Comments (12)
It appears as though pip is using a cached version of Ax. Could you either:
-
Install without using the cache:
pip install ax-platform --no-cache-dir
-
Install version 0.1.3 explicitly:
pip install ax-platform==0.1.3
Can you let me know if either of these works?
Thanks!
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pip uninstall ax-platform --no-cache-dir
8.3.0
Uninstalling ax-platform-0.0.0:
Would remove:
/opt/miniconda3/envs/py37/lib/python3.7/site-packages/ax_platform-0.0.0.dist-info/*
Proceed (y/n)? y
Successfully uninstalled ax-platform-0.0.0
pip install ax-platform==0.1.3
8.3.0
Collecting ax-platform==0.1.3
ERROR: Could not find a version that satisfies the requirement ax-platform==0.1.3 (from versions: 0.0.0)
ERROR: No matching distribution found for ax-platform==0.1.3
Still not installed successfully。 ubuntu 19.04
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@yiyinying - hmm, OK. Not sure off the top of my head why pip is not using the manylinux1
wheel that we provide for Python 3.7: https://files.pythonhosted.org/packages/1e/32/fe7b32395a8e2075369ffa826ab3d0e4e71ef13086f6b09fad7104132dc8/ax_platform-0.1.3-cp37-cp37m-manylinux1_x86_64.whl.
If you could try running the is_manylinux1_compatible
function from https://www.python.org/dev/peps/pep-0513/#platform-detection-for-installers and see why your system is not eligible for manylinux1
, that would be helpful.
If you could provide outputs for specific checks, that would be useful too, e.g. what do you get if you run the following command:
python3 -c "from distutils.util import get_platform; print(get_platform());"
We will investigate this further, but in the meantime you should be able to install the bleeding edge version directly from Git: https://ax.dev/versions/latest/docs/installation.html#installing-from-git.
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(py37) yiyin@yiyin-ThinkPad-T460p:~$ python3 -c "from distutils.util import get_platform; print(get_platform());"
linux-x86_64
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is_manylinux1_compatible() true
have_compatible_glibc('ubuntu','19.04') false
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Should I change to ubuntu 18.04?
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https://files.pythonhosted.org/packages/1e/32/fe7b32395a8e2075369ffa826ab3d0e4e71ef13086f6b09fad7104132dc8/ax_platform-0.1.3-cp37-cp37m- manylinux1_x86_64.whl。
pip install ax_platform-0.1.3-cp37-cp37m-manylinux1_x86_64.whl
8.3.0
ERROR: ax_platform-0.1.3-cp37-cp37m-manylinux1_x86_64.whl is not a supported wheel on this platform.
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ERROR: Command "git clone -q ssh://[email protected]/facebook/Ax.git /tmp/pip-req-build-9ufn0ckd" failed with error code 128 in None
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Git installation also failed
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Re: have_compatible_glibc
, you want to pass the major and minor version of glibc (see example in https://www.python.org/dev/peps/pep-0513/#platform-detection-for-installers). What's the version you have?
Re: the error you're getting cloning the repo ("failed with error code 128 in None"
), it seems like that can happen due to a variety of reasons (some authentication related), but it shouldn't be specific to our library. See for example, zulip/zulip#126.
Make sure you have an up-to-date pip and that your GitHub certs are in order, and then try:
pip3 install cython numpy # needed for compiling Cython code
pip3 install git+ssh://[email protected]/facebook/Ax.git#egg=Ax
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Python 3.6 installed successfully。
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Great, closing the issue then.
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Related Issues (20)
- Safe optimization in the Service API HOT 5
- The same point is evaluated multiple times during Integer Optimization with BO. HOT 5
- ax_client.generation_strategy.trials_as_df HOT 9
- Managing Objective Function Evaluation Failures in Ax for MOO HOT 6
- [GENERAL SUPPORT]: Managing Objective Function Evaluation Failures in Ax for MOO HOT 3
- [GENERAL SUPPORT]: Using qNegIntegratedPosteriorVariance HOT 3
- [GENERAL SUPPORT]: Reference point for multi-objective bayesian optimization HOT 4
- [GENERAL SUPPORT]: Adjusting search space or accommodating out-of-bounds initial data HOT 19
- [GENERAL SUPPORT]: Manual configuration, HOT 1
- [Bug]: Custom metric issue HOT 4
- [GENERAL SUPPORT]: CI_Level Paretofrontier
- [Bug]: Large sample time increase in ax-platform >= version 0.3.5 HOT 6
- [GENERAL SUPPORT]: Reference Point for Multi-Objective Bayesian Optimization HOT 5
- [GENERAL SUPPORT]: Plotting Pareto fronts / Posterior mean model HOT 6
- [GENERAL SUPPORT]: Getting best predicted point of a botorch model HOT 10
- [GENERAL SUPPORT]: Logical-or in outcome constraints HOT 4
- [GENERAL SUPPORT]: Parallelism and arbitrary parameter type support. HOT 5
- [GENERAL SUPPORT]: How to Standardize and Normalize Data for Service API HOT 6
- [FEATURE REQUEST]: Implement ImprovementGlobalStoppingStrategy for batch trials HOT 2
- [GENERAL SUPPORT]: Plot observed pareto frontier alongside remaining Arms HOT 2
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