Conda构建与该确切pc上的环境所生成的需求发生冲突

问题描述 投票:1回答:2

我在requirements.txt文件中具有以下要求:

# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: win-64
asn1crypto=0.24.0
attrs=19.1.0
backports=1.0
backports.shutil_get_terminal_size=1.0.0
backports.shutil_which=3.5.2
backports_abc=0.5=py27h0ec6b72_0
blas=1.0=mkl
bleach=3.1.0=py27_0
blist=1.3.6=py27_0
ca-certificates=2019.8.28=0
certifi=2019.9.11=py27_0
cffi=1.12.3=py27hcfb25f9_0
click=7.0=py27_0
colorama=0.4.1
comtypes=1.1.7=py27_0
configobj=5.0.6=py27_0
configparser=3.7.4=py27_0
cryptography=2.7=py27hcfb25f9_0
cycler=0.10.0
cython=0.29.13=py27hc56fc5f_0
decorator=4.4.0=py27_1
defusedxml=0.6.0=py_0
entrypoints=0.3=py27_0
enum34=1.1.6=py27_1
flask=0.12.2=py27_0
flask-swagger=0.2.8=py27_0
funcsigs=1.0.2
functools32=3.2.3.2=py27_1
future=0.16.0=py27_1
futures=3.3.0=py27_0
gitdb=0.6.0=py27_1
gitpython=0.3.2
hdf5=1.8.15.1=vc9_4
icc_rt=2019.0.0=h0cc432a_1
idna=2.8=py27_0
intel-openmp=2019.4=245
ipaddress=1.0.22=py27_0
ipykernel=4.10.0=py27_0
ipython=5.8.0=py27_0
ipython_genutils=0.2.0=py27hbe997df_0
ipywidgets=7.5.1=py_0
itsdangerous=1.1.0=py27_0
jdcal=1.0=py27_0
jinja2=2.10.1=py27_0
jsonschema=3.0.2=py27_0
jupyter=1.0.0=py27_3
jupyter_client=5.3.3=py_0
jupyter_console=5.2.0=py27_1
jupyter_core=4.5.0=py_0
kiwisolver=1.1.0
libsodium=1.0.16=h8b3e59e_0
line_profiler=1.0b3=py27_0
lxml=3.3.4=py27_0
markupsafe=1.1.1=py27h0c8e037_0
matplotlib=2.2.3
mistune=0.8.4=py27h0c8e037_0
mkl=2018.0.3=1
more-itertools=5.0.0
nbconvert=5.6.0=py27_1
nbformat=4.4.0=py27hf49b375_0
nose=1.3.7=py27_2
notebook=5.7.8=py27_0
numexpr=2.4.4
numpy=1.16.5
numpy-base=1.9.3=py27h0bb1d87_7
openpyxl=2.0.2=py27_0
openssl=1.1.1d=h0c8e037_0
pandas=0.16.2
pandoc=2.2.3.2=0
pandocfilters=1.4.2=py27_1
pathlib2=2.3.4=py27_0
patsy=0.5.1=py27_0
pickleshare=0.7.5=py27_0
pip=19.2.3=py27_0
pluggy=0.6.0
prometheus_client=0.7.1=py_0
prompt_toolkit=1.0.15=py27h3a8ec6a_0
py=1.8.0
pycparser=2.19=py27_0
pygments=2.4.2=py_0
pyodbc=4.0.17=py27_0
pyopenssl=19.0.0=py27_0
pyparsing=2.4.2
pyqt=4.10.4=py27_1
pyrsistent=0.14.11=py27h0c8e037_0
pysocks=1.7.1=py27_0
pytables=3.2.2
pytest=3.5.1
python=2.7.12=0
python-dateutil=2.8.0=py27_0
pytz=2019.2=py_0
pywin32=220=py27_2
pywinpty=0.5=py27_2
pyyaml=5.1.2=py27h0c8e037_0
pyzmq=18.1.0=py27hc56fc5f_0
qtconsole=4.5.5=py_0
redis=2.10.5=py27_0
requests=2.14.2=py27_0
requests-kerberos=0.11.0=py27_0
retrying=1.3.3=py27_0
scandir=1.10.0=py27h0c8e037_0
scikit-learn=0.16.1
scipy=0.16.0
send2trash=1.5.0=py27_0
setuptools=41.2.0=py27_0
simplegeneric=0.8.1=py27_2
singledispatch=3.4.0.3=py27h3f9d112_0
six=1.12.0
smmap=0.8.3=py27_0
sqlalchemy=1.1.13=py27_0
statsmodels=0.6.1
terminado=0.8.2=py27_0
testpath=0.4.2=py27_0
tornado=5.1.1=py27h0c8e037_0
traitlets=4.3.2=py27h1b1b3a5_0
traits=4.6.0=py27_0
typing=3.6.2=py27_0
urllib3=1.21.1=py27_0
vc=9=h7299396_1
vs2008_runtime=9.00.30729.5054=0
wcwidth=0.1.7=py27hb1a0d82_0
webencodings=0.5.1=py27_1
werkzeug=0.16.0=py_0
wheel=0.33.6=py27_0
widgetsnbextension=3.5.1=py27_0
win_inet_pton=1.1.0=py27_0
win_unicode_console=0.5=py27hc037021_0
wincertstore=0.2=py27hf04cefb_0
winkerberos=0.7.0=py27_1
winpty=0.4.3=4
xlrd=1.1.0=py27_0
xlsxwriter=0.9.8=py27_0
xlwings=0.11.4=py27_0
xlwt=1.3.0=py27_0
yaml=0.1.7=h3e6d941_2
zeromq=4.3.1=h2880e7c_3
zlib=1.2.11=h3cc03e0_3

运行时(此文件包含上面粘贴的内容):conda install -y --file "requirements.txt"

我得到:

UnsatisfiableError: The following specifications were found to be incompatible with each other:



Package blas conflicts for:
numpy-base==1.9.3=py27h0bb1d87_7 -> blas==1.0=mkl
pytables==3.2.2 -> numpy=1.13 -> blas[version='*|1.0',build=mkl]
scikit-learn==0.16.1 -> numpy=1.9 -> numpy-base==1.9.3=py27h0bb1d87_7 -> blas==1.0=mkl
scipy==0.16.0 -> numpy=1.9 -> blas=[build=mkl]
numpy==1.16.5 -> mkl-service[version='>=2,<3.0a0'] -> blas=[build=mkl]
scipy==0.16.0 -> numpy=1.9 -> numpy-base==1.9.3=py27h0bb1d87_7 -> blas==1.0=mkl
numexpr==2.4.4 -> numpy=1.9 -> numpy-base==1.9.3=py27h0bb1d87_7 -> blas==1.0=mkl
matplotlib==2.2.3 -> numpy -> blas[version='*|1.0',build=mkl]
pandas==0.16.2 -> numpy=1.9 -> numpy-base==1.9.3=py35h5c71026_7 -> blas==1.0=mkl
patsy==0.5.1=py27_0 -> numpy[version='>=1.4.0'] -> blas[version='*|1.0',build=mkl]
numpy==1.16.5 -> blas==1.0=mkl
scikit-learn==0.16.1 -> numpy=1.9 -> blas=[build=mkl]
numexpr==2.4.4 -> numpy=1.9 -> blas=[build=mkl]
pandas==0.16.2 -> numpy=1.9 -> blas=[build=mkl]
statsmodels==0.6.1 -> numpy=1.11 -> blas[version='*|1.0',build=mkl]
Package numpy-base conflicts for:
patsy==0.5.1=py27_0 -> numpy[version='>=1.4.0'] -> mkl_fft -> numpy-base[version='>=1.0.6,<2.0a0']
numpy-base==1.9.3=py27h0bb1d87_7
patsy==0.5.1=py27_0 -> numpy[version='>=1.4.0'] -> numpy-base[version='1.11.3|1.14.3|1.14.4|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.6|1.14.6|1.15.0|1.15.1|1.15.2|1.15.2|1.15.3|1.15.4|1.15.4|1.16.0|1.16.0|1.16.1|1.16.1|1.16.2|1.16.3|1.16.5|1.9.3|1.9.3',build='py27hb1d0314_1|py27h2753ae9_0|py27h2753ae9_0|py27hb1d0314_5|py27h0bb1d87_3|py27h0bb1d87_2|py27h0bb1d87_1|py27h0bb1d87_0|py27h0bb1d87_0|py27hb1d0314_12|py27h2753ae9_9|py27h0bb1d87_7|py27h0bb1d87_8|py27h2753ae9_10|py27hb1d0314_11|py27hfef472a_9|py27h917549b_1|py27h0bb1d87_4|py27h2753ae9_4|py27hfef472a_0|py27h2753ae9_0|py27h2753ae9_1|py27h2753ae9_0|py27hb1d0314_0|py27hb1d0314_0|py27hb1d0314_1|py27hb1d0314_0|py27hb1d0314_0|py27hb1d0314_0|py27hb1d0314_0|py27h0bb1d87_6|py27h0bb1d87_7']
numpy==1.16.5 -> numpy-base==1.16.5[build='py36hc3f5095_0|py27hb1d0314_0|py37hc3f5095_0']
numexpr==2.4.4 -> numpy=1.9 -> numpy-base==1.9.3[build='py27h0bb1d87_7|py27h0bb1d87_6|py35h5c71026_7']
pandas==0.16.2 -> numpy=1.9 -> numpy-base==1.9.3[build='py27h0bb1d87_7|py27h0bb1d87_6|py35h5c71026_7']
pytables==3.2.2 -> numpy=1.13 -> mkl_random -> numpy-base[version='>=1.0.2,<2.0a0|>=1.0.6,<2.0a0']
numpy==1.16.5 -> mkl_random[version='>=1.0.2,<2.0a0'] -> numpy-base[version='>=1.0.2,<2.0a0|>=1.0.6,<2.0a0']
pytables==3.2.2 -> numpy=1.13 -> numpy-base[version='1.11.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|>=1.9.3,<2.0a0',build='py36h5c71026_7|py35h5c71026_7|py27h0bb1d87_7|py27h0bb1d87_6|py36hc3f5095_12|py36h8128ebf_9|py27hb1d0314_12|py27h2753ae9_9|py27h0bb1d87_7|py27h0bb1d87_8|py27h2753ae9_10|py27hb1d0314_11|py27hfef472a_9|py35h4a99626_8|py35h4a99626_9|py35h8128ebf_10|py35h8128ebf_9|py36h2a9b21d_11|py36h4a99626_9|py36h5c71026_7|py36h5c71026_8|py36h8128ebf_10|py36h8128ebf_11|py36h5c71026_6']
matplotlib==2.2.3 -> numpy -> mkl_fft[version='>=1.0.6,<2.0a0'] -> numpy-base[version='>=1.0.2,<2.0a0|>=1.0.6,<2.0a0']
scikit-learn==0.16.1 -> numpy=1.9 -> numpy-base==1.9.3[build='py27h0bb1d87_7|py27h0bb1d87_6|py35h5c71026_7']
scipy==0.16.0 -> numpy=1.9 -> numpy-base==1.9.3[build='py27h0bb1d87_7|py27h0bb1d87_6|py35h5c71026_7']
statsmodels==0.6.1 -> numpy=1.11 -> numpy-base[version='1.11.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|>=1.9.3,<2.0a0',build='py36h5c71026_7|py35h5c71026_7|py27h0bb1d87_7|py27h0bb1d87_6|py36hc3f5095_12|py36h8128ebf_9|py27hb1d0314_12|py27h2753ae9_9|py27h0bb1d87_7|py27h0bb1d87_8|py27h2753ae9_10|py27hb1d0314_11|py27hfef472a_9|py35h4a99626_8|py35h4a99626_9|py35h8128ebf_10|py35h8128ebf_9|py36h2a9b21d_11|py36h4a99626_9|py36h5c71026_7|py36h5c71026_8|py36h8128ebf_10|py36h8128ebf_11|py36h5c71026_6']
matplotlib==2.2.3 -> numpy -> numpy-base[version='1.11.3|1.14.3|1.14.3|1.14.3|1.14.4|1.14.4|1.14.4|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.5|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.14.6|1.15.0|1.15.0|1.15.0|1.15.0|1.15.1|1.15.1|1.15.1|1.15.1|1.15.2|1.15.2|1.15.2|1.15.2|1.15.2|1.15.3|1.15.3|1.15.3|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.15.4|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.0|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.1|1.16.2|1.16.2|1.16.2|1.16.3|1.16.3|1.16.3|1.16.4|1.16.4|1.16.5|1.16.5|1.16.5|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|1.9.3|>=1.9.3,<2.0a0',build='py36h5c71026_7|py37hc3f5095_0|py37hc3f5095_0|py27hb1d0314_1|py37hc3f5095_0|py37h8128ebf_0|py36hc3f5095_0|py27hb1d0314_0|py27h2753ae9_0|py37h8128ebf_0|py36h8128ebf_0|py37h8128ebf_0|py37h8128ebf_0|py36h8128ebf_0|py35h8128ebf_0|py27h2753ae9_0|py27hfef472a_0|py37h8128ebf_4|py36h8128ebf_4|py27hb1d0314_5|py37h5c71026_2|py37h5c71026_1|py37h5c71026_0|py36h5c71026_4|py36h5c71026_2|py36h5c71026_1|py36h5c71026_0|py27h0bb1d87_3|py27h0bb1d87_0|py27h0bb1d87_0|py35h555522e_1|py27h917549b_1|py37hc3f5095_12|py37h8128ebf_9|py37h8128ebf_11|py37h2a9b21d_11|py36hc3f5095_12|py36h8128ebf_9|py27hb1d0314_12|py27h2753ae9_9|py27h0bb1d87_7|py27h0bb1d87_8|py27h2753ae9_10|py27hb1d0314_11|py27hfef472a_9|py35h4a99626_8|py35h4a99626_9|py35h8128ebf_10|py35h8128ebf_9|py36h2a9b21d_11|py36h4a99626_9|py36h5c71026_7|py36h5c71026_8|py36h8128ebf_10|py36h8128ebf_11|py37h4a99626_9|py37h5c71026_7|py37h5c71026_8|py37h8128ebf_10|py36h555522e_1|py35h5c71026_0|py36h5c71026_0|py27h0bb1d87_1|py27h0bb1d87_2|py27h0bb1d87_4|py35h4a99626_4|py35h5c71026_0|py36h5c71026_3|py37h5c71026_3|py37h5c71026_4|py27h2753ae9_4|py35h8128ebf_4|py36hc3f5095_5|py37hc3f5095_5|py35h4a99626_0|py36h4a99626_0|py37h4a99626_0|py27h2753ae9_0|py27h2753ae9_1|py35h8128ebf_0|py36h8128ebf_0|py27h2753ae9_0|py36h8128ebf_0|py27hb1d0314_0|py27hb1d0314_1|py36hc3f5095_0|py36hc3f5095_1|py37hc3f5095_0|py37hc3f5095_1|py27hb1d0314_0|py36hc3f5095_0|py36hc3f5095_1|py37hc3f5095_1|py27hb1d0314_0|py36hc3f5095_0|py37hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py36hc3f5095_0|py37hc3f5095_0|py27hb1d0314_0|py36hc3f5095_0|py37hc3f5095_0|py27h0bb1d87_6|py27h0bb1d87_7|py35h5c71026_7|py36h5c71026_6|py37h5c71026_6|py37h5c71026_7']
Package numpy conflicts for:
scikit-learn==0.16.1 -> numpy[version='1.10.*|1.9.*']
statsmodels==0.6.1 -> numpy=1.11 -> numpy[version='>=1.11.3,<2.0a0|>=1.11|>=1.11.3,<1.12.0a0|>=1.12|>=1.12.1,<2.0a0|>=1.4.0|>=1.7.0|>=1.9']
matplotlib==2.2.3 -> numpy
statsmodels==0.6.1 -> numpy[version='1.10.*|1.11.*|1.12.*|1.8.*|1.9.*']
scipy==0.16.0 -> numpy[version='1.10.*|1.9.*']
pytables==3.2.2 -> numpy[version='1.10.*|1.11.*|1.12.*|1.13.*|1.9.*']
pandas==0.16.2 -> numpy=1.9
numexpr==2.4.4 -> numpy[version='1.10.*|1.9.*']
patsy==0.5.1=py27_0 -> numpy[version='>=1.4.0']
numpy==1.16.5
Package mkl-service conflicts for:
pytables==3.2.2 -> numexpr -> mkl==11.3.3 -> mkl-service[version='1.0.0|>=2,<3.0a0']
matplotlib==2.2.3 -> numpy -> mkl[version='>=2018.0.0,<2019.0a0'] -> mkl-service==1.0.0
patsy==0.5.1=py27_0 -> numpy[version='>=1.4.0'] -> mkl==11.3.3 -> mkl-service==1.0.0
Package pyqt conflicts for:
matplotlib==2.2.3 -> pyqt[version='5.6.*|5.9.*']
jupyter==1.0.0=py27_3 -> qtconsole -> pyqt[version='4.*|5.*']
qtconsole==4.5.5=py_0 -> pyqt
pyqt==4.10.4=py27_1
Package mkl-rt conflicts for:
scikit-learn==0.16.1 -> mkl-rt==11.1
numexpr==2.4.4 -> mkl-rt==11.1
pytables==3.2.2 -> numexpr -> mkl==11.3.3 -> mkl-service==1.0.0 -> mkl-rt==11.0
statsmodels==0.6.1 -> numpy=1.11 -> numpy-base==1.11.3=py27hb1d0314_12 -> mkl-rt==11.0
Package mkl conflicts for:
mkl==2018.0.3=1

现在,奇怪的是,这个要求文件是由一个env在同一台PC上创建的,我在其中安装了所需的各种软件包。

我将如何解决这个问题?

谢谢

python conda
2个回答
0
投票

Conda v4.7 dropped a branch of the Anaconda Cloud repository called the free channel为了提高求解性能。不幸的是,这包括许多以前的软件包,这些软件包从未移植到保留的存储库分支中。您的要求受此影响,特别是我注意到SciPy 0.16.0相当旧,并且未显示在conda search scipy=0.16.0[subdir=win-64]中。


0
投票

Merv似乎非常了解情况,我并不是要破坏他的回答或为回答我的问题而付出的努力。

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