/importlib/__init__.py:169: UserWarning: The NumPy module was reloaded (imported a second time). This can in some cases result in small but subtle issues and is discouraged.\n", - " _bootstrap._exec(spec, module)\n", - "2023-04-20 23:40:30.102300: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", - "2023-04-20 23:40:30.102346: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/deepmd/utils/batch_size.py:61: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/utils/batch_size.py:62: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.config.list_physical_devices('GPU')` instead.\n", - "WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/deepmd/utils/batch_size.py:61: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/utils/batch_size.py:62: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.config.list_physical_devices('GPU')` instead.\n", "DEEPMD WARNING You can use the environment variable DP_INFER_BATCH_SIZE tocontrol the inference batch size (nframes * natoms). The default value is 1024.\n", "DEEPMD INFO # ---------------output of dp test--------------- \n", "DEEPMD INFO # testing system : ../00.data/validation_data\n", - "OMP: Info #155: KMP_AFFINITY: Initial OS proc set respected: 0-7\n", - "OMP: Info #216: KMP_AFFINITY: decoding x2APIC ids.\n", - "OMP: Info #216: KMP_AFFINITY: cpuid leaf 11 not supported.\n", - "OMP: Info #216: KMP_AFFINITY: decoding legacy APIC ids.\n", - "OMP: Info #157: KMP_AFFINITY: 8 available OS procs\n", - "OMP: Info #158: KMP_AFFINITY: Uniform topology\n", - "OMP: Info #287: KMP_AFFINITY: topology layer \"LL cache\" is equivalent to \"socket\".\n", - "OMP: Info #192: KMP_AFFINITY: 1 socket x 4 cores/socket x 2 threads/core (4 total cores)\n", - "OMP: Info #218: KMP_AFFINITY: OS proc to physical thread map:\n", - "OMP: Info #172: KMP_AFFINITY: OS proc 0 maps to socket 0 core 0 thread 0 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 1 maps to socket 0 core 0 thread 1 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 2 maps to socket 0 core 1 thread 0 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 3 maps to socket 0 core 1 thread 1 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 4 maps to socket 0 core 2 thread 0 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 5 maps to socket 0 core 2 thread 1 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 6 maps to socket 0 core 3 thread 0 \n", - "OMP: Info #172: KMP_AFFINITY: OS proc 7 maps to socket 0 core 3 thread 1 \n", - "OMP: Info #254: KMP_AFFINITY: pid 254 tid 265 thread 1 bound to OS proc set 2\n", - "OMP: Info #254: KMP_AFFINITY: pid 254 tid 267 thread 2 bound to OS proc set 4\n", - "OMP: Info #254: KMP_AFFINITY: pid 254 tid 268 thread 3 bound to OS proc set 6\n", - "OMP: Info #254: KMP_AFFINITY: pid 254 tid 269 thread 4 bound to OS proc set 1\n", - "OMP: Info #254: KMP_AFFINITY: pid 254 tid 270 thread 5 bound to OS proc set 3\n", - "OMP: Info #254: KMP_AFFINITY: pid 254 tid 271 thread 6 bound to OS proc set 5\n", - "OMP: Info #254: KMP_AFFINITY: pid 254 tid 272 thread 7 bound to OS proc set 7\n", - "OMP: Info #254: KMP_AFFINITY: pid 254 tid 273 thread 8 bound to OS proc set 0\n", "DEEPMD INFO # number of test data : 40 \n", - "DEEPMD INFO Energy MAE : 4.400922e-03 eV\n", - "DEEPMD INFO Energy RMSE : 5.258026e-03 eV\n", - "DEEPMD INFO Energy MAE/Natoms : 8.801843e-04 eV\n", - "DEEPMD INFO Energy RMSE/Natoms : 1.051605e-03 eV\n", - "DEEPMD INFO Force MAE : 4.277741e-02 eV/A\n", - "DEEPMD INFO Force RMSE : 5.514855e-02 eV/A\n", - "DEEPMD INFO Virial MAE : 6.080471e-02 eV\n", - "DEEPMD INFO Virial RMSE : 7.882116e-02 eV\n", - "DEEPMD INFO Virial MAE/Natoms : 1.216094e-02 eV\n", - "DEEPMD INFO Virial RMSE/Natoms : 1.576423e-02 eV\n", + "DEEPMD INFO Energy MAE : 1.473845e-03 eV\n", + "DEEPMD INFO Energy RMSE : 2.007936e-03 eV\n", + "DEEPMD INFO Energy MAE/Natoms : 2.947689e-04 eV\n", + "DEEPMD INFO Energy RMSE/Natoms : 4.015871e-04 eV\n", + "DEEPMD INFO Force MAE : 2.146239e-02 eV/A\n", + "DEEPMD INFO Force RMSE : 2.748797e-02 eV/A\n", + "DEEPMD INFO Virial MAE : 2.879183e-02 eV\n", + "DEEPMD INFO Virial RMSE : 3.817983e-02 eV\n", + "DEEPMD INFO Virial MAE/Natoms : 5.758366e-03 eV\n", + "DEEPMD INFO Virial RMSE/Natoms : 7.635965e-03 eV\n", "DEEPMD INFO # ----------------------------------------------- \n" ] } ], "source": [ - "! dp test -m graph.pb -s ../00.data/validation_data" + "! cd DeePMD-kit_Tutorial/01.train.finished/ && dp test -m graph.pb -s ../00.data/validation_data" ] }, { @@ -930,44 +1165,15 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 36, "id": "be03c8da-e4f9-439d-bb22-800b651a7737", "metadata": {}, - "outputs": [], - "source": [ - "import dpdata\n", - "\n", - "training_systems = dpdata.LabeledSystem(\"../00.data/training_data\", fmt=\"deepmd/npy\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "e3046e2b-7080-4be0-81d3-faaab3873002", - "metadata": { - "collapsed": true, - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-04-20 23:40:32.104716: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-04-20 23:40:34.426193: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", - "2023-04-20 23:40:34.427318: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", - "2023-04-20 23:40:34.427332: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From /opt/mamba/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "non-resource variables are not supported in the long term\n" ] @@ -983,7 +1189,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From /opt/mamba/lib/python3.10/site-packages/deepmd/utils/batch_size.py:61: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/utils/batch_size.py:62: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.config.list_physical_devices('GPU')` instead.\n" ] @@ -992,26 +1198,29 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-04-20 23:40:36.161142: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA\n", + "2024-03-24 23:05:17.177887: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-04-20 23:40:36.165078: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", - "2023-04-20 23:40:36.165119: W tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:265] failed call to cuInit: UNKNOWN ERROR (303)\n", - "2023-04-20 23:40:36.165142: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (bohrium-14076-1013950): /proc/driver/nvidia/version does not exist\n", - "2023-04-20 23:40:36.181810: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:357] MLIR V1 optimization pass is not enabled\n", - "WARNING:tensorflow:From /opt/mamba/lib/python3.10/site-packages/deepmd/utils/batch_size.py:61: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", + "2024-03-24 23:05:17.179243: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n", + "2024-03-24 23:05:17.197330: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n", + "WARNING:tensorflow:From /root/miniconda3/envs/deepmd/lib/python3.10/site-packages/deepmd/utils/batch_size.py:62: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use `tf.config.list_physical_devices('GPU')` instead.\n", - "WARNING:deepmd.tf.utils.batch_size:You can use the environment variable DP_INFER_BATCH_SIZE tocontrol the inference batch size (nframes * natoms). The default value is 1024.\n" + "WARNING:deepmd.utils.batch_size:You can use the environment variable DP_INFER_BATCH_SIZE tocontrol the inference batch size (nframes * natoms). The default value is 1024.\n" ] } ], "source": [ - "predict = training_systems.predict(\"graph.pb\")" + "import dpdata\n", + "\n", + "training_systems = dpdata.LabeledSystem(\n", + " \"./DeePMD-kit_Tutorial/00.data/training_data\", fmt=\"deepmd/npy\"\n", + ")\n", + "predict = training_systems.predict(\"./DeePMD-kit_Tutorial/01.train.finished/graph.pb\")" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 37, "id": "b4005a62-8bf2-4aaf-8865-20ea303ea0f2", "metadata": {}, "outputs": [ @@ -1021,13 +1230,13 @@ "[]" ] }, - "execution_count": 16, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -1055,14 +1264,14 @@ "id": "fffaad23-53a6-47dd-8d91-9beb68d1212b", "metadata": {}, "source": [ - "## Run MD with LAMMPS\n", + "### Run MD with LAMMPS\n", "\n", "The model can drive molecular dynamics in LAMMPS. \n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 38, "id": "d0ceac75-9753-4ae8-b954-c2cc6d005e46", "metadata": {}, "outputs": [ @@ -1070,16 +1279,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "conf.lmp graph.pb in.lammps\n" + "DeePMD-kit_Tutorial\n", + "\u001b[01;34m.\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;32mch4.dump\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;32mconf.lmp\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;32mgraph.pb\u001b[0m\n", + "β”œβ”€β”€ \u001b[01;32min.lammps\u001b[0m\n", + "└── \u001b[00mlog.lammps\u001b[0m\n", + "\n", + "0 directories, 5 files\n" ] } ], "source": [ - "! cd ../02.lmp && cp ../01.train/graph.pb ./ && ls" + "! ls\n", + "! cd ./DeePMD-kit_Tutorial/02.lmp && cp ../01.train.finished/graph.pb ./ && tree -L 1" ] }, { - "attachments": {}, "cell_type": "markdown", "id": "c794572f-7e9c-42f7-b78a-4d82bd3f3118", "metadata": {}, @@ -1091,7 +1308,7 @@ "```\n", "where the pair style deepmd is invoked and the model file `graph.pb` is provided, which means the atomic interaction will be computed by the DP model that is stored in the file `graph.pb`. \n", "\n", - "In an environment with a compatible version of LAMMPS, the deep potential molecular dynamics can be performed via \n", + "In an environment with a compatibable version of LAMMPS, the deep potential molecular dynamics can be performed via \n", "\n", "```bash\n", "lmp -i input.lammps\n", @@ -1100,52 +1317,47 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 39, "id": "f4501e9c-541c-431d-8847-d0d9eecfb0e0", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Warning:\n", - "This LAMMPS executable is in a conda environment, but the environment has\n", - "not been activated. Libraries may fail to load. To activate this environment\n", - "please see https://conda.io/activation.\n", - "LAMMPS (23 Jun 2022 - Update 1)\n", + "LAMMPS (2 Aug 2023 - Update 1)\n", "OMP_NUM_THREADS environment is not set. Defaulting to 1 thread. (src/comm.cpp:98)\n", " using 1 OpenMP thread(s) per MPI task\n", - "Loaded 1 plugins from /opt/deepmd-kit-2.2.1/lib/deepmd_lmp\n", + "Loaded 1 plugins from /root/miniconda3/envs/deepmd/lib/deepmd_lmp\n", "Reading data file ...\n", " triclinic box = (0 0 0) to (10.114259 10.263124 10.216793) with tilt (0.036749877 0.13833062 -0.056322169)\n", " 1 by 1 by 1 MPI processor grid\n", " reading atoms ...\n", " 5 atoms\n", - " read_data CPU = 0.004 seconds\n", + " read_data CPU = 0.002 seconds\n", "DeePMD-kit WARNING: Environmental variable OMP_NUM_THREADS is not set. Tune OMP_NUM_THREADS for the best performance. See https://deepmd.rtfd.io/parallelism/ for more information.\n", "Summary of lammps deepmd module ...\n", " >>> Info of deepmd-kit:\n", - " installed to: /opt/deepmd-kit-2.2.1\n", - " source: v2.2.1\n", + " installed to: /root/miniconda3/envs/deepmd\n", + " source: v2.2.7\n", " source branch: HEAD\n", - " source commit: 3ac8c4c7\n", - " source commit at: 2023-03-16 12:33:24 +0800\n", + " source commit: 839f4fe7\n", + " source commit at: 2023-10-27 21:10:24 +0800\n", " surpport model ver.:1.1 \n", - " build variant: cuda\n", - " build with tf inc: /opt/deepmd-kit-2.2.1/include;/opt/deepmd-kit-2.2.1/include\n", - " build with tf lib: /opt/deepmd-kit-2.2.1/lib/libtensorflow_cc.so\n", + " build variant: cpu\n", + " build with tf inc: /root/miniconda3/envs/deepmd/include;/root/miniconda3/envs/deepmd/include\n", + " build with tf lib: /root/miniconda3/envs/deepmd/lib/libtensorflow_cc.so\n", " set tf intra_op_parallelism_threads: 0\n", " set tf inter_op_parallelism_threads: 0\n", " >>> Info of lammps module:\n", - " use deepmd-kit at: /opt/deepmd-kit-2.2.1DeePMD-kit WARNING: Environmental variable OMP_NUM_THREADS is not set. Tune OMP_NUM_THREADS for the best performance. See https://deepmd.rtfd.io/parallelism/ for more information.\n", - "DeePMD-kit: Successfully load libcudart.so\n", - "2023-04-20 23:40:39.637091: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA\n", + " use deepmd-kit at: /root/miniconda3/envs/deepmdDeePMD-kit WARNING: Environmental variable OMP_NUM_THREADS is not set. Tune OMP_NUM_THREADS for the best performance. See https://deepmd.rtfd.io/parallelism/ for more information.\n", + "2024-03-24 23:05:49.768736: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA\n", "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "2023-04-20 23:40:39.643206: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/nvidia/lib:/usr/local/nvidia/lib64\n", - "2023-04-20 23:40:39.643234: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n", - "2023-04-20 23:40:39.643257: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (bohrium-14076-1013950): /proc/driver/nvidia/version does not exist\n", - "2023-04-20 23:40:39.645305: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n", - "2023-04-20 23:40:39.700559: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n", + "2024-03-24 23:05:49.770401: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n", + "2024-03-24 23:05:49.817983: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n", + "INVALID_ARGUMENT: Tensor spin_attr/ntypes_spin:0, specified in either feed_devices or fetch_devices was not found in the Graph\n", " >>> Info of model(s):\n", " using 1 model(s): graph.pb \n", " rcut in model: 6\n", @@ -1161,7 +1373,7 @@ "\n", "Generated 0 of 1 mixed pair_coeff terms from geometric mixing rule\n", "Neighbor list info ...\n", - " update every 10 steps, delay 0 steps, check no\n", + " update: every = 10 steps, delay = 0 steps, check = no\n", " max neighbors/atom: 2000, page size: 100000\n", " master list distance cutoff = 7\n", " ghost atom cutoff = 7\n", @@ -1176,73 +1388,73 @@ " Unit style : metal\n", " Current step : 0\n", " Time step : 0.001\n", - "Per MPI rank memory allocation (min/avg/max) = 3.809 | 3.809 | 3.809 Mbytes\n", + "Per MPI rank memory allocation (min/avg/max) = 2.559 | 2.559 | 2.559 Mbytes\n", " Step PotEng KinEng TotEng Temp Press Volume \n", - " 0 -219.77011 0.025852029 -219.74426 50 -810.10259 1060.5429 \n", - " 100 -219.76784 0.023303362 -219.74454 45.070664 -605.50113 1060.5429 \n", - " 200 -219.77863 0.032400378 -219.74622 62.665059 -53.929107 1060.5429 \n", - " 300 -219.77403 0.027115352 -219.74692 52.443373 642.24342 1060.5429 \n", - " 400 -219.77126 0.023079501 -219.74818 44.637697 861.365 1060.5429 \n", - " 500 -219.786 0.034433001 -219.75156 66.596322 256.47994 1060.5429 \n", - " 600 -219.78295 0.029039598 -219.75391 56.165027 -527.21506 1060.5429 \n", - " 700 -219.777 0.020227709 -219.75677 39.122091 -696.11258 1060.5429 \n", - " 800 -219.78394 0.022893217 -219.76105 44.277408 -77.227892 1060.5429 \n", - " 900 -219.77998 0.015506508 -219.76447 29.990893 663.84491 1060.5429 \n", - " 1000 -219.78328 0.015178419 -219.7681 29.356341 482.8228 1060.5429 \n", - " 1100 -219.7903 0.018763273 -219.77154 36.28975 -273.19351 1060.5429 \n", - " 1200 -219.78639 0.012922048 -219.77347 24.992328 -577.90459 1060.5429 \n", - " 1300 -219.79131 0.015848131 -219.77546 30.65162 -129.85247 1060.5429 \n", - " 1400 -219.78829 0.011969602 -219.77632 23.150218 545.58517 1060.5429 \n", - " 1500 -219.78735 0.010610097 -219.77674 20.520821 356.36805 1060.5429 \n", - " 1600 -219.78834 0.011547453 -219.77679 22.333746 -386.08305 1060.5429 \n", - " 1700 -219.78549 0.0095297364 -219.77596 18.431312 -522.29867 1060.5429 \n", - " 1800 -219.78787 0.01302987 -219.77484 25.200865 120.83085 1060.5429 \n", - " 1900 -219.7845 0.012341623 -219.77216 23.869737 643.66442 1060.5429 \n", - " 2000 -219.7857 0.017597987 -219.76811 34.035987 255.57892 1060.5429 \n", - " 2100 -219.7853 0.023253088 -219.76205 44.973429 -465.61243 1060.5429 \n", - " 2200 -219.77987 0.024650089 -219.75522 47.675348 -708.62743 1060.5429 \n", - " 2300 -219.78134 0.030690759 -219.75065 59.358512 -221.82549 1060.5429 \n", - " 2400 -219.77737 0.029446857 -219.74792 56.952699 635.02431 1060.5429 \n", - " 2500 -219.768 0.022122766 -219.74587 42.787292 826.89652 1060.5429 \n", - " 2600 -219.77246 0.02691536 -219.74554 52.056572 168.88834 1060.5429 \n", - " 2700 -219.77746 0.031963987 -219.7455 61.821042 -497.33107 1060.5429 \n", - " 2800 -219.7733 0.02814671 -219.74515 54.438107 -792.71093 1060.5429 \n", - " 2900 -219.77498 0.029131114 -219.74585 56.342026 -685.23164 1060.5429 \n", - " 3000 -219.78212 0.034326288 -219.74779 66.38993 -20.441816 1060.5429 \n", - " 3100 -219.77222 0.02366469 -219.74856 45.769502 708.42782 1060.5429 \n", - " 3200 -219.77252 0.022334468 -219.75019 43.196742 753.3138 1060.5429 \n", - " 3300 -219.78538 0.032458098 -219.75292 62.776693 36.172647 1060.5429 \n", - " 3400 -219.78047 0.026131264 -219.75434 50.540064 -661.25487 1060.5429 \n", - " 3500 -219.77926 0.022926821 -219.75633 44.342401 -623.5037 1060.5429 \n", - " 3600 -219.78369 0.024854728 -219.75884 48.071137 74.821258 1060.5429 \n", - " 3700 -219.7768 0.016731114 -219.76006 32.359382 709.57785 1060.5429 \n", - " 3800 -219.77927 0.017595175 -219.76168 34.03055 543.56168 1060.5429 \n", - " 3900 -219.7864 0.023003584 -219.7634 44.490867 -230.55364 1060.5429 \n", - " 4000 -219.78098 0.017102387 -219.76388 33.077456 -677.85161 1060.5429 \n", - " 4100 -219.78581 0.020907229 -219.7649 40.436341 -343.9622 1060.5429 \n", - " 4200 -219.78717 0.021708329 -219.76546 41.985736 491.95578 1060.5429 \n", - " 4300 -219.78328 0.018229256 -219.76505 35.256916 680.5279 1060.5429 \n", - " 4400 -219.79007 0.024931071 -219.76514 48.21879 -26.785455 1060.5429 \n", - " 4500 -219.78331 0.019795452 -219.76352 38.286071 -624.98799 1060.5429 \n", - " 4600 -219.78094 0.0196038 -219.76134 37.915399 -584.8297 1060.5429 \n", - " 4700 -219.78608 0.027516802 -219.75857 53.219812 74.218844 1060.5429 \n", - " 4800 -219.77656 0.023488867 -219.75307 45.429446 827.4406 1060.5429 \n", - " 4900 -219.78039 0.032832529 -219.74755 63.500874 634.64896 1060.5429 \n", - " 5000 -219.78237 0.040761952 -219.7416 78.837046 -224.81626 1060.5429 \n", - "Loop time of 12.1251 on 1 procs for 5000 steps with 5 atoms\n", + " 0 -219.77409 0.025852029 -219.74824 50 -799.80566 1060.5429 \n", + " 100 -219.77101 0.02250472 -219.7485 43.526023 -563.15562 1060.5429 \n", + " 200 -219.77525 0.025722761 -219.74953 49.749984 -55.768826 1060.5429 \n", + " 300 -219.78111 0.030123111 -219.75098 58.260632 415.50143 1060.5429 \n", + " 400 -219.78545 0.03264184 -219.7528 63.132067 724.77655 1060.5429 \n", + " 500 -219.7897 0.034591934 -219.75511 66.903712 664.01323 1060.5429 \n", + " 600 -219.78944 0.031599794 -219.75784 61.116661 307.82983 1060.5429 \n", + " 700 -219.78389 0.023121639 -219.76076 44.719197 -166.66606 1060.5429 \n", + " 800 -219.77712 0.013122374 -219.764 25.379775 -493.10259 1060.5429 \n", + " 900 -219.7791 0.011293959 -219.76781 21.843468 -609.86395 1060.5429 \n", + " 1000 -219.78712 0.01531002 -219.77181 29.610866 -422.5828 1060.5429 \n", + " 1100 -219.7939 0.018709632 -219.77519 36.186003 -61.443156 1060.5429 \n", + " 1200 -219.79395 0.016606919 -219.77734 32.11918 331.62678 1060.5429 \n", + " 1300 -219.79132 0.012642575 -219.77868 24.451803 505.6361 1060.5429 \n", + " 1400 -219.79314 0.013255468 -219.77989 25.637191 381.73541 1060.5429 \n", + " 1500 -219.79509 0.014397006 -219.78069 27.845022 48.696022 1060.5429 \n", + " 1600 -219.79313 0.012485864 -219.78064 24.148711 -302.67659 1060.5429 \n", + " 1700 -219.78841 0.0085717658 -219.77983 16.578516 -476.08062 1060.5429 \n", + " 1800 -219.78663 0.0081557171 -219.77847 15.773843 -407.83792 1060.5429 \n", + " 1900 -219.78715 0.010996426 -219.77615 21.268013 -98.699573 1060.5429 \n", + " 2000 -219.78836 0.016278673 -219.77209 31.484324 293.02315 1060.5429 \n", + " 2100 -219.78819 0.022161035 -219.76603 42.861306 587.40225 1060.5429 \n", + " 2200 -219.79165 0.031838471 -219.75981 61.578284 543.58893 1060.5429 \n", + " 2300 -219.79343 0.038239208 -219.75519 73.957846 104.54643 1060.5429 \n", + " 2400 -219.78301 0.031060153 -219.75195 60.072951 -293.72903 1060.5429 \n", + " 2500 -219.77209 0.022352657 -219.74974 43.231919 -606.61353 1060.5429 \n", + " 2600 -219.76604 0.017305685 -219.74873 33.47065 -623.66583 1060.5429 \n", + " 2700 -219.77552 0.026563069 -219.74895 51.375211 -332.34033 1060.5429 \n", + " 2800 -219.78594 0.0362724 -219.74967 70.153875 120.73427 1060.5429 \n", + " 2900 -219.78868 0.038558744 -219.75012 74.575856 542.93567 1060.5429 \n", + " 3000 -219.78351 0.03281317 -219.75069 63.463433 746.24646 1060.5429 \n", + " 3100 -219.78106 0.028937414 -219.75212 55.967395 583.87016 1060.5429 \n", + " 3200 -219.77929 0.025275432 -219.75402 48.884814 128.24387 1060.5429 \n", + " 3300 -219.77781 0.022017978 -219.75579 42.584622 -395.55332 1060.5429 \n", + " 3400 -219.77696 0.019305132 -219.75765 37.33775 -679.74745 1060.5429 \n", + " 3500 -219.78369 0.023714356 -219.75997 45.86556 -656.9891 1060.5429 \n", + " 3600 -219.79244 0.030071312 -219.76237 58.160448 -354.34542 1060.5429 \n", + " 3700 -219.79168 0.027557568 -219.76412 53.298657 199.00964 1060.5429 \n", + " 3800 -219.78639 0.021137515 -219.76525 40.881734 596.54224 1060.5429 \n", + " 3900 -219.77923 0.012972221 -219.76626 25.089367 713.41996 1060.5429 \n", + " 4000 -219.78185 0.014202505 -219.76765 27.46884 430.83529 1060.5429 \n", + " 4100 -219.78477 0.016041208 -219.76872 31.025047 -28.605377 1060.5429 \n", + " 4200 -219.78545 0.016332231 -219.76912 31.587909 -457.5328 1060.5429 \n", + " 4300 -219.78602 0.016882726 -219.76914 32.652612 -608.55966 1060.5429 \n", + " 4400 -219.78949 0.020680419 -219.76881 39.99767 -456.72943 1060.5429 \n", + " 4500 -219.79121 0.023411938 -219.7678 45.280658 -79.406734 1060.5429 \n", + " 4600 -219.7882 0.022574198 -219.76562 43.660398 414.11955 1060.5429 \n", + " 4700 -219.78521 0.022736692 -219.76248 43.974676 663.73939 1060.5429 \n", + " 4800 -219.7834 0.025050214 -219.75835 48.449222 598.39611 1060.5429 \n", + " 4900 -219.78291 0.030199797 -219.75271 58.408949 203.75805 1060.5429 \n", + " 5000 -219.77611 0.030245158 -219.74586 58.496682 -300.80549 1060.5429 \n", + "Loop time of 38.8363 on 1 procs for 5000 steps with 5 atoms\n", "\n", - "Performance: 35.629 ns/day, 0.674 hours/ns, 412.369 timesteps/s\n", - "242.0% CPU use with 1 MPI tasks x 1 OpenMP threads\n", + "Performance: 11.124 ns/day, 2.158 hours/ns, 128.746 timesteps/s, 643.728 atom-step/s\n", + "104.3% CPU use with 1 MPI tasks x 1 OpenMP threads\n", "\n", "MPI task timing breakdown:\n", "Section | min time | avg time | max time |%varavg| %total\n", "---------------------------------------------------------------\n", - "Pair | 12.072 | 12.072 | 12.072 | 0.0 | 99.56\n", - "Neigh | 0.0066181 | 0.0066181 | 0.0066181 | 0.0 | 0.05\n", - "Comm | 0.012792 | 0.012792 | 0.012792 | 0.0 | 0.11\n", - "Output | 0.0044695 | 0.0044695 | 0.0044695 | 0.0 | 0.04\n", - "Modify | 0.022737 | 0.022737 | 0.022737 | 0.0 | 0.19\n", - "Other | | 0.006263 | | | 0.05\n", + "Pair | 38.703 | 38.703 | 38.703 | 0.0 | 99.66\n", + "Neigh | 0.0079815 | 0.0079815 | 0.0079815 | 0.0 | 0.02\n", + "Comm | 0.0334 | 0.0334 | 0.0334 | 0.0 | 0.09\n", + "Output | 0.0065195 | 0.0065195 | 0.0065195 | 0.0 | 0.02\n", + "Modify | 0.070599 | 0.070599 | 0.070599 | 0.0 | 0.18\n", + "Other | | 0.01491 | | | 0.04\n", "\n", "Nlocal: 5 ave 5 max 5 min\n", "Histogram: 1 0 0 0 0 0 0 0 0 0\n", @@ -1257,21 +1469,12 @@ "Ave neighs/atom = 4\n", "Neighbor list builds = 500\n", "Dangerous builds not checked\n", - "Total wall time: 0:00:13\n" + "Total wall time: 0:00:39\n" ] } ], "source": [ - "! cd ../02.lmp && cp ../01.train/graph.pb ./ && lmp -i in.lammps" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "98a7aff7-daaf-494f-beb9-aa99688ed0a2", - "metadata": {}, - "source": [ - "\"Open" + "! cd ./DeePMD-kit_Tutorial/02.lmp && lmp -i in.lammps" ] } ], @@ -1291,7 +1494,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.13" } }, "nbformat": 4,