python - 无法 `pip install -r requirements.txt`

Question

我正在尝试在 Ubuntu 服务器上设置 Python2.7 环境。使用我们从开发系统上的pip freeze获取的requirements.txt，运行

pip install -r requirements.txt

在服务器上给出:

Collecting abstract-rendering==0.5.1 (from -r requirements.txt (line 1))
  Using cached abstract_rendering-0.5.1.tar.gz
    Complete output from command python setup.py egg_info:
    Traceback (most recent call last):
      File "<string>", line 20, in <module>
      File "/tmp/pip-build-JhBJBA/abstract-rendering/setup.py", line 6, in <module>
        from numpy import get_include
    ImportError: No module named numpy

    ----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in /tmp/pip-build-JhBJBA/abstract-rendering

所以我手动安装numpy使用

pip install numpy 

但这给出了一个以

结尾的长错误输出

      File "/tmp/pip-build-2uW9Y2/numpy/numpy/distutils/command/build_src.py", line 386, in generate_sources
        source = func(extension, build_dir)
      File "numpy/core/setup.py", line 669, in get_mathlib_info
        raise RuntimeError("Broken toolchain: cannot link a simple C program")
    RuntimeError: Broken toolchain: cannot link a simple C program

    ----------------------------------------
Command "/usr/bin/python2.7 -c "import setuptools, tokenize;__file__='/tmp/pip-build-2uW9Y2/numpy/setup.py';exec(compile(getattr(tokenize, 'open', open)(__file__).read().replace('\r\n', '\n'), __file__, 'exec'))" install --record /tmp/pip-ZQ5XJ7-record/install-record.txt --single-version-externally-managed --compile" failed with error code 1 in /tmp/pip-build-2uW9Y2/numpy

关于如何在服务器上正确设置 Python 环境的任何想法？下面是 requirements.txt 文件。

abstract-rendering==0.5.1
alabaster==0.7.6
anaconda-client==1.1.0
appnope==0.1.0
appscript==1.0.1
argcomplete==1.0.0
astropy==1.0.5
Babel==2.1.1
backports-abc==0.4
backports.ssl-match-hostname==3.4.0.2
bcolz==0.12.0
beautifulsoup4==4.3.2
binstar==0.11.0
bitarray==0.8.1
blaze==0.8.3
blz==0.6.2
bokeh==0.10.0
boto==2.38.0
boto3==1.2.2
botocore==1.3.8
Bottleneck==1.0.0
bz2file==0.98
cdecimal==2.3
certifi==14.5.14
cffi==1.2.1
clyent==0.4.0
colorama==0.3.3
configobj==5.0.6
cryptography==1.0.2
cssselect==0.9.1
cv2==1.0
cycler==0.9.0
Cython==0.23.4
cytoolz==0.7.4
datashape==0.4.7
decorator==4.0.4
docopt==0.6.2
docutils==0.12
enum34==1.0.4
et-xmlfile==1.0.1
fastcache==1.0.2
findspark==0.0.5
Flask==0.10.1
funcsigs==0.4
functools32==3.2.3.post2
futures==3.0.3
gdbn==0.1
gensim==0.12.2
gevent==1.0.1
gevent-websocket==0.9.3
gnumpy==0.2
greenlet==0.4.9
grin==1.2.1
h5py==2.5.0
httpretty==0.8.6
idna==2.0
ipaddress==1.0.14
ipykernel==4.1.1
ipython==4.0.0
ipython-genutils==0.1.0
ipywidgets==4.1.0
itsdangerous==0.24
jdcal==1.0
jedi==0.9.0
Jinja2==2.8
jmespath==0.9.0
joblib==0.9.2
jsonschema==2.4.0
jupyter==1.0.0
jupyter-client==4.1.1
jupyter-console==4.0.3
jupyter-core==4.0.6
Keras==0.2.0
Lasagne==0.1
llvmlite==0.7.0+3.g1ec568f
lxml==3.4.4
MarkupSafe==0.23
matplotlib==1.4.3
mistune==0.7.1
mock==1.0.1
multipledispatch==0.4.8
nbconvert==4.0.0
nbformat==4.0.1
networkx==1.10
nltk==3.1
nolearn==0.6a0.dev0
nose==1.3.7
notebook==4.0.6
numba==0.21.0
numexpr==2.4.4
numpy==1.10.1
odo==0.3.4
openpyxl==2.2.6
pandas==0.17.0
path.py==0.0.0
patsy==0.4.0
Pattern==2.6
pbr==1.8.1
pep8==1.6.2
pexpect==3.3
pickleshare==0.5
Pillow==3.0.0
ply==3.8
psutil==3.2.2
ptyprocess==0.5
PuLP==1.6.0
py==1.4.30
pyasn1==0.1.9
PyAudio==0.2.7
pycosat==0.6.1
pycparser==2.14
pycrypto==2.6.1
pycryptodome==3.3.1
pycurl==7.19.5.1
pyflakes==1.0.0
Pygments==2.0.2
pymongo==3.0.3
pyOpenSSL==0.15.1
pyparsing==2.0.3
pyquery==1.2.9
pytest==2.8.1
python-dateutil==2.4.2
pytz==2015.7
PyYAML==3.11
pyzmq==14.7.0
qtconsole==4.1.0
redis==2.10.3
requests==2.8.1
rope==0.10.3
runipy==0.1.3
scikit-image==0.11.3
scikit-learn==0.16.1
scipy==0.16.0
seaborn==0.6.0
secret==0.5.1
simplegeneric==0.8.1
singledispatch==3.4.0.3
six==1.10.0
sklearn==0.0
smart-open==1.3.0
snowballstemmer==1.2.0
sockjs-tornado==1.0.1
Sphinx==1.3.1
sphinx-rtd-theme==0.1.7
spyder==2.3.7
SQLAlchemy==1.0.9
statsmodels==0.6.1
sympy==0.7.6.1
tables==3.2.2
tabulate==0.7.5
tensorflow==0.5.0
terminado==0.5
Theano==0.7.0
toolz==0.7.4
tornado==4.2.1
traitlets==4.0.0
trollius==2.0
ujson==1.33
unicodecsv==0.14.1
Werkzeug==0.10.4
wheel==0.26.0
xlrd==0.9.4
XlsxWriter==0.7.7
xlwings==0.4.1
xlwt==1.0.0

Answer 1

安装 numpy 通常需要一个有效的 C 编译器环境。由于这可能是一项乏味的任务，人们喜欢使用(科学的)python 发行版，如 anaconda。至少你需要一个 cpmpiler 和 python 头文件，可能还需要更多。如果您没有使用 python 包和 C 扩展的经验，请选择 anaconda & co。您会找到许多关于 pip 和 numpy 等的答案。

sudo apt-get install build-essential python-dev