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Anaconda 를 설치하는 가장 좋은 방법은 최신판 Anaconda 설치 배시 스크립트를 다운로드 받아 검증 후 구동하는 것이다.
Download Anaconda Distribution 에서 파이썬 3 용 최신 버전을 찾을 수 있다. 현재 포스팅 작성 시점 기준에서는 5.30 발표된 5.2 버전이 최신이다.
임시(ephemeral) 아이템을 다운로드받을 /tmp 디렉토리로 변경하자. curl 을 이용하여 다음과 같이 최신판을 /tmp 디렉토리에 다운로드하자.
1 2 3 4 | :/tmp# curl -O https://repo.continuum.io/archive/Anaconda3-5.2.0-Linux-x86_64.sh % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 100 621M 100 621M 0 0 12.0M 0 0:00:51 0:00:51 --:--:-- 11.2M | cs |
이제 SHA-256 체크섬을 통해 다운로드 받은 파일을 검증해보자. 다음과 같이 sha256sum 명령어를 사용한다. 검증이 끝나면 다음과 같은 결과를 보게 될 것이다.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | :/tmp# ls -al total 636512 drwxrwxrwt 9 root root 4096 Jun 12 18:07 . drwxr-xr-x 23 root root 4096 Jun 12 06:24 .. -rw-r--r-- 1 root root 651745206 Jun 12 18:08 Anaconda3-5.2.0-Linux-x86_64.sh drwxrwxrwt 2 root root 4096 Jun 7 19:15 .font-unix drwxrwxrwt 2 root root 4096 Jun 7 19:15 .ICE-unix drwx------ 3 root root 4096 Jun 7 19:15 systemd-private-cf2cb4abf7df4bcbbdc988b782780905-systemd-resolved.service-d6Yy6k drwx------ 3 root root 4096 Jun 7 19:15 systemd-private-cf2cb4abf7df4bcbbdc988b782780905-systemd-timesyncd.service-vAtkvJ drwxrwxrwt 2 root root 4096 Jun 7 19:15 .Test-unix drwxrwxrwt 2 root root 4096 Jun 7 19:15 .X11-unix drwxrwxrwt 2 root root 4096 Jun 7 19:15 .XIM-unix :/tmp# sha256sum Anaconda3-5.2.0-Linux-x86_64.sh 09f53738b0cd3bb96f5b1bac488e5528df9906be2480fe61df40e0e0d19e3d48 Anaconda3-5.2.0-Linux-x86_64.sh | cs |
그러면 위의 결과를 Anaconda with Python 3 on 64-bit Linux 페이지의 결과와 비교해야 한다. 방금 우리는 Anaconda3-5.2.0-Linux-x86_64.sh 를 다운로드 받았으므로 해당 링크를 클릭한다.
해당 파일의 해쉬 값은 다음과 같다. 위에서 sha256sum 명령어를 사용한 검증 결과가 sha256 열의 값과 일치한다면 정상적으로 다운로드된 것이다. 이제 스크립트르르 실행하자.
검증이 끝났으면 다음과 같이 스크립트를 실행합니다.
1 2 3 4 5 6 7 8 9 10 11 12 13 | :/tmp# bash Anaconda3-5.2.0-Linux-x86_64.sh Welcome to Anaconda3 5.2.0 In order to continue the installation process, please review the license agreement. Please, press ENTER to continue >>> =================================== Anaconda End User License Agreement =================================== Copyright 2015, Anaconda, Inc. | cs |
ENTER 를 눌러서 설치를 진행한다. 라이센스 사용 동의를 하고, 설치 위치를 확인한 후 ENTER 를 눌러 설치한다.
설치가 완료되면 다음과 같은 결과를 보게 된다. conda 명령어를 사용하기 위해 yes 를 타이핑한다.
설치가 끝나면 다음과 같은 결과를 보게 된다.
다음과 같이 아나콘다를 활성화시킨다. 설치를 검증하기 위해, 예를 들어 conda lis 명령어를 통해 설치된 패키지를 확인해보자.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 | :/tmp# source ~/.bashrc :/tmp# conda list # packages in environment at /root/anaconda3: # # Name Version Build Channel _ipyw_jlab_nb_ext_conf 0.1.0 py36he11e457_0 alabaster 0.7.10 py36h306e16b_0 anaconda 5.2.0 py36_3 anaconda-client 1.6.14 py36_0 anaconda-navigator 1.8.7 py36_0 anaconda-project 0.8.2 py36h44fb852_0 asn1crypto 0.24.0 py36_0 astroid 1.6.3 py36_0 astropy 3.0.2 py36h3010b51_1 attrs 18.1.0 py36_0 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 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 0.12.16 py36_0 boto 2.48.0 py36h6e4cd66_1 bottleneck 1.2.1 py36haac1ea0_0 bzip2 1.0.6 h14c3975_5 ca-certificates 2018.03.07 0 cairo 1.14.12 h7636065_2 certifi 2018.4.16 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 conda 4.5.4 py36_0 conda-build 3.10.5 py36_0 conda-env 2.6.0 h36134e3_1 conda-verify 2.0.0 py36h98955d8_0 contextlib2 0.5.5 py36h6c84a62_0 cryptography 2.2.2 py36h14c3975_0 curl 7.60.0 h84994c4_0 cycler 0.10.0 py36h93f1223_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 datashape 0.5.4 py36h3ad6b5c_0 dbus 1.13.2 h714fa37_1 decorator 4.3.0 py36_0 distributed 1.21.8 py36_0 docutils 0.14 py36hb0f60f5_0 entrypoints 0.2.3 py36h1aec115_2 et_xmlfile 1.0.1 py36hd6bccc3_0 expat 2.2.5 he0dffb1_0 fastcache 1.0.2 py36h14c3975_2 filelock 3.0.4 py36_0 flask 1.0.2 py36_1 flask-cors 3.0.4 py36_0 fontconfig 2.12.6 h49f89f6_0 freetype 2.8 hab7d2ae_1 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 greenlet 0.4.13 py36h14c3975_0 gst-plugins-base 1.14.0 hbbd80ab_1 gstreamer 1.14.0 hb453b48_1 h5py 2.7.1 py36ha1f6525_2 harfbuzz 1.7.6 h5f0a787_1 hdf5 1.10.2 hba1933b_1 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 ipython 6.4.0 py36_0 ipython_genutils 0.2.0 py36hb52b0d5_0 ipywidgets 7.2.1 py36_0 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 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 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 7.2.0 hdf63c60_3 libgfortran-ng 7.2.0 hdf63c60_3 libpng 1.6.34 hb9fc6fc_0 libsodium 1.0.16 h1bed415_0 libssh2 1.8.0 h9cfc8f7_4 libstdcxx-ng 7.2.0 hdf63c60_3 libtiff 4.0.9 he85c1e1_1 libtool 2.4.6 h544aabb_3 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 py36h0e671d2_1 mccabe 0.6.1 py36h5ad9710_1 mistune 0.8.3 py36h14c3975_1 mkl 2018.0.2 1 mkl-service 1.1.2 py36h17a0993_4 mkl_fft 1.0.1 py36h3010b51_0 mkl_random 1.0.1 py36h629b387_0 more-itertools 4.1.0 py36_0 mpc 1.0.3 hec55b23_5 mpfr 3.1.5 h11a74b3_2 mpmath 1.0.0 py36hfeacd6b_2 msgpack-python 0.5.6 py36h6bb024c_0 multipledispatch 0.5.0 py36_0 navigator-updater 0.2.1 py36_0 nbconvert 5.3.1 py36hb41ffb7_0 nbformat 4.4.0 py36h31c9010_0 ncurses 6.1 hf484d3e_0 networkx 2.1 py36_0 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 1.14.3 py36hcd700cb_1 numpy-base 1.14.3 py36h9be14a7_1 numpydoc 0.8.0 py36_0 odo 0.5.1 py36h90ed295_0 olefile 0.45.1 py36_0 openpyxl 2.5.3 py36_0 openssl 1.0.2o h20670df_0 packaging 17.1 py36_0 pandas 0.23.0 py36h637b7d7_0 pandoc 1.19.2.1 hea2e7c5_1 pandocfilters 1.4.2 py36ha6701b7_1 pango 1.41.0 hd475d92_0 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 5.1.0 py36h3deb7b8_0 pip 10.0.1 py36_0 pixman 0.34.0 hceecf20_3 pkginfo 1.4.2 py36_1 pluggy 0.6.0 py36hb689045_0 ply 3.11 py36_0 prompt_toolkit 1.0.15 py36h17d85b1_0 psutil 5.4.5 py36h14c3975_0 ptyprocess 0.5.2 py36h69acd42_0 py 1.5.3 py36_0 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 pygments 2.2.0 py36h0d3125c_0 pylint 1.8.4 py36_0 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 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 pytz 2018.4 py36_0 pywavelets 0.5.2 py36he602eb0_0 pyyaml 3.12 py36hafb9ca4_1 pyzmq 17.0.0 py36h14c3975_0 qt 5.9.5 h7e424d6_0 qtawesome 0.4.4 py36h609ed8c_0 qtconsole 4.3.1 py36h8f73b5b_0 qtpy 1.4.1 py36_0 readline 7.0 ha6073c6_4 requests 2.18.4 py36he2e5f8d_1 rope 0.10.7 py36h147e2ec_0 ruamel_yaml 0.15.35 py36h14c3975_1 scikit-image 0.13.1 py36h14c3975_1 scikit-learn 0.19.1 py36h7aa7ec6_0 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 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 1.2.7 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 tk 8.6.7 hc745277_3 toolz 0.9.0 py36_0 tornado 5.0.2 py36_0 traitlets 4.3.2 py36h674d592_0 typing 3.6.4 py36_0 unicodecsv 0.14.1 py36ha668878_0 unixodbc 2.3.6 h1bed415_0 urllib3 1.22 py36hbe7ace6_0 wcwidth 0.1.7 py36hdf4376a_0 webencodings 0.5.1 py36h800622e_1 werkzeug 0.14.1 py36_0 wheel 0.31.1 py36_0 widgetsnbextension 3.2.1 py36_0 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 | cs |
그럼 이제 Anaconda 환경 설정으로 넘어가자. Anaconda 가상 환경은 파이썬 버전이나 패키지별로 프로젝트 관리가 가능하도록 도와준다. 먼저 어떤 파이썬 버전이 사용가능한지부터 확인해보자. 아래와 같이 검색해보면 파이썬 2와 파이썬 3의 다양한 버전의 파이썬이 사용가능함을 알 수 있다.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | :/tmp# conda search "^python$" Loading channels: done # Name Version Build Channel python 1.0.1 0 pkgs/free python 2.6.8 1 pkgs/free python 2.6.8 2 pkgs/free python 2.6.8 3 pkgs/free python 2.6.8 4 pkgs/free python 2.6.8 5 pkgs/free python 2.6.8 6 pkgs/free python 2.6.8 7 pkgs/free python 2.6.9 0 pkgs/free python 2.6.9 1 pkgs/free python 2.7.3 2 pkgs/free python 2.7.3 3 pkgs/free python 2.7.3 4 pkgs/free python 2.7.3 5 pkgs/free python 2.7.3 6 pkgs/free python 2.7.3 7 pkgs/free python 2.7.4 0 pkgs/free python 2.7.5 0 pkgs/free python 2.7.5 1 pkgs/free python 2.7.5 2 pkgs/free python 2.7.5 3 pkgs/free python 2.7.6 0 pkgs/free python 2.7.6 1 pkgs/free python 2.7.6 2 pkgs/free python 2.7.7 0 pkgs/free python 2.7.7 2 pkgs/free python 2.7.8 0 pkgs/free python 2.7.8 1 pkgs/free python 2.7.9 0 pkgs/free python 2.7.9 1 pkgs/free python 2.7.9 2 pkgs/free python 2.7.9 3 pkgs/free python 2.7.9 d1 pkgs/free python 2.7.9 d2 pkgs/free python 2.7.10 0 pkgs/free python 2.7.10 1 pkgs/free python 2.7.10 2 pkgs/free python 2.7.10 d0 pkgs/free python 2.7.11 0 pkgs/free python 2.7.11 5 pkgs/free python 2.7.11 d0 pkgs/free python 2.7.12 0 pkgs/free python 2.7.12 1 pkgs/free python 2.7.13 0 pkgs/free python 2.7.13 hac47a24_15 pkgs/main python 2.7.13 heccc3f1_16 pkgs/main python 2.7.13 hfff3488_13 pkgs/main python 2.7.14 h1571d57_29 pkgs/main python 2.7.14 h1571d57_30 pkgs/main python 2.7.14 h1571d57_31 pkgs/main python 2.7.14 h1aa7481_19 pkgs/main python 2.7.14 h435b27a_18 pkgs/main python 2.7.14 h89e7a4a_22 pkgs/main python 2.7.14 h91f54f5_26 pkgs/main python 2.7.14 h931c8b0_15 pkgs/main python 2.7.14 h9b67528_20 pkgs/main python 2.7.14 ha6fc286_23 pkgs/main python 2.7.14 hc2b0042_21 pkgs/main python 2.7.14 hdd48546_24 pkgs/main python 2.7.14 hf918d8d_16 pkgs/main python 2.7.15 h1571d57_0 pkgs/main python 3.3.0 2 pkgs/free python 3.3.0 3 pkgs/free python 3.3.0 4 pkgs/free python 3.3.0 pro0 pkgs/pro python 3.3.0 pro1 pkgs/pro python 3.3.1 0 pkgs/free python 3.3.2 0 pkgs/free python 3.3.2 1 pkgs/free python 3.3.3 0 pkgs/free python 3.3.4 0 pkgs/free python 3.3.5 0 pkgs/free python 3.3.5 1 pkgs/free python 3.3.5 2 pkgs/free python 3.3.5 3 pkgs/free python 3.3.5 4 pkgs/free python 3.4.0 0 pkgs/free python 3.4.1 0 pkgs/free python 3.4.1 1 pkgs/free python 3.4.1 2 pkgs/free python 3.4.1 3 pkgs/free python 3.4.1 4 pkgs/free python 3.4.2 0 pkgs/free python 3.4.3 0 pkgs/free python 3.4.3 1 pkgs/free python 3.4.3 2 pkgs/free python 3.4.4 0 pkgs/free python 3.4.4 5 pkgs/free python 3.4.5 0 pkgs/free python 3.5.0rc4 0 pkgs/free python 3.5.0 0 pkgs/free python 3.5.0 1 pkgs/free python 3.5.1 0 pkgs/free python 3.5.1 5 pkgs/free python 3.5.2 0 pkgs/free python 3.5.3 0 pkgs/free python 3.5.3 1 pkgs/free python 3.5.4 0 pkgs/free python 3.5.4 h00c01ad_19 pkgs/main python 3.5.4 h0b4c808_22 pkgs/main python 3.5.4 h2170f06_12 pkgs/main python 3.5.4 h3075507_18 pkgs/main python 3.5.4 h417fded_24 pkgs/main python 3.5.4 h56e0582_23 pkgs/main python 3.5.4 h72f0b78_15 pkgs/main python 3.5.4 hb43c6bb_21 pkgs/main python 3.5.4 hc053d89_14 pkgs/main python 3.5.4 hc3d631a_27 pkgs/main python 3.5.4 he2c66cf_20 pkgs/main python 3.5.5 hc3d631a_0 pkgs/main python 3.5.5 hc3d631a_1 pkgs/main python 3.5.5 hc3d631a_3 pkgs/main python 3.5.5 hc3d631a_4 pkgs/main python 3.6.0 0 pkgs/free python 3.6.1 0 pkgs/free python 3.6.1 2 pkgs/free python 3.6.2 0 pkgs/free python 3.6.2 h02fb82a_12 pkgs/main python 3.6.2 h0b30769_14 pkgs/main python 3.6.2 h33255ae_18 pkgs/main python 3.6.2 hca45abc_19 pkgs/main python 3.6.2 hdfe5801_15 pkgs/main python 3.6.3 h0ef2715_3 pkgs/main python 3.6.3 h1284df2_4 pkgs/main python 3.6.3 h6c0c0dc_5 pkgs/main python 3.6.3 hc9025b9_1 pkgs/main python 3.6.3 hcad60d5_0 pkgs/main python 3.6.3 hefd0734_2 pkgs/main python 3.6.4 hc3d631a_0 pkgs/main python 3.6.4 hc3d631a_1 pkgs/main python 3.6.4 hc3d631a_3 pkgs/main python 3.6.5 hc3d631a_0 pkgs/main python 3.6.5 hc3d631a_1 pkgs/main python 3.6.5 hc3d631a_2 pkgs/main | cs |
그러면 최신 파이썬 3 버전을 사용하여 가상 환경을 구성해보자. 지금 구성하는 환경을 예를 들어 redsparrow 라고 명명하면 다음과 같이 구성이 가능하다. 그러면 어떤 패키지가 다운로드되는지 확인할 수 있다.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | :/tmp# conda create --name redsparrow python=3 Solving environment: done ## Package Plan ## environment location: /root/anaconda3/envs/my_conda added / updated specs: - python=3 The following packages will be downloaded: package | build ---------------------------|----------------- setuptools-39.2.0 | py36_0 551 KB The following NEW packages will be INSTALLED: ca-certificates: 2018.03.07-0 certifi: 2018.4.16-py36_0 libedit: 3.1.20170329-h6b74fdf_2 libffi: 3.2.1-hd88cf55_4 libgcc-ng: 7.2.0-hdf63c60_3 libstdcxx-ng: 7.2.0-hdf63c60_3 ncurses: 6.1-hf484d3e_0 openssl: 1.0.2o-h20670df_0 pip: 10.0.1-py36_0 python: 3.6.5-hc3d631a_2 readline: 7.0-ha6073c6_4 setuptools: 39.2.0-py36_0 sqlite: 3.23.1-he433501_0 tk: 8.6.7-hc745277_3 wheel: 0.31.1-py36_0 xz: 5.2.4-h14c3975_4 zlib: 1.2.11-ha838bed_2 Proceed ([y]/n)? | cs |
1 2 3 4 5 6 7 8 9 10 11 12 | Downloading and Extracting Packages setuptools-39.2.0 | 551 KB | ########################################################################################################################################## | 100% Preparing transaction: done Verifying transaction: done Executing transaction: done # # To activate this environment, use: # > source activate redsparrow # # To deactivate an active environment, use: # > source deactivate # | cs |
구성된 환경은 다음과 같이 활성화시킬 수 있다. 활성화되고 나면 가상환경 이름이 앞 부분에 붙는 것을 확인할 수 있다.
1 2 3 | :/tmp# source activate my_conda (redsparrow) :/tmp# | cs |
해당 환경에서 어떤 버전의 파이썬이 사용되고 있는지는 다음과 같이 확인활 수 있다.
1 2 | (redsparrow) fukaerii@server:/$ python --version Python 3.6.5 :: Anaconda, Inc. | cs |
비활성화는 다음과 같이 가능하다.
1 2 3 4 | (redsparrow) :/tmp# source deactivate :/tmp# | cs |
여기서 source 대신 . 을 입력해도 같은 결과를 얻을 수 있다.
1 2 3 4 | (redsparrow) fukaerii@server:/$ . deactivate fukaerii@server:/$ fukaerii@server:/$ . activate redsparrow (redsparrow) fukaerii@server:/$ | cs |
3이 아니라 3.5와 같이 구체적인 파이썬 버전을 인수로 쓸 수도 있다.
1 | fukaerii@server:/$ conda create -n my_env35 python=3.5 | cs |
3.5.1 에서 3.5.2 로 업데이트하는 것처럼 같은 브랜치 내에서 파이썬 버전을 다음과 같이 업데이트할 수 있다.
1 2 3 4 | (redsparrow) fukaerii@133-130-107-97:/$ conda update python Solving environment: done # All requested packages already installed. | cs |
구성되어 있는 가상환경 목록은 다음과 같이 확인할 수 있다.
1 2 3 4 5 | (redsparrow) fukaerii@server:/$ conda info --envs # conda environments: # base /home/fukaerii/anaconda3 redsparrow * /home/fukaerii/anaconda3/envs/redsparrow | cs |
여기서 *는 현재 활성화된 환경을 나타낸다.
아래 패키지는 기본적으로 각각의 환경에 설치된다.
openssl
pip
python
readline
setuptools
sqlite
tk
wheel
xz
zlib
예를 들어 numpy 와 같이 추가적으로 필요한 패키지는 다음과 같이 설치한다.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | fukaerii@server:/$ conda install --name redsparrow numpy Solving environment: done ## Package Plan ## environment location: /home/fukaerii/anaconda3/envs/redsparrow added / updated specs: - numpy The following packages will be downloaded: package | build ---------------------------|----------------- numpy-base-1.14.5 | py36hdbf6ddf_0 4.1 MB numpy-1.14.5 | py36hcd700cb_0 94 KB mkl-2018.0.3 | 1 198.7 MB intel-openmp-2018.0.3 | 0 705 KB ------------------------------------------------------------ Total: 203.6 MB The following NEW packages will be INSTALLED: blas: 1.0-mkl intel-openmp: 2018.0.3-0 libgfortran-ng: 7.2.0-hdf63c60_3 mkl: 2018.0.3-1 mkl_fft: 1.0.1-py36h3010b51_0 mkl_random: 1.0.1-py36h629b387_0 numpy: 1.14.5-py36hcd700cb_0 numpy-base: 1.14.5-py36hdbf6ddf_0 Proceed ([y]/n)? y Downloading and Extracting Packages intel-openmp-2018.0. | 705 KB | ############################################### | 100% numpy-base-1.14.5 | 4.1 MB | ############################################### | 100% mkl-2018.0.3 | 198.7 MB | ############################################## | 100% numpy-1.14.5 | 94 KB | ############################################### | 100% Preparing transaction: done Verifying transaction: done Executing transaction: done | cs |
물론 아나콘다 설치 단계에서 아래와 같이 numpy 패키지를 같이 설치할 수도 있다.
1 2 | fukaerii@server:/$ conda create --name redsparrow python=3 numpy | cs |
해당 가상환경을 더 이상 사용하지 않아 삭제가 필요한 경우 다음과 같이 하면 된다.
1 2 | fukaerii@server:/$ conda remove --name redsparrow --all | cs |
이와 관련해 자세한 내용은 다음 링크에서 확인가능하다.
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