Develop Reference

Conda does not create environment from yml file on MacOS

I'm trying to create an environment with conda after having installed Miniconda3, on macOS. I am using the conda env create -f SEM1-CB.yml command through terminal but it gives me the following error:
Collecting package metadata (repodata.json): done
Solving environment: failed
ResolvePackageNotFound:
- xz==5.2.5=h62dcd97_1
- mkl-service==2.4.0=py310h2bbff1b_0
- numpy-base==1.21.2=py310h0829f74_0
- libzlib==1.2.11=h8ffe710_1013
- vc==14.2=hb210afc_5
- lz4-c==1.9.3=h8ffe710_1
- numpy==1.21.2=py310hfca59bb_0
- jpeg==9d=h8ffe710_0
- mkl_random==1.2.2=py310h4ed8f06_0
- zlib==1.2.11=h8ffe710_1013
- libffi==3.4.2=h8ffe710_5
- mkl==2021.4.0=haa95532_640
- setuptools==58.5.3=py310h5588dad_0
- mkl_fft==1.3.1=py310ha0764ea_0
- ca-certificates==2021.10.26=haa95532_4
- libtiff==4.3.0=hd413186_2
- openssl==3.0.0=h8ffe710_2
- intel-openmp==2021.4.0=haa95532_3556
- libdeflate==1.8=h8ffe710_0
- zstd==1.5.0=h6255e5f_0
- python==3.10.0=hcf16a7b_2_cpython
- vs2015_runtime==14.29.30037=h902a5da_5
- lcms2==2.12=h2a16943_0
- lerc==3.0=h0e60522_0
- openjpeg==2.4.0=hb211442_1
- pillow==8.4.0=py310h22f3323_0
- tk==8.6.11=h8ffe710_1
- ucrt==10.0.20348.0=h57928b3_0
- freetype==2.10.4=h546665d_1
- jbig==2.1=h8d14728_2003
- libpng==1.6.37=h1d00b33_2
- sqlite==3.36.0=h8ffe710_2
- bzip2==1.0.8=h8ffe710_4
The SEM1-CB.yml file is as follows:
name: SEM1-CB
channels:
- conda-forge
- defaults
dependencies:
- blas=1.0=mkl
- bzip2=1.0.8=h8ffe710_4
- ca-certificates=2021.10.26=haa95532_4
- freetype=2.10.4=h546665d_1
- intel-openmp=2021.4.0=haa95532_3556
- jbig=2.1=h8d14728_2003
- jpeg=9d=h8ffe710_0
- lcms2=2.12=h2a16943_0
- lerc=3.0=h0e60522_0
- libdeflate=1.8=h8ffe710_0
- libffi=3.4.2=h8ffe710_5
- libpng=1.6.37=h1d00b33_2
- libtiff=4.3.0=hd413186_2
- libzlib=1.2.11=h8ffe710_1013
- lz4-c=1.9.3=h8ffe710_1
- mkl=2021.4.0=haa95532_640
- mkl-service=2.4.0=py310h2bbff1b_0
- mkl_fft=1.3.1=py310ha0764ea_0
- mkl_random=1.2.2=py310h4ed8f06_0
- numpy=1.21.2=py310hfca59bb_0
- numpy-base=1.21.2=py310h0829f74_0
- olefile=0.46=pyh9f0ad1d_1
- openjpeg=2.4.0=hb211442_1
- openssl=3.0.0=h8ffe710_2
- pillow=8.4.0=py310h22f3323_0
- pip=21.3.1=pyhd8ed1ab_0
- pysimplegui=4.55.1=pyhd8ed1ab_0
- python=3.10.0=hcf16a7b_2_cpython
- python_abi=3.10=2_cp310
- setuptools=58.5.3=py310h5588dad_0
- six=1.16.0=pyhd3eb1b0_0
- sqlite=3.36.0=h8ffe710_2
- tk=8.6.11=h8ffe710_1
- tzdata=2021e=he74cb21_0
- ucrt=10.0.20348.0=h57928b3_0
- vc=14.2=hb210afc_5
- vs2015_runtime=14.29.30037=h902a5da_5
- wheel=0.37.0=pyhd8ed1ab_1
- xz=5.2.5=h62dcd97_1
- zlib=1.2.11=h8ffe710_1013
- zstd=1.5.0=h6255e5f_0
- pip:
- click==8.0.3
- colorama==0.4.4
- flask==2.0.2
- itsdangerous==2.0.1
- jinja2==3.0.3
- markupsafe==2.0.1
- pymata4==1.15
- pyserial==3.5
- werkzeug==2.0.2
prefix:
I need to create and activate this environment in order to add it as interpreter on visual studio code. I have tried the procedure on microsoft windows and it works, but I need it to work even on macOS.
Thank you in advance for any help!

In SEM1-CB.yml file you have Windows-specific conda package builds specified, which aren't available in OSX index, hence ends up showing you ResolvePackageNotFound error. Check the screenshot below I took from: https://anaconda.org/conda-forge/xz/files
To fix this, when you export your conda environment file in Windows run this command: conda env export --from-history --name env_name > file_name.yml or manually remove build numbers from the file.

Conda - how to update only cudatoolkit in an existing environment?

This is a specific instance of a general problem that I run into when updating packages using conda.
I have an environment that is working great on machine A. I want to transfer it to machine B.
But, machine A has GTX1080 gpus, and due to configuration I cannot control, requires cudatoolkit 10.2.
Machine B has A100 gpus, and due to configuration I cannot control, requires cudatoolkit 11.1
I can easily export Machine A's environment to yml, and create a new environment on Machine B using that yml.
However, I cannot seem to update cudatoolkit to 11.1 on that environment on Machine B.
I try
conda install cudatoolkit=11.1 -c conda-forge
and I am met with ~5 minutes of conflicts and retrying solve messages, that ultimately don't really tell me anything useful (sorry, I did not capture that to post here, it is quite voluminous).
Short of re-creating the environment from scratch on Machine B, is there any way to update just cudatoolkit to the version that is required for that machine's GPUs?
I have also tried various permutations of conda update ... with no success.
If it helps, here is the yml file from Machine A:
name: VAE180
channels:
- pytorch
- conda-forge
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- absl-py=0.12.0=pyhd8ed1ab_0
- aiohttp=3.7.4=py38h27cfd23_1
- argh=0.26.2=pyh9f0ad1d_1002
- async-timeout=3.0.1=py_1000
- attrs=20.3.0=pyhd3deb0d_0
- blas=1.0=mkl
- blinker=1.4=py_1
- brotlipy=0.7.0=py38h8df0ef7_1001
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.17.1=h36c2ea0_0
- ca-certificates=2020.12.5=ha878542_0
- cachetools=4.2.1=pyhd8ed1ab_0
- certifi=2020.12.5=py38h578d9bd_1
- cffi=1.14.5=py38h261ae71_0
- chardet=3.0.4=py38h924ce5b_1008
- click=7.1.2=pyh9f0ad1d_0
- cloudpickle=1.6.0=py_0
- configparser=5.0.2=pyhd8ed1ab_0
- cryptography=3.4.6=py38ha5dfef3_0
- cudatoolkit=10.2.89=hfd86e86_1
- cycler=0.10.0=py38_0
- dbus=1.13.18=hb2f20db_0
- docker-pycreds=0.4.0=py_0
- expat=2.2.10=he6710b0_2
- ffmpeg=4.3=hf484d3e_0
- fontconfig=2.13.1=h6c09931_0
- freetype=2.10.4=h5ab3b9f_0
- fsspec=0.8.7=pyhd8ed1ab_0
- future=0.18.2=py38h578d9bd_3
- gitdb=4.0.5=pyhd8ed1ab_1
- gitpython=3.1.14=pyhd8ed1ab_0
- glib=2.67.4=h36276a3_1
- gmp=6.2.1=h2531618_2
- gnutls=3.6.5=h71b1129_1002
- google-auth=1.24.0=pyhd3deb0d_0
- google-auth-oauthlib=0.4.1=py_2
- gql=0.1.0=py_0
- graphql-core=3.1.3=pyhd8ed1ab_0
- grpcio=1.33.2=py38heead2fc_2
- gst-plugins-base=1.14.0=h8213a91_2
- gstreamer=1.14.0=h28cd5cc_2
- gym=0.18.0=py38h81c977d_0
- icu=58.2=he6710b0_3
- idna=2.10=pyh9f0ad1d_0
- importlib-metadata=3.7.3=py38h578d9bd_0
- intel-openmp=2020.2=254
- joblib=1.0.1=pyhd3eb1b0_0
- jpeg=9b=h024ee3a_2
- kiwisolver=1.3.1=py38h2531618_0
- lame=3.100=h7b6447c_0
- lcms2=2.11=h396b838_0
- ld_impl_linux-64=2.33.1=h53a641e_7
- libffi=3.3=he6710b0_2
- libgcc-ng=9.1.0=hdf63c60_0
- libgfortran-ng=7.3.0=hdf63c60_0
- libiconv=1.15=h63c8f33_5
- libpng=1.6.37=hbc83047_0
- libprotobuf=3.14.0=h8c45485_0
- libstdcxx-ng=9.1.0=hdf63c60_0
- libtiff=4.2.0=h3942068_0
- libuuid=1.0.3=h1bed415_2
- libuv=1.40.0=h7b6447c_0
- libwebp-base=1.2.0=h27cfd23_0
- libxcb=1.14=h7b6447c_0
- libxml2=2.9.10=hb55368b_3
- lz4-c=1.9.3=h2531618_0
- markdown=3.3.4=pyhd8ed1ab_0
- matplotlib=3.3.4=py38h06a4308_0
- matplotlib-base=3.3.4=py38h62a2d02_0
- mkl=2020.2=256
- mkl-service=2.3.0=py38he904b0f_0
- mkl_fft=1.3.0=py38h54f3939_0
- mkl_random=1.1.1=py38h0573a6f_0
- multidict=5.1.0=py38h27cfd23_2
- ncurses=6.2=he6710b0_1
- nettle=3.4.1=hbb512f6_0
- ninja=1.10.2=py38hff7bd54_0
- numpy=1.19.2=py38h54aff64_0
- numpy-base=1.19.2=py38hfa32c7d_0
- nvidia-ml=7.352.0=py_0
- oauthlib=3.0.1=py_0
- olefile=0.46=py_0
- openh264=2.1.0=hd408876_0
- openssl=1.1.1j=h27cfd23_0
- packaging=20.9=pyh44b312d_0
- pandas=1.2.3=py38ha9443f7_0
- pathtools=0.1.2=py_1
- pcre=8.44=he6710b0_0
- pillow=8.1.2=py38he98fc37_0
- pip=21.0.1=py38h06a4308_0
- promise=2.3=py38h578d9bd_3
- protobuf=3.14.0=py38h2531618_1
- psutil=5.7.3=py38h8df0ef7_0
- pyasn1=0.4.8=py_0
- pyasn1-modules=0.2.7=py_0
- pycparser=2.20=pyh9f0ad1d_2
- pyglet=1.5.15=py38h578d9bd_0
- pyjwt=2.0.1=pyhd8ed1ab_0
- pyopenssl=20.0.1=pyhd8ed1ab_0
- pyparsing=2.4.7=pyh9f0ad1d_0
- pyqt=5.9.2=py38h05f1152_4
- pysocks=1.7.1=py38h578d9bd_3
- python=3.8.8=hdb3f193_4
- python-dateutil=2.8.1=pyhd3eb1b0_0
- python_abi=3.8=1_cp38
- pytorch=1.8.0=py3.8_cuda10.2_cudnn7.6.5_0
- pytorch-lightning=1.2.4=pyhd8ed1ab_0
- pytz=2021.1=pyhd3eb1b0_0
- pyyaml=5.3.1=py38h8df0ef7_1
- qt=5.9.7=h5867ecd_1
- readline=8.1=h27cfd23_0
- requests=2.25.1=pyhd3deb0d_0
- requests-oauthlib=1.3.0=pyh9f0ad1d_0
- rsa=4.7.2=pyh44b312d_0
- scikit-learn=0.24.1=py38ha9443f7_0
- scipy=1.6.1=py38h91f5cce_0
- sentry-sdk=0.20.3=pyh44b312d_0
- setuptools=52.0.0=py38h06a4308_0
- shortuuid=1.0.1=py38h578d9bd_4
- sip=4.19.13=py38he6710b0_0
- six=1.15.0=py38h06a4308_0
- smmap=3.0.5=pyh44b312d_0
- sqlite=3.35.2=hdfb4753_0
- subprocess32=3.5.4=py_1
- tensorboard=2.4.1=pyhd8ed1ab_0
- tensorboard-plugin-wit=1.8.0=pyh44b312d_0
- tensorboardx=2.1=py_0
- threadpoolctl=2.1.0=pyh5ca1d4c_0
- tk=8.6.10=hbc83047_0
- torchaudio=0.8.0=py38
- torchvision=0.9.0=py38_cu102
- tornado=6.1=py38h27cfd23_0
- tqdm=4.59.0=pyhd8ed1ab_0
- typing-extensions=3.7.4.3=0
- typing_extensions=3.7.4.3=py_0
- urllib3=1.26.4=pyhd8ed1ab_0
- wandb=0.10.20=pyhd8ed1ab_0
- watchdog=0.10.4=py38h578d9bd_0
- werkzeug=1.0.1=pyh9f0ad1d_0
- wheel=0.36.2=pyhd3eb1b0_0
- xz=5.2.5=h7b6447c_0
- yaml=0.2.5=h516909a_0
- yarl=1.6.3=py38h25fe258_0
- zipp=3.4.1=pyhd8ed1ab_0
- zlib=1.2.11=h7b6447c_3
- zstd=1.4.5=h9ceee32_0
- pip:
- pytorch-lightning-bolts==0.3.0
prefix: /home/eric/miniconda3/envs/VAE180

Overly-Restrictive Constraints
I'd venture the issue is that recreating from a YAML that includes versions and builds will establish those versions and builds as explicit specifications for that environment moving forward. That is, Conda will regard explicit specifications as hard requirements that it cannot mutate and so if even a single one of the dependencies of cudatoolkit also needs to be updated in order to use version 11, Conda will not know how to satisfy it without violating those previously specified constraints.
Specifically, this is what I see when searching (assuming linux-64 platform):
$ conda search --info 'cudatoolkit[subdir="linux-64",version="11.1.*"]'
Loading channels: done
...
cudatoolkit 11.1.1 h6406543_8
-----------------------------
file name : cudatoolkit-11.1.1-h6406543_8.tar.bz2
name : cudatoolkit
version : 11.1.1
build : h6406543_8
build number: 8
size : 1.20 GB
license : NVIDIA End User License Agreement
subdir : linux-64
url : https://conda.anaconda.org/conda-forge/linux-64/cudatoolkit-11.1.1-h6406543_8.tar.bz2
md5 : 4851e7f19b684e517dc8e6b5b375dda0
timestamp : 2021-02-12 16:31:01 UTC
constraints :
- __cuda >=11.1
dependencies:
- __glibc >=2.17,<3.0.a0
- libgcc-ng >=9.3.0
- libstdcxx-ng >=9.3.0
Note how the dependencies conflict with your YAML specification. That is, the YAML locks libgcc-ng and libstdcxx-ng into versions 9.1.0, whereas cudatoolkit==11.1.1 requires versions 9.3.0 or greater.
Minimal Relaxing
It may be sufficient to edit only the specifications noted above. However, there is no simple way of telling if this might conflict with something else. It's worth a try though. That is, edit the YAML to have:
dependencies:
...
- cudatoolkit=11.1
- libgcc-ng
- libstdcxx-ng
...
with the idea of leaving off the constraints on the dependencies, and let Conda solve them with whatever works.
Liberal Constraints
A more liberal way of exporting an environment is to use the --from-history flag:
conda env export --from-history -n VAE180 > VAE180.minimal.yaml
This will only export the explicit constraints from the user history, which may or may not include versions and builds, depending on how the user originally created the environment. Usually, it is far less constrained than what the default export will generate. One could then edit this YAML to have cudatoolkit=11.1 and then try to create the env with that.
The downside to this approach is that many of the other packages will likely take on newer versions, so it isn't as faithful a replication of the original environment as before.

Basically, I will always reinstall a new 11.1 Conda env for a new A100. Conda will install almost everything you need. Then, you can install extra packages for you projects. The count of extra packages is not that much for me. Even torch or tf env, or all scipy/pandas/sklearn stack would take you long.

How can I install conda packages on Linux if they were originally installed on Windows?

I have conda installed locally on my Windows PC and also installed remotely on a Linux server. I already have conda packages installed locally on my Windows PC, and I want to install the same packages on the Linux server. I have already tried the following steps:
Create a requirements.txt file containing the currently installed packages and their versions using the Anaconda Prompt on my Windows PC using the command conda list -e > requirements.txt.
Transfer this requirements.txt file to my Linux server.
Install these packages in my conda base environment using the command conda install --yes --file requirements.txt.
However, I get the following error message on my Linux server when I try to complete step 3:
Solving environment: failed
PackagesNotFoundError: The following packages are not available from current channels:
- ca-certificates==2020.12.8=haa95532_0
- m2w64-gcc-libs-core==5.3.0=7
- audioread==2.1.8=pypi_0
- pywin32-ctypes==0.2.0=py38_1000
- notebook==6.1.5=py38haa95532_0
- librosa==0.8.0=pypi_0
- numpy-base==1.19.2=py38ha3acd2a_0
- psutil==5.7.2=py38he774522_0
- numpy==1.19.2=py38hadc3359_0
- regex==2020.11.13=py38h2bbff1b_0
- spyder-kernels==1.10.0=py38haa95532_0
- appdirs==1.4.4=pypi_0
- ujson==4.0.1=py38ha925a31_0
- setuptools==51.0.0=py38haa95532_2
- sklearn-crfsuite==0.3.6=pypi_0
- pywinpty==0.5.7=py38_0
- m2w64-gmp==6.1.0=2
- pyyaml==5.3.1=py38he774522_1
- bzip2==1.0.8=he774522_0
- sounddevice==0.4.1=pypi_0
- certifi==2020.12.5=py38haa95532_0
- gpytorch==1.3.0=pypi_0
- winpty==0.4.3=4
- pyzmq==20.0.0=py38hd77b12b_1
- pytorch==1.7.1=py3.8_cpu_0
- lazy-object-proxy==1.4.3=py38h2bbff1b_2
- zeromq==4.3.3=ha925a31_3
- ipython==7.19.0=py38hd4e2768_0
- mkl_fft==1.2.0=py38h45dec08_0
- conda-package-handling==1.7.2=py38h76e460a_0
- vc==14.2=h21ff451_1
- cpuonly==1.0=0
- pip==20.3.1=py38haa95532_0
- tornado==6.1=py38h2bbff1b_0
- libarchive==3.4.2=h5e25573_0
- msys2-conda-epoch==20160418=1
- pandocfilters==1.4.3=py38haa95532_1
- scikit-learn==0.23.2=pypi_0
- torchaudio==0.7.2=py38
- soundfile==0.10.3.post1=pypi_0
- gsl==2.4=hfa6e2cd_4
- kiwisolver==1.3.0=py38hd77b12b_0
- argon2-cffi==20.1.0=py38he774522_1
- dataclasses==0.6=pypi_0
- libtiff==4.1.0=h56a325e_1
- torchvision==0.8.2=py38_cpu
- m2w64-libwinpthread-git==5.0.0.4634.697f757=2
- numba==0.51.2=pypi_0
- pooch==1.2.0=pypi_0
- cvxopt==1.2.0=py38hdc3235a_0
- tabulate==0.8.7=pypi_0
- pillow==8.0.1=py38h4fa10fc_0
- libpng==1.6.37=h2a8f88b_0
- libiconv==1.15=h1df5818_7
- rtree==0.9.4=py38h21ff451_1
- qt==5.9.7=vc14h73c81de_0
- ruamel_yaml==0.15.87=py38he774522_1
- libsodium==1.0.18=h62dcd97_0
- yaml==0.2.5=he774522_0
- m2w64-gcc-libs==5.3.0=7
- libspatialindex==1.9.3=h33f27b4_0
- jedi==0.17.2=py38haa95532_1
- tk==8.6.10=he774522_0
- six==1.15.0=py38haa95532_0
- python-crfsuite==0.9.7=pypi_0
- spyder==4.2.0=py38haa95532_0
- cffi==1.14.4=py38hcd4344a_0
- xz==5.2.5=h62dcd97_0
- console_shortcut==0.1.1=4
- sqlite==3.33.0=h2a8f88b_0
- pycosat==0.6.3=py38h2bbff1b_0
- pyrsistent==0.17.3=py38he774522_0
- markupsafe==1.1.1=py38he774522_0
- bcrypt==3.2.0=py38he774522_0
- libuv==1.40.0=he774522_0
- brotlipy==0.7.0=py38h2bbff1b_1003
- mistune==0.8.4=py38he774522_1000
- wrapt==1.11.2=py38he774522_0
- powershell_shortcut==0.0.1=3
- mkl-service==2.3.0=py38h196d8e1_0
- pysocks==1.7.1=py38haa95532_0
- typeguard==2.10.0=pypi_0
- jpeg==9b=hb83a4c4_2
- libxml2==2.9.10=hb89e7f3_3
- freetype==2.10.4=hd328e21_0
- python==3.8.5=h5fd99cc_1
- liblief==0.10.1=ha925a31_0
- sip==4.19.13=py38ha925a31_0
- scipy==1.5.4=pypi_0
- pywin32==227=py38he774522_1
- nltk==3.5=pypi_0
- py-lief==0.10.1=py38ha925a31_0
- threadpoolctl==2.1.0=pypi_0
- zlib==1.2.11=h62dcd97_4
- cudatoolkit==10.2.89=h74a9793_1
- zstd==1.4.5=h04227a9_0
- mkl_random==1.1.1=py38h47e9c7a_0
- glpk==4.65=hdc00fd2_2
- ninja==1.10.2=py38h6d14046_0
- joblib==0.17.0=pypi_0
- typed-ast==1.4.1=py38he774522_0
- pandas==1.1.3=py38ha925a31_0
- llvmlite==0.34.0=pypi_0
- resampy==0.2.2=pypi_0
- pynacl==1.4.0=py38h62dcd97_1
- vs2015_runtime==14.27.29016=h5e58377_2
- icu==58.2=ha925a31_3
- matplotlib-base==3.3.2=py38hba9282a_0
- menuinst==1.4.16=py38he774522_1
- pyqt==5.9.2=py38ha925a31_4
- cryptography==3.3.1=py38hcd4344a_0
- jupyter_core==4.7.0=py38haa95532_0
- ax-platform==0.1.19=pypi_0
- botorch==0.3.3=pypi_0
- win_inet_pton==1.1.0=py38haa95532_0
- pkginfo==1.6.1=py38haa95532_0
- openssl==1.1.1i=h2bbff1b_0
- wincertstore==0.2=py38_0
- matplotlib==3.3.2=pypi_0
- lz4-c==1.9.2=hf4a77e7_3
- pandoc==2.11=h9490d1a_0
- conda==4.9.2=py38haa95532_0
- ad3==2.3.dev0=pypi_0
- retrying==1.3.3=pypi_0
- plotly==4.14.1=pypi_0
- m2w64-gcc-libgfortran==5.3.0=6
- watchdog==0.10.4=py38haa95532_0
- chardet==3.0.4=py38haa95532_1003
Current channels:
- https://repo.anaconda.com/pkgs/main/linux-64
- https://repo.anaconda.com/pkgs/main/noarch
- https://repo.anaconda.com/pkgs/free/linux-64
- https://repo.anaconda.com/pkgs/free/noarch
- https://repo.anaconda.com/pkgs/r/linux-64
- https://repo.anaconda.com/pkgs/r/noarch
- https://repo.anaconda.com/pkgs/pro/linux-64
- https://repo.anaconda.com/pkgs/pro/noarch
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and use the search bar at the top of the page.
I am aware that the problem is that I am not looking in the correct conda channels, as the error message suggests, but I am not sure how to solve this problem.
Thanks for the help.

The official documentation has some additional steps to have environment working cross-platform. Here is the link to that.
However, if you are not using packages that are only available in anaconda channels, you can do the following.
Have pip on both your conda environment (windows and linux server)
Make requirements.txt file using pip freeze instead of conda's one. From the windows machine
$ pip freeze > requirements.txt
Install the packages in linux server normally with pip.
$ pip install -r requirements.txt
I am not saying that it is the best option. But this way usually is easier can support other environment management packages like pyenv.

ResolvePackageNotFound: Create env using conda and yml file on mac OSX

Fist of all, I'm a total newbie, please bear my idiocy :)
I run this:
conda env create -f env.yml
Here's the yml file:
name: DAND
channels: !!python/tuple
- defaults
dependencies:
- _nb_ext_conf=0.3.0=py27_0
- anaconda-client=1.6.0=py27_0
- appnope=0.1.0=py27_0
- backports=1.0=py27_0
- backports_abc=0.5=py27_0
- beautifulsoup4=4.5.1=py27_0
- clyent=1.2.2=py27_0
- configparser=3.5.0=py27_0
- cycler=0.10.0=py27_0
- decorator=4.0.10=py27_1
- entrypoints=0.2.2=py27_0
- enum34=1.1.6=py27_0
- freetype=2.5.5=1
- functools32=3.2.3.2=py27_0
- get_terminal_size=1.0.0=py27_0
- icu=54.1=0
- ipykernel=4.5.2=py27_0
- ipython=5.1.0=py27_1
- ipython_genutils=0.1.0=py27_0
- ipywidgets=5.2.2=py27_0
- jinja2=2.8=py27_1
- jsonschema=2.5.1=py27_0
- jupyter=1.0.0=py27_3
- jupyter_client=4.4.0=py27_0
- jupyter_console=5.0.0=py27_0
- jupyter_core=4.2.1=py27_0
- libpng=1.6.22=0
- markupsafe=0.23=py27_2
- matplotlib=1.5.3=np111py27_1
- mistune=0.7.3=py27_1
- mkl=11.3.3=0
- nb_anacondacloud=1.2.0=py27_0
- nb_conda=2.0.0=py27_0
- nb_conda_kernels=2.0.0=py27_0
- nbconvert=4.2.0=py27_0
- nbformat=4.2.0=py27_0
- nbpresent=3.0.2=py27_0
- nltk=3.2.1=py27_0
- notebook=4.3.0=py27_0
- numpy=1.11.2=py27_0
- openssl=1.0.2j=0
- pandas=0.19.1=np111py27_0
- path.py=8.2.1=py27_0
- pathlib2=2.1.0=py27_0
- pexpect=4.0.1=py27_0
- pickleshare=0.7.4=py27_0
- pip=9.0.1=py27_1
- prompt_toolkit=1.0.9=py27_0
- ptyprocess=0.5.1=py27_0
- pygments=2.1.3=py27_0
- pymongo=3.3.0=py27_0
- pyparsing=2.1.4=py27_0
- pyqt=5.6.0=py27_1
- python=2.7.12=1
- python-dateutil=2.6.0=py27_0
- python.app=1.2=py27_4
- pytz=2016.10=py27_0
- pyyaml=3.12=py27_0
- pyzmq=16.0.2=py27_0
- qt=5.6.2=0
- qtconsole=4.2.1=py27_1
- readline=6.2=2
- requests=2.12.3=py27_0
- scikit-learn=0.17.1=np111py27_2
- scipy=0.18.1=np111py27_0
- seaborn=0.7.1=py27_0
- setuptools=27.2.0=py27_0
- simplegeneric=0.8.1=py27_1
- singledispatch=3.4.0.3=py27_0
- sip=4.18=py27_0
- six=1.10.0=py27_0
- sqlite=3.13.0=0
- ssl_match_hostname=3.4.0.2=py27_1
- terminado=0.6=py27_0
- tk=8.5.18=0
- tornado=4.4.2=py27_0
- traitlets=4.3.1=py27_0
- unicodecsv=0.14.1=py27_0
- wcwidth=0.1.7=py27_0
- wheel=0.29.0=py27_0
- widgetsnbextension=1.2.6=py27_0
- xlrd=1.0.0=py27_0
- yaml=0.1.6=0
- zlib=1.2.8=3
- pip:
- backports-abc==0.5
- backports.shutil-get-terminal-size==1.0.0
- backports.ssl-match-hostname==3.4.0.2
- ipython-genutils==0.1.0
- jupyter-client==4.4.0
- jupyter-console==5.0.0
- jupyter-core==4.2.1
- nb-anacondacloud==1.2.0
- nb-conda==2.0.0
- nb-conda-kernels==2.0.0
- prompt-toolkit==1.0.9
prefix: /Users/mat/anaconda/envs/DAND
The error I run into:
Collecting package metadata (repodata.json): done
Solving environment: failed
ResolvePackageNotFound:
- jupyter_console==5.0.0=py27_0
- freetype==2.5.5=1
- pyzmq==16.0.2=py27_0
- configparser==3.5.0=py27_0
- scipy==0.18.1=np111py27_0
- libpng==1.6.22=0
- ...then the list goes on and list all of the dependencies in the yml file, except the ones under pip
Things I've attempted:
I got this yaml file from a Udacity online class I'm taking, I downloaded from the website, so I don't think conda env export --no-builds > env.yml method applies to me.
I tried the solution in here, I simply moved everything under pip block, and run into a new error. Maybe I'm misunderstanding the solution.
The new error I run into:
Warning: you have pip-installed dependencies in your environment file, but you do not list pip itself as one of your conda dependencies. Conda may not use the correct pip to install your packages, and they may end up in the wrong place. Please add an explicit pip dependency. I'm adding one for you, but still nagging you.
Collecting package metadata (repodata.json): done
Solving environment: done
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Ran pip subprocess with arguments:
['/Users/yulia/anaconda3/envs/DAND/bin/python', '-m', 'pip', 'install', '-U', '-r', '/Users/yulia/data analysis -uda/condaenv.mo_ctuap.requirements.txt']
Pip subprocess output:
Pip subprocess error:
ERROR: Double requirement given: backports_abc==0.5=py27_0 (from -r /Users/yulia/data analysis -uda/condaenv.mo_ctuap.requirements.txt (line 12)) (already in backports-abc==0.5 (from -r /Users/yulia/data analysis -uda/condaenv.mo_ctuap.requirements.txt (line 1)), name='backports-abc')
CondaEnvException: Pip failed
I read some other posts suggesting to use pip to install the requirements.txt file, and some posts about "CondaEnvException: Pip failed" situation. But they didn't write explicit solutions, most of the time I'm really confused about those solutions.
Please let me know what I'm missing here, this is getting frustrating as I cannot set up the proper environment to continue the class. Thank you so much in advance!

UPDATE
It seems that things might work better in the end when you skip using the env file. Instead, create an env with required dependencies manually, this way libraries are up-to-date and notebooks appear to work properly.
$ conda create -n DAND python=2 numpy pandas matplotlib seaborn
Look for required libraries in your course's "Setting up your system" (or similar) section. The ones in my example are based on Udacity's "Intro to Data Analysis" course.
Older answer
I had a similar problem and what eventually worked for me was adding two more channels in the channels section of this YAML file.
Before:
channels: !!python/tuple
- defaults
After:
channels: !!python/tuple
- defaults
- conda-forge
- anaconda
Then all the packages even with the version restrictions were found.
In case you get some errors about conflicting version, make sure to set conda config channel_priority to false:
$ conda config --set channel_priority false

Avoiding combination of pip and conda [duplicate]

I work with conda environments and need some pip packages as well, e.g. pre-compiled wheels from ~gohlke.
At the moment I have two files: environment.yml for conda with:
# run: conda env create --file environment.yml
name: test-env
dependencies:
- python>=3.5
- anaconda
and requirements.txt for pip which can be used after activating above conda environment:
# run: pip install -i requirements.txt
docx
gooey
http://www.lfd.uci.edu/~gohlke/pythonlibs/bofhrmxk/opencv_python-3.1.0-cp35-none-win_amd64.whl
Is there a possibility to combine them in one file (for conda)?

Pip dependencies can be included in the environment.yml file like this (docs):
# run: conda env create --file environment.yml
name: test-env
dependencies:
- python>=3.5
- anaconda
- pip
- numpy=1.13.3 # pin version for conda
- pip:
# works for regular pip packages
- docx
- gooey
- matplotlib==2.0.0 # pin version for pip
# and for wheels
- http://www.lfd.uci.edu/~gohlke/pythonlibs/bofhrmxk/opencv_python-3.1.0-cp35-none-win_amd64.whl
It also works for .whl files in the same directory (see Dengar's answer) as well as with common pip packages.

One can also use the requirements.txt directly in the YAML. For example,
name: test-env
dependencies:
- python>=3.5
- anaconda
- pip
- pip:
- -r requirements.txt
Basically, any option you can run with pip install you can run in a YAML. See the Advanced Pip Example for a showcase of other capabilities.
Important Note
A previous version of this answer (and Conda's Advanced Pip Example) used a substandard file URI syntax:
- -r file:requirements.txt
Pip v21.2.1 introduced stricter behavior for URI parsing and no longer supports this. See this answer for details.

Just want to add that adding a wheel in the directory also works. I was getting this error when using the entire URL:
HTTP error 404 while getting http://www.lfd.uci.edu/~gohlke/pythonlibs/f9r7rmd8/opencv_python-3.1.0-cp35-none-win_amd64.whl
Ended up downloading the wheel and saving it into the same directory as the yml file.
name: test-env
dependencies:
- python>=3.5
- anaconda
- pip
- pip:
- opencv_python-3.1.0-cp35-none-win_amd64.whl

If you want to do it automatically it seems that if you do:
conda env export > environment.yml
already has the pip things you need. No need to run pip freeze > requirements4pip.txt separately for me or include it as a
- pip:
- -r file:requirements.txt
as another answer has mentioned.
See my yml file:
$ cat environment.yml
name: myenv
channels:
- pytorch
- dglteam
- defaults
- conda-forge
dependencies:
- _libgcc_mutex=0.1=main
- absl-py=0.12.0=py38h06a4308_0
- aiohttp=3.7.4=py38h27cfd23_1
- async-timeout=3.0.1=py38h06a4308_0
- attrs=20.3.0=pyhd3eb1b0_0
- beautifulsoup4=4.9.3=pyha847dfd_0
- blas=1.0=mkl
- blinker=1.4=py38h06a4308_0
- brotlipy=0.7.0=py38h27cfd23_1003
- bzip2=1.0.8=h7b6447c_0
- c-ares=1.17.1=h27cfd23_0
- ca-certificates=2021.4.13=h06a4308_1
- cachetools=4.2.1=pyhd3eb1b0_0
- cairo=1.14.12=h8948797_3
- certifi=2020.12.5=py38h06a4308_0
- cffi=1.14.0=py38h2e261b9_0
- chardet=3.0.4=py38h06a4308_1003
- click=7.1.2=pyhd3eb1b0_0
- conda=4.10.1=py38h06a4308_1
- conda-build=3.21.4=py38h06a4308_0
- conda-package-handling=1.7.3=py38h27cfd23_1
- coverage=5.5=py38h27cfd23_2
- cryptography=3.4.7=py38hd23ed53_0
- cudatoolkit=11.0.221=h6bb024c_0
- cycler=0.10.0=py38_0
- cython=0.29.23=py38h2531618_0
- dbus=1.13.18=hb2f20db_0
- decorator=4.4.2=pyhd3eb1b0_0
- dgl-cuda11.0=0.6.1=py38_0
- dill=0.3.3=pyhd3eb1b0_0
- expat=2.3.0=h2531618_2
- filelock=3.0.12=pyhd3eb1b0_1
- fontconfig=2.13.1=h6c09931_0
- freetype=2.10.4=h7ca028e_0
- fribidi=1.0.10=h7b6447c_0
- gettext=0.21.0=hf68c758_0
- glib=2.66.3=h58526e2_0
- glob2=0.7=pyhd3eb1b0_0
- google-auth=1.29.0=pyhd3eb1b0_0
- google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
- graphite2=1.3.14=h23475e2_0
- graphviz=2.40.1=h21bd128_2
- grpcio=1.36.1=py38h2157cd5_1
- gst-plugins-base=1.14.0=h8213a91_2
- gstreamer=1.14.0=h28cd5cc_2
- harfbuzz=1.8.8=hffaf4a1_0
- icu=58.2=he6710b0_3
- idna=2.10=pyhd3eb1b0_0
- importlib-metadata=3.10.0=py38h06a4308_0
- intel-openmp=2021.2.0=h06a4308_610
- jinja2=2.11.3=pyhd3eb1b0_0
- joblib=1.0.1=pyhd3eb1b0_0
- jpeg=9b=h024ee3a_2
- kiwisolver=1.3.1=py38h2531618_0
- lcms2=2.12=h3be6417_0
- ld_impl_linux-64=2.33.1=h53a641e_7
- libarchive=3.4.2=h62408e4_0
- libffi=3.2.1=hf484d3e_1007
- libgcc-ng=9.1.0=hdf63c60_0
- libgfortran-ng=7.3.0=hdf63c60_0
- libglib=2.66.3=hbe7bbb4_0
- libiconv=1.16=h516909a_0
- liblief=0.10.1=he6710b0_0
- libpng=1.6.37=h21135ba_2
- libprotobuf=3.14.0=h8c45485_0
- libstdcxx-ng=9.1.0=hdf63c60_0
- libtiff=4.1.0=h2733197_1
- libuuid=1.0.3=h1bed415_2
- libuv=1.40.0=h7b6447c_0
- libxcb=1.14=h7b6447c_0
- libxml2=2.9.10=hb55368b_3
- lz4-c=1.9.2=he1b5a44_3
- markdown=3.3.4=py38h06a4308_0
- markupsafe=1.1.1=py38h7b6447c_0
- matplotlib=3.3.4=py38h06a4308_0
- matplotlib-base=3.3.4=py38h62a2d02_0
- mkl=2020.2=256
- mkl-service=2.3.0=py38h1e0a361_2
- mkl_fft=1.3.0=py38h54f3939_0
- mkl_random=1.2.0=py38hc5bc63f_1
- multidict=5.1.0=py38h27cfd23_2
- ncurses=6.2=he6710b0_1
- networkx=2.5.1=pyhd3eb1b0_0
- ninja=1.10.2=hff7bd54_1
- numpy=1.19.2=py38h54aff64_0
- numpy-base=1.19.2=py38hfa32c7d_0
- oauthlib=3.1.0=py_0
- olefile=0.46=pyh9f0ad1d_1
- openssl=1.1.1k=h27cfd23_0
- pandas=1.2.4=py38h2531618_0
- pango=1.42.4=h049681c_0
- patchelf=0.12=h2531618_1
- pcre=8.44=he6710b0_0
- pillow=8.2.0=py38he98fc37_0
- pip=21.0.1=py38h06a4308_0
- pixman=0.40.0=h7b6447c_0
- pkginfo=1.7.0=py38h06a4308_0
- protobuf=3.14.0=py38h2531618_1
- psutil=5.8.0=py38h27cfd23_1
- py-lief=0.10.1=py38h403a769_0
- pyasn1=0.4.8=py_0
- pyasn1-modules=0.2.8=py_0
- pycosat=0.6.3=py38h7b6447c_1
- pycparser=2.20=py_2
- pyjwt=2.0.1=pyhd8ed1ab_1
- pyopenssl=20.0.1=pyhd3eb1b0_1
- pyparsing=2.4.7=pyhd3eb1b0_0
- pyqt=5.9.2=py38h05f1152_4
- pysocks=1.7.1=py38h06a4308_0
- python=3.8.2=hcf32534_0
- python-dateutil=2.8.1=pyhd3eb1b0_0
- python-libarchive-c=2.9=pyhd3eb1b0_1
- python_abi=3.8=1_cp38
- pytorch=1.7.1=py3.8_cuda11.0.221_cudnn8.0.5_0
- pytz=2021.1=pyhd3eb1b0_0
- pyyaml=5.4.1=py38h27cfd23_1
- qt=5.9.7=h5867ecd_1
- readline=8.1=h27cfd23_0
- requests=2.25.1=pyhd3eb1b0_0
- requests-oauthlib=1.3.0=py_0
- ripgrep=12.1.1=0
- rsa=4.7.2=pyhd3eb1b0_1
- ruamel_yaml=0.15.100=py38h27cfd23_0
- scikit-learn=0.24.1=py38ha9443f7_0
- scipy=1.6.2=py38h91f5cce_0
- setuptools=52.0.0=py38h06a4308_0
- sip=4.19.13=py38he6710b0_0
- six=1.15.0=pyh9f0ad1d_0
- soupsieve=2.2.1=pyhd3eb1b0_0
- sqlite=3.35.4=hdfb4753_0
- tensorboard=2.4.0=pyhc547734_0
- tensorboard-plugin-wit=1.6.0=py_0
- threadpoolctl=2.1.0=pyh5ca1d4c_0
- tk=8.6.10=hbc83047_0
- torchaudio=0.7.2=py38
- torchtext=0.8.1=py38
- torchvision=0.8.2=py38_cu110
- tornado=6.1=py38h27cfd23_0
- typing-extensions=3.7.4.3=0
- typing_extensions=3.7.4.3=py_0
- urllib3=1.26.4=pyhd3eb1b0_0
- werkzeug=1.0.1=pyhd3eb1b0_0
- wheel=0.36.2=pyhd3eb1b0_0
- xz=5.2.5=h7b6447c_0
- yaml=0.2.5=h7b6447c_0
- yarl=1.6.3=py38h27cfd23_0
- zipp=3.4.1=pyhd3eb1b0_0
- zlib=1.2.11=h7b6447c_3
- zstd=1.4.5=h9ceee32_0
- pip:
- aioconsole==0.3.1
- lark-parser==0.6.5
- lmdb==0.94
- pexpect==4.6.0
- progressbar2==3.39.3
- ptyprocess==0.7.0
- pycapnp==1.0.0
- python-utils==2.5.6
- sexpdata==0.0.3
- tqdm==4.56.0
prefix: /home/miranda9/miniconda3/envs/myenv
Note that at the time of this writing doing conda env create --file environment.yml to create the yml env results in an error:
$ conda env create --file environment.yml
CondaValueError: prefix already exists: /home/miranda9/miniconda3/envs/myenv