Hi everyone, running into an error when the flow is Boostrapping the conda virtual environment running on arm (M1) laptop:

mbp in ...path on  branch [!?] via 🅒 dev 
took 6s ✗ python test.py --environment=conda --datastore=s3 run
Metaflow 2.11.10 executing Test for user:pmiron
Project: test, Branch: user.pmiron
Validating your flow...
    The graph looks good!
Running pylint...
    Pylint not found, so extra checks are disabled.
Bootstrapping virtual environment(s) ...
    Micromamba ran into an error while setting up environment:
    command '/opt/homebrew/bin/micromamba create --yes --quiet --dry-run --no-extra-safety-checks --repodata-ttl=86400 --retry-clean-cache --prefix=/var/folders/_6/hdhmyzr120jgn1d_45q65zkh0000gn/T/tmp8oq8ubfo/prefix --channel=conda-forge --channel=defaults  ... ... ... zstd==1.5.6 python==3.11.9' returned error (1)
    nothing provides __glibc >=2.17,<3.0.a0 needed by nss-3.98-h1d7d5a4_0
...
...
    nothing provides __glibc >=2.17,<3.0.a0 needed by libgdal-3.4.1-h42fb820_3

My guess right now is that those pinned dependencies solved using

"linux-64"

as the platform can't be solve on arm64. Is there a way to pass the platform to make sure it is bootstrapped on

linux-64

? For example in Docker, one can passed the

--platform linux/amd64

parameter.

👀

what environment are you trying to resolve? It should resolve for linux-64 if you are trying to run for kubernetes/batch/etc.

Oh ok.. so it might be something else then. This is my environment:

name: env
channels:
  - conda-forge
  - nodefaults
dependencies:
  - geopandas
  - pandas
  - python=3.11
  - pyarrow
  - numpy
  - s3fs

which I resolved for

linux-64

to get the following pinned dependencies dictionary:

dependencies = {
    "_libgcc_mutex": "0.1",
    "_openmp_mutex": "4.5",
    "aiobotocore": "2.12.2",
    "aiohttp": "3.9.5",
    "aioitertools": "0.11.0",
    "aiosignal": "1.3.1",
    "attrs": "23.2.0",
    "aws-c-auth": "0.7.18",
    "aws-c-cal": "0.6.11",
    "aws-c-common": "0.9.15",
    "aws-c-compression": "0.2.18",
    "aws-c-event-stream": "0.4.2",
    "aws-c-http": "0.8.1",
    "aws-c-io": "0.14.7",
    "aws-c-mqtt": "0.10.4",
    "aws-c-s3": "0.5.7",
    "aws-c-sdkutils": "0.1.15",
    "aws-checksums": "0.1.18",
    "aws-crt-cpp": "0.26.8",
    "aws-sdk-cpp": "1.11.267",
    "azure-core-cpp": "1.11.1",
    "azure-identity-cpp": "1.6.0",
    "azure-storage-blobs-cpp": "12.10.0",
    "azure-storage-common-cpp": "12.5.0",
    "blosc": "1.21.5",
    "botocore": "1.34.51",
    "branca": "0.7.2",
    "brotli": "1.1.0",
    "brotli-bin": "1.1.0",
    "brotli-python": "1.1.0",
    "bzip2": "1.0.8",
    "c-ares": "1.28.1",
    "ca-certificates": "2024.2.2",
    "cairo": "1.18.0",
    "certifi": "2024.2.2",
    "cfitsio": "4.4.0",
    "charset-normalizer": "3.3.2",
    "click": "8.1.7",
    "click-plugins": "1.1.1",
    "cligj": "0.7.2",
    "contourpy": "1.2.1",
    "cycler": "0.12.1",
    "expat": "2.6.2",
    "fiona": "1.9.6",
    "fmt": "10.2.1",
    "folium": "0.16.0",
    "font-ttf-dejavu-sans-mono": "2.37",
    "font-ttf-inconsolata": "3.000",
    "font-ttf-source-code-pro": "2.038",
    "font-ttf-ubuntu": "0.83",
    "fontconfig": "2.14.2",
    "fonts-conda-ecosystem": "1",
    "fonts-conda-forge": "1",
    "fonttools": "4.51.0",
    "freetype": "2.12.1",
    "freexl": "2.0.0",
    "frozenlist": "1.4.1",
    "fsspec": "2024.3.1",
    "gdal": "3.8.5",
    "geopandas": "0.14.4",
    "geopandas-base": "0.14.4",
    "geos": "3.12.1",
    "geotiff": "1.7.1",
    "gflags": "2.2.2",
    "giflib": "5.2.2",
    "glog": "0.7.0",
    "hdf4": "4.2.15",
    "hdf5": "1.14.3",
    "icu": "73.2",
    "idna": "3.7",
    "jinja2": "3.1.3",
    "jmespath": "1.0.1",
    "joblib": "1.4.2",
    "json-c": "0.17",
    "kealib": "1.5.3",
    "keyutils": "1.6.1",
    "kiwisolver": "1.4.5",
    "krb5": "1.21.2",
    "lcms2": "2.16",
    "ld_impl_linux-64": "2.40",
    "lerc": "4.0.0",
    "libabseil": "20240116.2",
    "libaec": "1.1.3",
    "libarchive": "3.7.2",
    "libarrow": "15.0.2",
    "libarrow-acero": "15.0.2",
    "libarrow-dataset": "15.0.2",
    "libarrow-flight": "15.0.2",
    "libarrow-flight-sql": "15.0.2",
    "libarrow-gandiva": "15.0.2",
    "libarrow-substrait": "15.0.2",
    "libblas": "3.9.0",
    "libboost-headers": "1.84.0",
    "libbrotlicommon": "1.1.0",
    "libbrotlidec": "1.1.0",
    "libbrotlienc": "1.1.0",
    "libcblas": "3.9.0",
    "libcrc32c": "1.1.2",
    "libcurl": "8.7.1",
    "libdeflate": "1.20",
    "libedit": "3.1.20191231",
    "libev": "4.33",
    "libevent": "2.1.12",
    "libexpat": "2.6.2",
    "libffi": "3.4.2",
    "libgcc-ng": "13.2.0",
    "libgdal": "3.8.5",
    "libgfortran-ng": "13.2.0",
    "libgfortran5": "13.2.0",
    "libglib": "2.80.0",
    "libgomp": "13.2.0",
    "libgoogle-cloud": "2.23.0",
    "libgoogle-cloud-storage": "2.23.0",
    "libgrpc": "1.62.2",
    "libiconv": "1.17",
    "libjpeg-turbo": "3.0.0",
    "libkml": "1.3.0",
    "liblapack": "3.9.0",
    "libllvm16": "16.0.6",
    "libnetcdf": "4.9.2",
    "libnghttp2": "1.58.0",
    "libnl": "3.9.0",
    "libnsl": "2.0.1",
    "libopenblas": "0.3.27",
    "libparquet": "15.0.2",
    "libpng": "1.6.43",
    "libpq": "16.2",
    "libprotobuf": "4.25.3",
    "libre2-11": "2023.09.01",
    "librttopo": "1.1.0",
    "libspatialindex": "1.9.3",
    "libspatialite": "5.1.0",
    "libsqlite": "3.45.3",
    "libssh2": "1.11.0",
    "libstdcxx-ng": "13.2.0",
    "libthrift": "0.19.0",
    "libtiff": "4.6.0",
    "libutf8proc": "2.8.0",
    "libuuid": "2.38.1",
    "libwebp-base": "1.4.0",
    "libxcb": "1.15",
    "libxcrypt": "4.4.36",
    "libxml2": "2.12.6",
    "libzip": "1.10.1",
    "libzlib": "1.2.13",
    "lz4-c": "1.9.4",
    "lzo": "2.10",
    "mapclassify": "2.6.1",
    "markupsafe": "2.1.5",
    "matplotlib-base": "3.8.4",
    "minizip": "4.0.5",
    "multidict": "6.0.5",
    "munkres": "1.1.4",
    "ncurses": "6.4.20240210",
    "networkx": "3.3",
    "nspr": "4.35",
    "nss": "3.98",
    "numpy": "1.26.4",
    "openjpeg": "2.5.2",
    "openssl": "3.3.0",
    "orc": "2.0.0",
    "packaging": "24.0",
    "pandas": "2.2.2",
    "pcre2": "10.43",
    "pillow": "10.3.0",
    "pip": "24.0",
    "pixman": "0.43.2",
    "poppler": "24.04.0",
    "poppler-data": "0.4.12",
    "postgresql": "16.2",
    "proj": "9.4.0",
    "pthread-stubs": "0.4",
    "pyarrow": "15.0.2",
    "pyparsing": "3.1.2",
    "pyproj": "3.6.1",
    "pysocks": "1.7.1",
    "python-dateutil": "2.9.0",
    "python-tzdata": "2024.1",
    "python_abi": "3.11",
    "pytz": "2024.1",
    "rdma-core": "51.0",
    "re2": "2023.09.01",
    "readline": "8.2",
    "requests": "2.31.0",
    "rtree": "1.2.0",
    "s2n": "1.4.12",
    "s3fs": "2024.3.1",
    "scikit-learn": "1.4.2",
    "scipy": "1.13.0",
    "setuptools": "69.5.1",
    "shapely": "2.0.4",
    "six": "1.16.0",
    "snappy": "1.2.0",
    "spdlog": "1.13.0",
    "sqlite": "3.45.3",
    "threadpoolctl": "3.5.0",
    "tiledb": "2.22.0",
    "tk": "8.6.13",
    "typing_extensions": "4.11.0",
    "tzcode": "2024a",
    "tzdata": "2024a",
    "ucx": "1.15.0",
    "uriparser": "0.9.7",
    "urllib3": "2.0.7",
    "wheel": "0.43.0",
    "wrapt": "1.16.0",
    "xerces-c": "3.2.5",
    "xorg-kbproto": "1.0.7",
    "xorg-libice": "1.1.1",
    "xorg-libsm": "1.2.4",
    "xorg-libx11": "1.8.9",
    "xorg-libxau": "1.0.11",
    "xorg-libxdmcp": "1.1.3",
    "xorg-libxext": "1.3.4",
    "xorg-libxrender": "0.9.11",
    "xorg-renderproto": "0.11.1",
    "xorg-xextproto": "7.3.0",
    "xorg-xproto": "7.0.31",
    "xyzservices": "2024.4.0",
    "xz": "5.2.6",
    "yarl": "1.9.4",
    "zlib": "1.2.13",
    "zstd": "1.5.6"
}
@conda_base(libraries=dependencies, python='3.11.9')

that I use in the flow. And yes it is on Kubernetes,

@conda_base(libraries=dependencies, python=python_version)
class Test(FlowSpec):
    @kubernetes(cpu=2, memory=8000, disk=10000)
    @step
    def start(self):
        self.next(self.end)

    @conda(disabled=True)
    @step
    def end(self):
        pass


if __name__ == "__main__":
    Test()

I am not sure why it doesn’t work with the mainline conda/pypi decorators but it should work with the netflix extension ones. I just resolved it on my m2 mac:

metaflow environment resolve --arch=linux-64 --dry-run -f philippe.yml;

where the yml file has:

dependencies:
  - geopandas
  - pandas
  - python=3.11
  - pyarrow
  - numpy
  - s3fs

and get:

### Environment for architecture linux-64
Environment of type conda-only full hash 59f0dad7faf45217e16bb1bdd4f174a51513dfe3:03390c90372f5f07844ba32bb331dc557fb1f91f
Arch linux-64
Available on linux-64

Resolved on 2024-05-03 11:33:14.606793
Resolved by rcledat

User-requested packages sys::__glibc==2.27, conda::boto3==>=1.14.0, conda::cffi==>=1.13.0,!=1.15.0, conda::fastavro==>=1.6.0, conda::geopandas, conda::numpy, conda::pandas==>=0.24.0, conda::pyarrow==>=0.17.1, conda::python==3.11, conda::requests==>=2.21.0, conda::s3fs
User sources conda::conda-forge

Conda Packages installed _libgcc_mutex==0.1-conda_forge, _openmp_mutex==4.5-2_gnu, attrs==23.2.0-pyh71513ae_0, aws-c-auth==0.7.18-he0b1f16_0, aws-c-cal==0.6.11-heb1d5e4_0, aws-c-common==0.9.15-hd590300_0, aws-c-compression==0.2.18-hce8ee76_3, aws-c-event-stream==0.4.2-h01f5eca_8, aws-c-http==0.8.1-hdb68c23_10, aws-c-io==0.14.7-hbfbeace_6, aws-c-mqtt==0.10.4-h50844eb_0, aws-c-s3==0.5.7-h6be9164_2, aws-c-sdkutils==0.1.15-hce8ee76_3, aws-checksums==0.1.18-hce8ee76_3, aws-crt-cpp==0.26.8-h2150271_2, aws-sdk-cpp==1.11.267-hddb5a97_7, azure-core-cpp==1.11.1-h91d86a7_1, azure-identity-cpp==1.6.0-hf1915f5_1, azure-storage-blobs-cpp==12.10.0-h00ab1b0_1, azure-storage-common-cpp==12.5.0-h94269e2_4, blosc==1.21.5-hc2324a3_1, boto3==1.34.97-pyhd8ed1ab_0, botocore==1.34.97-pyge310_1234567_0, branca==0.7.2-pyhd8ed1ab_0, brotli==1.1.0-hd590300_1, brotli-bin==1.1.0-hd590300_1, brotli-python==1.1.0-py311hb755f60_1, bzip2==1.0.8-hd590300_5, c-ares==1.28.1-hd590300_0, ca-certificates==2024.2.2-hbcca054_0, cairo==1.18.0-h3faef2a_0, certifi==2024.2.2-pyhd8ed1ab_0, cffi==1.16.0-py311hb3a22ac_0, cfitsio==4.4.0-hbdc6101_1, charset-normalizer==3.3.2-pyhd8ed1ab_0, click==8.1.7-unix_pyh707e725_0, click-plugins==1.1.1-py_0, cligj==0.7.2-pyhd8ed1ab_1, contourpy==1.2.1-py311h9547e67_0, cycler==0.12.1-pyhd8ed1ab_0, expat==2.6.2-h59595ed_0, fastavro==1.9.4-py311h459d7ec_0, fiona==1.9.6-py311hf8e0aa6_0, fmt==10.2.1-h00ab1b0_0, folium==0.16.0-pyhd8ed1ab_0, font-ttf-dejavu-sans-mono==2.37-hab24e00_0, font-ttf-inconsolata==3.000-h77eed37_0, font-ttf-source-code-pro==2.038-h77eed37_0, font-ttf-ubuntu==0.83-h77eed37_2, fontconfig==2.14.2-h14ed4e7_0, fonts-conda-ecosystem==1-0, fonts-conda-forge==1-0, fonttools==4.51.0-py311h459d7ec_0, freetype==2.12.1-h267a509_2, freexl==2.0.0-h743c826_0, fsspec==2024.3.1-pyhca7485f_0, gdal==3.8.5-py311hd032c08_2, geopandas==0.14.4-pyhd8ed1ab_0, geopandas-base==0.14.4-pyha770c72_0, geos==3.12.1-h59595ed_0, geotiff==1.7.1-h6cf1f90_16, gflags==2.2.2-he1b5a44_1004, giflib==5.2.2-hd590300_0, glog==0.7.0-hed5481d_0, hdf4==4.2.15-h2a13503_7, hdf5==1.14.3-nompi_h4f84152_101, icu==73.2-h59595ed_0, idna==3.7-pyhd8ed1ab_0, jinja2==3.1.3-pyhd8ed1ab_0, jmespath==1.0.1-pyhd8ed1ab_0, joblib==1.4.2-pyhd8ed1ab_0, json-c==0.17-h7ab15ed_0, kealib==1.5.3-h2f55d51_0, keyutils==1.6.1-h166bdaf_0, kiwisolver==1.4.5-py311h9547e67_1, krb5==1.21.2-h659d440_0, lcms2==2.16-hb7c19ff_0, ld_impl_linux-64==2.40-h55db66e_0, lerc==4.0.0-h27087fc_0, libabseil==20240116.2-cxx17_h59595ed_0, libaec==1.1.3-h59595ed_0, libarchive==3.7.2-h2aa1ff5_1, libarrow==15.0.2-hefa796f_6_cpu, libarrow-acero==15.0.2-hbabe93e_6_cpu, libarrow-dataset==15.0.2-hbabe93e_6_cpu, libarrow-flight==15.0.2-hc4f8a93_6_cpu, libarrow-flight-sql==15.0.2-he4f5ca8_6_cpu, libarrow-gandiva==15.0.2-hc1954e9_6_cpu, libarrow-substrait==15.0.2-he4f5ca8_6_cpu, libblas==3.9.0-22_linux64_openblas, libboost-headers==1.84.0-ha770c72_2, libbrotlicommon==1.1.0-hd590300_1, libbrotlidec==1.1.0-hd590300_1, libbrotlienc==1.1.0-hd590300_1, libcblas==3.9.0-22_linux64_openblas, libcrc32c==1.1.2-h9c3ff4c_0, libcurl==8.7.1-hca28451_0, libdeflate==1.20-hd590300_0, libedit==3.1.20191231-he28a2e2_2, libev==4.33-hd590300_2, libevent==2.1.12-hf998b51_1, libexpat==2.6.2-h59595ed_0, libffi==3.4.2-h7f98852_5, libgcc-ng==13.2.0-h77fa898_6, libgdal==3.8.5-hf9625ee_2, libgfortran-ng==13.2.0-h69a702a_6, libgfortran5==13.2.0-h43f5ff8_6, libglib==2.80.0-hf2295e7_6, libgomp==13.2.0-h77fa898_6, libgoogle-cloud==2.23.0-h9be4e54_1, libgoogle-cloud-storage==2.23.0-hc7a4891_1, libgrpc==1.62.2-h15f2491_0, libiconv==1.17-hd590300_2, libjpeg-turbo==3.0.0-hd590300_1, libkml==1.3.0-h01aab08_1018, liblapack==3.9.0-22_linux64_openblas, libllvm16==16.0.6-hb3ce162_3, libnetcdf==4.9.2-nompi_h9612171_113, libnghttp2==1.58.0-h47da74e_1, libnl==3.9.0-hd590300_0, libnsl==2.0.1-hd590300_0, libopenblas==0.3.27-pthreads_h413a1c8_0, libparquet==15.0.2-hacf5a1f_6_cpu, libpng==1.6.43-h2797004_0, libpq==16.2-h33b98f1_1, libprotobuf==4.25.3-h08a7969_0, libre2-11==2023.09.01-h5a48ba9_2, librttopo==1.1.0-h8917695_15, libspatialindex==1.9.3-h9c3ff4c_4, libspatialite==5.1.0-h6f065fc_5, libsqlite==3.45.3-h2797004_0, libssh2==1.11.0-h0841786_0, libstdcxx-ng==13.2.0-hc0a3c3a_6, libthrift==0.19.0-hb90f79a_1, libtiff==4.6.0-h1dd3fc0_3, libutf8proc==2.8.0-h166bdaf_0, libuuid==2.38.1-h0b41bf4_0, libwebp-base==1.4.0-hd590300_0, libxcb==1.15-h0b41bf4_0, libxml2==2.12.6-h232c23b_2, libzip==1.10.1-h2629f0a_3, libzlib==1.2.13-hd590300_5, lz4-c==1.9.4-hcb278e6_0, lzo==2.10-hd590300_1001, mapclassify==2.6.1-pyhd8ed1ab_0, markupsafe==2.1.5-py311h459d7ec_0, matplotlib-base==3.8.4-py311h54ef318_0, minizip==4.0.5-h0ab5242_0, munkres==1.1.4-pyh9f0ad1d_0, ncurses==6.4.20240210-h59595ed_0, networkx==3.3-pyhd8ed1ab_1, nspr==4.35-h27087fc_0, nss==3.98-h1d7d5a4_0, numpy==1.26.4-py311h64a7726_0, openjpeg==2.5.2-h488ebb8_0, openssl==3.3.0-hd590300_0, orc==2.0.0-h17fec99_1, packaging==24.0-pyhd8ed1ab_0, pandas==2.2.2-py311h320fe9a_0, pcre2==10.43-hcad00b1_0, pillow==10.3.0-py311h18e6fac_0, pip==24.0-pyhd8ed1ab_0, pixman==0.43.2-h59595ed_0, poppler==24.04.0-hb6cd0d7_0, poppler-data==0.4.12-hd8ed1ab_0, postgresql==16.2-h82ecc9d_1, proj==9.4.0-h1d62c97_1, pthread-stubs==0.4-h36c2ea0_1001, pyarrow==15.0.2-py311hd5e4297_6_cpu, pycparser==2.22-pyhd8ed1ab_0, pyparsing==3.1.2-pyhd8ed1ab_0, pyproj==3.6.1-py311hb3a3e68_6, pysocks==1.7.1-pyha2e5f31_6, python==3.11.0-he550d4f_1_cpython, python-dateutil==2.9.0-pyhd8ed1ab_0, python-tzdata==2024.1-pyhd8ed1ab_0, python_abi==3.11-4_cp311, pytz==2024.1-pyhd8ed1ab_0, rdma-core==51.0-hd3aeb46_0, re2==2023.09.01-h7f4b329_2, readline==8.2-h8228510_1, requests==2.31.0-pyhd8ed1ab_0, rtree==1.2.0-py311h3bb2b0f_0, s2n==1.4.12-h06160fa_0, s3fs==0.4.2-py_0, s3transfer==0.10.1-pyhd8ed1ab_0, scikit-learn==1.4.2-py311hc009520_0, scipy==1.13.0-py311h64a7726_0, setuptools==69.5.1-pyhd8ed1ab_0, shapely==2.0.4-py311h2032efe_0, six==1.16.0-pyh6c4a22f_0, snappy==1.2.0-hdb0a2a9_1, spdlog==1.13.0-hd2e6256_0, sqlite==3.45.3-h2c6b66d_0, threadpoolctl==3.5.0-pyhc1e730c_0, tiledb==2.22.0-h27f064a_3, tk==8.6.13-noxft_h4845f30_101, tzcode==2024a-h3f72095_0, tzdata==2024a-h0c530f3_0, ucx==1.15.0-ha691c75_8, uriparser==0.9.7-h59595ed_1, urllib3==2.2.1-pyhd8ed1ab_0, wheel==0.43.0-pyhd8ed1ab_1, xerces-c==3.2.5-hac6953d_0, xorg-kbproto==1.0.7-h7f98852_1002, xorg-libice==1.1.1-hd590300_0, xorg-libsm==1.2.4-h7391055_0, xorg-libx11==1.8.9-h8ee46fc_0, xorg-libxau==1.0.11-hd590300_0, xorg-libxdmcp==1.1.3-h7f98852_0, xorg-libxext==1.3.4-h0b41bf4_2, xorg-libxrender==0.9.11-hd590300_0, xorg-renderproto==0.11.1-h7f98852_1002, xorg-xextproto==7.3.0-h0b41bf4_1003, xorg-xproto==7.0.31-h7f98852_1007, xyzservices==2024.4.0-pyhd8ed1ab_0, xz==5.2.6-h166bdaf_0, zlib==1.2.13-hd590300_5, zstd==1.5.6-ha6fb4c9_0

(you might get some small differences — we inject a few more packages by default at Netflix). You can try that one if that unblocks you.

I'll try both suggestions.

you mean one of the transitive dependenices?

Not sure how to call this, but for example

"botocore"

needed by s3fs when my env is:

name: env
channels:
  - conda-forge
  - nodefaults
dependencies:
  - geopandas
  - pandas
  - python=3.11
  - pyarrow
  - numpy
  - s3fs

ok, yes, that makes sense. So with the mainline conda/pypi, as long as you are on the same machine (well mostly), it will keep your environment locked (ie: not resolve and change botocore for example if it gets updated). For the extension one, same behavior but you also have something called named environment where you can resolve your enviornment once and give it a name and then re-use it. Something like:

metaflow environment resolve --arch=linux-64 -f myenv.yml --alias philippe/awesome_env

and then instead of

@conda

, use

@named_env(name="philippe/awesome_env")

. You can add tags too like

--alias philippe/awesome_env:v1

and refer to that specifically. Tags are immutable except for

latest

(the default tag) so you can update your env (it’s kind of like docker tags).

True, I think we are actually using this already. Once it is bootstrapped, it reuses the environment for the future runs of the flow.

that yes — both versions do this. If it is deployed to argo/airflow, the enviornment is locked. If it is not deployed, it will look up if it has a cached environment locally (same machine) and use that if it can. Else it will re-resolve.

Not sure if the named env would work in our use case. The idea is to be able to launch flow from my laptop (in that step I guess the

@named_env(name="philippe/awesome_env")

tag would work once I resolved it. But once debug, it is connected to argo and would trigger on a event/schedule.

you can resolve same name with different arch if that is what you want to do

ok so those envs are saved somewhere that would be accessible from the cluster nodes?

yes.

Hi team - I am running into the following error:

Bootstrapping virtual environment(s) ...
    Micromamba ran into an error while setting up environment:
    command '/root/.metaflowconfig/micromamba/bin/micromamba create --yes --quiet --dry-run --no-extra-safety-checks --repodata-ttl=86400 --retry-clean-cache --prefix=/tmp/tmpqisl16cr/prefix --channel=conda-forge requests==>=2.21.0 boto3==>=1.14.0 pandas==2.0.0 snowflake-connector-python==3.0.4 pyyaml==6.0.1 slack-sdk==3.23.0 python==3.9.18' returned error (1)
    critical libmamba Error parsing version ">=2.21.0". Version contains invalid characters in >=2.21.0.

Any suggested solutions?

which version of metalfow and micromamba?