The Fast Cross-Platform Package Manager

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The Fast Cross-Platform Package Manager

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Package Manager mamba Package Server quetz Package Builder boa

mamba

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Mamba is a reimplementation of the conda package manager in C++.

  • parallel downloading of repository data and package files using multi-threading
  • libsolv for much faster dependency solving, a state of the art library used in the RPM package manager of Red Hat, Fedora and OpenSUSE
  • core parts of mamba are implemented in C++ for maximum efficiency

At the same time, mamba utilize the same command line parser, package installation and deinstallation code and transaction verification routines as conda to stay as compatible as possible.

Mamba is part of a bigger ecosystem to make scientific packaging more sustainable. You can read our announcement blog post. The ecosystem also consists of quetz, an open source conda package server and boa, a fast conda package builder.

Installation

It's advised to install mamba from conda-forge. If you already have conda:

conda install mamba -c conda-forge

otherwise it's best to start with Miniconda. If you want to experiment with the latest software, you can also try micromamba (more below).

Installing conda packages with mamba

Now you are ready to install packages with

mamba install xtensor-r -c conda-forge

for example.

Additional features

Mamba comes with features on top of stock conda. To efficiently query repositories and query package dependencies you can use mamba repoquery.

Here are some examples:

mamba repoquery search xtensor will show you all available xtensor packages. You can also specify more constraints on this search query, for example mamba repoquery search "xtensor>=0.18"

mamba repoquery depends xtensor will show you a tree view of the dependencies of xtensor.

$ mamba repoquery depends xtensor

xtensor == 0.21.5
  ├─ libgcc-ng [>=7.3.0]
  │ ├─ _libgcc_mutex [0.1 conda_forge]
  │ └─ _openmp_mutex [>=4.5]
  │   ├─ _libgcc_mutex already visited
  │   └─ libgomp [>=7.3.0]
  │     └─ _libgcc_mutex already visited
  ├─ libstdcxx-ng [>=7.3.0]
  └─ xtl [>=0.6.9,<0.7]
    ├─ libgcc-ng already visited
    └─ libstdcxx-ng already visited

And you can ask for the inverse, which packages depend on some other package (e.g. ipython) using whoneeds.

$ mamba repoquery whoneeds ipython

 Name            Version Build          Channel
──────────────────────────────────────────────────
 ipykernel       5.2.1   py37h43977f1_0 installed
 ipywidgets      7.5.1   py_0           installed
 jupyter_console 6.1.0   py_1           installed

With the --tree (or -t) flag, you can get the same information in a tree.

micromamba

micromamba is a tiny version of the mamba package manager. It is a pure C++ package with a separate command line interface. It can be used to bootstrap environments (as an alternative to miniconda), but it's currently experimental. The benefit is that it's very tiny and does not come with a default version of Python.

micromamba works in the bash & zsh shell on Linux & OS X. It's completely statically linked, which allows you to drop it in some place and just execute it.

Note: it's advised to use micromamba in containers & CI only.

Download and unzip the executable (from the official conda-forge package):

wget -qO- https://micromamba.snakepit.net/api/micromamba/linux-64/latest | tar -xvj bin/micromamba

We can use ./micromamba shell init ... to initialize a shell (.bashrc) and a new root environment in ~/micromamba:

./bin/micromamba shell init -s bash -p ~/micromamba
source ~/.bashrc

Now you can activate the base environment and install new packages, or create other environments.

Note: currently the -c arguments have to come at the end of the command line.

micromamba activate
micromamba install python=3.6 jupyter -c conda-forge
# or
micromamba create -p /some/new/prefix xtensor -c conda-forge
micromamba activate /some/new/prefix

For more instructions (including OS X) check out https://gist.github.com/wolfv/fe1ea521979973ab1d016d95a589dcde

Development installation

Make sure to have the following requirements in your conda environment:

mamba install cmake compilers pybind11 libsolv libarchive libcurl nlohmann_json pip cpp-filesystem yaml-cpp -c conda-forge

If you build mamba in a different environment than base, you must also install conda in that environment:

mamba install conda -c conda-forge

For a local (dev) build, run pip install -e .. This will build and install mamba in the conda environment.

cmake based build

You will additionally need to install cmake and cli11 for micromamba:

mamba install -c conda-forge cli11 cmake

For the C++ tests, you need Google Tests installed (e.g. mamba install gtest). To build the program using CMake, the following lines need to be used:

mkdir -p build
cd build
cmake .. \
    -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX \
    -DPYTHON_EXECUTABLE=$CONDA_PREFIX/bin/python3 \
    -DPYTHON_LIBRARIES=$CONDA_PREFIX/lib/libpython3.7m.so \
    -DENABLE_TESTS=ON
cmake --build . -j

This would generate the test program ./test/test_mamba under the build directory which you can execute with make test. To generate the executable micromamba also include the CMake option -DBUILD_EXE=ON in the above step.

Support us

For questions, you can also join us on the QuantStack Chat or on the conda channel (note that this project is not officially affiliated with conda or Anaconda Inc.).

License

We use a shared copyright model that enables all contributors to maintain the copyright on their contributions.

This software is licensed under the BSD-3-Clause license. See the LICENSE file for details.

Comments
  • Micromamba sporadically hangs  (QEMU + ARM)

    Micromamba sporadically hangs (QEMU + ARM)

    Edit: Summary

    Initial report

    Over at Jupyter Docker Stacks, we have been trying create the initial Conda environment for our base-notebook by using Micromamba instead of Miniforge. We have been plagued by sporadic hangs in our multi-arch Docker build process on GH Actions at a rate of ~20% of all runs when for aarch64 we RUN the command

    ./micromamba install --root-prefix=/opt/conda --prefix=/opt/conda -vv --yes python=3.9 mamba notebook jupyterhub jupyterlab
    

    The hang occurs sometime between the end of extraction and the beginning of linking. The last message printed during an unsuccessful run is something like

      #29 188.0 debug    libmamba Extracted to '/opt/conda/pkgs/mistune-0.8.4-py39h14843e3_1005'
    

    without proceeding to a line like

    #29 178.0 Linking pandoc-2.17.1.1-h8af1aa0_0
    

    Execution hangs without any further output until the 6h limit is reached.

    References

    (in chronological order)

  • Reproducible micromamba segfault when installing from lockfile after previous install

    Reproducible micromamba segfault when installing from lockfile after previous install

    I can reproduce in a Dockerfile built on GitHub Actions.

    I put some effort into making this more minimal, but didn't get very far. I hope this makes it possible for someone to easily do a trace. :)

    High-level overview:

    • Select some particular dependencies
    • Do micromamba install, with or without lockfile. (I used a lockfile for the first install command to guarantee reproducibility.)
    • Do micromamba install from a new-style lockfile produced by conda-lock
  • SHA256 mismatch on homebrew micromamba installation

    SHA256 mismatch on homebrew micromamba installation

    SHA256 mismatch when installing micromamba 0.27.0-2 on a fresh installation of Monterey via homebrew. Everything else brew related seems to be working correctly. Successfully installed a few days ago so not sure what's changed.

  • DLL load error

    DLL load error

    With the latest mamba=0.2.7 I'm seeing DLL load errors:

    (dev) C:\>mamba install diagrams
    Traceback (most recent call last):
      File "C:\Miniconda3\Scripts\mamba-script.py", line 6, in <module>
        from mamba.mamba import main
      File "C:\Miniconda3\lib\site-packages\mamba\mamba.py", line 52, in <module>
        import mamba.mamba_api as api
    ImportError: DLL load failed: %1 is not a valid Win32 application.
    

    I saw this with 0.2.6 but I'm not sure if it's specifically related to updating mamba or I also did a conda update --all in my base (mamba) env around the same time.

  • Extract packages in subproc

    Extract packages in subproc

    Based on the work in https://github.com/mamba-org/mamba/pull/1187. Refs https://github.com/mamba-org/mamba/pull/1174.

    For some reason, on my machine reproc always reports an exit code of 32 (Broken pipe), but the child processes run just fine, which is why the code currently skips error checking. I have spent quite some time debugging this but have not succeeded in identifying the root cause. Things I've tried: Replacing mamba with echo; putting a mutex around extract_subproc; removing any output from the extract subcommand; others.

  • Parse and use explicit platform specifications.

    Parse and use explicit platform specifications.

    Rather than trying to figure out whether a channel string/url contains a platform, we instead make platform specification explicit with CHANNEL[PLATFORM1,PLATFORM2,...].

    There is a refactor of the channel class here to hide implementation details/functions which are only relevant for testing.

    This is a rework related to discussion at #934, and will close #794.

    Note that this introduces breaking CLI changes as well as breaking Python API changes (the Channel class is improved), so these should be noted in the changelog.

  • micromamba resolves differently (and wrong) in ci environment while resolving locally correctly

    micromamba resolves differently (and wrong) in ci environment while resolving locally correctly

    Both have micromamba 0.27 We do

    micromamba create -f conda.yaml -y -n env-name
    # And then
    micromamba install -c conda-forge -n env-name pytest=7.1.3 pyspark=3.2.1 openjdk=8
    # as those are dev dependencies we don't want in our final conda file which later is used in mlflow
    

    Locally this works, and upgrades two packages but in CI in upgrades (consistently) a third package - tokenizers

     Upgrade:
    ───────────────────────────────────────────────────────────────────────────────────────────
      - libprotobuf               3.20.0  h6239696_0          conda-forge              Cached
      + libprotobuf               3.21.9  h6239696_0          conda-forge/linux-64        3MB
      - protobuf                  3.20.0  py38hfa26641_5      conda-forge              Cached
      + protobuf                  4.21.9  py38hfa26641_0      conda-forge/linux-64      348kB
      - tokenizers                0.12.1  py38h8bed557_1      conda-forge              Cached
      + tokenizers                0.13.1  py38hb35c9e2_2      conda-forge/linux-64        5MB
    

    The problem is it's a version we don't support and is specified in the conda.yaml

    name: survey-answers-clustering-mlflow-env
    channels:
      - conda-forge
    
    dependencies:
          - python=3.8
          - hdbscan=0.8.28
          - joblib=1.1.0 #because of problematic hdbscan dependency
          - bertopic=0.9.4
          - python-flair>=0.11.3,<0.12 #it was open, and to avoid future problems I am locking
          - spacy>=2.0.0,<3.0.0
          - numpy=1.21.0
          - pandas=1.3.5
          - spacy-model-en_core_web_sm=2.2.0
          - inflection=0.5.1
          - protobuf>=3.12.0,3.20
          - transformers=4.19.4
          - tokenizers>=0.11.1,!=0.11.3,<0.13 #other dependency require this limitation
          - pip
          - pip:
            - mlflow==1.29.0
            - cloudpickle==1.6.0
    

    How should I debug this? what should I do differently? Trying to install both in the same command fails with another error

     micromamba create -f conda.yaml -y -n survey-answers-clustering-mlflow-env-4 pytest=7.1.3 pyspark=3.2.1 openjdk=8
    

    Thanks for this tool!

  • Micromamba download performance is slow compared to just parallel curl

    Micromamba download performance is slow compared to just parallel curl

    I've been testing some things on large ec2 machines and found micromamba seems to max out around 400Mbps sustained download.

    Comparatively using parallel and curl I was able to hit 7Gbps (over 10x speedup!). I played with micromamba config set download_threads but nothing really got me past the 400Mbps wall.

    This is surprising and maybe points to something wrong considering micromamba uses libcurl. Very few people will be hitting 4Gbps, but in CI/Cloud systems where 1Gbps is the norm and 100Gbps is possible, fixing this could have a nice beneficial effect.

    Here's a sample command and environment I was using

    parallel -j 64 --verbose curl -O < testfile.txt
    
    testfile.txt
    https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2022.6.15-ha878542_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.36.1-hea4e1c9_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-12.1.0-hdcd56e2_16.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-12.1.0-ha89aaad_16.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/pandoc-2.18-ha770c72_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libgcc-devel_linux-64-12.1.0-h1ec3361_16.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libgomp-12.1.0-h8d9b700_16.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libgfortran-ng-12.1.0-h69a702a_16.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-12.1.0-h8d9b700_16.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.1.0-ha89aaad_16.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.5-h516909a_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h7f98852_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h7f98852_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/snappy-1.1.9-hbd366e4_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/re2-2022.06.01-h27087fc_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/pcre-8.45-h9c3ff4c_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1q-h166bdaf_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/nspr-4.32-h9c3ff4c_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.3-h27087fc_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.3-h9c3ff4c_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.2.12-h166bdaf_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.2.3-h166bdaf_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h7f98852_1000.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.7.0-h7f98852_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libudev1-249-h166bdaf_4.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libtool-2.4.6-h9c3ff4c_1008.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libsodium-1.0.18-h36c2ea0_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.20-pthreads_h78a6416_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.4-h7f98852_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.0-h7f98852_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.16-h516909a_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-h516909a_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.12-h166bdaf_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libdb-6.2.32-h9c3ff4c_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_7.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/lerc-3.0-h9c3ff4c_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/jpeg-9e-h166bdaf_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/icu-70.1-h27087fc_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/giflib-5.2.1-h36c2ea0_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-he1b5a44_1004.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/fftw-3.3.10-nompi_h77c792f_102.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/expat-2.4.8-h27087fc_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.18.1-h7f98852_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h7f98852_4.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.6.2-h7f98852_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.6.1-h7f98852_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/abseil-cpp-20210324.2-h9c3ff4c_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h7f98852_1004.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/s2n-1.0.10-h9b69904_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/mysql-common-8.0.29-haf5c9bc_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.10-h9b69904_4.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/readline-8.1.2-h0f457ee_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20191231-he28a2e2_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.2-h8a70e8d_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.12-h166bdaf_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.12-h27826a3_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libllvm14-14.0.6-he0ac6c6_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libllvm11-11.1.0-hf817b99_3.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/zeromq-4.3.4-h9c3ff4c_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-15_linux64_openblas.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libvorbis-1.3.7-h9c3ff4c_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-h73d1719_1008.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.0.9-h166bdaf_7.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.0.9-h166bdaf_7.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/glog-0.6.0-h6f12383_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.1.11-ha31a3da_7.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.5.11-h95a6274_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libcap-2.64-ha37c62d_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/portaudio-19.6.0-h57a0ea0_5.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/xcb-util-wm-0.4.1-h166bdaf_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.9-h166bdaf_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.0-h166bdaf_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.0-h166bdaf_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.16.0-h519c5ea_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/krb5-1.19.3-h3790be6_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/mysql-libs-8.0.29-h28c427c_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.4.0-hc85c160_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/blosc-1.21.1-h83bc5f7_3.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.39.2-h4ff8645_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.14-h22db469_3.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.10.0-ha56f1ee_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-3.20.1-h6239696_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-h753d276_3.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.47.0-h727a467_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libclang13-14.0.6-default_h3a83d3e_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-15_linux64_openblas.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-15_linux64_openblas.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/libglib-2.72.1-h2d90d5f_0.tar.bz2
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    https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.1.1-py39h4037b75_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/gst-plugins-base-1.20.3-hf6a322e_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/phik-0.12.2-py39ha791e8c_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/shap-0.41.0-py39h1832856_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/qt-main-5.15.4-ha5833f6_2.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/pyqt-5.15.7-py39h18e9c17_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.5.2-py39hf3d152e_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/dask-core-2022.7.1-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.2-pyhd8ed1ab_1.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/aiosignal-1.2.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/markdown-3.4.1-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/mako-1.2.1-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/jsonschema-4.7.2-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-4.11.4-hd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/jupyter_client-7.3.4-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/patsy-0.5.2-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/imagehash-4.2.1-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/visions-0.7.4-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/parquet-cpp-1.5.1-2.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/pyopenssl-22.0.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/oauthlib-3.2.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-21.3.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.11.2-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/flask-2.1.3-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/alembic-1.8.1-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/nbformat-5.4.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/ipykernel-6.15.1-pyh210e3f2_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/urllib3-1.26.10-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/prometheus_flask_exporter-0.20.2-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/nbclient-0.6.6-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/requests-2.28.1-pyhd8ed1ab_1.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/distributed-2022.7.1-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/botocore-1.24.21-pyhd8ed1ab_1.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/nbconvert-core-6.5.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/querystring_parser-1.2.4-py_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/databricks-cli-0.17.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/nbconvert-pandoc-6.5.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/jupyter_server-1.18.1-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/nbconvert-6.5.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/notebook-shim-0.1.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/jupyterlab_server-2.15.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/notebook-6.4.12-pyha770c72_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/nbclassic-0.4.3-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/jupyterlab-3.4.4-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/bokeh-2.4.3-py39hf3d152e_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/aiohttp-3.8.1-py39hb9d737c_1.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.13.2-py39hd257fcd_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/pyarrow-8.0.0-py39h42d110c_4_cpu.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/docker-py-5.0.3-py39hf3d152e_2.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/dask-2022.7.1-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/aiobotocore-2.3.4-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/seaborn-0.11.2-hd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/s3fs-2022.5.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/missingno-0.4.2-py_1.tar.bz2
    https://conda.anaconda.org/conda-forge/noarch/pandas-profiling-3.2.0-pyhd8ed1ab_0.tar.bz2
    https://conda.anaconda.org/conda-forge/linux-64/mlflow-1.27.0-py39ha39b057_0.tar.bz2
    
  • Faster pyc compilation

    Faster pyc compilation

    This PR included all but one of the changes I've mentioned on gitter. My C++ knowlege is lacking so I'm not sure how idiomatic this PR is, feel free to sugest major changes. I'm also happy for someone else to take over this PR if it's easier.

    Fixes #1413

    Performance comparision

    I picked a few packages I'm familiar with to use for benchmarking here:

    micromamba create --prefix /tmp/test-env --yes celery pandas tqdm sqlalchemy uproot scikit-hep jupyterlab
    

    I've ran the command several times to make sure the caches were populated.

    I've also tried various other sets of packages, installers generated using conda-constructor and running on slower filesystems (NFS, Ceph volumes, ...). All of my test cases have been significantly faster.

    Lastest micromamba release from inside an exisiting environment

    Slowed by calling deactivate (see #1413)

    ________________________________________________________
    Executed in   45.73 secs    fish           external
       usr time   41.04 secs  610.00 micros   41.04 secs
       sys time    8.31 secs  193.00 micros    8.31 secs
    

    Lastest micromamba release outside an exisiting environment

    ________________________________________________________
    Executed in   19.87 secs    fish           external
       usr time   16.14 secs    1.67 millis   16.13 secs
       sys time    3.48 secs    0.00 millis    3.48 secs
    

    This PR

    ________________________________________________________
    Executed in    9.57 secs    fish           external
       usr time   11.92 secs  800.00 micros   11.92 secs
       sys time    1.81 secs    0.00 micros    1.81 secs
    
  • filesystem error: cannot make absolute path with 0.26

    filesystem error: cannot make absolute path with 0.26

    Hello;

    Our package doesn't build anymore using Mamba, although it has always been in the past. It might be related to the new 0.26 version.

    Details https://app.travis-ci.com/github/radis/radis/jobs/584458524#L310

    info     libmamba ****************** Backtrace Start ******************
    debug    libmamba Loading configuration
    trace    libmamba Compute configurable 'create_base'
    trace    libmamba Compute configurable 'no_env'
    trace    libmamba Compute configurable 'no_rc'
    trace    libmamba Compute configurable 'rc_files'
    trace    libmamba Compute configurable 'root_prefix'
    trace    libmamba Get RC files configuration from locations up to HomeDir
    trace    libmamba Configuration not found at '/home/travis/.mambarc'
    trace    libmamba Configuration not found at '/home/travis/.condarc'
    trace    libmamba Configuration not found at '/home/travis/.conda/condarc.d'
    trace    libmamba Configuration not found at '/home/travis/.conda/condarc'
    trace    libmamba Configuration not found at '/home/travis/.conda/.condarc'
    trace    libmamba Configuration not found at '/home/travis/micromamba/.mambarc'
    trace    libmamba Configuration not found at '/home/travis/micromamba/condarc.d'
    trace    libmamba Configuration not found at '/home/travis/micromamba/condarc'
    trace    libmamba Configuration not found at '/home/travis/micromamba/.condarc'
    trace    libmamba Configuration not found at '/var/lib/conda/.mambarc'
    trace    libmamba Configuration not found at '/var/lib/conda/condarc.d/'
    trace    libmamba Configuration not found at '/var/lib/conda/condarc'
    trace    libmamba Configuration not found at '/var/lib/conda/.condarc'
    trace    libmamba Configuration not found at '/etc/conda/.mambarc'
    trace    libmamba Configuration not found at '/etc/conda/condarc.d/'
    trace    libmamba Configuration not found at '/etc/conda/condarc'
    trace    libmamba Configuration not found at '/etc/conda/.condarc'
    trace    libmamba Update configurable 'no_env'
    trace    libmamba Compute configurable 'file_specs'
    critical libmamba filesystem error: cannot make absolute path: Invalid argument []
    

    I do not understand what's wrong from the error message. What informations do you need to debug ?

  • ImportError: cannot import name 'init_std_stream_encoding' from 'conda.common.compat'

    ImportError: cannot import name 'init_std_stream_encoding' from 'conda.common.compat'

    My mamba install was working fine till now when I suddenly get this today on any mamba command.

    ❯ mamba
    Traceback (most recent call last):
      File "/home/user/miniconda3/bin/mamba", line 11, in <module>
        sys.exit(main())
      File "/home/user/miniconda3/lib/python3.9/site-packages/mamba/mamba.py", line 892, in main
        from conda.common.compat import ensure_text_type, init_std_stream_encoding
    ImportError: cannot import name 'init_std_stream_encoding' from 'conda.common.compat' (/home/user/miniconda3/lib/python3.9/site-packages/conda/common/compat.py)
    
  • micromamba in powershell Invoke-Expression

    micromamba in powershell Invoke-Expression "warning" despite shell init

    Hello, I ran into the following issue: I use micromamba under Windows 11 with Powershell 7.3. I did

    \...\micromamba.exe shell init -s powershell -p C:\mamba\base\
    

    where -p is where I want (and have) my base environment. This resulted in the following modification to my Powershell-profile (which I know is excuted):

    #region mamba initialize
    # !! Contents within this block are managed by 'mamba shell init' !!
    $Env:MAMBA_ROOT_PREFIX = "C:\mamba\base\"
    $Env:MAMBA_EXE = "C:\mamba\micromamba.exe"
    (& $Env:MAMBA_EXE 'shell' 'hook' -s 'powershell' -p $Env:MAMBA_ROOT_PREFIX) | Out-String | Invoke-Expression
    #endregion
    

    This looks good to me. Now calling e. g. micromamba install numpy works as expected but with the following flaw: After Transaction finished is printed, there is some additional output: issue Despite micromamba working correctly as far as I can tell, this is pretty tedious and finishing basically every install with an error message is painful. Using

    C:\mamba\micromamba.exe install numpy
    

    works without the additional error message. Of the "mamba-related directories" I only have C:\mamba\base\Scripts\ in my PATH. I have no problems in git bash. Could this be an issue with the newest Powershell version and the way micomamba.exe shell init works? I have little to no Powershell knowledge so please excuse me if this is senseless.

    I would be grateful for any help. Keep up the good work!

  • .\micromamba.exe --help has no output

    .\micromamba.exe --help has no output

    Following the instructions here: https://mamba.readthedocs.io/en/latest/installation.html#windows

    I've ran those commands:

    Invoke-Webrequest -URI https://micro.mamba.pm/api/micromamba/win-64/latest -OutFile micromamba.tar.bz2
    C:\PROGRA~1\7-Zip\7z.exe x micromamba.tar.bz2 -aoa
    C:\PROGRA~1\7-Zip\7z.exe x micromamba.tar -ttar -aoa -r Library\bin\micromamba.exe
    
    MOVE -Force Library\bin\micromamba.exe micromamba.exe
    .\micromamba.exe --help
    

    The download and extraction worked fine, but the executable itself doesn't seem to work. image

    No commands seem to work. -v doesn't change the output.

  • mamba being slow to start

    mamba being slow to start

    I have a feeling that mamba used to be much faster at least on my system (that's why I switched to it anyway :D )

    But it's now much slower, and I have about 10s startup time for an install command:

    /usr/bin/time mamba install -d numpy
    
                      __    __    __    __
                     /  \  /  \  /  \  /  \
                    /    \/    \/    \/    \
    ███████████████/  /██/  /██/  /██/  /████████████████████████
                  /  / \   / \   / \   / \  \____
                 /  /   \_/   \_/   \_/   \    o \__,
                / _/                       \_____/  `
                |/
            ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗
            ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗
            ██╔████╔██║███████║██╔████╔██║██████╔╝███████║
            ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║
            ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║
            ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝
    
            mamba (1.0.0) supported by @QuantStack
    
            GitHub:  https://github.com/mamba-org/mamba
            Twitter: https://twitter.com/QuantStack
    
    █████████████████████████████████████████████████████████████
    
    
    Looking for: ['numpy']
    
    conda-forge/linux-64                                        Using cache
    conda-forge/noarch                                          Using cache
    
    Pinned packages:
      - python 3.10.*
    
    
    Transaction
    
      Prefix: /home/adrin/miniforge3/envs/skops
    
      All requested packages already installed
    
    10.49user 4.31system 0:19.79elapsed 74%CPU (0avgtext+0avgdata 329648maxresident)k
    0inputs+0outputs (0major+240235minor)pagefaults 0swaps
    

    even mamba info seems to be quite slow:

    /usr/bin/time mamba info
    
                      __    __    __    __
                     /  \  /  \  /  \  /  \
                    /    \/    \/    \/    \
    ███████████████/  /██/  /██/  /██/  /████████████████████████
                  /  / \   / \   / \   / \  \____
                 /  /   \_/   \_/   \_/   \    o \__,
                / _/                       \_____/  `
                |/
            ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗
            ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗
            ██╔████╔██║███████║██╔████╔██║██████╔╝███████║
            ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║
            ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║
            ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝
    
            mamba (1.0.0) supported by @QuantStack
    
            GitHub:  https://github.com/mamba-org/mamba
            Twitter: https://twitter.com/QuantStack
    
    █████████████████████████████████████████████████████████████
    
    
         active environment : skops
        active env location : /home/adrin/miniforge3/envs/skops
                shell level : 1
           user config file : /home/adrin/.condarc
     populated config files : /home/adrin/miniforge3/.condarc
                              /home/adrin/.condarc
              conda version : 22.9.0
        conda-build version : not installed
             python version : 3.8.13.final.0
           virtual packages : __linux=6.0.9=0
                              __glibc=2.36=0
                              __unix=0=0
                              __archspec=1=x86_64
           base environment : /home/adrin/miniforge3  (writable)
          conda av data dir : /home/adrin/miniforge3/etc/conda
      conda av metadata url : None
               channel URLs : https://conda.anaconda.org/conda-forge/linux-64
                              https://conda.anaconda.org/conda-forge/noarch
              package cache : /home/adrin/miniforge3/pkgs
                              /home/adrin/.conda/pkgs
           envs directories : /home/adrin/miniforge3/envs
                              /home/adrin/.conda/envs
                   platform : linux-64
                 user-agent : conda/22.9.0 requests/2.28.1 CPython/3.8.13 Linux/6.0.9-arch1-1 arch/ glibc/2.36
                    UID:GID : 1000:1000
                 netrc file : /home/adrin/.netrc
               offline mode : False
    
    8.16user 4.15system 0:17.28elapsed 71%CPU (0avgtext+0avgdata 90384maxresident)k
    0inputs+0outputs (0major+186509minor)pagefaults 0swaps
    
  • Timeout

    Timeout

    An unexpected error has occurred. Conda has prepared the above report.

    Operation too slow. Less than 30 bytes/sec transferred the last 60 seconds.

    It's downloading for a big package, above 1GB, Why this always happens? Whether there are network speed requirements for using MAMBA ?

  • Mamba env create ignores multiple --fille arguments

    Mamba env create ignores multiple --fille arguments

    When creating an environment from multiple environment.yml files, only the last environement.yml file is considered.

    mamba env create -n test_env --file=environment1.yml --file=environment2.yml

    Here are my used environment files

    environment1.yml 
    name: env1
    
    dependencies:
    - svglib
    
    
    environment1.yml 
    name: env2
    
    dependencies:
    - libpng
    

    When checking the created envionment with conda list, svglib is not installed. No error is prompted.

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