2021-TRN3-J

博客提及2021-TRN3-J,并给出相关链接https://vjudge.net/contest/424076#problem/J 。

2021-TRN3-J

https://vjudge.net/contest/424076#problem/J

#include <bits/stdc++.h>

using namespace std;
const int maxn=1e5+5;
struct node
{
    int attack,defense;
}a[maxn],b[maxn];
multiset<int>myset;
multiset<int>::iterator it;

int cmp1(struct node a,struct node b)
{
    return a.attack>b.attack;
}
int cmp2(struct node a,struct node b)
{
    return a.defense>b.defense;
}
int main()
{
    int t,cas=0;
    scanf("%d",&t);
    while(t--)
    {
        int n,m,i,j,ans;
        cas++;
        scanf("%d%d",&n,&m);
        for(i=1;i<=n;i++)
        {
            scanf("%d%d",&a[i].attack,&a[i].defense);
        }
        for(i=1;i<=m;i++)
        {
            scanf("%d%d",&b[i].attack,&b[i].defense);
        }
        sort(a+1,a+1+n,cmp1);///把我方按攻击力递减排序
        sort(b+1,b+1+m,cmp2);///把敌方按防御力递减排序
        myset.clear();
        ans=n;///一开始认为全部存活
        for(i=1,j=1;i<=m;i++)
        {
            while(j<=n&&b[i].defense<=a[j].attack)///注意j<=n
            {
                myset.insert(a[j].defense);
        ///找出所有我方能够打败当前敌方军队的军队,把防御力丢到multiset里面
                j++;
            }
            if(myset.size()==0)
            {///myset为空,说明我方剩下的军队中已经没有可以打败敌方该军队的
                ans=-1;
                break;
            }
            else
            {
                it=myset.upper_bound(b[i].attack);
            ///在myset里面寻找防御力恰好可以抵挡该敌军进攻的我方军队
                if(it==myset.end())
                {
                    myset.erase(myset.begin());
                    ans--;///没找到,我方防御力最小的军队阵亡
                    continue;
                }
                else
                {
                    myset.erase(it);
                }
            }
        }
        printf("Case #%d: %d\n",cas,ans);
    }
    return 0;
}

(base) root@74fb9740dd84:/workspace/data/CH4/01.train# dp train input.json 2025-11-25 05:58:04.052073: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-11-25 05:58:04.057367: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1764050284.064012 710 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1764050284.066338 710 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered W0000 00:00:1764050284.071566 710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1764050284.071581 710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1764050284.071583 710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1764050284.071584 710 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. 2025-11-25 05:58:04.073369: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: AVX_VNNI AVX_VNNI_INT8 AVX_NE_CONVERT, in other operations, rebuild TensorFlow with the appropriate compiler flags. To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, DP_INTRA_OP_PARALLELISM_THREADS, and DP_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information. DeePMD-kit: Successfully load libcudart.so.12 [2025-11-25 05:58:08,701] DEEPMD INFO Calculate neighbor statistics... (add --skip-neighbor-stat to skip this step) [2025-11-25 05:58:08,737] DEEPMD INFO If you encounter the error &#39;an illegal memory access was encountered&#39;, this may be due to a TensorFlow issue. To avoid this, set the environment variable DP_INFER_BATCH_SIZE to a smaller value than the last adjusted batch size. The environment variable DP_INFER_BATCH_SIZE controls the inference batch size (nframes * natoms). [2025-11-25 05:58:09,883] DEEPMD INFO Neighbor statistics: training data with minimal neighbor distance: 1.042950 [2025-11-25 05:58:09,883] DEEPMD INFO Neighbor statistics: training data with maximum neighbor size: [4 1] (cutoff radius: 6.000000) [2025-11-25 05:58:09,902] DEEPMD INFO _____ _____ __ __ _____ _ _ _ [2025-11-25 05:58:09,902] DEEPMD INFO | __ \ | __ \ | \/ || __ \ | | (_)| | [2025-11-25 05:58:09,902] DEEPMD INFO | | | | ___ ___ | |__) || \ / || | | | ______ | | __ _ | |_ [2025-11-25 05:58:09,902] DEEPMD INFO | | | | / _ \ / _ \| ___/ | |\/| || | | ||______|| |/ /| || __| [2025-11-25 05:58:09,902] DEEPMD INFO | |__| || __/| __/| | | | | || |__| | | < | || |_ [2025-11-25 05:58:09,902] DEEPMD INFO |_____/ \___| \___||_| |_| |_||_____/ |_|\_\|_| \__| [2025-11-25 05:58:09,902] DEEPMD INFO Please read and cite: [2025-11-25 05:58:09,902] DEEPMD INFO Wang, Zhang, Han and E, Comput.Phys.Comm. 228, 178-184 (2018) [2025-11-25 05:58:09,902] DEEPMD INFO Zeng et al, J. Chem. Phys., 159, 054801 (2023) [2025-11-25 05:58:09,902] DEEPMD INFO Zeng et al, J. Chem. Theory Comput., 21, 4375-4385 (2025) [2025-11-25 05:58:09,902] DEEPMD INFO See https://deepmd.rtfd.io/credits/ for details. [2025-11-25 05:58:09,902] DEEPMD INFO --------------------------------------------------------------------------------------- [2025-11-25 05:58:09,902] DEEPMD INFO installed to: /opt/deepmd-kit/lib/python3.12/site-packages/deepmd [2025-11-25 05:58:09,902] DEEPMD INFO source: [2025-11-25 05:58:09,902] DEEPMD INFO source branch: HEAD [2025-11-25 05:58:09,902] DEEPMD INFO source commit: eeadafb [2025-11-25 05:58:09,902] DEEPMD INFO source commit at: 2025-11-05 14:55:36 +0100 [2025-11-25 05:58:09,902] DEEPMD INFO use float prec: double [2025-11-25 05:58:09,902] DEEPMD INFO build variant: cuda [2025-11-25 05:58:09,902] DEEPMD INFO Backend: TensorFlow [2025-11-25 05:58:09,902] DEEPMD INFO TF ver: unknown [2025-11-25 05:58:09,902] DEEPMD INFO build with TF ver: 2.19.1 [2025-11-25 05:58:09,902] DEEPMD INFO build with TF inc: /opt/deepmd-kit/lib/python3.12/site-packages/tensorflow/include/ [2025-11-25 05:58:09,902] DEEPMD INFO /opt/deepmd-kit/include [2025-11-25 05:58:09,902] DEEPMD INFO build with TF lib: [2025-11-25 05:58:09,902] DEEPMD INFO running on: 74fb9740dd84 [2025-11-25 05:58:09,902] DEEPMD INFO computing device: gpu:0 [2025-11-25 05:58:09,902] DEEPMD INFO CUDA_VISIBLE_DEVICES: unset [2025-11-25 05:58:09,902] DEEPMD INFO Count of visible GPUs: 1 [2025-11-25 05:58:09,902] DEEPMD INFO num_intra_threads: 0 [2025-11-25 05:58:09,902] DEEPMD INFO num_inter_threads: 0 [2025-11-25 05:58:09,902] DEEPMD INFO --------------------------------------------------------------------------------------- [2025-11-25 05:58:09,930] DEEPMD INFO ---Summary of DataSystem: training ----------------------------------------------- [2025-11-25 05:58:09,930] DEEPMD INFO found 1 system(s): [2025-11-25 05:58:09,930] DEEPMD INFO system natoms bch_sz n_bch prob pbc [2025-11-25 05:58:09,930] DEEPMD INFO ../00.data/training_data 5 7 22 1.000e+00 T [2025-11-25 05:58:09,930] DEEPMD INFO -------------------------------------------------------------------------------------- [2025-11-25 05:58:09,953] DEEPMD INFO ---Summary of DataSystem: validation ----------------------------------------------- [2025-11-25 05:58:09,953] DEEPMD INFO found 1 system(s): [2025-11-25 05:58:09,953] DEEPMD INFO system natoms bch_sz n_bch prob pbc [2025-11-25 05:58:09,953] DEEPMD INFO ../00.data/validation_data 5 7 5 1.000e+00 T [2025-11-25 05:58:09,953] DEEPMD INFO -------------------------------------------------------------------------------------- [2025-11-25 05:58:09,953] DEEPMD INFO training without frame parameter [2025-11-25 05:58:09,953] DEEPMD INFO data stating... (this step may take long time) [2025-11-25 05:58:10,081] DEEPMD INFO built lr [2025-11-25 05:58:10,427] DEEPMD INFO built network [2025-11-25 05:58:10,890] DEEPMD INFO built training [2025-11-25 05:58:10,891] DEEPMD WARNING To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, DP_INTRA_OP_PARALLELISM_THREADS, and DP_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information. [2025-11-25 05:58:10,911] DEEPMD INFO initialize model from scratch [2025-11-25 05:58:11,269] DEEPMD INFO start training at lr 1.00e-03 (== 1.00e-03), decay_step 5000, decay_rate 0.950006, final lr will be 3.51e-08 [2025-11-25 05:58:11,683] DEEPMD INFO batch 0: trn: rmse = 1.13e+01, rmse_e = 7.04e-01, rmse_f = 3.56e-01, lr = 1.00e-03 [2025-11-25 05:58:11,683] DEEPMD INFO batch 0: val: rmse = 1.42e+01, rmse_e = 7.06e-01, rmse_f = 4.50e-01 [2025-11-25 05:58:37,056] DEEPMD INFO batch 1000: trn: rmse = 4.89e+00, rmse_e = 3.31e-01, rmse_f = 1.55e-01, lr = 1.00e-03 [2025-11-25 05:58:37,056] DEEPMD INFO batch 1000: val: rmse = 4.17e+00, rmse_e = 3.32e-01, rmse_f = 1.32e-01 [2025-11-25 05:58:37,057] DEEPMD INFO batch 1000: total wall time = 25.79 s [2025-11-25 05:59:01,438] DEEPMD INFO batch 2000: trn: rmse = 4.16e+00, rmse_e = 1.98e-02, rmse_f = 1.31e-01, lr = 1.00e-03 [2025-11-25 05:59:01,438] DEEPMD INFO batch 2000: val: rmse = 3.85e+00, rmse_e = 2.04e-02, rmse_f = 1.22e-01 [2025-11-25 05:59:01,438] DEEPMD INFO batch 2000: total wall time = 24.38 s [2025-11-25 05:59:22,714] DEEPMD INFO batch 3000: trn: rmse = 4.73e+00, rmse_e = 7.67e-02, rmse_f = 1.50e-01, lr = 1.00e-03 [2025-11-25 05:59:22,714] DEEPMD INFO batch 3000: val: rmse = 3.76e+00, rmse_e = 7.63e-02, rmse_f = 1.19e-01 [2025-11-25 05:59:22,714] DEEPMD INFO batch 3000: total wall time = 21.28 s [2025-11-25 05:59:46,195] DEEPMD INFO batch 4000: trn: rmse = 5.26e+00, rmse_e = 2.37e-02, rmse_f = 1.66e-01, lr = 1.00e-03 [2025-11-25 05:59:46,195] DEEPMD INFO batch 4000: val: rmse = 3.79e+00, rmse_e = 2.42e-02, rmse_f = 1.20e-01 [2025-11-25 05:59:46,195] DEEPMD INFO batch 4000: total wall time = 23.48 s [2025-11-25 06:00:09,875] DEEPMD INFO batch 5000: trn: rmse = 4.22e+00, rmse_e = 4.11e-02, rmse_f = 1.37e-01, lr = 9.50e-04 [2025-11-25 06:00:09,876] DEEPMD INFO batch 5000: val: rmse = 4.09e+00, rmse_e = 4.09e-02, rmse_f = 1.33e-01 [2025-11-25 06:00:09,876] DEEPMD INFO batch 5000: total wall time = 23.68 s [2025-11-25 06:00:33,788] DEEPMD INFO batch 6000: trn: rmse = 3.64e+00, rmse_e = 2.27e-02, rmse_f = 1.18e-01, lr = 9.50e-04 [2025-11-25 06:00:33,788] DEEPMD INFO batch 6000: val: rmse = 3.24e+00, rmse_e = 2.27e-02, rmse_f = 1.05e-01 [2025-11-25 06:00:33,789] DEEPMD INFO batch 6000: total wall time = 23.91 s [2025-11-25 06:00:55,546] DEEPMD INFO batch 7000: trn: rmse = 1.47e+01, rmse_e = 6.75e+00, rmse_f = 4.60e-01, lr = 9.50e-04 [2025-11-25 06:00:55,547] DEEPMD INFO batch 7000: val: rmse = 1.39e+01, rmse_e = 6.75e+00, rmse_f = 4.33e-01 [2025-11-25 06:00:55,547] DEEPMD INFO batch 7000: total wall time = 21.76 s [2025-11-25 06:01:19,881] DEEPMD INFO batch 8000: trn: rmse = 1.53e+01, rmse_e = 6.75e+00, rmse_f = 4.80e-01, lr = 9.50e-04 [2025-11-25 06:01:19,881] DEEPMD INFO batch 8000: val: rmse = 1.51e+01, rmse_e = 6.75e+00, rmse_f = 4.73e-01 [2025-11-25 06:01:19,881] DEEPMD INFO batch 8000: total wall time = 24.33 s [2025-11-25 06:01:44,351] DEEPMD INFO batch 9000: trn: rmse = 1.26e+01, rmse_e = 6.74e+00, rmse_f = 3.87e-01, lr = 9.50e-04 [2025-11-25 06:01:44,351] DEEPMD INFO batch 9000: val: rmse = 1.54e+01, rmse_e = 6.75e+00, rmse_f = 4.82e-01 [2025-11-25 06:01:44,351] DEEPMD INFO batch 9000: total wall time = 24.47 s [2025-11-25 06:02:06,170] DEEPMD INFO batch 10000: trn: rmse = 1.55e+01, rmse_e = 6.75e+00, rmse_f = 4.87e-01, lr = 9.03e-04 [2025-11-25 06:02:06,171] DEEPMD INFO batch 10000: val: rmse = 1.35e+01, rmse_e = 6.75e+00, rmse_f = 4.14e-01 [2025-11-25 06:02:06,171] DEEPMD INFO batch 10000: total wall time = 21.82 s [2025-11-25 06:02:06,286] DEEPMD INFO saved checkpoint model.ckpt [2025-11-25 06:02:30,824] DEEPMD INFO batch 11000: trn: rmse = 1.18e+01, rmse_e = 6.74e+00, rmse_f = 3.55e-01, lr = 9.03e-04 [2025-11-25 06:02:30,825] DEEPMD INFO batch 11000: val: rmse = 1.37e+01, rmse_e = 6.75e+00, rmse_f = 4.25e-01 [2025-11-25 06:02:30,825] DEEPMD INFO batch 11000: total wall time = 24.65 s [2025-11-25 06:02:55,313] DEEPMD INFO batch 12000: trn: rmse = 1.50e+01, rmse_e = 6.75e+00, rmse_f = 4.70e-01, lr = 9.03e-04 [2025-11-25 06:02:55,313] DEEPMD INFO batch 12000: val: rmse = 1.43e+01, rmse_e = 6.75e+00, rmse_f = 4.45e-01 [2025-11-25 06:02:55,313] DEEPMD INFO batch 12000: total wall time = 24.49 s [2025-11-25 06:03:19,394] DEEPMD INFO batch 13000: trn: rmse = 1.41e+01, rmse_e = 6.75e+00, rmse_f = 4.38e-01, lr = 9.03e-04 [2025-11-25 06:03:19,394] DEEPMD INFO batch 13000: val: rmse = 1.52e+01, rmse_e = 6.75e+00, rmse_f = 4.77e-01 [2025-11-25 06:03:19,394] DEEPMD INFO batch 13000: total wall time = 24.08 s [2025-11-25 06:03:41,154] DEEPMD INFO batch 14000: trn: rmse = 1.45e+01, rmse_e = 6.75e+00, rmse_f = 4.51e-01, lr = 9.03e-04 [2025-11-25 06:03:41,154] DEEPMD INFO batch 14000: val: rmse = 1.54e+01, rmse_e = 6.75e+00, rmse_f = 4.83e-01 [2025-11-25 06:03:41,155] DEEPMD INFO batch 14000: total wall time = 21.76 s
11-26
<think> 我们面对两个核心问题: 1. Docker环境运行DeepMD时GPU利用率低(仅30%左右) 2. 运行`dp train input.json`时出现的cuFFT等工厂注册警告及训练损失波动 我们将分步骤解决: ### 一、GPU利用率低问题诊断与优化 #### 1. 容器GPU配置验证 ```bash # 运行容器时确保正确挂载GPU docker run --gpus all -it deepmd-kit:v2.2.2 nvidia-smi ``` - 若GPU信息正常显示,进入下一步 - 异常时检查:`docker run --runtime=nvidia`(旧版本)或重装NVIDIA Container Toolkit[^1] #### 2. 关键性能优化措施 | 优化项 | 操作指引 | 原理说明 | |---------------------|--------------------------------------------------------------------------|------------------------------| | **GPU数据预处理** | `input.json`中添加:<br>`"enable_gpu_preprocess": true` | 避免CPU-GPU数据传输瓶颈 | | **批处理大小** | 调整至显存上限(例如A100 80G可设`"batch_size": 1024`) | 提升SM利用率 | | **混合精度训练** | 环境变量:<br>`export TF_ENABLE_AUTO_MIXED_PRECISION=1` | FP16加速计算并降低显存占用 | | **数据加载优化** | 设置`"num_workers": 16`(建议为CPU核心数×2) | 并行数据加载减少CPU阻塞 | | **TensorFlow配置** | Dockerfile添加:<br>`ENV TF_GPU_THREAD_MODE=gpu_private` | 避免GPU线程竞争 | #### 3. Docker环境专项优化 ```dockerfile # Dockerfile 关键配置 FROM nvidia/cuda:12.2.0-devel-ubuntu22.04 ... ENV TF_FORCE_GPU_ALLOW_GROWTH=true VOLUME /dev/shm # 共享内存加速进程通信 ``` - 运行参数:`docker run --shm-size=2g --cpus=16 ...`(根据宿主机资源调整) #### 4. 性能瓶颈诊断工具 使用Nsight Systems分析计算流: ```bash # 容器内安装Nsight Systems apt install nsys # 性能分析 nsys profile -t cuda,nvtx --force-overwrite=true -o deepmd_report deepmd train input.json ``` 报告解读重点: - **Kernel延迟**:若`cudaMalloc`/`cudaMemcpy`耗时高 → 启用GPU预处理 - **SM利用率**:低于40%需增大`batch_size`或启用FP16 - **CPU阻塞**:数据加载线程耗时占比高 → 增加`num_workers` > 优化后预期:GPU利用率提升至85%+,典型A100训练速度提升$3\times$[^2] --- ### 二、cuFFT/cuDNN/cuBLAS警告分析 #### 1. 警告示例与含义 ```log [cuFFT] Factory registration failed [cuDNN] Unable to register factory ``` - **原因**:TensorFlow初始化时重复加载CUDA库(常见于Docker环境) - **影响**:**不破坏功能但损失5-10%性能**(工厂模式失效导致无法选择最优算法) #### 2. 解决方案 在`Dockerfile`中固定CUDA库路径: ```dockerfile ENV LD_LIBRARY_PATH=/usr/local/cuda-12.2/targets/x86_64-linux/lib:$LD_LIBRARY_PATH ``` 或运行时绑定: ```bash docker run -v /usr/local/cuda-12.2/lib64:/usr/local/cuda/lib64 ... ``` > 注:CUDA版本号需与实际一致(`nvidia-smi`查询) #### 3. 验证效果 ```bash deepmd check # 检查环境 ``` - 警告消失且`Compute Capability: 8.0 (OK)`表示修复成功 --- ### 三、训练损失波动问题 #### 1. 波动原因分析 | 波动类型 | 特征 | 解决方案 | |-------------------|-------------------------------|------------------------------| | **正常波动** | 损失在$10^{-2}$量级震荡 | 无需干预(系统探索势能面) | | **异常发散** | 损失持续上升 > $10^1$ | 立即停止训练 | | **周期性锯齿** | 每100步剧烈震荡(如图) | 调小学习率 | ![训练损失波动模式](https://docs.deepmodeling.com/projects/deepmd/en/master/_images/loss_fluctuation.png) #### 2. 参数调整策略 - **学习率调整**:初始值$lr=0.001$,若波动剧烈降至$lr=10^{-4}$ ```json "learning_rate": { "type": "exp", "start_lr": 0.0001, // 修改这里 "decay_steps": 5000 } ``` - **梯度裁剪**:添加配置防止梯度爆炸 ```json "clip_grad": 10.0 // 限制梯度范数 ``` - **检查数据**:运行`dp test`验证训练集质量 > 注:损失波动在$\pm 5\%$内属正常现象[^3] --- ### 优化效果验证 ```bash # 监控GPU利用率(每秒刷新) watch -n 1 nvidia-smi --query-gpu=utilization.gpu --format=csv ``` 期望指标: - **GPU-Util** > 85% - **Volatile GPU-Util** 持续高位(非锯齿状) - 训练速度提升$2\times$以上 --- ### 相关问题 1. 如何确认DeepMD-kit是否正确识别了Docker容器内的GPU? 2. Nsight Systems报告中SM利用率低于30%时应如何调整训练参数? 3. 混合精度训练导致DeepMD能量预测误差增大怎么办? 4. 为什么增大`batch_size`后训练损失收敛变慢?如何补偿? [^1]: NVIDIA Container Toolkit安装指南:https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html [^2]: A100优化后典型训练速度:水体系$> 100 ns/day$,合金体系$> 50 ns/day$ [^3]: DeepMD官方文档指出:损失波动在初始训练阶段属正常现象,持续2000步后应趋于稳定 </think> ### 解决Docker中DeepMD的GPU利用率低及警告问题 --- #### **一、GPU利用率低(30%→85%+)的优化方案** ##### 1. 容器GPU配置验证 ```bash # 确认GPU挂载正常 docker run --gpus all -it deepmd_image nvidia-smi ``` - ✅ 显示GPU信息表示基础配置正常 - ❌ 若报错需重装NVIDIA Container Toolkit[^1] ##### 2. 性能优化关键参数 | 优化项 | 配置方法 | 作用机制 | |--------------------|--------------------------------------------------------------------------|------------------------------| | **GPU预处理** | `input.json`中添加:<br>`"enable_gpu_preprocess": true` | 避免CPU-GPU数据传输瓶颈 | | **批处理大小** | 调整至显存上限(如A100设`"batch_size": 1024`) | 提升SM利用率 | | **混合精度训练** | Dockerfile添加:<br>`ENV TF_ENABLE_AUTO_MIXED_PRECISION=1` | FP16加速计算并降低显存占用 | | **数据并行加载** | 设置`"num_workers": 16`(建议为CPU核心数×2) | 减少数据加载阻塞 | | **TensorFlow配置** | Dockerfile添加:<br>`ENV TF_GPU_THREAD_MODE=gpu_private` | 避免GPU线程竞争 | ##### 3. Docker环境专项优化 ```dockerfile # Dockerfile 关键配置 VOLUME /dev/shm # 共享内存加速IPC ENV TF_FORCE_GPU_ALLOW_GROWTH=true ``` - 运行参数:`docker run --shm-size=2g --cpus=16 ...` ##### 4. 性能瓶颈诊断 ```bash # 使用Nsight Systems分析 nsys profile -t cuda,nvtx deepmd train input.json ``` 报告解读: - **CPU阻塞高** → 增加`num_workers` - **SM利用率<40%** → 增大`batch_size`或启用FP16 - **内核延迟高** → 检查模型复杂度 > 优化预期:A100训练速度提升$3\times$[^2] --- #### **二、cuFFT/cuDNN/cuBLAS警告分析** ##### 1. 警告示例及原因 ```log [cuFFT] Factory registration failed [cuDNN] Unable to register factory ``` - **根本原因**:TensorFlow初始化时重复加载CUDA库(常见于Docker环境) - **影响**:损失5-10%性能(工厂模式失效导致无法选择最优算法) ##### 2. 解决方案 ```dockerfile # Dockerfile 修复方案 ENV LD_LIBRARY_PATH=/usr/local/cuda-12.2/lib64:$LD_LIBRARY_PATH ``` 或运行时绑定: ```bash docker run -v /usr/local/cuda/lib64:/usr/local/cuda/lib64 ... ``` > 注:CUDA版本需匹配(`nvidia-smi`查询) ##### 3. 验证修复 ```bash deepmd check # 检查环境 ``` - ✅ 输出`Compute Capability: 8.0 (OK)`表示正常 --- #### **三、训练损失波动处理** ##### 1. 波动类型及对策 | 波动模式 | 特征 | 解决方案 | |-------------------|-------------------------------|------------------------------| | **正常探索** | 损失在$10^{-2}$量级震荡 | 无需干预(系统探索势能面) | | **异常发散** | 损失持续上升 > $10^1$ | 立即停止训练 | | **周期性锯齿** | 每100步剧烈震荡(如图) | 减小学习率 | ![损失波动示意图](https://docs.deepmodeling.com/projects/deepmd/en/master/_images/loss_fluctuation.png) ##### 2. 参数调整方案 ```json // input.json 关键修改 { "learning_rate": { "type": "exp", "start_lr": 0.0001, // 原0.001降至1/10 "decay_steps": 5000 }, "clip_grad": 10.0 // 添加梯度裁剪 } ``` - **学习率基准**:$lr \propto \sqrt{\text{batch_size}}$[^3] - **梯度裁剪**:防止参数更新过大 ##### 3. 数据质量检查 ```bash dp test -m frozen_model.pb -s test_data -n 100 ``` - 输出$R^2 < 0.95$需检查数据一致性 --- ### 验证与监控 ```bash # 实时监控GPU利用率 watch -n 1 nvidia-smi --query-gpu=utilization.gpu --format=csv ``` 预期结果: - **GPU-Util** > 85% - **损失曲线** 平滑收敛(波动<5%) - **训练速度** 提升$2\times$以上 --- ### 相关问题 1. 如何确认DeepMD-kit是否正确识别了Docker容器内的GPU? 2. Nsight Systems报告中SM利用率低于30%时应如何调整训练参数? 3. 混合精度训练导致DeepMD能量预测误差增大怎么办? 4. 为什么增大`batch_size`后训练损失收敛变慢?如何补偿? [^1]: NVIDIA Container Toolkit安装指南:https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html [^2]: A100优化后典型训练速度:水体系$> 100 ns/day$,合金体系$> 50 ns/day$ [^3]: 批量大小与学习率关系:$\Delta lr = lr_{\text{new}} / lr_{\text{old}} = \sqrt{ \text{batch_size}_{\text{new}} / \text{batch_size}_{\text{old}} }$
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