# Parallel processing with different distributions causing Theano.gof.compilelock warning

**URL:** https://discourse.pymc.io/t/parallel-processing-with-different-distributions-causing-theano-gof-compilelock-warning/3689
**Category:** Questions
**Tags:** theano
**Created:** [August 7, 2019, 11:59am UTC](https://discourse.pymc.io/t/parallel-processing-with-different-distributions-causing-theano-gof-compilelock-warning/3689 "2019-08-07T11:59:37Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![LMMorgan](https://avatars.discourse-cdn.com/v4/letter/l/b5e925/32.png) [@LMMorgan](https://discourse.pymc.io/u/LMMorgan)
#### Post date: [August 7, 2019, 11:59am UTC](https://discourse.pymc.io/t/parallel-processing-with-different-distributions-causing-theano-gof-compilelock-warning/3689/1 "2019-08-07T11:59:37Z")

</div>

Hi,

I am currently trying to run pymc3 with lammps (MD code), passing lammps potential values (floats) for each run. This works very well within `pymc3.Model` block, using `pymc3.Simulator` and `pymc3.sample` when using a Normal distribution (passing mu and sd). However when I switch out the distribution type to Uniform (passing lower and upper) it doesn’t work. From what I can gather, for Normal you are passing a dict of values around, whereas Uniform is passing `Elemwise{add,no_inplace}.0` (Theano object), which can not be read by lammps. To fix this I have use `.eval()` to return the float value for `Elemwise{add,no_inplace}.0`. This runs, but keeps throwing up the following messages repeatedly:

```auto
INFO (theano.gof.compilelock): Waiting for existing lock by process ‘77206’ (I am process ‘77113’)
INFO (theano.gof.compilelock): To manually release the lock, delete /Users/lmm/.theano/compiledir_Darwin-17.7.0-x86_64-i386-64bit-i386-3.7.3-64/lock_dir

```

I have also noticed that when running with `parallel=True` (Default) it doesn’t utilise full capacity of all cores, but combined maxis at 100% (i.e. over 4 cores, it will run something like 70%, 10%, 15%, 5% respectively). When I set `parallel=False` I no long receive the Theano warnings, and the single core runs at ~100%. It is however exceptionally slow.

I don’t have these issues when using a Normal distribution, which leads me to believe the issue is with passing the `Elemwise{add,no_inplace}.0` object instead of the explicit value, or running `.eval()`?

I have searched through a lot of Theano troubleshooting, adding bits to the .theanorc etc, but nothing prevents the warnings or core capacity issues.

Any suggestions would be most appreciated!