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27
OnRunning.md
27
OnRunning.md
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# Running Experiments with Lambda:
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This is not supposed to be a instruction on how to do it properly, but it is a writeup on how i did it.
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If you want to do it properly, extend the command line Arguments for haga-lambda and allow runtime tweaking of Hyperparams and Datasets. While at it, generalizing LamdaCalculusV1 would be smart, too. You can use LamdaCalculusV2 as a template on how to do it more properly. (I wrote that later, and was IMO quite a bit smarter about it. I sadly didn't have time to fix up V1...)
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You just want to do the same hack i did or know about it?
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create a branch for each Dataset-experiment pair. e.g. iris_1 ... iris_9
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here git is your friend, especially if you inevitably screw up.
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e.g. echo git\ checkout\ iris_{1..9}\;\ git\ cherry-pick\ 7ced1e1\; will create a command for applying the commit 7ced1e1 to every iris branch.
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Adapt the build.sbatch and run.sbatch and **commit them**!
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clone the branch you committed to on the cluster.
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create the required folders! If you forget the output one, slurm will fail silently!
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Make sure to sbatch an adapted **build.sbatch before run.sbatch**!
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build.sbatch will need to be adapted for and run on every node you will use!
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Otherwise stuff WILL break!
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sbatch run.sbatch
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You can use squeue to monitor progress.
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A huge slew of raw data will be dumped into the output Folder. The error files contain results, the output files stats during training.
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On how to process these results, see: https://merl.dnshome.de/git/Hans/haga-graphics
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29
build.sbatch
29
build.sbatch
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#!/usr/bin/env bash
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#!/usr/bin/env bash
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#SBATCH --time=00:10:00
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#SBATCH --time=00:10:00
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#SBATCH --partition=cpu
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#SBATCH --partition=cpu
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# 9 Experiments * 3 Datasets
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#SBATCH --output=./output/output_build.txt
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#SBATCH --array=0-27
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#SBATCH --error=./output/error_build.txt
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# ensure output exists, is a folder and is writable in your working directory
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#SBATCH --output=./output/output_run_%a.txt
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#SBATCH --error=./output/error_run_%a.txt
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# run once for every node you plan to use
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#SBATCH --nodelist=oc-compute02
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#SBATCH --nodelist=oc-compute02
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#SBATCH --mem=2G
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#SBATCH --mem=4G
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#SBATCH -c16
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srun nix develop --command stack --no-nix --system-ghc --no-install-ghc build
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# list your branches
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problems=("iris" "nurse" "german")
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#9 Experiments
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current_problem=${problems[(${SLURM_ARRAY_TASK_ID}/9)]}
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#9 Experiments
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current_variant=$(((${SLURM_ARRAY_TASK_ID}) % 9 + 1))
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current_branch="${current_problem}_${current_variant}"
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# ensure [full path to writable folder on node *] exists
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git clone -b $current_branch --single-branch "[your git repo]" [full path to writable folder on node 1]/$current_branch
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git clone -b $current_branch --single-branch "[your git repo]" [full path to writable folder on node 1]/$current_branch
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#... for every node
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srun bash -c "cd /data/$SLURMD_NODENAME/merljoha/$current_branch; nix develop --command stack --no-nix --system-ghc --no-install-ghc build"
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@@ -89,10 +89,10 @@ lE =
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maxDepth = 9,
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maxDepth = 9,
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weights =
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weights =
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ExpressionWeights
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ExpressionWeights
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{ lambdaSpucker = 0,
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{ lambdaSpucker = 1,
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lambdaSchlucker = 10,
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lambdaSchlucker = 2,
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symbol = 100,
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symbol = 30,
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variable = 5,
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variable = 10,
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constant = 5
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constant = 5
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}
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}
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}
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}
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maxDepth = 9,
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maxDepth = 9,
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weights =
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weights =
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ExpressionWeights
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ExpressionWeights
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{ lambdaSpucker = 0,
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{ lambdaSpucker = 1,
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lambdaSchlucker = 10,
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lambdaSchlucker = 2,
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symbol = 100,
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symbol = 30,
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variable = 5,
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variable = 10,
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constant = 5
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constant = 5
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}
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}
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}
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}
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@@ -77,10 +77,10 @@ lE =
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maxDepth = 9,
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maxDepth = 9,
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weights =
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weights =
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ExpressionWeights
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ExpressionWeights
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{ lambdaSpucker = 0,
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{ lambdaSpucker = 1,
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lambdaSchlucker = 10,
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lambdaSchlucker = 2,
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symbol = 100,
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symbol = 30,
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variable = 5,
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variable = 10,
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constant = 5
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constant = 5
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}
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}
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}
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}
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@@ -8,8 +8,8 @@ import Pipes
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import Pretty
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import Pretty
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import Protolude hiding (for)
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import Protolude hiding (for)
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import System.IO
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import System.IO
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import LambdaDatasets.IrisDataset
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-- import LambdaDatasets.IrisDataset
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-- import LambdaDatasets.NurseryDataset
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import LambdaDatasets.NurseryDataset
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-- import LambdaDatasets.GermanDataset
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-- import LambdaDatasets.GermanDataset
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import Debug.Trace as DB
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import Debug.Trace as DB
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import qualified Data.Map.Strict as Map
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import qualified Data.Map.Strict as Map
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36
run.sbatch
36
run.sbatch
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#!/usr/bin/env bash
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#!/usr/bin/env bash
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# test this timing, it scales with result sizes
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#SBATCH --time=18:00:00
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#SBATCH --time=12:00:00
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#SBATCH --partition=cpu
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#SBATCH --partition=cpu
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# 30 Runs * 9 Experiments * 3 Datasets
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#SBATCH --array=0-30
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#SBATCH --array=0-809
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#SBATCH --output=./output/output_run_%j.txt
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# ensure output exists, is a folder and is writable in your working directory
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#SBATCH --error=./output/error_run_%j.txt
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#SBATCH --output=./output/output_run_%a.txt
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#SBATCH --nodelist=oc-compute02
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#SBATCH --error=./output/error_run_%a.txt
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#SBATCH --mem=3G
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# exclude nodes with weaker CPUs
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srun nix develop --command stack --no-nix --system-ghc --no-install-ghc run haga-lambda
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#SBATCH --exclude=oc222
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# test memory usage, it scales **Exponentially** with max Depth. Implement some countermeasures if that's a problem, e.g. raise max depth over time.
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#SBATCH --mem=6G
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#SBATCH --nodes=1
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# list your branches
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problems=("iris" "nurse" "german")
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# 30 Runs * 9 Experiments
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current_problem=${problems[(${SLURM_ARRAY_TASK_ID}/270)]}
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# 30 Runs, 9 Experiments
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current_variant=$(((${SLURM_ARRAY_TASK_ID} / 30) % 9 + 1))
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current_branch="${current_problem}_${current_variant}"
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# ensure [full path to writable folder on node *] exists
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git clone -b $current_branch --single-branch "[your git repo]" [full path to writable folder on node 1]/$current_branch
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git clone -b $current_branch --single-branch "[your git repo]" [full path to writable folder on node 2]/$current_branch
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#... for every node
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srun bash -c "cd /data/$SLURMD_NODENAME/merljoha/$current_branch; nix develop --command stack --no-nix --system-ghc --no-install-ghc run haga-lambda"
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