HyperNOMAD
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The starting point cannot be evaluated
If I try to use HyperNOMAD I have the following problem
caterina99@pc0425:/mnt/c/Users/calessi/Desktop/HyperNomad_linux/examples$ hypernomad.exe mnist_x0.txt
WARNING: hyperparameters explicitely set by name are superseded by settings done using X0, LOWER_BOUND and UPPER_BOUND.
BLOCKS OF HYPERPARAMETERS Each block has a head hyperparameters and possibly several groups of associated hyperameters.
Convolutionnal layers {
Head of block NUM_CON_LAYERS -> x0=2, lb=0, ub=100, is VARIABLE Multiple times associated hyperparameters: 2 groups Group #0 NUM_OUTPUT_LAYERS -> x0=6, lb=1, ub=1000, is VARIABLE KERNELS -> x0=5, lb=1, ub=20, is FIXED STRIDES -> x0=1, lb=1, ub=3, is VARIABLE PADDINGS -> x0=0, lb=0, ub=2, is VARIABLE POOLING_SIZE -> x0=1, lb=1, ub=5, is VARIABLE Group #1 NUM_OUTPUT_LAYERS -> x0=16, lb=1, ub=1000, is VARIABLE KERNELS -> x0=5, lb=1, ub=20, is FIXED STRIDES -> x0=1, lb=1, ub=3, is VARIABLE PADDINGS -> x0=0, lb=0, ub=2, is VARIABLE POOLING_SIZE -> x0=1, lb=1, ub=5, is VARIABLE }
Full layers {
Head of block NUM_FC_LAYERS -> x0=2, lb=0, ub=500, is VARIABLE Multiple times associated hyperparameters: 2 groups Group #0 SIZE_FC_LAYER -> x0=128, lb=1, ub=1000, is VARIABLE Group #1 SIZE_FC_LAYER -> x0=84, lb=1, ub=1000, is VARIABLE }
Batch size {
Head of block BATCH_SIZE -> x0=128, lb=1, ub=400, is VARIABLE No associated hyperparameters }
Optimizer {
Head of block OPTIMIZER_CHOICE -> x0=3, lb=1, ub=4, is VARIABLE One time associated hyperparameters (always 1 group) Group #0 OPT_PARAM_1 -> x0=0.1, lb=0, ub=1, is VARIABLE OPT_PARAM_2 -> x0=0.9, lb=0, ub=1, is VARIABLE OPT_PARAM_3 -> x0=0.0005, lb=0, ub=1, is VARIABLE OPT_PARAM_4 -> x0=0, lb=0, ub=1, is VARIABLE }
Dropout rate {
Head of block DROPOUT_RATE -> x0=0.2, lb=0, ub=0.95, is FIXED No associated hyperparameters }
Activation function {
Head of block ACTIVATION_FUNCTION -> x0=1, lb=1, ub=3, is VARIABLE No associated hyperparameters }
Warning: { Model use is disabled for problem with categorical variables. } Warning: { Setting granularity different than 0 is disabled for problem with categorical variables. } Warning: { Default anisotropic mesh is disabled with categorical and binary variables. }
HyperNomad - version 1.0
Using Nomad version 3.9.1 - www.gerad.ca/nomad
Nomad parameters {
dimension : n=22
lower bounds : ( NaN 1 1 1 0 1 1 1 1 0 1 NaN 1 1 1 NaN 0 0 0 0 0 1 )
upper bounds : ( NaN 1000 20 3 2 5 1000 20 3 2 5 NaN 1000 1000 400 NaN 1 1 1 1 0.95 3 )
fixed variables : ( NaN NaN 5 NaN NaN NaN NaN 5 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.2 NaN )
blackbox input types : ( cat(C) int(I) int(I) int(I) int(I) int(I) int(I) int(I) int(I) int(I) int(I) cat(C) int(I) int(I) int(I) cat(C) cont(R) cont(R) cont(R) cont(R) cont(R) int(I) )
extended poll trigger: 10
variable groups {
group #0 {
indexes: { 14 }
directions {
n : 1
types : { [Ortho-MADS n+1 NEG] }
sec poll types: { }
int poll types: { }
seed : 0
}
}
group #1 {
indexes: { 21 }
directions {
n : 1
types : { [Ortho-MADS n+1 NEG] }
sec poll types: { }
int poll types: { }
seed : 0
}
}
group #2 {
indexes: { 12 13 }
directions {
n : 2
types : { [Ortho-MADS n+1 NEG] }
sec poll types: { }
int poll types: { }
seed : 0
}
}
group #3 {
indexes: { 0 11 15 }
no directions (categorical variables)
}
group #4 {
indexes: { 16 17 18 19 }
directions {
n : 4
types : { [Ortho-MADS n+1 NEG] }
sec poll types: { }
int poll types: { }
seed : 0
}
}
group #5 {
indexes: { 1 3 4 5 6 8 9 10 }
directions {
n : 8
types : { [Ortho-MADS n+1 NEG] }
sec poll types: { }
int poll types: { }
seed : 0
}
}
}
blackbox outputs (m=1) {
#0 OBJ $python /mnt/c/Users/calessi/Desktop/HyperNomad_linux/src/blackbox/pytorch_bb.py MNIST $python /mnt/c/Users/calessi/Desktop/HyperNomad_linux/src/blackbox/pytorch_sgte.py MNIST
}
signature : standard
has surrogate : yes
sort trial points with surrogate: yes
surrogate cost : none
sort trial points randomly : no
add seed to output file names : yes
solution file : none
history file : history.0.txt
stats file : (stats.0.txt) BBE ( SOL ) OBJ
cache file : none
x0 : ( 2 6 5 1 0 1 16 5 1 0 1 2 128 84 128 3 0.1 0.9 0.0005 0 0.2 1 )
directions : Ortho-MADS n+1 NEG
gmesh (isotropic) {
granularity : 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
coarsening exponent : 1
refining exponent : -1
initial mesh index : 0
initial mesh size : ( 1 10 1 1 1 1 10 1 1 1 1 1 10 10 10 1 1 1 1 1 1 1 )
initial poll size : ( 1 50 5 5 5 5 50 5 5 5 5 1 50 50 50 1 1 1 1 1 1 5 )
}
snap to bounds : yes
opportunistic evaluations : yes
use models (search and sort) : no
speculative search : yes
VNS search : no
NelderMead (NM) search {
max_trial_pts_nfactor: 80
gamma (shrink):0.5
delta_oc (outside contraction):0.5
delta_ic (inside contraction):-0.5
delta_e (expansion):2
intensive: no
opportunistic: no
use_only_Y: no
init_Y_best_von: no
use_short_Y0: no
include_factor: 8 }
Latin-Hypercube (LH) search : no
cache search : no
random seed / run id : 0
epsilon : 1e-13
undefined string : NaN
infinity string : inf
display degrees {
general : full (3)
search : full (3)
poll : full (3)
iterative: full (3)
}
display stats : BBE ( SOL ) OBJ
display all evaluations : no
point display limit : no limit
max number of blackbox eval. : 100
max cache memory : 2000 MB
}
MADS run {
starting point evaluation {
x0 eval point: ( 2 6 5 1 0 1 16 5 1 0 1 2 128 84 128 3 0.1 0.9 0.0005 0 0.2 1 )
list of points evaluation (x0 evaluation) {
submitted evaluation 1/1 {
point #0 ( 2 6 5 1 0 1 16 5 1 0 1 2 128 84 128 3 0.1 0.9 0.0005 0 0.2 1 )
mesh indices : ( 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 )
not in cache
}
sh: 1: python: not found
evaluation 1/1 {
point #0
x0 evaluation unsuccessful point #0: evaluation failed (you may need to check the source of the problem).
}
} end of evaluations
terminate MADS : yes
termination cause : problem with starting point evaluation
iteration status : unsuccessful
new feas. incumbent : none
new infeas. incumbent: none
} end of starting point evaluation
} end of run (problem with starting point evaluation)
NOMAD final display {
cache {
number of cache points: 1
size in memory : 619 B
cache file : -
}
stats {
MADS iterations : 0
blackbox evaluations : 1
simulated blackbox evaluations : 0
evaluations : 1
failed evaluations : 1 (all evaluations failed)
interrupted sequences of eval. : 0
cache hits : 0
number of poll searches : 0
dyn. direction successes : 0
number of speculative searches : 0
number of user searches : 0
number of LH searches : 0
number of NM searches : 0
number of TM line searches : 0
number of cache searches : 0
number of VNS searches : 0
no model has been constructed
wall-clock time : 0s
}
miscellaneous {
mesh indices : min= ( 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ), max = ( 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ), last= ( 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 )
best feasible solution : no feasible solution has been found
}
}
The starting point cannot be evaluated. Please verify that the Pytorch script is available and runs correctly. The default setting for bbExe is $python /mnt/c/Users/calessi/Desktop/HyperNomad_linux/src/blackbox/pytorch_bb.py MNIST. Make sure it works correctly on its own.
The way to test that the python script is working correctly is to put the initial point coordinates (2 6 5 1 0 1 16 5 1 0 1 2 128 84 128 3 0.1 0.9 0.0005 0 0.2 1, that is, just the value, no "(" or ")") into a X0.txt file and run on the command line
python /mnt/c/Users/calessi/Desktop/HyperNomad_linux/src/blackbox/pytorch_bb.py MNIST X0.txt
it gives caterina99@pc0425:~$ python /mnt/c/Users/calessi/Desktop/HyperNomad_linux/src/blackbox/pytorch_bb.py MNIST /mnt/c/Users/calessi/Desktop/HyperNomad_linux/src/blackbox/X0.txt -88.310
I just saw in the log that when running HyperNomad you have the following message: "sh: 1: python: not found". This means the python interpreter is not found when running the command. But when running a single evaluation on the command line it works.
Maybe you can try to provide the full path to the python interpreter into HyperNomad parameter file.
For the example you tried, this can be done by adding
BB_EXE $/full/path/to/python /mnt/c/Users/calessi/Desktop/HyperNomad_linux/src/blackbox/pytorch_bb.py
in the file examples/mnist_x0.txt