最近在用並行超算雲GPU伺服器（中國國家網格12區）搭建畢設的環境，這裡記錄一下。
首先，超算雲伺服器的登入可以採用網頁版、也可以採用客戶端（超算雲地址：https://cloud.paratera.com/ ）。需要注意的是，並行超算雲只提供windows和mac的客戶端，Linux使用者可能只有使用網頁版的介面了（或者用pappcloud直接遠端練ssh用vim寫:( 哈哈，pappcloud的用法可參見官網下載的《papp_cloud使用手冊》）。
超算雲上最常見的是用module進行包管理（文件：https://modules.readthedocs.io/en/latest/module.html）。我們可以用module avail命令來檢視現有的包：
[macong@paratera01 ~]$ module avail
------------------------- /usr/share/Modules/modulefiles -------------------------
dot module-git module-info modules null use.own
-------------------------------- /etc/modulefiles --------------------------------
mpi/compat-openmpi16-x86_64 mpi/mpich-x86_64
mpi/mpich-3.0-x86_64 mpi/openmpi-x86_64
mpi/mpich-3.2-x86_64
----------------------------- /software/modulefiles ------------------------------
alphafold/2.0
anaconda/2.7
anaconda/3.7(default)
anaconda/3.7.4
bcftools/1.10.1
具體新建環境相關操作大家可以參見module的文件，此處不再贅述。大家需要注意的是，因為Pytorch和Tensorflow對應的CUDA版本有出入，我們建議Pytorch和Tensorflow分別裝兩個不同的環境。
然後根據下列不同的命令對Pytorch和Tensorflow進行裝載。
1. Pytorch環境裝載與測試
Pytorch 1.9.0 環境裝載：
[macong@paratera01 project]$ module load anaconda/3.7.4(tensflow)
[macong@paratera01 project]$ source activate torch
(torch) [macong@paratera01 project]$
可以檢視此時的torch版本：
(torch) [macong@paratera01 ~]$ pip list |grep torch
torch 1.9.0+cu111
torchvision 0.10.0+cu111
接下來我們編寫test_torch.py測試檔案：
# test_torch.py
import torch
print(torch.cuda.is_available())
採用以下的sub_torch.sh指令碼提交到GPU運算節點執行(注意，提交指令碼里面一定要有裝載環境操作（在計算節點裝載），在使用者節點裡裝載環境沒用（使用者節點只能用於安裝依賴包）)
#!/bin/bash
#SBATCH -N 1
#SBATCH -n 5
#SBATCH -p gpu
#SBATCH --gres=gpu:1
#SBATCH --no-requeue
module load anaconda/3.7.4
source activate torch
export PYTHONUNBUFFERED=1
python test_torch.py
提交命令為
sbatch sub_torch.sh
(注意，不是bash sub_torch.sh，bash不能提交到計算節點)
用squeue檢視佇列情況
[macong@paratera01 project]$ squeue
CLUSTER: priv
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
CLUSTER: swarm
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
16601003 gpu sub_tens macong R INVALID 1 g0002
一段時間後，檢視同目錄下的slurm-16601003.out輸出檔案，我們看到
True
說明Pytorch環境配置成功。
2. Tensorflow 2.5.1 環境裝載與測試：
Tensorflow 2.5.1 環境裝載：
[macong@paratera01 project]$ module load anaconda/3.7.4
[macong@paratera01 project]$ export LD_LIBRARY_PATH=/home/macong/project/cuda/lib64:$LD_LIBRARY_PATH
[macong@paratera01 project]$ source activate tensflow
(tensflow) [macong@paratera01 project]$
載入完畢後，可以檢視此時的tensorflow版本
(tensflow) [macong@paratera01 project]$ pip list |grep tensorflow
tensorflow-estimator 2.4.0
tensorflow-gpu 2.4.1
接下來我們編寫以下test_tensorflow.py檔案：
# test_tensorflow.py
import tensorflow as tf
print(tf.test.is_gpu_available())
採用以下的sub_tensorflow.sh指令碼提交到GPU運算節點執行(同樣地，提交指令碼里面一定要有裝載環境操作。另外注意，因為Tensorflow需要cudnn，這裡要額外地增加cuda動態連結庫的載入地址）
#!/bin/bash
#SBATCH -N 1
#SBATCH -n 5
#SBATCH -p gpu
#SBATCH --gres=gpu:1
#SBATCH --no-requeue
module load anaconda/3.7.4
export LD_LIBRARY_PATH=/home/macong/project/cuda/lib64:$LD_LIBRARY_PATH
source activate tensflow
export PYTHONUNBUFFERED=1
python test_tensorflow.py
提交命令為
sbatch sub_tensorflow.sh
輸出顯示
job 16601097 on cluster swarm
同樣，我們可以用squeue檢視佇列情況
[macong@paratera01 project]$ squeue
CLUSTER: priv
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
CLUSTER: swarm
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
16601097 gpu sub_tens macong R INVALID 1 g0039
一段時間後，檢視同目錄下的slurm-16601097.out輸出檔案，我們看到一長串列印輸出
2021-11-28 15:29:22.848812: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
WARNING:tensorflow:From test_tensorflow.py:2: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
2021-11-28 15:30:04.558903: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-11-28 15:30:04.592168: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-11-28 15:30:04.596694: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-11-28 15:30:04.736951: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:84:00.0 name: Tesla V100-SXM2-16GB computeCapability: 7.0
coreClock: 1.53GHz coreCount: 80 deviceMemorySize: 15.78GiB deviceMemoryBandwidth: 836.37GiB/s
2021-11-28 15:30:04.737540: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-11-28 15:30:05.810351: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-11-28 15:30:05.810525: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-11-28 15:30:06.033285: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-11-28 15:30:06.193055: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-11-28 15:30:06.630374: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-11-28 15:30:06.820341: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-11-28 15:30:06.847036: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-11-28 15:30:06.850769: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-11-28 15:30:06.850852: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-11-28 15:30:09.592923: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-11-28 15:30:09.593017: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2021-11-28 15:30:09.593043: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2021-11-28 15:30:09.628099: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/device:GPU:0 with 14761 MB memory) -> physical GPU (device: 0, name: Tesla V100-SXM2-16GB, pci bus id: 0000:84:00.0, compute capability: 7.0)
True
當然，我們只需要關注最後一行的“True”，說明Tensorflow環境配置成功。
3. 常用命令：
(1) squeue
squeue可用於檢視當前的任務佇列資訊，如之前我們看到的：
[macong@paratera01 project]$ squeue
CLUSTER: priv
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
CLUSTER: swarm
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
16601097 gpu sub_tens macong R INVALID 1 g0039
(2) scancel
scancel+任務id可用於將正在執行的任務殺掉，如殺掉正在執行的16601167任務
scancel 16601167
對應的slurm-16601167.out檔案中會顯示:
slurmstepd: error: *** JOB 16601167 ON g0011 CANCELLED AT 2021-11-28T10:10:00 ***
更多命令可詳見官網《中國國家網格12區使用者手冊v2.4》