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Merge branch 'master' of github.com:ECP-CANDLE/Supervisor
2 parents f00f31b + 07f17c6 commit 3a904af

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‎workflows/nt3_mlrMBO/swift/cori_settings.sh‎

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@@ -4,5 +4,7 @@ module swap PrgEnv-intel PrgEnv-gnu
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# if PrgEnv-intel is not loaded then PrgEnv-gnu won't load via the swap
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# so we load gcc explicitly
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module load gcc
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module load intel-tensorflow
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export PATH=/global/homes/w/wozniak/Public/sfw/compute/swift-t-r/stc/bin:$PATH

‎workflows/nt3_mlrMBO/swift/cori_workflow.sh‎

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@@ -74,7 +74,7 @@ COMMON_DIR=$EMEWS_PROJECT_ROOT/../common/python
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export PYTHONPATH=$BENCHMARK_DIR:$COMMON_DIR:$EMEWS_PROJECT_ROOT/python
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export PYTHONHOME=/global/common/cori/software/python/2.7-anaconda/envs/deeplearning/
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export TURBINE_DIRECTIVE="#SBATCH --constraint=haswell\n#SBATCH --license=SCRATCH"
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export TURBINE_DIRECTIVE="#SBATCH --constraint=knl,quad,cache\n#SBATCH --license=SCRATCH\n#SBATCH --account=m2759"
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# Resident task workers and ranks
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export TURBINE_RESIDENT_WORK_WORKERS=1

‎workflows/nt3_mlrMBO/swift/cori_workflow3.sh‎

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@@ -75,7 +75,7 @@ COMMON_DIR=$EMEWS_PROJECT_ROOT/../common/python
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export PYTHONPATH=$EMEWS_PROJECT_ROOT/python:$EMEWS_PROJECT_ROOT/ext/EQ-Py:$BENCHMARK_DIR:$COMMON_DIR
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export PYTHONHOME=/global/common/cori/software/python/2.7-anaconda/envs/deeplearning/
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export TURBINE_DIRECTIVE="#SBATCH --constraint=haswell\n#SBATCH --license=SCRATCH"
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export TURBINE_DIRECTIVE="#SBATCH --constraint=knl,quad,cache\n#SBATCH --license=SCRATCH\n#SBATCH --account=m2759"
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# Resident task workers and ranks
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export TURBINE_RESIDENT_WORK_WORKERS=1

‎workflows/p1b1_mlrMBO/swift/cori_workflow.sh‎

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@@ -72,7 +72,7 @@ COMMON_DIR=$EMEWS_PROJECT_ROOT/../common/python
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export PYTHONPATH=$BENCHMARK_DIR:$COMMON_DIR:$EMEWS_PROJECT_ROOT/python
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export PYTHONHOME=/global/common/cori/software/python/2.7-anaconda/envs/deeplearning/
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export TURBINE_DIRECTIVE="#SBATCH --constraint=haswell\n#SBATCH --license=SCRATCH"
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export TURBINE_DIRECTIVE="#SBATCH --constraint=knl,quad,cache\n#SBATCH --license=SCRATCH\n#SBATCH --account=m2759"
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# Resident task workers and ranks
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export TURBINE_RESIDENT_WORK_WORKERS=1

‎workflows/p1b3_mlrMBO/swift/cori_workflow.sh‎

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@@ -71,7 +71,7 @@ COMMON_DIR=$EMEWS_PROJECT_ROOT/../common/python
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export PYTHONPATH=$EMEWS_PROJECT_ROOT/python:$BENCHMARK_DIR:$COMMON_DIR
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export PYTHONHOME=/global/common/cori/software/python/2.7-anaconda/envs/deeplearning/
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export TURBINE_DIRECTIVE="#SBATCH --constraint=haswell\n#SBATCH --license=SCRATCH"
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export TURBINE_DIRECTIVE="#SBATCH --constraint=knl,quad,cache\n#SBATCH --license=SCRATCH\n#SBATCH --account=m2759"
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# Resident task workers and ranks
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export TURBINE_RESIDENT_WORK_WORKERS=1

‎workflows/p1b3_mlrMBO/swift/cori_workflow3.sh‎

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@@ -75,7 +75,7 @@ COMMON_DIR=$EMEWS_PROJECT_ROOT/../common/python
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export PYTHONPATH=$EMEWS_PROJECT_ROOT/python:$BENCHMARK_DIR:$COMMON_DIR
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export PYTHONHOME=/global/common/cori/software/python/2.7-anaconda/envs/deeplearning/
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export TURBINE_DIRECTIVE="#SBATCH --constraint=haswell\n#SBATCH --license=SCRATCH"
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export TURBINE_DIRECTIVE="#SBATCH --constraint=knl,quad,cache\n#SBATCH --license=SCRATCH\n#SBATCH --account=m2759"
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# Resident task workers and ranks
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export TURBINE_RESIDENT_WORK_WORKERS=1

‎workflows/p2b1_mlrMBO/scripts/theta_run_model.sh‎

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@@ -72,7 +72,7 @@ MODEL_CMD="python ${arg_array[@]}"
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set +e
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echo $MODEL_CMD
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$TIMEOUT_CMD python "${arg_array[@]}"
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sleep 60
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RES=$?
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if [ "$RES" -ne 0 ]; then

‎workflows/p2b1_mlrMBO/swift/cori_workflow3.sh‎

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@@ -74,7 +74,7 @@ COMMON_DIR=$EMEWS_PROJECT_ROOT/../common/python
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export PYTHONPATH=$EMEWS_PROJECT_ROOT/python:$EMEWS_PROJECT_ROOT/ext/EQ-Py:$BENCHMARK_DIR:$COMMON_DIR
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export PYTHONHOME=/global/common/cori/software/python/2.7-anaconda/envs/deeplearning/
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export TURBINE_DIRECTIVE="#SBATCH --constraint=haswell\n#SBATCH --license=SCRATCH"
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export TURBINE_DIRECTIVE="#SBATCH --constraint=knl,quad,cache\n#SBATCH --license=SCRATCH\n#SBATCH --account=m2759"
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# Resident task workers and ranks
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export TURBINE_RESIDENT_WORK_WORKERS=1

‎workflows/p2b1_mlrMBO/swift/workflow3.swift‎

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@@ -32,7 +32,7 @@ if not os.path.exists(outdir):
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hyper_parameter_map = json.loads('%s')
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hyper_parameter_map['framework'] = 'keras'
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hyper_parameter_map['save'] = '{}/output'.format(outdir)
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hyper_parameter_map['save_path'] = '{}/output'.format(outdir)
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hyper_parameter_map['instance_directory'] = outdir
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hyper_parameter_map['experiment_id'] = '%s'
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hyper_parameter_map['run_id'] = '%s'

‎workflows/p3b1_mlrMBO/data/parameter_set3.R‎

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# see https://cran.r-project.org/web/packages/ParamHelpers/ParamHelpers.pdfmakeNum
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# the parameter names should match names of the arguments expected by the benchmark
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param.set <- makeParamSet(
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makeDiscreteParam("batch_size", values = c(16, 32, 64, 128, 256, 512)),
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makeIntegerParam("epochs", lower = 5, upper = 500),
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makeDiscreteParam("activation", values = c("softmax", "elu", "softplus", "softsign", "relu", "tanh", "sigmoid", "hard_sigmoid", "linear")),
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makeDiscreteParam("optimizer", values = c("adam", "sgd", "rmsprop", "adagrad", "adadelta")),
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makeNumericParam("dropout", lower = 0, upper = 0.9),
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makeNumericParam("learning_rate", lower = 0.00001, upper = 0.1)
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## DEBUG PARAMETERS: DON'T USE THESE IN PRODUCTION RUN
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## makeDiscreteParam("conv", values = c("32 20 16 32 10 1"))
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makeNumericParam("learning_rate", lower= 0.00001, upper= 0.1 ),
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makeNumericParam("dropout", lower= 0, upper= 0.9 ),
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makeDiscreteParam("activation",
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values= c( "softmax","elu","softplus","softsign",
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"relu", "tanh","sigmoid","hard_sigmoid",
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"linear") ),
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makeDiscreteParam("optimizer",
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values = c("adam", "sgd", "rmsprop","adagrad",
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"adadelta")),
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makeDiscreteParam("shared_nnet_spec",
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values= c( "400", "500", "600", "700"
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#"800", "900", "1000", "1100", "1200",
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#"400,400", "500,500", "600,600", "700,700",
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#"800,800", "900,900", "1000,1000", "1100,1100",
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#"1200,1200"
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) ),
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makeDiscreteParam("ind_nnet_spec",
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values= c( "400:400:400", "600:600:600"
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#"800:800:800", "1000:1000:1000",
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#"1200:1200:1200",
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#"400,400:400,400:400,400", "600,600:600,600:600,600",
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#"800,800:800,800:800,800", "1000,1000:1000,1000:1000,1000",
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#"1200,1200:1200,1200:1200,1200",
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#"800,400:800,400:800,400",
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#"1200,400:1200,400:1200,400",
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#"1200,800,400:1200,800,400:1200,800,400"
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)),
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makeDiscreteParam("batch_size", values = c(16,32,64,128,256)),
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makeIntegerParam("epochs", lower = 5, upper = 50)
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)
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