Python 为什么火炬不是由torchvision安装的？
不知何故，当我安装时，它会安装Python 为什么火炬不是由torchvision安装的？,python,pytorch,conda,Python,Pytorch,Conda,不知何故，当我安装时，它会安装torchvision，但不会安装torch。我按照主网站的指示运行的命令： conda install pytorch torchvision cudatoolkit=10.0 -c pytorch 然后我做了一个列表，但看： $ conda list # packages in environment at /home/ubuntu/anaconda3/envs/pytorch_p36: # # Name Version
torchvisiontorchconda install pytorch torchvision cudatoolkit=10.0 -c pytorch
$ conda list
# packages in environment at /home/ubuntu/anaconda3/envs/pytorch_p36:
#
# Name Version Build Channel
alabaster 0.7.10 py36h306e16b_0
anaconda-client 1.6.14 py36_0
anaconda-project 0.8.2 py36h44fb852_0
argparse 1.4.0 <pip>
asn1crypto 0.24.0 py36_0
astroid 1.6.3 py36_0
astropy 3.0.2 py36h3010b51_1
attrs 18.1.0 py36_0
autovizwidget 0.12.7 <pip>
babel 2.5.3 py36_0
backcall 0.1.0 py36_0
backports 1.0 py36hfa02d7e_1
backports.shutil_get_terminal_size 1.0.0 py36hfea85ff_2
bcrypt 3.1.6 <pip>
beautifulsoup4 4.6.0 py36h49b8c8c_1
bitarray 0.8.1 py36h14c3975_1
bkcharts 0.2 py36h735825a_0
blas 1.0 mkl
blaze 0.11.3 py36h4e06776_0
bleach 2.1.3 py36_0
blosc 1.14.3 hdbcaa40_0
bokeh 1.0.4 py36_0
boto 2.48.0 py36h6e4cd66_1
boto3 1.9.146 <pip>
boto3 1.9.134 py_0
botocore 1.12.146 <pip>
botocore 1.12.134 py_0
bottleneck 1.2.1 py36haac1ea0_0
bzip2 1.0.6 h14c3975_5
ca-certificates 2019.1.23 0
cached-property 1.5.1 <pip>
cairo 1.14.12 h8948797_3
certifi 2019.3.9 py36_0
cffi 1.11.5 py36h9745a5d_0
chardet 3.0.4 py36h0f667ec_1
click 6.7 py36h5253387_0
cloudpickle 0.5.3 py36_0
clyent 1.2.2 py36h7e57e65_1
colorama 0.3.9 py36h489cec4_0
contextlib2 0.5.5 py36h6c84a62_0
cryptography 2.3.1 py36hc365091_0
cudatoolkit 10.0.130 0
curl 7.60.0 h84994c4_0
cycler 0.10.0 py36h93f1223_0
cymem 2.0.2 py36hfd86e86_0
cython 0.28.2 py36h14c3975_0
cytoolz 0.9.0.1 py36h14c3975_0
dask 0.17.5 py36_0
dask-core 0.17.5 py36_0
dataclasses 0.6 py_0 fastai
datashape 0.5.4 py36h3ad6b5c_0
dbus 1.13.2 h714fa37_1
decorator 4.3.0 py36_0
defusedxml 0.6.0 py_0
dill 0.2.9 py36_0
distributed 1.21.8 py36_0
docker 3.7.2 <pip>
docker-compose 1.24.0 <pip>
docker-pycreds 0.4.0 <pip>
dockerpty 0.4.1 <pip>
docopt 0.6.2 <pip>
docutils 0.14 py36hb0f60f5_0
entrypoints 0.2.3 py36h1aec115_2
environment-kernels 1.1.1 <pip>
et_xmlfile 1.0.1 py36hd6bccc3_0
expat 2.2.5 he0dffb1_0
fastai 1.0.52 1 fastai
fastcache 1.0.2 py36h14c3975_2
fastprogress 0.1.21 py_0 fastai
filelock 3.0.4 py36_0
flask 1.0.2 py36_1
flask-cors 3.0.4 py36_0
fontconfig 2.13.0 h9420a91_0
freetype 2.9.1 h8a8886c_1
fribidi 1.0.5 h7b6447c_0
get_terminal_size 1.0.0 haa9412d_0
gevent 1.3.0 py36h14c3975_0
glib 2.56.1 h000015b_0
glob2 0.6 py36he249c77_0
gmp 6.1.2 h6c8ec71_1
gmpy2 2.0.8 py36hc8893dd_2
graphite2 1.3.11 h16798f4_2
graphviz 2.40.1 h21bd128_2
greenlet 0.4.13 py36h14c3975_0
gst-plugins-base 1.14.0 hbbd80ab_1
gstreamer 1.14.0 hb453b48_1
h5py 2.8.0 py36h989c5e5_3
harfbuzz 1.8.4 hec2c2bc_0
hdf5 1.10.2 hba1933b_1
hdijupyterutils 0.12.7 <pip>
heapdict 1.0.0 py36_2
html5lib 1.0.1 py36h2f9c1c0_0
icu 58.2 h9c2bf20_1
idna 2.6 py36h82fb2a8_1
imageio 2.3.0 py36_0
imagesize 1.0.0 py36_0
intel-openmp 2018.0.0 8
ipykernel 4.8.2 py36_0
ipyparallel 6.2.2 <pip>
ipython 6.4.0 py36_0
ipython_genutils 0.2.0 py36hb52b0d5_0
ipywidgets 7.2.1 py36_0
ipywidgets 7.4.0 <pip>
isort 4.3.4 py36_0
itsdangerous 0.24 py36h93cc618_1
jbig 2.1 hdba287a_0
jdcal 1.4 py36_0
jedi 0.12.0 py36_1
jinja2 2.10 py36ha16c418_0
jmespath 0.9.4 py_0
jpeg 9b h024ee3a_2
jsonschema 2.6.0 py36h006f8b5_0
jupyter 1.0.0 py36_4
jupyter_client 5.2.3 py36_0
jupyter_console 5.2.0 py36he59e554_1
jupyter_core 4.4.0 py36h7c827e3_0
jupyterlab 0.32.1 py36_0
jupyterlab_launcher 0.10.5 py36_0
kiwisolver 1.0.1 py36h764f252_0
krb5 1.14.2 hcdc1b81_6
lazy-object-proxy 1.3.1 py36h10fcdad_0
libcurl 7.60.0 h1ad7b7a_0
libedit 3.1.20170329 h6b74fdf_2
libffi 3.2.1 hd88cf55_4
libgcc-ng 8.2.0 hdf63c60_1
libgfortran 3.0.0 1 conda-forge
libgfortran-ng 7.2.0 hdf63c60_3
libpng 1.6.37 hbc83047_0
libprotobuf 3.5.2 hd28b015_1 conda-forge
libsodium 1.0.16 h1bed415_0
libssh2 1.8.0 h9cfc8f7_4
libstdcxx-ng 8.2.0 hdf63c60_1
libtiff 4.0.9 he85c1e1_1
libtool 2.4.6 h544aabb_3
libuuid 1.0.3 h1bed415_2
libxcb 1.13 h1bed415_1
libxml2 2.9.8 h26e45fe_1
libxslt 1.1.32 h1312cb7_0
llvmlite 0.23.1 py36hdbcaa40_0
locket 0.2.0 py36h787c0ad_1
lxml 4.2.1 py36h23eabaa_0
lzo 2.10 h49e0be7_2
markupsafe 1.0 py36hd9260cd_1
matplotlib 2.2.2 <pip>
matplotlib 3.0.3 py36h5429711_0
mccabe 0.6.1 py36h5ad9710_1
mistune 0.8.3 py36h14c3975_1
mkl 2018.0.3 1
mkl-service 1.1.2 py36h17a0993_4
mkl_fft 1.0.6 py36h7dd41cf_0
mkl_random 1.0.1 py36h629b387_0
mock 3.0.5 <pip>
more-itertools 4.1.0 py36_0
mpc 1.0.3 hec55b23_5
mpfr 3.1.5 h11a74b3_2
mpi 1.0 openmpi conda-forge
mpmath 1.0.0 py36hfeacd6b_2
msgpack 0.6.0 <pip>
msgpack-numpy 0.4.3.2 py36_0
msgpack-python 0.5.6 py36h6bb024c_0
multipledispatch 0.5.0 py36_0
murmurhash 1.0.2 py36he6710b0_0
nb_conda 2.2.1 py36_2 conda-forge
nb_conda_kernels 2.2.1 py36_0 conda-forge
nbconvert 5.4.1 py36_3
nbformat 4.4.0 py36h31c9010_0
ncurses 6.1 hf484d3e_0
networkx 2.1 py36_0
ninja 1.8.2 py36h6bb024c_1
nltk 3.3.0 py36_0
nose 1.3.7 py36hcdf7029_2
notebook 5.5.0 py36_0
numba 0.38.0 py36h637b7d7_0
numexpr 2.6.5 py36h7bf3b9c_0
numpy 1.15.4 py36h1d66e8a_0
numpy 1.15.4 <pip>
numpy-base 1.15.4 py36h81de0dd_0
numpydoc 0.8.0 py36_0
nvidia-ml-py3 7.352.0 py_0 fastai
odo 0.5.1 py36h90ed295_0
olefile 0.45.1 py36_0
onnx 1.4.1 <pip>
openmpi 3.1.0 h26a2512_3 conda-forge
openpyxl 2.5.3 py36_0
openssl 1.0.2r h7b6447c_0
packaging 17.1 py36_0
pandas 0.24.2 <pip>
pandas 0.23.0 py36h637b7d7_0
pandoc 1.19.2.1 hea2e7c5_1
pandocfilters 1.4.2 py36ha6701b7_1
pango 1.42.3 h8589676_0
paramiko 2.4.2 <pip>
parso 0.2.0 py36_0
partd 0.3.8 py36h36fd896_0
patchelf 0.9 hf79760b_2
path.py 11.0.1 py36_0
pathlib2 2.3.2 py36_0
patsy 0.5.0 py36_0
pcre 8.42 h439df22_0
pep8 1.7.1 py36_0
pexpect 4.5.0 py36_0
pickleshare 0.7.4 py36h63277f8_0
pillow 5.2.0 py36heded4f4_0
pip 10.0.1 py36_0
pixman 0.34.0 hceecf20_3
pkginfo 1.4.2 py36_1
plac 0.9.6 py36_0
plotly 2.7.0 <pip>
pluggy 0.6.0 py36hb689045_0
ply 3.11 py36_0
preshed 2.0.1 py36he6710b0_0
prompt_toolkit 1.0.15 py36h17d85b1_0
protobuf 3.5.2 py36hd28b015_0 conda-forge
protobuf3-to-dict 0.1.5 <pip>
psutil 5.4.5 py36h14c3975_0
psycopg2 2.7.5 <pip>
ptyprocess 0.5.2 py36h69acd42_0
py 1.5.3 py36_0
py4j 0.10.7 <pip>
pyasn1 0.4.5 <pip>
pycodestyle 2.4.0 py36_0
pycosat 0.6.3 py36h0a5515d_0
pycparser 2.18 py36hf9f622e_1
pycrypto 2.6.1 py36h14c3975_8
pycurl 7.43.0.1 py36hb7f436b_0
pyflakes 1.6.0 py36h7bd6a15_0
pygal 2.4.0 <pip>
pygments 2.2.0 py36h0d3125c_0
pykerberos 1.2.1 py36h14c3975_0
pylint 1.8.4 py36_0
PyNaCl 1.3.0 <pip>
pyodbc 4.0.23 py36hf484d3e_0
pyopenssl 18.0.0 py36_0
pyparsing 2.2.0 py36hee85983_1
pyqt 5.9.2 py36h751905a_0
pysocks 1.6.8 py36_0
pyspark 2.3.2 <pip>
pytables 3.4.3 py36h02b9ad4_2
pytest 3.5.1 py36_0
pytest-arraydiff 0.2 py36_0
pytest-astropy 0.3.0 py36_0
pytest-doctestplus 0.1.3 py36_0
pytest-openfiles 0.3.0 py36_0
pytest-remotedata 0.2.1 py36_0
python 3.6.5 hc3d631a_2
python-dateutil 2.7.3 py36_0
pytorch 1.1.0 py3.6_cuda10.0.130_cudnn7.5.1_0 pytorch
pytz 2018.4 py36_0
pywavelets 0.5.2 py36he602eb0_0
pyyaml 3.12 py36hafb9ca4_1
pyzmq 17.0.0 py36h14c3975_0
qt 5.9.6 h52aff34_0
qtawesome 0.4.4 py36h609ed8c_0
qtconsole 4.3.1 py36h8f73b5b_0
qtpy 1.4.1 py36_0
readline 7.0 ha6073c6_4
regex 2018.01.10 py36h14c3975_1000 fastai
requests 2.20.0 py36_1000 conda-forge
requests-kerberos 0.12.0 <pip>
rope 0.10.7 py36h147e2ec_0
ruamel_yaml 0.15.35 py36h14c3975_1
s3fs 0.1.5 py36_0
s3transfer 0.2.0 <pip>
s3transfer 0.2.0 py36_0
sagemaker 1.20.1 <pip>
sagemaker-pyspark 1.2.4 <pip>
scikit-image 0.13.1 py36h14c3975_1
scikit-learn 0.19.1 py36h7aa7ec6_0
scikit-learn 0.20.3 <pip>
scipy 1.1.0 py36hfc37229_0
seaborn 0.8.1 py36hfad7ec4_0
send2trash 1.5.0 py36_0
setuptools 39.1.0 py36_0
simplegeneric 0.8.1 py36_2
singledispatch 3.4.0.3 py36h7a266c3_0
sip 4.19.8 py36hf484d3e_0
six 1.11.0 py36h372c433_1
snappy 1.1.7 hbae5bb6_3
snowballstemmer 1.2.1 py36h6febd40_0
sortedcollections 0.6.1 py36_0
sortedcontainers 1.5.10 py36_0
spacy 2.0.18 py36hf484d3e_1000 fastai
sparkmagic 0.12.5 <pip>
sphinx 1.7.4 py36_0
sphinxcontrib 1.0 py36h6d0f590_1
sphinxcontrib-websupport 1.0.1 py36hb5cb234_1
spyder 3.2.8 py36_0
SQLAlchemy 1.2.11 <pip>
sqlalchemy 1.2.7 py36h6b74fdf_0
sqlite 3.23.1 he433501_0
statsmodels 0.9.0 py36h3010b51_0
sympy 1.1.1 py36hc6d1c1c_0
tblib 1.3.2 py36h34cf8b6_0
terminado 0.8.1 py36_1
testpath 0.3.1 py36h8cadb63_0
texttable 0.9.1 <pip>
thinc 6.12.1 py36h637b7d7_1000 fastai
tk 8.6.8 hbc83047_0
toolz 0.9.0 py36_0
torchvision 0.2.2 py_3 pytorch
tornado 5.0.2 py36_0
tqdm 4.31.1 py36_1
traitlets 4.3.2 py36h674d592_0
typing 3.6.4 py36_0
typing-extensions 3.7.2 <pip>
ujson 1.35 py36h14c3975_0
unicodecsv 0.14.1 py36ha668878_0
unixodbc 2.3.6 h1bed415_0
urllib3 1.23 py36_0
wcwidth 0.1.7 py36hdf4376a_0
webencodings 0.5.1 py36h800622e_1
websocket-client 0.56.0 <pip>
werkzeug 0.14.1 py36_0
wheel 0.31.1 py36_0
widgetsnbextension 3.2.1 py36_0
widgetsnbextension 3.4.2 <pip>
wrapt 1.10.11 py36h28b7045_0
xlrd 1.1.0 py36h1db9f0c_1
xlsxwriter 1.0.4 py36_0
xlwt 1.3.0 py36h7b00a1f_0
xz 5.2.4 h14c3975_4
yaml 0.1.7 had09818_2
zeromq 4.2.5 h439df22_0
zict 0.1.3 py36h3a3bf81_0
zlib 1.2.11 ha838bed_2
(pytorch_p36) ubuntu@ip-123-12-21-123:~/project/folder$ python high_performing_data_point_models_cifar10.py
Traceback (most recent call last):
File "high_performing_data_point_models_cifar10.py", line 5, in <module>
import torch
ModuleNotFoundError: No module named 'torch'
（pytorch_p36）ubuntu@ip-123-12-21-123:~/project/folder$python high\u performing\u data\u point\u models\u cifar10.py
回溯（最近一次呼叫最后一次）：
文件“高性能数据点模型cifar10.py”，第5行，中
进口火炬
ModuleNotFoundError:没有名为“torch”的模块
我也不能导入torchvision
$ python
Python 3.6.5 |Anaconda, Inc.| (default, Apr 29 2018, 16:14:56)
[GCC 7.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torchvision
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'torchvision'
>>>
$python
Python 3.6.5 | Anaconda，Inc.|（默认，2018年4月29日，16:14:56）
linux上的[GCC 7.2.0]
有关详细信息，请键入“帮助”、“版权”、“信用证”或“许可证”。
>>>进口火炬视觉
回溯（最近一次呼叫最后一次）：
文件“”，第1行，在
ModuleNotFoundError:没有名为“torchvision”的模块
>>>
conda listautoml# packages in environment at /home/ubuntu/anaconda3/envs/automl:
（pytorch_p36）您应该在这个
pytorch_p36conda install在使用多台机器和多个conda/python环境时，很容易错过smth。我认为从头开始是个好主意。这是一个适合我的安装过程 0使用SSH或PuTTY连接到您的
aws实例。
1.在aws机器上使用python 3.6创建名为pytorch_p36的conda envconda（pytorch_p36）(pytorch_p36) user@aws-instance:~$
condanohupAWS机器学习AMI已经在预定义的
pytorch_p36pytorchtorchvisionpytorch$ source activate pytorch_p36
（显然没有美元符号）。然后，如果运行
pip show torch哪个pythoncondaconda更新condauser@aws-instance:~$ conda create -n pytorch_p36 python=3.6
user@aws-instance:~$ conda activate pytorch_p36
user@aws-instance:~$ source activate pytorch_p36
(pytorch_p36) user@aws-instance:~$
(pytorch_p36) user@aws-instance:~$ conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
(pytorch_p36) user@aws-instance:~$ conda list
# packages in environment at /home/user/anaconda3/envs/pytorch_p36:
#
# Name Version Build Channel
...
cudatoolkit 10.0.130 0
...
python 3.6.9 h265db76_0
pytorch 1.1.0 py3.6_cuda10.0.130_cudnn7.5.1_0 pytorch
...
torchvision 0.3.0 py36_cu10.0.130_1 pytorch
...
(pytorch_p36) user@aws-instance:~$ nohup python -c "import torch; import torchvision; print('PyTorch is fine!')" && cat nohup.out
nohup: ignoring input and appending output to 'nohup.out'
PyTorch is fine!
$ source activate pytorch_p36