Python环境检测，人工智能编程环境

天池

  

Python环境检测

   
# 1.1 系统 & 硬件
!uname -a                    # OS / 内核
!lscpu | head -10            # CPU 型号 & 核数
!free -h                     # 内存
!df -h | grep '/'            # 磁盘剩余
!nvidia-smi 2>/dev/null || echo "No GPU"   # GPU 型号 / 显存

# 1.2 预装软件版本
!python --version
!python3 --version
!pip --version
!git --version
!which gcc && gcc --version  # 编译器

# 1.3 网络连通性（可选）
!ping -c 2 gitee.com
Linux dsw-536437-89dcd8865-mzwzh 4.19.91-012.ali4000.alios7.x86_64 #1 SMP Wed Sep 15 17:27:09 CST 2021 x86_64 x86_64 x86_64 GNU/Linux
架构：                           x86_64
CPU 运行模式：                   32-bit, 64-bit
字节序：                         Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU:                             32
在线 CPU 列表：                  0-31
每个核的线程数：                 2
每个座的核数：                   16
座：                             1
NUMA 节点：                      1
              总计         已用        空闲      共享    缓冲/缓存    可用
内存：       6.0Gi       477Mi       5.2Gi          0B       368Mi       5.5Gi
交换：          0B          0B          0B
overlay         492G  117G  355G   25% /
tmpfs            64M     0   64M    0% /dev
tmpfs            62G     0   62G    0% /sys/fs/cgroup
/dev/vda2       492G  117G  355G   25% /tmp
shm              64M     0   64M    0% /dev/shm
overlay         492G  117G  355G   25% /etc/dsw
tmpfs           124G   12K  124G    1% /run/secrets/kubernetes.io/serviceaccount
tmpfs            62G     0   62G    0% /proc/acpi
tmpfs            62G     0   62G    0% /proc/scsi
tmpfs            62G     0   62G    0% /sys/firmware
No GPU
Python 3.7.13
Python 3.7.13
pip 24.0 from /opt/conda/lib/python3.7/site-packages/pip (python 3.7)
git version 2.25.1
/usr/bin/gcc
gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Copyright (C) 2019 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

/bin/bash: ping：未找到命令

　　

  
  
import sys, os, platform, subprocess, pkg_resources, psutil, torch

print("=" * 60)
print("🔍 Python 运行环境")
print("=" * 60)
print("Python:", sys.version)
print("Platform:", platform.platform())
print("PWD:", os.getcwd())
print("=" * 60)

# 2.1 关键库版本
libs = ["torch", "transformers", "accelerate", "sentencepiece", "modelscope"]
for lib in libs:
    try:
        ver = pkg_resources.get_distribution(lib).version
        print(f"{lib:<15} {ver}")
    except:
        print(f"{lib:<15} ❌ 未安装")

# 2.2 硬件资源
mem = psutil.virtual_memory()
print("\n🔍 资源快照")
print(f"Memory Total   : {mem.total/1024**3:.1f} GB")
print(f"Memory Free    : {mem.available/1024**3:.1f} GB")
print(f"GPU Available  : {torch.cuda.is_available()}")
if torch.cuda.is_available():
    for i in range(torch.cuda.device_count()):
        print(f"  - GPU {i}     : {torch.cuda.get_device_name(i)} "
              f"{torch.cuda.memory_reserved(i)/1024**3:.1f} GB")

print("=" * 60)

　　

============================================================
🔍 Python 运行环境
============================================================
Python: 3.7.13 (default, Mar 29 2022, 02:18:16) 
[GCC 7.5.0]
Platform: Linux-4.19.91-012.ali4000.alios7.x86_64-x86_64-with-debian-bullseye-sid
PWD: /mnt/workspace
============================================================
torch           1.11.0+cpu
transformers    4.24.0
accelerate      0.20.3
sentencepiece   0.1.97
modelscope      1.28.1

🔍 资源快照
Memory Total   : 6.0 GB
Memory Free    : 5.5 GB
GPU Available  : False
============================================================

　　

def llm_ready():
    ok = True
    if sys.version_info < (3, 7):
        print("❌ Python 版本过低，建议 ≥3.8")
        ok = False
    if torch.__version__ < "1.10":
        print("⚠️  PyTorch 版本较低，可能影响性能")
    if not torch.cuda.is_available():
        print("⚠️  无 GPU，仅 CPU 推理，速度会慢")
    try:
        from transformers import AutoTokenizer, AutoModel
        print("✅ transformers 可用")
    except ImportError:
        print("❌ 未安装 transformers")
        ok = False
    return ok

print("LLM 环境就绪？" , "✅ 可以跑" if llm_ready() else "❌ 需升级/安装")
⚠️  无 GPU，仅 CPU 推理，速度会慢
✅ transformers 可用
LLM 环境就绪？ ✅ 可以跑

　　

  
   

 浙大平台

 

  
  
# 1.1 系统 & 硬件
!uname -a                    # OS / 内核
!lscpu | head -10            # CPU 型号 & 核数
!free -h                     # 内存
!df -h | grep '/'            # 磁盘剩余
!nvidia-smi 2>/dev/null || echo "No GPU"   # GPU 型号 / 显存

# 1.2 预装软件版本
!python --version
!python3 --version
!pip --version
!git --version
!which gcc && gcc --version  # 编译器

# 1.3 网络连通性（可选）
!ping -c 2 gitee.com
Linux notebook 3.10.0-1127.el7.x86_64 #1 SMP Tue Mar 31 23:36:51 UTC 2020 x86_64 x86_64 x86_64 GNU/Linux
Architecture:        x86_64
CPU op-mode(s):      32-bit, 64-bit
Byte Order:          Little Endian
CPU(s):              64
On-line CPU(s) list: 0-63
Thread(s) per core:  2
Core(s) per socket:  16
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
              total        used        free      shared  buff/cache   available
Mem:           187G         13G        150G        4.1G         23G        169G
Swap:            0B          0B          0B
overlay                                                                              503G  289G  215G  58% /
tmpfs                                                                                 64M     0   64M   0% /dev
tmpfs                                                                                 94G     0   94G   0% /sys/fs/cgroup
/dev/mapper/centos-root                                                               50G   35G   16G  70% /etc/hosts
/dev/mapper/centos-home                                                              503G  289G  215G  58% /etc/hostname
shm                                                                                   64M     0   64M   0% /dev/shm
10.203.10.174:/mnt/user_directory/68a555116eba6373f7fd90d3/68a55fe9b0a1e361589b21a3   28T   28T  411G  99% /home/jovyan/work
tmpfs                                                                                 94G   12K   94G   1% /run/secrets/kubernetes.io/serviceaccount
tmpfs                                                                                 94G     0   94G   0% /proc/acpi
tmpfs                                                                                 94G     0   94G   0% /proc/scsi
tmpfs                                                                                 94G     0   94G   0% /sys/firmware
No GPU
Python 3.7.5
Python 3.7.5
pip 21.1.3 from /home/jovyan/.virtualenvs/basenv/lib/python3.7/site-packages/pip (python 3.7)
git version 2.17.1
/usr/bin/gcc
gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Copyright (C) 2017 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

PING gitee.com-31ba39d0fd3.baiduads.com (180.76.199.13) 56(84) bytes of data.

--- gitee.com-31ba39d0fd3.baiduads.com ping statistics ---
2 packets transmitted, 0 received, 100% packet loss, time 999ms

　　

  
  
import sys, os, platform, subprocess, pkg_resources, psutil, torch

print("=" * 60)
print("🔍 Python 运行环境")
print("=" * 60)
print("Python:", sys.version)
print("Platform:", platform.platform())
print("PWD:", os.getcwd())
print("=" * 60)

# 2.1 关键库版本
libs = ["torch", "transformers", "accelerate", "sentencepiece", "modelscope"]
for lib in libs:
    try:
        ver = pkg_resources.get_distribution(lib).version
        print(f"{lib:<15} {ver}")
    except:
        print(f"{lib:<15} ❌ 未安装")

# 2.2 硬件资源
mem = psutil.virtual_memory()
print("\n🔍 资源快照")
print(f"Memory Total   : {mem.total/1024**3:.1f} GB")
print(f"Memory Free    : {mem.available/1024**3:.1f} GB")
print(f"GPU Available  : {torch.cuda.is_available()}")
if torch.cuda.is_available():
    for i in range(torch.cuda.device_count()):
        print(f"  - GPU {i}     : {torch.cuda.get_device_name(i)} "
              f"{torch.cuda.memory_reserved(i)/1024**3:.1f} GB")

print("=" * 60)
============================================================
🔍 Python 运行环境
============================================================
Python: 3.7.5 (default, Dec  9 2021, 17:04:37) 
[GCC 8.4.0]
Platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-Ubuntu-18.04-bionic
PWD: /home/jovyan/work
============================================================
torch           1.8.1+cpu
transformers    4.1.1
accelerate      ❌ 未安装
sentencepiece   0.1.91
modelscope      ❌ 未安装

🔍 资源快照
Memory Total   : 187.4 GB
Memory Free    : 169.1 GB
GPU Available  : False
============================================================

 

  
  
def llm_ready():
    ok = True
    if sys.version_info < (3, 7):
        print("❌ Python 版本过低，建议 ≥3.8")
        ok = False
    if torch.__version__ < "1.10":
        print("⚠️  PyTorch 版本较低，可能影响性能")
    if not torch.cuda.is_available():
        print("⚠️  无 GPU，仅 CPU 推理，速度会慢")
    try:
        from transformers import AutoTokenizer, AutoModel
        print("✅ transformers 可用")
    except ImportError:
        print("❌ 未安装 transformers")
        ok = False
    return ok

print("LLM 环境就绪？" , "✅ 可以跑" if llm_ready() else "❌ 需升级/安装")
⚠️  无 GPU，仅 CPU 推理，速度会慢
2025-08-20 13:47:59.415739: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory
2025-08-20 13:47:59.415772: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
✅ transformers 可用
LLM 环境就绪？ ✅ 可以跑
​

 

  
   
   
 XEdu  
  
import sys, os, platform, subprocess, pkg_resources, psutil, torch

print("=" * 60)
print("🔍 Python 运行环境")
print("=" * 60)
print("Python:", sys.version)
print("Platform:", platform.platform())
print("PWD:", os.getcwd())
print("=" * 60)

# 2.1 关键库版本
libs = ["torch", "transformers", "accelerate", "sentencepiece", "modelscope"]
for lib in libs:
    try:
        ver = pkg_resources.get_distribution(lib).version
        print(f"{lib:<15} {ver}")
    except:
        print(f"{lib:<15} ❌ 未安装")

# 2.2 硬件资源
mem = psutil.virtual_memory()
print("\n🔍 资源快照")
print(f"Memory Total   : {mem.total/1024**3:.1f} GB")
print(f"Memory Free    : {mem.available/1024**3:.1f} GB")
print(f"GPU Available  : {torch.cuda.is_available()}")
if torch.cuda.is_available():
    for i in range(torch.cuda.device_count()):
        print(f"  - GPU {i}     : {torch.cuda.get_device_name(i)} "
              f"{torch.cuda.memory_reserved(i)/1024**3:.1f} GB")

print("=" * 60)
============================================================
🔍 Python 运行环境
============================================================
Python: 3.8.3 (default, Jul  2 2020, 17:30:36) [MSC v.1916 64 bit (AMD64)]
Platform: Windows-10-10.0.22621-SP0
PWD: D:\XEdu\人工智能 
============================================================
torch           2.4.1
transformers    4.46.3
accelerate      1.0.1
sentencepiece   ❌ 未安装
modelscope      1.22.3

🔍 资源快照
Memory Total   : 15.7 GB
Memory Free    : 0.3 GB
GPU Available  : False
============================================================

　　

  

 requirements.txt

 

  
  
absl-py==0.9.0
alembic==1.12.1
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
ase==3.21.1
astor==0.8.1
asttokens==2.4.1
astunparse==1.6.3
async-generator==1.10
attrs==19.3.0
Augmentor==0.2.8
backcall==0.2.0
baytune==0.4.0
bleach==5.0.0
blis==0.4.1
boto3==1.16.25
botocore==1.19.25
cachetools==3.1.1
cairocffi==1.3.0
CairoSVG==2.5.2
calysto==1.0.6
catalogue==1.0.0
certifi==2022.9.24
certipy==0.1.3
cffi==1.15.0
charset-normalizer==2.1.1
click==8.1.2
cloudpickle==1.2.2
cmake==3.21.1
configparser==5.2.0
copulas==0.3.3
cryptography==36.0.2
cssselect2==0.5.0
cycler==0.11.0
cymem==2.0.6
Cython==0.29.20
debugpy==1.6.0
decorator==4.4.2
defusedxml==0.7.1
distlib==0.3.4
dlib==19.22.0
dm-tree==0.1.7
easydict==1.9
en-core-web-sm @ https://files.momodel.cn/en_core_web_sm-2.3.0.tar.gz
entrypoints==0.4
et-xmlfile==1.1.0
fastjsonschema==2.15.3
filelock==3.6.0
func-timeout==4.3.5
future==0.18.2
gast==0.3.3
gensim==3.8.3
google-auth==2.14.0
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
googledrivedownloader==0.4
graphviz==0.14
greenlet==1.1.2
grpcio==1.29.0
gym==0.15.7
h5py==2.10.0
idna==3.4
imageio==2.8.0
imageio-ffmpeg==0.5.1
imbalanced-learn==0.6.2
imgaug==0.4.0
importlib-metadata==4.13.0
importlib-resources==5.7.0
ipdb==0.13.2
ipykernel==6.13.0
ipython==7.32.0
ipython-genutils==0.2.0
ipywidgets==7.4.0
isodate==0.6.1
jdcal==1.4.1
jedi==0.18.1
jieba==0.42.1
Jinja2==3.0.3
jmespath==0.10.0
joblib==1.1.0
jsonschema==4.4.0
jupyter-client==7.2.2
jupyter-core==4.9.2
jupyter-telemetry==0.1.0
jupyterhub==1.4.2
jupyterlab==1.0.0a1
jupyterlab-server==0.2.0
kanren==0.2.3
Keras==2.4.3
Keras-Preprocessing==1.1.2
kiwisolver==1.3.2
llvmlite==0.39.1
Mako==1.2.0
Markdown==3.4.1
MarkupSafe==2.1.1
matplotlib==3.0.3
matplotlib-inline==0.1.3
metakernel==0.29.0
mindspore @ https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.2.14/MindSpore/unified/x86_64/mindspore-2.2.14-cp37-cp37m-linux_x86_64.whl
minepy==1.2.4
minio==5.0.10
mistune==0.8.4
mock==5.2.0
moviepy==1.0.3
mpmath==1.2.1
multipledispatch==0.6.0
murmurhash==1.0.6
nbconvert==5.6.1
nbformat==5.3.0
nest-asyncio==1.5.5
networkx==2.6.3
nltk==3.5
notebook==6.2.0
numba==0.56.4
numexpr==2.8.6
numpy==1.18.5
numpyencoder==0.3.0
oauthlib==3.2.2
opencv-python==4.5.1.48
openpyxl==3.0.9
opt-einsum==3.3.0
packaging==21.3
paddlepaddle==2.0.1
pamela==1.0.0
pandas==1.3.5
pandocfilters==1.5.0
parso==0.8.3
pbr==5.8.1
pexpect==4.8.0
pickleshare==0.7.5
Pillow==8.1.0
plac==1.1.3
platformdirs==2.5.1
plotly==4.8.1
portpicker==1.3.9
preshed==3.0.6
proglog==0.1.10
prometheus-client==0.14.1
prompt-toolkit==3.0.29
protobuf==3.20.3
psutil==5.9.0
ptyprocess==0.7.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
PyAudio==0.2.11
pycparser==2.21
pydot==1.4.1
pyenchant==3.1.1
pygame==2.0.1
pyglet==1.5.0
Pygments==2.11.2
pyOpenSSL==22.0.0
pyparsing==3.0.7
pyrsistent==0.18.1
python-dateutil==2.8.2
python-json-logger==2.0.2
python-louvain==0.16
pytorch-pretrained-bert==0.6.2
pytorch-transformers==1.2.0
pytz==2022.1
PyWavelets==1.3.0
PyYAML==6.0
pyzmq==22.3.0
rdflib==6.3.2
regex==2022.3.15
requests==2.28.1
requests-oauthlib==1.3.1
retrying==1.3.3
rouge==1.0.0
rsa==4.9
ruamel.yaml==0.17.21
ruamel.yaml.clib==0.2.6
s3transfer==0.3.3
sacremoses==0.0.49
scikit-image==0.15.0
scikit-learn==0.22.2.post1
scipy==1.7.3
seaborn==0.10.1
semantic-version==2.8.5
Send2Trash==1.8.0
sentencepiece==0.1.91
Shapely==1.7.0
six==1.16.0
smart-open==5.2.1
spacy==2.3.2
SQLAlchemy==1.4.35
srsly==1.0.5
stevedore==3.5.0
svgwrite==1.4.2
sympy==1.6.2
tables==3.5.1
tensorboard==2.11.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorboardX==2.0
tensorflow==2.3.1
tensorflow-addons==0.11.2
tensorflow-estimator==2.3.0
tensorflow-federated==0.17.0
tensorflow-model-optimization==0.4.1
tensorflow-privacy==0.5.2
termcolor==2.1.1
terminado==0.13.3
testpath==0.6.0
tf-slim==1.1.0
thinc==7.4.1
tinycss2==1.1.1
tokenizers==0.9.4
toolz==0.11.2
torch @ https://download.pytorch.org/whl/cpu/torch-1.8.1%2Bcpu-cp37-cp37m-linux_x86_64.whl
torch-geometric==1.7.0
torch-scatter @ https://data.pyg.org/whl/torch-1.8.0%2Bcpu/torch_scatter-2.0.8-cp37-cp37m-linux_x86_64.whl
torch-sparse @ https://data.pyg.org/whl/torch-1.8.0%2Bcpu/torch_sparse-0.6.12-cp37-cp37m-linux_x86_64.whl
torch-spline-conv @ https://data.pyg.org/whl/torch-1.8.0%2Bcpu/torch_spline_conv-1.2.1-cp37-cp37m-linux_x86_64.whl
torchinfo==1.7.2
torchtext==0.6.0
torchvision @ https://download.pytorch.org/whl/cpu/torchvision-0.9.1%2Bcpu-cp37-cp37m-linux_x86_64.whl
tornado==6.1
tqdm==4.46.1
traitlets==5.1.1
transformers==4.1.1
typeguard==2.13.3
typing-extensions==4.1.1
unification==0.2.2
urllib3==1.26.13
virtualenv==20.13.1
virtualenv-clone==0.5.7
virtualenvwrapper==4.7.0
wasabi==0.9.1
wcwidth==0.2.5
webencodings==0.5.1
Werkzeug==2.2.2
widgetsnbextension==3.4.2
word2vec==0.11.1
wrapt==1.14.1
xlrd==1.2.0
XlsxWriter==1.4.3
yellowbrick==1.1
zipp==3.11.0

　　

  
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
   
posted @ 2025-08-20 13:56  aiplus  阅读(15)  评论(0)    收藏  举报
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