# # This file is autogenerated by pip-compile with Python 3.11 # by the following command: # # pip-compile # --extra-index-url https://download.pytorch.org/whl/cpu absl-py==2.2.2 # via # keras # tensorboard alembic==1.16.1 # via mlflow altair==4.2.2 # via # -r requirements.in # altair-data-server # altair-saver # altair-viewer # deneb altair-data-server==0.4.1 # via # altair-saver # altair-viewer altair-saver==0.5.0 # via deneb altair-viewer==0.4.0 # via altair-saver annotated-types==0.7.0 # via pydantic antlr4-python3-runtime==4.9.3 # via omegaconf anyio==4.9.0 # via starlette arviz==0.21.0 # via sbi asttokens==3.0.0 # via stack-data attrs==25.3.0 # via # jsonschema # outcome # referencing # trio autograd==1.8.0 # via sbibm bayesflow @ git+https://github.com/bayesflow-org/bayesflow.git@ac0461a08b792b4e222d83c3d1079aad466d7cc3 # via -r requirements.in blinker==1.9.0 # via flask cachetools==5.5.2 # via # google-auth # mlflow-skinny certifi==2025.4.26 # via # requests # selenium charset-normalizer==3.4.2 # via requests click==8.2.0 # via # dask # distributed # flask # mlflow-skinny # pyabc # uvicorn cloudpickle==3.1.1 # via # dask # distributed # mlflow-skinny # pyabc comm==0.2.2 # via ipykernel contourpy==1.3.2 # via matplotlib cycler==0.12.1 # via matplotlib cython==3.1.0 # via gpy dask[distributed]==2025.5.0 # via # distributed # elfi databricks-sdk==0.53.0 # via mlflow-skinny debugpy==1.8.14 # via ipykernel decorator==5.2.1 # via # ipyparallel # ipython # paramz deneb==1.0.1 # via sbibm deprecated==1.2.18 # via # opentelemetry-api # opentelemetry-semantic-conventions diffeqtorch==1.0.0 # via sbibm distributed==2025.5.0 # via # dask # pyabc docker==7.1.0 # via mlflow elfi==0.8.6 # via sbibm entrypoints==0.4 # via altair executing==2.2.0 # via stack-data fastapi==0.115.12 # via mlflow-skinny filelock==3.18.0 # via torch flask==3.1.1 # via mlflow flatlatex==0.15 # via deneb fonttools==4.58.0 # via matplotlib fsspec==2025.3.2 # via # dask # torch future==1.0.0 # via -r requirements.in gitdb==4.0.12 # via gitpython gitpython==3.1.44 # via # mlflow-skinny # pyabc google-auth==2.40.1 # via databricks-sdk gpy==1.13.2 # via elfi graphene==3.4.3 # via mlflow graphql-core==3.2.6 # via # graphene # graphql-relay graphql-relay==3.2.0 # via graphene greenlet==3.2.2 # via sqlalchemy grpcio==1.71.0 # via tensorboard gunicorn==23.0.0 # via mlflow h11==0.16.0 # via # uvicorn # wsproto h5netcdf==1.6.1 # via arviz h5py==3.13.0 # via # h5netcdf # keras idna==3.10 # via # anyio # requests # trio importlib-metadata==8.6.1 # via # dask # mlflow-skinny # opentelemetry-api ipykernel==6.29.5 # via ipyparallel ipyparallel==9.0.1 # via elfi ipython==9.2.0 # via # ipykernel # ipyparallel ipython-pygments-lexers==1.1.1 # via ipython itsdangerous==2.2.0 # via flask jabbar==0.0.16 # via pyabc jax==0.6.0 # via -r requirements.in jaxlib==0.6.0 # via jax jedi==0.19.2 # via ipython jinja2==3.1.6 # via # altair # distributed # flask # mlflow # torch joblib==1.5.0 # via # sbi # sbibm # scikit-learn jsonschema==4.23.0 # via altair jsonschema-specifications==2025.4.1 # via jsonschema julia==0.6.2 # via diffeqtorch jupyter-client==8.6.3 # via # ipykernel # ipyparallel jupyter-core==5.7.2 # via # ipykernel # jupyter-client keras==3.9.2 # via # -r requirements.in # bayesflow # mlflow-bayesflow-benchmark-plugin kiwisolver==1.4.8 # via matplotlib locket==1.0.0 # via # distributed # partd mako==1.3.10 # via alembic markdown==3.8 # via # mlflow # tensorboard markdown-it-py==3.0.0 # via rich markupsafe==3.0.2 # via # flask # jinja2 # mako # werkzeug matplotlib==3.10.3 # via # -r requirements.in # arviz # bayesflow # elfi # mlflow # nflows # pyabc # pyknos # sbi # sbibm # seaborn matplotlib-inline==0.1.7 # via # ipykernel # ipython mdurl==0.1.2 # via markdown-it-py mergedeep==1.3.4 # via deneb ml-dtypes==0.5.1 # via # jax # jaxlib # keras mlflow==2.22.0 # via mlflow-bayesflow-benchmark-plugin mlflow-bayesflow-benchmark-plugin @ git+https://codeberg.org/vpratz/mlflow-bayesflow-benchmark-plugin.git@af16db3c0735b9f3260de89d4226b1ab82788244 # via -r requirements.in mlflow-skinny==2.22.0 # via # -r requirements.in # mlflow mpmath==1.3.0 # via sympy msgpack==1.1.0 # via distributed namex==0.0.9 # via keras nest-asyncio==1.6.0 # via ipykernel networkx==3.4.2 # via # elfi # torch nflows==0.14 # via pyknos numdifftools==0.9.41 # via elfi numpy==1.26.4 # via # altair # arviz # autograd # bayesflow # contourpy # elfi # gpy # h5py # jax # jaxlib # keras # matplotlib # ml-dtypes # mlflow # nflows # numdifftools # pandas # paramz # pyabc # pyknos # pyro-ppl # sbi # sbibm # scikit-learn # scipy # seaborn # tensorboard # xarray # xarray-einstats omegaconf==2.3.0 # via -r requirements.in opentelemetry-api==1.33.1 # via # mlflow-skinny # opentelemetry-sdk # opentelemetry-semantic-conventions opentelemetry-sdk==1.33.1 # via mlflow-skinny opentelemetry-semantic-conventions==0.54b1 # via opentelemetry-sdk opt-einsum==3.4.0 # via # diffeqtorch # jax # pyro-ppl optree==0.15.0 # via keras outcome==1.3.0.post0 # via # trio # trio-websocket packaging==24.2 # via # arviz # dask # distributed # gunicorn # h5netcdf # ipykernel # keras # matplotlib # mlflow-skinny # tensorboard # xarray pandas==2.2.3 # via # altair # arviz # bayesflow # mlflow # mlflow-bayesflow-benchmark-plugin # pyabc # sbibm # seaborn # vega-datasets # xarray paramz==0.9.6 # via gpy parso==0.8.4 # via jedi partd==1.4.2 # via dask pexpect==4.9.0 # via ipython pillow==11.2.1 # via # matplotlib # sbi platformdirs==4.3.8 # via jupyter-core portpicker==1.6.0 # via altair-data-server prompt-toolkit==3.0.51 # via ipython protobuf==6.31.0 # via # mlflow-skinny # tensorboard psutil==7.0.0 # via # distributed # ipykernel # ipyparallel # portpicker ptyprocess==0.7.0 # via pexpect pure-eval==0.2.3 # via stack-data pyabc==0.12.15 # via sbibm pyabcranger==0.0.72 # via sbibm pyarrow==19.0.1 # via mlflow pyasn1==0.6.1 # via # pyasn1-modules # rsa pyasn1-modules==0.4.2 # via google-auth pydantic==2.11.4 # via # fastapi # mlflow-bayesflow-benchmark-plugin # mlflow-skinny pydantic-core==2.33.2 # via pydantic pygments==2.19.1 # via # ipython # ipython-pygments-lexers # rich pyknos==0.16.0 # via sbi pyparsing==3.2.3 # via matplotlib pyro-api==0.1.2 # via pyro-ppl pyro-ppl==1.9.1 # via # sbi # sbibm pysocks==1.7.1 # via urllib3 python-dateutil==2.9.0.post0 # via # graphene # ipyparallel # jupyter-client # matplotlib # pandas pytz==2025.2 # via pandas pyyaml==6.0.2 # via # dask # distributed # mlflow-skinny # omegaconf pyzmq==26.4.0 # via # ipykernel # ipyparallel # jupyter-client redis==6.1.0 # via pyabc referencing==0.36.2 # via # jsonschema # jsonschema-specifications regex==2024.11.6 # via flatlatex requests==2.32.3 # via # databricks-sdk # docker # mlflow-skinny rich==14.0.0 # via keras rpds-py==0.25.0 # via # jsonschema # referencing rsa==4.9.1 # via google-auth sbi==0.21.0 # via sbibm sbibm==1.1.0 # via -r requirements.in scikit-learn==1.6.1 # via # elfi # mlflow # pyabc # sbi # sbibm scipy==1.12.0 # via # arviz # bayesflow # elfi # gpy # jax # jaxlib # mlflow # numdifftools # paramz # pyabc # sbi # scikit-learn # xarray-einstats seaborn==0.13.2 # via bayesflow selenium==4.32.0 # via altair-saver six==1.17.0 # via # gpy # paramz # python-dateutil # tensorboard smmap==5.0.2 # via gitdb sniffio==1.3.1 # via # anyio # trio sortedcontainers==2.4.0 # via # distributed # trio sqlalchemy==2.0.41 # via # alembic # mlflow # pyabc sqlparse==0.5.3 # via mlflow-skinny stack-data==0.6.3 # via ipython starlette==0.46.2 # via fastapi sympy==1.14.0 # via torch tblib==3.1.0 # via distributed tensorboard==2.19.0 # via # nflows # pyknos # sbi tensorboard-data-server==0.7.2 # via tensorboard threadpoolctl==3.6.0 # via scikit-learn toolz==1.0.0 # via # altair # dask # distributed # elfi # partd torch==2.7.0+cpu # via # -r requirements.in # diffeqtorch # nflows # pyknos # pyro-ppl # sbi # sbibm tornado==6.5 # via # altair-data-server # distributed # ipykernel # ipyparallel # jupyter-client tqdm==4.67.1 # via # -r requirements.in # bayesflow # ipyparallel # nflows # pyknos # pyro-ppl # sbi # sbibm traitlets==5.14.3 # via # comm # ipykernel # ipyparallel # ipython # jupyter-client # jupyter-core # matplotlib-inline trio==0.30.0 # via # selenium # trio-websocket trio-websocket==0.12.2 # via selenium typing-extensions==4.13.2 # via # alembic # anyio # arviz # fastapi # graphene # ipython # mlflow-skinny # opentelemetry-sdk # optree # pydantic # pydantic-core # referencing # selenium # sqlalchemy # torch # typing-inspection typing-inspection==0.4.0 # via pydantic tzdata==2025.2 # via pandas urllib3[socks]==2.4.0 # via # distributed # docker # requests # selenium uvicorn==0.34.2 # via mlflow-skinny vega-datasets==0.9.0 # via deneb wcwidth==0.2.13 # via prompt-toolkit websocket-client==1.8.0 # via selenium werkzeug==3.1.3 # via # flask # tensorboard wrapt==1.17.2 # via deprecated wsproto==1.2.0 # via trio-websocket xarray==2025.4.0 # via # arviz # xarray-einstats xarray-einstats==0.8.0 # via arviz zict==3.0.0 # via distributed zipp==3.21.0 # via importlib-metadata # The following packages are considered to be unsafe in a requirements file: # setuptools