Ralf Gommers rgommers

Conda-forge library names and pkg-config output

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	/***************** See f2py2e/cb_rules.py: buildcallback *****************/

	/******************* cb_cselect_in_gees__user__routines *******************/
	typedef struct {
	PyObject *capi;
	PyTupleObject *args_capi;
	int nofargs;
	jmp_buf jmpbuf;
	} cb_cselect_in_gees__user__routines_t;

	$ # For top-level dependencies, see the conda-forge metadata browser:
	$ # https://conda-metadata-app.streamlit.app/?q=conda-forge%2Flinux-64%2Ftorchdata-0.7.1-py39h02e9b37_5.conda

	$ mamba create -n torchdata torchdata # on macOS arm64
	$ mamba activate torchdata
	$ mamba repoquery depends torchdata

	Executing the query torchdata

	==> py-scipy: Executing phase: 'install'
	==> [2022-10-13-12:45:50.325962] '/home/rgommers/code/spack/opt/spack/linux-endeavourosrolling-skylake_avx512/oneapi-2022.2.0/python-3.9.13-mn4ovd4dkbyg62orinqikrnvaxq6r5tf/bin/python3.9' '-m' 'pip' '-vvv' '--no-input' '--no-cache-dir' '--disable-pip-version-check' 'install' '--no-deps' '--ignore-installed' '--no-build-isolation' '--no-warn-script-location' '--no-index' '--prefix=/home/rgommers/code/spack/opt/spack/linux-endeavourosrolling-skylake_avx512/oneapi-2022.2.0/py-scipy-1.8.1-ww5y2ruckqwn44vypo4e7yl3yzmxiwsh' '.'
	Using pip 22.2.2 from /home/rgommers/code/spack/opt/spack/linux-endeavourosrolling-skylake_avx512/oneapi-2022.2.0/py-pip-22.2.2-42kbxmhhxyigyp2224oarapc3o5xxpga/lib/python3.9/site-packages/pip (python 3.9)
	Non-user install due to --prefix or --target option
	Ignoring indexes: https://pypi.org/simple
	Created temporary directory: /tmp/pip-ephem-wheel-cache-vju1m2iu
	Created temporary directory: /tmp/pip-build-tracker-h5l54x96
	Initialized build tracking at

	"""
	Implement `jax.random` APIs with NumPy, and `numpy.random` APIs with JAX.
	The purpose of this is to be able to compare APIs more easily, and clarify
	where they are and aren't similar.
	"""

	import secrets
	import multiprocessing

	import numpy as np

	To create an environment with the most recent version of all mainstream Python array/tensor libraries installed:

	conda create -n many-libs python=3.7
	conda activate many-libs
	conda install cudatoolkit=10.2
	pip install numpy torch jax jaxlib tensorflow mxnet cupy-cuda102 dask toolz sparse

	Conda doesn't manage to find a winning combination here; pip has a hard time
	too and probably not all constraints are satisfied, but nothing crashes
	and basic tests work as they are supposed to.

	==> 2018-03-06 17:49:03 <==
	# cmd: /home/rgommers/anaconda3/bin/conda install pytorch torchvision -c pytorch --no-deps
	+defaults::_ipyw_jlab_nb_ext_conf-0.1.0-py36he11e457_0
	+defaults::alabaster-0.7.10-py36h306e16b_0
	+defaults::anaconda-5.1.0-py36_2
	+defaults::anaconda-client-1.6.9-py36_0
	+defaults::anaconda-navigator-1.7.0-py36_0
	+defaults::anaconda-project-0.8.2-py36h44fb852_0
	+defaults::asn1crypto-0.24.0-py36_0
	+defaults::astroid-1.6.1-py36_0

	import numpy as np
	import matplotlib.pyplot as plt
	from autograd import grad
	import autograd.scipy.special as special


	plt.style.use('ggplot')


	x = np.linspace(-10, 10, num=1000)