How to use AMBNAS algorithm? #63
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Hello @anuragverma77 , For the error, can you tell me which deephyper version you are using? >>> import deephyper
>>> deephyper.__version__Sorry for the lack of the documentation on this part, I will try to add more soon. $ deephyper nas --help (dh) I
usage: deephyper nas [-h] {ambs,random,regevo,agebo,ambsmixed,regevomixed} ...
positional arguments:
{ambs,random,regevo,agebo,ambsmixed,regevomixed}
optional arguments:
-h, --help show this help message and exitWhich give you a clear list of acceptable keyword for the search algorithms. Also if you want more information about a specific algorithm use the same trick but after adding the search argument, such as: $ deephyper nas ambs --help (dh) I
usage: deephyper nas ambs [-h] [--problem PROBLEM] [--backend BACKEND]
[--max-evals MAX_EVALS]
[--eval-timeout-minutes EVAL_TIMEOUT_MINUTES]
[--ray-address RAY_ADDRESS]
[--ray-password RAY_PASSWORD]
[--num-cpus-per-task NUM_CPUS_PER_TASK]
[--num-gpus-per-task NUM_GPUS_PER_TASK]
[--seed SEED] [--cache-key {uuid,to_dict}]
[--num-ranks-per-node NUM_RANKS_PER_NODE]
[--num-evals-per-node NUM_EVALS_PER_NODE]
[--num-nodes-per-eval NUM_NODES_PER_EVAL]
[--num-threads-per-rank NUM_THREADS_PER_RANK]
[--num-threads-per-node NUM_THREADS_PER_NODE]
[--num-workers NUM_WORKERS] [--log-dir LOG_DIR]
[--run RUN] [--evaluator EVALUATOR]
[--surrogate-model {RF,ET,GBRT,DUMMY,GP}]
[--liar-strategy {cl_min,cl_mean,cl_max}]
[--acq-func {LCB,EI,PI,gp_hedge}] [--kappa KAPPA]
[--xi XI] [--n-jobs N_JOBS]
optional arguments:
-h, --help show this help message and exit
--problem PROBLEM Module path to the Problem instance you want to use
for the search.
--backend BACKEND Keras backend module name
--max-evals MAX_EVALS
maximum number of evaluations
--eval-timeout-minutes EVAL_TIMEOUT_MINUTES
Kill evals that take longer than this
--ray-address RAY_ADDRESS
This parameter is mandatory when using evaluator==ray.
It reference the "IP:PORT" redis address for the RAY-
driver to connect on the RAY-head.
--ray-password RAY_PASSWORD
--num-cpus-per-task NUM_CPUS_PER_TASK
--num-gpus-per-task NUM_GPUS_PER_TASK
--seed SEED Random seed used.
--cache-key {uuid,to_dict}
Cache policy.
--num-ranks-per-node NUM_RANKS_PER_NODE
Number of ranks per nodes for each evaluation. Only
valid if evaluator==balsam and balsam job-mode is
'mpi'.
--num-evals-per-node NUM_EVALS_PER_NODE
Number of evaluations performed on each node. Only
valid if evaluator==balsam and balsam job-mode is
'serial'.
--num-nodes-per-eval NUM_NODES_PER_EVAL
Number of nodes used for each evaluation. This
Parameter is usefull when using data-parallelism or
model-parallism with evaluator==balsam and balsam job-
mode is 'mpi'.
--num-threads-per-rank NUM_THREADS_PER_RANK
Number of threads per MPI rank. Only valid if
evaluator==balsam and balsam job-mode is 'mpi'.
--num-threads-per-node NUM_THREADS_PER_NODE
Number of threads per node. Only valid if
evaluator==balsam and balsam job-mode is 'mpi'.
--num-workers NUM_WORKERS
Number of parallel workers for the search. By default,
it is being automatically computed depending on the
chosen evaluator. If fixed then the default number of
workers is override by this value.
--log-dir LOG_DIR Path of the directory where to store information about
the run.
--run RUN Defaults to 'deephyper.nas.run.alpha.run'.
--evaluator EVALUATOR
Defaults to 'ray'.
--surrogate-model {RF,ET,GBRT,DUMMY,GP}
Type of surrogate model (learner).
--liar-strategy {cl_min,cl_mean,cl_max}
Constant liar strategy
--acq-func {LCB,EI,PI,gp_hedge}
Acquisition function type
--kappa KAPPA Controls how much of the variance in the predicted
values should be taken into account. If set to be very
high, then we are favouring exploration over
exploitation and vice versa. Used when the acquisition
is "LCB".
--xi XI Controls how much improvement one wants over the
previous best values. If set to be very high, then we
are favouring exploration over exploitation and vice
versa. Used when the acquisition is "EI", "PI".
--n-jobs N_JOBS number of cores to use for the 'surrogate model'
(learner), if n_jobs=-1 then it will use all cores
available. |
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Hi @Deathn0t Thanks. Please don't be sorry. You are helping people and that's more than enough. |
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Hi @Deathn0t
For regevo we can use
deephyper nas regevo --problem nas_problems...................................................For random we can use
deephyper nas random --problem nas_problems...................................................What is command for using AMBNAS? Nothing is mentioned here. Is it still under development?
Is it something like
deephyper nas ambs --problem nas_problems...................................................?When I tried with
deephyper nas regevo --evaluator subprocess --problem nas_problems_train_final.polynome2.problem.Problem --max-evals 1, it is working but when I tried withdeephyper nas ambs --evaluator subprocess --problem nas_problems_train_final.polynome2.problem.Problem --max-evals 1but I am getting the following error:The text was updated successfully, but these errors were encountered: