GPU Model Information:
name
NVIDIA H100 80GB HBM3
NVIDIA H100 80GB HBM3
NVIDIA H100 80GB HBM3
NVIDIA H100 80GB HBM3
NVIDIA H100 80GB HBM3
NVIDIA H100 80GB HBM3
NVIDIA H100 80GB HBM3
NVIDIA H100 80GB HBM3

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/fsx/ivan.laptev/Deepfake/CLIP/cliptest.py:91: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
  all_features = [torch.tensor(feature) for feature in all_features]

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Accuracy = 81.147
 This problem is unconstrained.
RUNNING THE L-BFGS-B CODE

           * * *

Machine precision = 2.220D-16
 N =          513     M =           10

At X0         0 variables are exactly at the bounds

At iterate    0    f=  1.38623D+04    |proj g|=  1.24655D+03

At iterate   50    f=  8.45446D+03    |proj g|=  1.29566D+02

At iterate  100    f=  8.25161D+03    |proj g|=  7.22117D+01

At iterate  150    f=  8.21490D+03    |proj g|=  8.54758D+01

At iterate  200    f=  8.20412D+03    |proj g|=  4.46181D+01

At iterate  250    f=  8.19127D+03    |proj g|=  2.23226D+01

At iterate  300    f=  8.17730D+03    |proj g|=  6.97138D+00

At iterate  350    f=  8.17138D+03    |proj g|=  7.08601D+00

At iterate  400    f=  8.16992D+03    |proj g|=  4.80929D+00

At iterate  450    f=  8.16951D+03    |proj g|=  3.01999D+00

At iterate  500    f=  8.16941D+03    |proj g|=  3.46523D+00

At iterate  550    f=  8.16939D+03    |proj g|=  4.46374D-01

           * * *

Tit   = total number of iterations
Tnf   = total number of function evaluations
Tnint = total number of segments explored during Cauchy searches
Skip  = number of BFGS updates skipped
Nact  = number of active bounds at final generalized Cauchy point
Projg = norm of the final projected gradient
F     = final function value

           * * *

   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F
  513    557    642      1     0     0   4.396D-01   8.169D+03
  F =   8169.3873911253249     

CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH