add tutorial1

Author: njzjz

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+---
+title: "DP Tutorial 1: How to Setup a DeePMD-kit Training within 5 Minutes?"
+date: 2021-06-12
+category: tutorial
+---
+
+DeePMD-kit is a software to implement Deep Potential.
+There is a lot of information on the Internet, but there are not so many tutorials for the new hand, and the official guide is too long.
+Today, I’ll take you 5 minutes to get started with DeePMD-kit. 
+
+Let's take a look at the training process of DeePMD-kit:
+
+{% mermaid graph LR %}
+A[Prepare data] --> B[Training]
+B --> C[Freeze the model]
+{% endmermaid %}
+
+What? Only three steps?
+Yes, it's that simple.
+Preparing data is converting the computational results of DFT to data that can be recongized by the DeePMD-kit.
+Training is train a Deep Potential model using the DeePMD-kit with data prepared in the previous step.
+Finally, what we need to do is to freeze the restart file in the training process into a model, in other words is to extract the neural network parameters into a file for subsequent use.
+I believe you can't wait to get started. Let's go!
+
+The data format of the DeePMD-kit is introduced in the [official document](https://deepmd.readthedocs.io/) but seems complex.
+Don't worry, I'd like to introduce a data processing tool: dpdata!
+You can use only one line Python scripts to process data.
+So easy!
+
+```py
+import dpdata
+dpdata.LabeledSystem('OUTCAR').to('deepmd/npy', 'data', set_size=200)
+```
+
+In this example, we converted the computaional results of the VASP in the `OUTCAR` to the data format of the DeePMD-kit and saved in to a directory named `data`,
+where `npy` is the compressed format of the numpy, which is required by the DeePMD-kit training.
+We assume `OUTCAR` stores 1000 frames of molecular dynamics trajectory, then where will be 1000 points after converting.
+`set_size=200` means these 1000 points will be divided into 5 subsets, which is named as `data/set.000`~`data/set.004`, respectively.
+The size of each set is 200.
+In these 5 sets, `data/set.000`~`data/set.003` will be considered as the trainign set by the DeePMD-kit, and `data/set.004` will be considered as the test set.
+The last set will be considered as the test set by the DeePMD-kit by default.
+If there is only one set, the set will be both the training set and the test set. (Of course, such the test set is meaningless.)
+It's required to prepare an input script to start the DeePMD-kit training.
+Are you still out of the fear of being dominated by INCAR script? 
+Don't worry, it's much easier to configure the DeePMD-kit than configuring the VASP.
+First, let's download an example and save to `input.json`:
+
+```sh
+wget https://raw.githubusercontent.com/deepmodeling/deepmd-kit/v1.3.3/examples/water/train/water_se_a.json -O input.json
+```
+
+The strength of the DeePMD-kit is that the same training parameters are suitable for different systems, so we only need to slightly modify `input.json` to start training.
+Here is the first parameter to modify:
+
+```json
+"type_map":     ["O", "H"],
+```
+
+In the DeePMD-kit data, each atom type is numbered as an integer starting from 0.
+The parameter gices an element name to each atom in the numbering system.
+Here, we can copy from the content of `data/type_map.raw`.
+For example,
+
+```json
+"type_map":    ["A", "B","C"],
+```
+
+Next, we are going to modify the neighbour searching parameter:
+
+```json
+"sel":       [46, 92],
+```
+
+Each number in this list gives the maximum number of atoms of each type among neighbor atoms of an atom.
+For example, `46` means there are at most 46 `O` (type `0`) neighbours.
+Here, our elements were modified to `A`, `B`, and `C`, so this parameters is also required to modify.
+What to do if you don’t know the maximum number of neighbors?
+You can be roughly estimate one by the density of the system, or try a number blindly.
+If it is not big enough, the DeePMD-kit will tell you. 
+Below we changed it to 
+
+```json
+"sel":       [64, 64, 64]
+```
+
+In addtion, we need to modify
+
+```
+"systems":     ["../data/"],
+```
+
+to
+
+```
+"systems":     ["./data/"],
+```
+
+It is the reason that the directory to write to is `./data/` in the current directory.
+Here I'd like to introduce the defination of the data system.
+The DeePMD-kit considers that data with the same atomic number and elements can form a system.
+Our data is generated from a molecular dynamics simulation and meets this condition, so we can put them into one system.
+Dpdata also did so.
+If data cannot be put into a system, multiple systems is required to be set as a list.
+
+Finnally, we are likely to modify another two parameters:
+
+```json
+"stop_batch":   1000000,
+"batch_size":   1,
+```
+`stop_batch` is the numebr of training step using the SGD method of deep learning, and `batch_size` is the mini-batch size of data in each step.
+If we want to reduce `stop_batch` and use `batch_size` that the DeePMD-kit recommends, we can use
+
+```json
+"stop_batch":   500000,
+"batch_size":   "auto",
+```
+
+Now we have succesfully set a input file! To start training, we execuate
+
+```sh
+dp train input.json
+```
+
+and wait for results. During the training process, we can see `lcurve.out` to observe the error reduction.
+Among them, Column 4 and 5 are the test and training errors of energy (normalize the number of atoms), and Column 6 and 7 are the test and training errors of the force. 
+
+After training, we can use the following script to freeze the model:
+
+```sh
+dp freeze
+```
+
+The default filename of the output model is `frozen_model.pb`. As so, we have got a good or bad DP model. 
+As for the reliability of this model and how to use it, I will give you a detailed tutorial in the next post.
+