tri-amdd / camd Goto Github PK

Right now CI is limited to one test at a time because we're storing the state of the worker, meta_agent_test in the same place. I think we should provision the tests to use a unique prefix so tests can run in parallel (e. g. with a uuid or something).

As of right now, the campaign bears the responsibility of choosing data. This is a decision, and I believe should be the purview of the agent. Also, the current initialization logic does not guarantee that the seed will be populated (for example if all of the experiments in the initialization fail). I think we should discuss initialization logic, where it belongs, and how to implement it.

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The issue line here.

apply_gradients expects a list, but receives an iterator (from call to zip, which returns an iterator). Should we convert to list for Python 3 compatibility?

I think we should refactor to use plotly's backend for phase diagram plotting as pymatgen does now by default.

CAMD is not currently windows compatible because of the qmpy dependency. GH Actions enables a relatively straightforward windows CI that should be enabled once this is resolved.

I am running Ubuntu 18.04.5 LTS, I created the python3.7 virtual environment for the installation:
mkvirtualenv -p /usr/bin/python3.7 camd

I installed pymatgen==2020.12.31
and proceeded

git clone https://github.com/TRI-AMDD/CAMD.git
cd CAMD
pip install numpy
pip install -r requirements.txt
python setup.py develop

I ran the tri hackathon 2020 tutorial here , in CAMD_tutorial.ipynb, when running the cell:

# Load the data
from hackathon.helper import load_tutorial_data
data = load_tutorial_data()
# Inspect the first five rows
data.head()

I get the error:

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-38-adaecc4da224> in <module>
      1 # Load the data
----> 2 from hackathon.helper import load_tutorial_data
      3 data = load_tutorial_data()
      4 # Inspect the first five rows
      5 data.head()

~/tri-hackathon-2020/hackathon/helper.py in <module>
      1 # Copyright 2020 Toyota Research Institute
      2 
----> 3 from camd.analysis import AnalyzerBase
      4 from matminer.datasets.convenience_loaders import load_elastic_tensor
      5 

~/CAMD/camd/analysis.py in <module>
     18 from qmpy.analysis.thermodynamics.space import PhaseSpace
     19 from multiprocessing import Pool, cpu_count
---> 20 from pymatgen import Composition
     21 from pymatgen.entries.computed_entries import ComputedEntry
     22 from pymatgen.analysis.phase_diagram import (

ImportError: cannot import name 'Composition' from 'pymatgen' (unknown location)

Please advise.

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As I was reading the manuscript, I was curious about what a comparison with Dragonfly via RayTune or Ax (latter would just be for small data experiments).

We're using gpflow 1.5's implementation and API for the SVGP, but the api has changed significantly for their latest version. There's some info on this here, which should be able to partially guide us, but will likely take some time.

Coverage in the docker container we're using for testing doesn't seem to be working. Will investigate and update this issue.

Currently, CAMD depends on a custom fork of qmpy, which was used to ensure python3 compatibility. qmpy has recently updated their github repo to be python3 compatible, but I don't think they've released yet. When they do, we should explore reintegrating the official version.