A Tensorflow based library for Time Series Modelling with Gaussian Processes

Related tags

Deep Learningmarkovflow
Overview

Markovflow

Quality checks and Tests Docs build

Documentation | Tutorials | API reference | Slack

What does Markovflow do?

Markovflow is a Python library for time-series analysis via probabilistic inference in generative models with Markovian Gaussian Processes.

Markovflow uses the mathematical building blocks from GPflow and marries these with objects and operators dedicated to run inference and learning in linear dynamical systems. This combination leads to a framework that can be used for:

  • researching new Markovian Gaussian process based models, and
  • building, training, evaluating and deploying Gaussian processes in a modern way — making use of the tools developed by the deep learning community.

Getting started

In the Documentation, we have multiple Tutorials showing the basic functionality of the toolbox, and a comprehensive API reference.

Install Markovflow

This project is assuming you are using python3.

For users

To install the latest (stable) release of the toolbox from PyPI, use pip:

$ pip install markovflow

For contributors

To install this project in editable mode, run the commands below from the root directory of the markovflow repository.

poetry install

Check that the installation was successful by running the tests:

poetry run task test

You can have a peek at the pyproject.toml for the commands.

The Secondmind Labs Community

Getting help

Bugs, feature requests, pain points, annoying design quirks, etc: Please use GitHub issues to flag up bugs/issues/pain points, suggest new features, and discuss anything else related to the use of Markovflow that in some sense involves changing the Markovflow code itself. We positively welcome comments or concerns about usability, and suggestions for changes at any level of design. We aim to respond to issues promptly, but if you believe we may have forgotten about an issue, please feel free to add another comment to remind us.

Slack workspace

We have a public Secondmind Labs slack workspace. Please use this invite link and join the #markovflow channel, whether you'd just like to ask short informal questions or want to be involved in the discussion and future development of Markovflow.

Contributing

All constructive input is very much welcome. For detailed information, see the guidelines for contributors.

Maintainers

Markovflow was originally created at Secondmind Labs and is now maintained by (in alphabetical order) Vincent Adam, Stefanos Eleftheriadis, Samuel Willis. We are grateful to all contributors who have helped shape Markovflow.

Markovflow is an open source project. If you have relevant skills and are interested in contributing then please do contact us (see "The Secondmind Labs Community" section above).

We are very grateful to our Secondmind Labs colleagues, maintainers of GPflow, GPflux, Trieste and Bellman, for their help with creating contributing guidelines, instructions for users and open-sourcing in general.

License

Apache License 2.0

Comments
  • Add mean function support for spatiotemporal GPs

    Add mean function support for spatiotemporal GPs

    This PR addresses two issues:

    • Though children of SpatioTemporalBase take a mean function as argument, they don't seem to use the function at all.
    • The mean functions currently implemented all support only a single temporal dimension and no spatial dimensions.

    This PR adds support for using separable spatial and temporal mean functions within the current children of SpatioTemporalBase. ~It does this by adding an abstract class SpatioTemporalMeanFunction and a subclass SeparableMeanFunction, which is the sum of a spatial mean function (should be a GPflow mean function) and a temporal mean function (should be a markovflow mean function).~

    ~I'm not certain whether the separability is a requirement or not, but since ConditionalPosterior needs a pure temporal mean function, it seemed much simpler to support these than to support all possible spatiotemporal mean functions.~ I'm also not convinced I have overloaded all methods of SpatioTemporalBase necessary for all functionality to correctly use the mean function.

    @vincentadam87 does this look like a suitable approach? If so I will

    • [ ] ~add a unit test for the new mean function~
    • [ ] ~add a notebook demonstrating use of a spatial mean function, which will also be an integration test~
    • [x] add an integration test using the mean function
    opened by sethaxen 3
  • Fix SpatioTemporalSparseCVI and add spatiotemporal tests

    Fix SpatioTemporalSparseCVI and add spatiotemporal tests

    This PR fixes #22

    As suggested by @vincentadam87 in https://github.com/secondmind-labs/markovflow/issues/22#issuecomment-1269856451, it also adds integration tests comparing the elbo of the variational model against the log-likelihood of GPR. It also checks that the predicted means are the same (predicted variances do not agree; should they?)

    opened by sethaxen 2
  • Spatiotemporal variational demo model always emits mean 0

    Spatiotemporal variational demo model always emits mean 0

    Describe the bug

    With SpatioTemporalSparseCVI, after training, space_time_predict_f emits only zeros for the mean in the demo.

    To reproduce

    1. Run the complete spatiotemporal variational example
    2. Call the following:
    >>> mu_f, var_f = model.space_time_predict_f(X_grid)
>>> mu_f
<tf.Tensor: shape=(2500, 1), dtype=float64, numpy=
array([[0.],
       [0.],
       [0.],
       ...,
       [0.],
       [0.],
       [0.]])>
>>> 
>>> np.max(np.abs(mu_f.numpy()))
0.0

    Expected behaviour

    It is surprising that the predicted mean is zero everywhere. This is visible in the plots in the notebook, where the prediction looks nothing like the data even after training: https://secondmind-labs.github.io/markovflow/notebooks/spatio_temporal.html?highlight=spatiotemporalsparsecvi:

    I expected the mean to look like it had learned something from the data.

    System information

    • OS: Pop!_OS 22.04 LTS
    • Python version: 3.7
    • Markovflow version: develop branch
    • TensorFlow version: 2.4.3
    • GPflow version: 2.2.1

    Additional context If I modify the demo to instead use SpatioTemporalSparseVariational, space_time_predict_f does not predict all zeros. After fitting for many more iterations I get a reasonable-looking result:

    It appears that 73232004e453baffeff871733c91b6a7fe7432ea may have been the commit that broke SpatioTemporalSparseCVI If we check out the previous commit (fc82c0abe777dd29edbb1eb7186a40579a83519b), this is the plot at the end of the notebook:

    I suspect the culprit is these lines: https://github.com/secondmind-labs/markovflow/blob/67e80c8117d256767c5ff255af29a5f5ecb37ee9/markovflow/models/spatio_temporal_variational.py#L433-L437 Since dist_q is a property that is generated each time it is called, perhaps declaring _posterior in the constructor makes it out-of-sync with the trained parameters?

    If we just convert these lines to

        @property
    def posterior(self) -> PosteriorProcess:
        return ConditionalProcess(
            posterior_dist=self.dist_q,
            kernel=self.kernel,
            conditioning_time_points=self.inducing_time,
        )

    then we again get a non-zero mean with predictions that look like the data:

    bug 
    opened by sethaxen 2
  • Feat/state mean property

    Feat/state mean property

    1. state_means functionality for PiecewiseKernel added.
    2. A setter method for state_mean and a getter (property) for the same in StationaryKernel class.
    3. Test for state_mean setter and getter.
    opened by prakharverma 1
  • Adding SDEs and basic functionalities

    Adding SDEs and basic functionalities

    1. Added SDE base class and two SDEs: Ornstein-Uhlenbeck and Double-Well.
    2. Numerical simulation method : Euler-Maruyama is also added to simulate the SDE.
    3. Functionality to linearize a non-linear SDE using Girsanov theorem.
    opened by prakharverma 0
  • Support for priors on parameters

    Support for priors on parameters

    This PR adds support for users specifying priors on parameters, similarly to in GPflow. Specifically it makes 2 changes

    • All classes defined in markovflow that currently inherit tf.Module now inherit gpflow.Module (also a child of tf.Module), which adds a trainable_parameters attribute to all of these classes. Unlike trainable_variables, this attribute returns gpflow.Parameter objects, which are variables that also have priors.
    • Add log_prior_density methods to the model base classes, so that model.log_prior_density() can be used to compute the log prior density for all trainable parameters with priors. This function is copied from gpflow.

    As an added benefit, the classes that inherit gpflow.Module inherit some of the useful pretty-printing. e.g. from https://github.com/secondmind-labs/markovflow/blob/7c5ef608340f0a259b8111ab77ea5924c07d7dbb/docs/notebooks/spatio_temporal.py

    In [3]: kernel_time
Out[3]: 
<markovflow.kernels.matern.Matern32 object at 0x7f43fb4e3c90>
╒═══════════════════════╤═══════════╤═════════════╤═════════╤═════════════╤═════════╤═════════╤═════════╕
│ name                  │ class     │ transform   │ prior   │ trainable   │ shape   │ dtype   │ value   │
╞═══════════════════════╪═══════════╪═════════════╪═════════╪═════════════╪═════════╪═════════╪═════════╡
│ Matern32._state_mean  │ Parameter │ Identity    │         │ False       │ (2,)    │ float64 │ [0. 0.] │
├───────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────┤
│ Matern32._lengthscale │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 2.5     │
├───────────────────────┼───────────┼─────────────┼─────────┼─────────────┼─────────┼─────────┼─────────┤
│ Matern32._variance    │ Parameter │ Softplus    │         │ True        │ ()      │ float64 │ 1.0     │
╘═══════════════════════╧═══════════╧═════════════╧═════════╧═════════════╧═════════╧═════════╧═════════╛

In [5]: model
Out[5]: 
<markovflow.models.spatio_temporal_variational.SpatioTemporalSparseCVI object at 0x7f43fb501cd0>
╒═══════════════════════════════════════════════════════════╤═══════════╤══════════════════╤═════════╤═════════════╤═════════════╤═════════╤═════════════════════╕
│ name                                                      │ class     │ transform        │ prior   │ trainable   │ shape       │ dtype   │ value               │
╞═══════════════════════════════════════════════════════════╪═══════════╪══════════════════╪═════════╪═════════════╪═════════════╪═════════╪═════════════════════╡
│ SpatioTemporalSparseCVI._kernel_space.variance            │ Parameter │ Softplus         │         │ True        │ ()          │ float64 │ 1.0                 │
│ SpatioTemporalSparseCVI._kernel.kernel_space.variance     │           │                  │         │             │             │         │                     │
├───────────────────────────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────────┼─────────┼─────────────────────┤
│ SpatioTemporalSparseCVI._kernel_space.lengthscales        │ Parameter │ Softplus         │         │ True        │ ()          │ float64 │ 0.5                 │
│ SpatioTemporalSparseCVI._kernel.kernel_space.lengthscales │           │                  │         │             │             │         │                     │
├───────────────────────────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────────┼─────────┼─────────────────────┤
│ SpatioTemporalSparseCVI._kernel_time._state_mean          │ Parameter │ Identity         │         │ False       │ (2,)        │ float64 │ [0. 0.]             │
│ SpatioTemporalSparseCVI._kernel.kernel_time._state_mean   │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[0]._state_mean   │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[1]._state_mean   │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[2]._state_mean   │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[3]._state_mean   │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[4]._state_mean   │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[5]._state_mean   │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[6]._state_mean   │           │                  │         │             │             │         │                     │
├───────────────────────────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────────┼─────────┼─────────────────────┤
│ SpatioTemporalSparseCVI._kernel_time._lengthscale         │ Parameter │ Softplus         │         │ True        │ ()          │ float64 │ 2.5                 │
│ SpatioTemporalSparseCVI._kernel.kernel_time._lengthscale  │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[0]._lengthscale  │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[1]._lengthscale  │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[2]._lengthscale  │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[3]._lengthscale  │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[4]._lengthscale  │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[5]._lengthscale  │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[6]._lengthscale  │           │                  │         │             │             │         │                     │
├───────────────────────────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────────┼─────────┼─────────────────────┤
│ SpatioTemporalSparseCVI._kernel_time._variance            │ Parameter │ Softplus         │         │ True        │ ()          │ float64 │ 1.0                 │
│ SpatioTemporalSparseCVI._kernel.kernel_time._variance     │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[0]._variance     │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[1]._variance     │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[2]._variance     │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[3]._variance     │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[4]._variance     │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[5]._variance     │           │                  │         │             │             │         │                     │
│ SpatioTemporalSparseCVI._kernel._kernels[6]._variance     │           │                  │         │             │             │         │                     │
├───────────────────────────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────────┼─────────┼─────────────────────┤
│ SpatioTemporalSparseCVI._likelihood.variance              │ Parameter │ Softplus + Shift │         │ True        │ ()          │ float64 │ 0.09999999999999999 │
├───────────────────────────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────────┼─────────┼─────────────────────┤
│ SpatioTemporalSparseCVI._kernel._state_mean               │ Parameter │ Identity         │         │ False       │ (14,)       │ float64 │ [0., 0., 0....      │
├───────────────────────────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────────┼─────────┼─────────────────────┤
│ SpatioTemporalSparseCVI.nat1                              │ Parameter │ Identity         │         │ True        │ (8, 28)     │ float64 │ [[0., 0., 0....     │
├───────────────────────────────────────────────────────────┼───────────┼──────────────────┼─────────┼─────────────┼─────────────┼─────────┼─────────────────────┤
│ SpatioTemporalSparseCVI.nat2                              │ Parameter │ Identity         │         │ True        │ (8, 28, 28) │ float64 │ [[[0., 0., 0....    │
╘═══════════════════════════════════════════════════════════╧═══════════╧══════════════════╧═════════╧═════════════╧═════════════╧═════════╧═════════════════════╛
    opened by sethaxen 0
  • Don't hardcode float64 in BlockTriDiagonal

    Don't hardcode float64 in BlockTriDiagonal

    Currently if we use parameters and data with a float32 eltype, update_sites with SpatioTemporalSparseCVI fails because the _convert_to_band method of BlockTriDiagonal initializes a tensor with a dtype of float64. This PR changes the behavior to use the dtype of a user-provided variable. If the user is using float32, then this method now uses float32, and no error is thrown.

    opened by sethaxen 0
  • Issue with Installation

    Issue with Installation

    I believe there is some inconsistency with the python version requirements in the pyproject.toml file. that prevents me from installing the package.

    To Reproduce I do the pip install with a few options:

    • Directly from pip pip install markovflow,
    • Pip install from the tarball file
    • Pip install from the cloned repo

    Error

    ...
Collecting banded-matrices==0.0.6
  Using cached banded_matrices-0.0.6.tar.gz (63 kB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
ERROR: Package 'markovflow' requires a different Python: 3.7.0 not in '<3.8,>3.7'

    Note: This is probably an issue on my end because I'm very surprised no other users have experienced this.

    Proposed Solution

    The setup.py file has the following requirements: python_requires=">=3.7,<4.0",.

    However, I believe the error is within the pyproject.toml file because it says python = ">3.7,<3.8" which is consistent with the error message. To confirm, I cloned the repo and changed this to >=3.7,<3.8 and I didn't receive this error message. However, I received another error message which is related to the banded_matrices library installation (which is a different issue - see details below for more info).

    (markovflow) [[email protected]: projects]$ git clone https://github.com/secondmind-labs/markovflow.git Cloning into 'markovflow'... remote: Enumerating objects: 1335, done. remote: Counting objects: 100% (568/568), done. remote: Compressing objects: 100% (252/252), done. remote: Total 1335 (delta 347), reused 470 (delta 308), pack-reused 767 Receiving objects: 100% (1335/1335), 26.95 MiB | 23.02 MiB/s, done. Resolving deltas: 100% (597/597), done. (markovflow) [[email protected]: projects]$ cd markovflow/ (markovflow) [[email protected]: markovflow]$ pip install -e . Obtaining file:///gpfsdswork/projects/rech/cli/uvo53rl/projects/markovflow

    Installing build dependencies ... done Checking if build backend supports build_editable ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting banded-matrices==0.0.6 Using cached banded_matrices-0.0.6.tar.gz (63 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting scipy<2.0.0,>=1.4.1 Using cached scipy-1.7.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (38.1 MB) Collecting setuptools<42.0.0,>=41.0.0 Using cached setuptools-41.6.0-py2.py3-none-any.whl (582 kB) Collecting gpflow<3.0,>=2.1 Using cached gpflow-2.6.4-py3-none-any.whl (446 kB) Collecting tensorflow-probability==0.11.0 Using cached tensorflow_probability-0.11.0-py2.py3-none-any.whl (4.3 MB) Collecting importlib_metadata<2.0,>=1.6 Using cached importlib_metadata-1.7.0-py2.py3-none-any.whl (31 kB) Collecting numpy<2.0.0,>=1.18.0 Using cached numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB) Collecting tensorflow>2.2.0 Using cached tensorflow-2.11.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (588.3 MB) Collecting google-auth<2.0.0,>=1.16.0 Using cached google_auth-1.35.0-py2.py3-none-any.whl (152 kB) ERROR: Package 'markovflow' requires a different Python: 3.7.0 not in '<3.8,>3.7' (markovflow) [[email protected]: markovflow]$ (markovflow) [[email protected]: markovflow]$ nano pyproject.toml (markovflow) [[email protected]: markovflow]$ pip install -e . Obtaining file:///gpfsdswork/projects/rech/cli/uvo53rl/projects/markovflow Installing build dependencies ... done Checking if build backend supports build_editable ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting banded-matrices==0.0.6 Using cached banded_matrices-0.0.6.tar.gz (63 kB) Installing build dependencies ... done Getting requirements to build wheel ... done Preparing metadata (pyproject.toml) ... done Collecting importlib_metadata<2.0,>=1.6 Using cached importlib_metadata-1.7.0-py2.py3-none-any.whl (31 kB) Collecting google-auth<2.0.0,>=1.16.0 Using cached google_auth-1.35.0-py2.py3-none-any.whl (152 kB) Collecting tensorflow-probability==0.11.0 Using cached tensorflow_probability-0.11.0-py2.py3-none-any.whl (4.3 MB) Collecting numpy<2.0.0,>=1.18.0 Using cached numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB) Collecting scipy<2.0.0,>=1.4.1 Using cached scipy-1.7.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (38.1 MB) Collecting gpflow<3.0,>=2.1 Using cached gpflow-2.6.4-py3-none-any.whl (446 kB) Collecting setuptools<42.0.0,>=41.0.0 Using cached setuptools-41.6.0-py2.py3-none-any.whl (582 kB) Collecting tensorflow>2.2.0 Using cached tensorflow-2.11.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (588.3 MB) Collecting cmake<3.19.0,>=3.18.0 Using cached cmake-3.18.4.post1-py3-none-manylinux1_x86_64.whl (17.7 MB) Collecting tensorflow>2.2.0 Using cached tensorflow-2.4.4-cp37-cp37m-manylinux2010_x86_64.whl (394.5 MB) Collecting dm-tree Downloading dm_tree-0.1.7-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (143 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 143.3/143.3 kB 18.6 MB/s eta 0:00:00 Collecting decorator Downloading decorator-5.1.1-py3-none-any.whl (9.1 kB) Collecting cloudpickle==1.3 Downloading cloudpickle-1.3.0-py2.py3-none-any.whl (26 kB) Collecting gast>=0.3.2 Downloading gast-0.5.3-py3-none-any.whl (19 kB) Collecting six>=1.10.0 Using cached six-1.16.0-py2.py3-none-any.whl (11 kB) Collecting rsa<5,>=3.1.4 Using cached rsa-4.9-py3-none-any.whl (34 kB) Collecting cachetools<5.0,>=2.0.0 Downloading cachetools-4.2.4-py3-none-any.whl (10 kB) Collecting pyasn1-modules>=0.2.1 Using cached pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB) Collecting lark>=1.1.0 Downloading lark-1.1.4-py3-none-any.whl (107 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 107.8/107.8 kB 34.7 MB/s eta 0:00:00 Collecting multipledispatch>=0.6 Downloading multipledispatch-0.6.0-py3-none-any.whl (11 kB) Collecting packaging Using cached packaging-21.3-py3-none-any.whl (40 kB) Collecting gpflow<3.0,>=2.1 Downloading gpflow-2.6.3-py3-none-any.whl (445 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 445.3/445.3 kB 77.5 MB/s eta 0:00:00 Downloading gpflow-2.6.2-py3-none-any.whl (445 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 445.2/445.2 kB 72.9 MB/s eta 0:00:00 Downloading gpflow-2.6.1-py3-none-any.whl (444 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 444.6/444.6 kB 91.0 MB/s eta 0:00:00 Downloading gpflow-2.6.0-py3-none-any.whl (443 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 443.5/443.5 kB 33.5 MB/s eta 0:00:00 Downloading gpflow-2.5.2-py3-none-any.whl (383 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 383.9/383.9 kB 69.5 MB/s eta 0:00:00 Downloading gpflow-2.5.1-py3-none-any.whl (381 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 381.9/381.9 kB 75.0 MB/s eta 0:00:00 Downloading gpflow-2.4.0-py3-none-any.whl (334 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 334.0/334.0 kB 47.5 MB/s eta 0:00:00 Collecting tabulate Downloading tabulate-0.9.0-py3-none-any.whl (35 kB) Collecting deprecated Downloading Deprecated-1.2.13-py2.py3-none-any.whl (9.6 kB) Collecting typing-extensions Using cached typing_extensions-4.4.0-py3-none-any.whl (26 kB) Collecting zipp>=0.5 Using cached zipp-3.11.0-py3-none-any.whl (6.6 kB) Collecting numpy<2.0.0,>=1.18.0 Using cached numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl (14.8 MB) Collecting tensorboard~=2.4 Using cached tensorboard-2.11.0-py3-none-any.whl (6.0 MB) Collecting google-pasta~=0.2 Using cached google_pasta-0.2.0-py3-none-any.whl (57 kB) Collecting six>=1.10.0 Using cached six-1.15.0-py2.py3-none-any.whl (10 kB) Collecting typing-extensions Using cached typing_extensions-3.7.4.3-py3-none-any.whl (22 kB) Collecting tensorflow-estimator<2.5.0,>=2.4.0 Using cached tensorflow_estimator-2.4.0-py2.py3-none-any.whl (462 kB) Requirement already satisfied: wheel~=0.35 in /gpfsssd/scratch/rech/cli/uvo53rl/miniconda3/envs/markovflow/lib/python3.7/site-packages (from tensorflow>2.2.0->markovflow==0.0.12) (0.37.1) Collecting wrapt~=1.12.1 Using cached wrapt-1.12.1-cp37-cp37m-linux_x86_64.whl Collecting grpcio~=1.32.0 Using cached grpcio-1.32.0-cp37-cp37m-manylinux2014_x86_64.whl (3.8 MB) Collecting h5py~=2.10.0 Using cached h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB) Collecting termcolor~=1.1.0 Using cached termcolor-1.1.0-py3-none-any.whl Collecting absl-py~=0.10 Using cached absl_py-0.15.0-py3-none-any.whl (132 kB) Collecting gast>=0.3.2 Using cached gast-0.3.3-py2.py3-none-any.whl (9.7 kB) Collecting opt-einsum~=3.3.0 Using cached opt_einsum-3.3.0-py3-none-any.whl (65 kB) Collecting protobuf>=3.9.2 Using cached protobuf-4.21.10-cp37-abi3-manylinux2014_x86_64.whl (408 kB) Collecting keras-preprocessing~=1.1.2 Using cached Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB) Collecting flatbuffers~=1.12.0 Using cached flatbuffers-1.12-py2.py3-none-any.whl (15 kB) Collecting astunparse~=1.6.3 Using cached astunparse-1.6.3-py2.py3-none-any.whl (12 kB) Collecting pyasn1<0.5.0,>=0.4.6 Using cached pyasn1-0.4.8-py2.py3-none-any.whl (77 kB) Collecting werkzeug>=1.0.1 Using cached Werkzeug-2.2.2-py3-none-any.whl (232 kB) Collecting markdown>=2.6.8 Using cached Markdown-3.4.1-py3-none-any.whl (93 kB) Collecting protobuf>=3.9.2 Using cached protobuf-3.20.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (1.0 MB) Collecting requests<3,>=2.21.0 Using cached requests-2.28.1-py3-none-any.whl (62 kB) Collecting tensorboard-plugin-wit>=1.6.0 Using cached tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB) Collecting google-auth-oauthlib<0.5,>=0.4.1 Using cached google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB) Collecting tensorboard-data-server<0.7.0,>=0.6.0 Using cached tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB) Collecting pyparsing!=3.0.5,>=2.0.2 Using cached pyparsing-3.0.9-py3-none-any.whl (98 kB) Collecting requests-oauthlib>=0.7.0 Using cached requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB) Collecting markdown>=2.6.8 Using cached Markdown-3.4-py3-none-any.whl (93 kB) Using cached Markdown-3.3.7-py3-none-any.whl (97 kB) Using cached Markdown-3.3.6-py3-none-any.whl (97 kB) Using cached Markdown-3.3.4-py3-none-any.whl (97 kB) Collecting idna<4,>=2.5 Using cached idna-3.4-py3-none-any.whl (61 kB) Collecting charset-normalizer<3,>=2 Using cached charset_normalizer-2.1.1-py3-none-any.whl (39 kB) Requirement already satisfied: certifi>=2017.4.17 in /gpfsssd/scratch/rech/cli/uvo53rl/miniconda3/envs/markovflow/lib/python3.7/site-packages (from requests<3,>=2.21.0->tensorboard~=2.4->tensorflow>2.2.0->markovflow==0.0.12) (2022.9.24) Collecting urllib3<1.27,>=1.21.1 Using cached urllib3-1.26.13-py2.py3-none-any.whl (140 kB) Collecting MarkupSafe>=2.1.1 Using cached MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (25 kB) Collecting oauthlib>=3.0.0 Using cached oauthlib-3.2.2-py3-none-any.whl (151 kB) Building wheels for collected packages: banded-matrices Building wheel for banded-matrices (pyproject.toml) ... error error: subprocess-exited-with-error

    × Building wheel for banded-matrices (pyproject.toml) did not run successfully. │ exit code: 1 ╰─> [201 lines of output] A setup.py file already exists. Using it. running build running build_py creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37 creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices copying banded_matrices/init.py -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices copying banded_matrices/banded.py -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices copying banded_matrices/library.py -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices copying banded_matrices/platform.py -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices copying banded_matrices/types.py -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices running egg_info creating banded_matrices.egg-info writing banded_matrices.egg-info/PKG-INFO writing dependency_links to banded_matrices.egg-info/dependency_links.txt writing requirements to banded_matrices.egg-info/requires.txt writing top-level names to banded_matrices.egg-info/top_level.txt writing manifest file 'banded_matrices.egg-info/SOURCES.txt' reading manifest file 'banded_matrices.egg-info/SOURCES.txt' writing manifest file 'banded_matrices.egg-info/SOURCES.txt' copying banded_matrices/CMakeLists.txt -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc copying banded_matrices/cc/CMakeLists.txt -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/include creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/include/banded_matrices copying banded_matrices/cc/include/banded_matrices/banded_matrix.hpp -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/include/banded_matrices copying banded_matrices/cc/include/banded_matrices/cholesky.hpp -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/include/banded_matrices copying banded_matrices/cc/include/banded_matrices/common.hpp -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/include/banded_matrices copying banded_matrices/cc/include/banded_matrices/product.hpp -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/include/banded_matrices copying banded_matrices/cc/include/banded_matrices/solve.hpp -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/include/banded_matrices copying banded_matrices/cc/include/banded_matrices/unary_broadcastable_op_kernel.hpp -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/include/banded_matrices creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/block_band.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/cholesky.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/inverse.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/outer_vec_vec.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/pack_matrix.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/product_band_band.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/product_band_mat.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/reverse_inverse.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/solve_triang_band.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/solve_triang_mat.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/square_band.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/symmetrise.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices copying banded_matrices/cc/src/banded_matrices/transpose_band.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/src/banded_matrices creating /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/test copying banded_matrices/cc/test/common.hpp -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/test copying banded_matrices/cc/test/main.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/test copying banded_matrices/cc/test/test_banded_matrix.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/test copying banded_matrices/cc/test/test_product_band_band.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/test copying banded_matrices/cc/test/test_solve.cc -> /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/cc/test running build_ext cmake /tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/banded_matrices -Wno-dev -DPYTHON_BIN=/gpfsscratch/rech/cli/uvo53rl/miniconda3/envs/markovflow/bin/python -DCMAKE_BUILD_TYPE=release -DCMAKE_CXX_COMPILER=g++-7 -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=/tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/lib -DCMAKE_RUNTIME_OUTPUT_DIRECTORY=/tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/lib.linux-x86_64-cpython-37/banded_matrices/bin -DCMAKE_VERBOSE_MAKEFILE:BOOL=on -- The C compiler identification is GNU 8.4.1 -- The CXX compiler identification is unknown -- Detecting C compiler ABI info -- Detecting C compiler ABI info - done -- Check for working C compiler: /usr/bin/cc - skipped -- Detecting C compile features -- Detecting C compile features - done CMake Error at CMakeLists.txt:17 (project): The CMAKE_CXX_COMPILER:

          g++-7

    is not a full path and was not found in the PATH.

    Tell CMake where to find the compiler by setting either the environment
    variable "CXX" or the CMake cache entry CMAKE_CXX_COMPILER to the full path
    to the compiler, or to the compiler name if it is in the PATH.


  -- Configuring incomplete, errors occurred!
  See also "/tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/temp.linux-x86_64-cpython-37/CMakeFiles/CMakeOutput.log".
  See also "/tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build/temp.linux-x86_64-cpython-37/CMakeFiles/CMakeError.log".
  /tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'banded_matrices.cc' as data is deprecated, please list it in `packages`.
      !!


      ############################
      # Package would be ignored #
      ############################
      Python recognizes 'banded_matrices.cc' as an importable package,
      but it is not listed in the `packages` configuration of setuptools.

      'banded_matrices.cc' has been automatically added to the distribution only
      because it may contain data files, but this behavior is likely to change
      in future versions of setuptools (and therefore is considered deprecated).

      Please make sure that 'banded_matrices.cc' is included as a package by using
      the `packages` configuration field or the proper discovery methods
      (for example by using `find_namespace_packages(...)`/`find_namespace:`
      instead of `find_packages(...)`/`find:`).

      You can read more about "package discovery" and "data files" on setuptools
      documentation page.


  !!

    check.warn(importable)
  /tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'banded_matrices.cc.include.banded_matrices' as data is deprecated, please list it in `packages`.
      !!


      ############################
      # Package would be ignored #
      ############################
      Python recognizes 'banded_matrices.cc.include.banded_matrices' as an importable package,
      but it is not listed in the `packages` configuration of setuptools.

      'banded_matrices.cc.include.banded_matrices' has been automatically added to the distribution only
      because it may contain data files, but this behavior is likely to change
      in future versions of setuptools (and therefore is considered deprecated).

      Please make sure that 'banded_matrices.cc.include.banded_matrices' is included as a package by using
      the `packages` configuration field or the proper discovery methods
      (for example by using `find_namespace_packages(...)`/`find_namespace:`
      instead of `find_packages(...)`/`find:`).

      You can read more about "package discovery" and "data files" on setuptools
      documentation page.


  !!

    check.warn(importable)
  /tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'banded_matrices.cc.src.banded_matrices' as data is deprecated, please list it in `packages`.
      !!


      ############################
      # Package would be ignored #
      ############################
      Python recognizes 'banded_matrices.cc.src.banded_matrices' as an importable package,
      but it is not listed in the `packages` configuration of setuptools.

      'banded_matrices.cc.src.banded_matrices' has been automatically added to the distribution only
      because it may contain data files, but this behavior is likely to change
      in future versions of setuptools (and therefore is considered deprecated).

      Please make sure that 'banded_matrices.cc.src.banded_matrices' is included as a package by using
      the `packages` configuration field or the proper discovery methods
      (for example by using `find_namespace_packages(...)`/`find_namespace:`
      instead of `find_packages(...)`/`find:`).

      You can read more about "package discovery" and "data files" on setuptools
      documentation page.


  !!

    check.warn(importable)
  /tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/setuptools/command/build_py.py:202: SetuptoolsDeprecationWarning:     Installing 'banded_matrices.cc.test' as data is deprecated, please list it in `packages`.
      !!


      ############################
      # Package would be ignored #
      ############################
      Python recognizes 'banded_matrices.cc.test' as an importable package,
      but it is not listed in the `packages` configuration of setuptools.

      'banded_matrices.cc.test' has been automatically added to the distribution only
      because it may contain data files, but this behavior is likely to change
      in future versions of setuptools (and therefore is considered deprecated).

      Please make sure that 'banded_matrices.cc.test' is included as a package by using
      the `packages` configuration field or the proper discovery methods
      (for example by using `find_namespace_packages(...)`/`find_namespace:`
      instead of `find_packages(...)`/`find:`).

      You can read more about "package discovery" and "data files" on setuptools
      documentation page.


  !!

    check.warn(importable)
  error: command '/tmp/pip-build-env-z3dhjd43/overlay/bin/cmake' failed with exit code 1
  Traceback (most recent call last):
    File "/gpfsscratch/rech/cli/uvo53rl/miniconda3/envs/markovflow/lib/python3.7/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 363, in <module>
      main()
    File "/gpfsscratch/rech/cli/uvo53rl/miniconda3/envs/markovflow/lib/python3.7/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 345, in main
      json_out['return_val'] = hook(**hook_input['kwargs'])
    File "/gpfsscratch/rech/cli/uvo53rl/miniconda3/envs/markovflow/lib/python3.7/site-packages/pip/_vendor/pep517/in_process/_in_process.py", line 262, in build_wheel
      metadata_directory)
    File "/tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/poetry/core/masonry/api.py", line 67, in build_wheel
      return WheelBuilder.make_in(poetry, Path(wheel_directory))
    File "/tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/poetry/core/masonry/builders/wheel.py", line 80, in make_in
      wb.build(target_dir=directory)
    File "/tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/poetry/core/masonry/builders/wheel.py", line 114, in build
      self._build(zip_file)
    File "/tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/poetry/core/masonry/builders/wheel.py", line 168, in _build
      self._run_build_command(setup)
    File "/tmp/pip-build-env-z3dhjd43/overlay/lib/python3.7/site-packages/poetry/core/masonry/builders/wheel.py", line 212, in _run_build_command
      str(self._path / "build"),
    File "/gpfsscratch/rech/cli/uvo53rl/miniconda3/envs/markovflow/lib/python3.7/subprocess.py", line 328, in check_call
      raise CalledProcessError(retcode, cmd)
  subprocess.CalledProcessError: Command '['/gpfsscratch/rech/cli/uvo53rl/miniconda3/envs/markovflow/bin/python', '/tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/setup.py', 'build', '-b', '/tmp/pip-install-53c0n0oi/banded-matrices_2fdeb4f02f09479eb65cfa50867bc5f7/build']' returned non-zero exit status 1.
  [end of output]

    note: This error originates from a subprocess, and is likely not a problem with pip. ERROR: Failed building wheel for banded-matrices Failed to build banded-matrices ERROR: Could not build wheels for banded-matrices, which is required to install pyproject.toml-based projects

    System information

    • OS: macOS, Linux
    • Python version: 3.7, 3.8, 3.10
    • Markovflow version: 0.0.1, 0.0.13
    • TensorFlow version: N/A
    • GPflow version: N/A

    Additional context

    bug 
    opened by jejjohnson 2
  • Adding the Wiener process

    Adding the Wiener process

    Describe the feature you'd like An implementation of the kernel for the Wiener process.

    Additional context It's possible functionality is already available in this repository under a different name, though I couldn't find it.

    opened by sethaxen 0
Releases(v0.0.13)
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Secondmind Labs
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