PythonAPI | Python API for Simulator | Machine Learning library

Charles Duffy explained the problem in the comments, thank you! 😃

When I checked the platform in Python, it was indeed ARM:

The reasons to set an initial point in Mosek are:

1. Starting feasible point for the mixed-integer solver when the problem has integer variables, in that case set itg

2. Warm-starting the simplex solver, in that case set bas.

3. Warm-starting the conic/interior-point solver, in that case set itr, although this option is only theoretical because currently Mosek will not warm-start and just ignores that solution.

Normally if there are any integer variables in the problem, then only the itg solution is relevant. If the problem is linear and the simplex solver is explicitly chosen via a Mosek parameter then only the bas solution is relevant.

However imagine that the problem has integer variables but the user sets the Mosek parameter iparam.mio_mode=ignore because then now want to solve only the LP relaxation. That is extremely unlikely that someone seriously needs such a combination but at least in principle it is possible. What then? There are lots of corner cases like this, and you would have to explicitly check lots of Mosek parameters (which can change over time) to make a decision.

One way out of this is to set all three primal solutions, and also set the parameter iparam.remove_unused_solutions. Then, once Mosek internally decides which optimizer will be used, it will purge those solutions which are not relevant at this point.

Another option is that you only set the itg solution. It is very unlikely that any cvxpy users would use this feature for anything else than 1. above.

pip expects your setup.py on the root of your git repository, like:

it's hard to answer this question without having a reproducible example with your data.

Your python code seems fine, so I believe the problem is in your data: if I must take a guess, I assume you have NaNs somewhere in your data which causes the failure.

I'm not sure if cvxpy and mosek handle nans at all, so it's a good idea to verify your data integrity before using them.

Solution was to add the following to the Dockerfile:

I got this error when upgrading from tensorflow 1.2.1 to 2.4. It appears the statement "x, K.shape(input_image)[1:3]))(input_gt_boxes)" is causing the bug. This is possibly due to an API change in tensorflow and/or Keras. I suspect the code you're trying to run is made for a different version of tensorflow than the one you got installed. You could try to install a matching version of tensorflow and keras or you can try to fix the code to comply with your current version. In my case I had to make slight changes to the way I constructed the model, but it is not easy to see how that can be done for your case without downloading the model library.

Wrap thread_id in ctypes.c_long as:

Try running it as follows:

Either way :) the nans will be grouped in a way that minimizes error, not discarded