Update random alpha

eval/6_star.edges

+6
+0 1
+0 2
+0 3
+0 4
+0 5
+1 0
+2 0
+3 0
+4 0
+5 0
\ No newline at end of file

eval/run_celeba_synchronous.sh

+#!/bin/bash
+# Documentation
+# Note: documentation was not written for this run file, so actual behaviour may differ
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use Adam.
+#   For SGD these need to be changed manually
+# - The script will set '--test_after' and '--train_evaluate_after' to comm_rounds_per_global_epoch, i.e., the eavaluation
+#   on the train set and on the test set is carried out every global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=6_star.edges
+config_file=~/tmp/config.ini
+procs_per_machine=6
+machines=1
+global_epochs=20
+eval_file=testing.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_celeba_synchronous.ini")
+# Learning rates
+lr="0.001"
+# Batch size
+batchsize="8"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="2000"
+# testing every x communication rounds
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+# Celeba has 63741 samples
+# Reddit has 70642
+# Femnist 734463
+# Shakespeares 3678451
+dataset_size=63741
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("90" "91" "92" "93" "94")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')/machine$m
+    echo results are stored in: $log_dir
+    mkdir -p $log_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_grid.sh

@@ -18,10 +18,8 @@
 # Each node needs a folder called 'tmp' in the user's home directory
 #
 # Note:
-# - The script does not change the optimizer. All configs are writen to use Adam.
-#   For SGD these need to be changed manually
-# - The script will set '--test_after' and '--train_evaluate_after' to comm_rounds_per_global_epoch, i.e., the eavaluation
-#   on the train set and on the test set is carried out every global epoch.
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
 # - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
 #   relevant for Adams and other optimizers with internal state)
 #
@@ -39,7 +37,7 @@ decpy_path=$nfs_home/decentralizepy/eval
 cd $decpy_path
 
 env_python=$python_bin/python3
-graph=192_regular.edges
+graph=96_regular.edges
 config_file=~/tmp/config.ini
 procs_per_machine=16
 machines=6
@@ -62,7 +60,6 @@ batchsize=("8" "16")
 comm_rounds_per_global_epoch=("1" "5" "10")
 procs=`expr $procs_per_machine \* $machines`
 echo procs: $procs
-# Celeba has 63741 samples
 dataset_size=63741
 # Calculating the number of samples that each user/proc will have on average
 samples_per_user=`expr $dataset_size / $procs`
@@ -86,6 +83,8 @@ do
     new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $r); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $r)/x)*$iterations); print($iterations if y<$iterations else y)")
     echo batches per communication round: $batches_per_comm_round
     echo corrected iterations: $new_iterations
+    test_after=$(($new_iterations / $global_epochs))
+    echo test after: $test_after
     for lr in "${lrs[@]}"
     do
       for i in "${tests[@]}"
@@ -102,7 +101,7 @@ do
         $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
         $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
         $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $b
-        $env_python $eval_file -ro 0 -tea $r -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $r -cf $config_file -ll $log_level
+        $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
         echo $i is done
         sleep 1
         echo end of sleep

eval/run_xtimes.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config.ini
+procs_per_machine=16
+machines=6
+global_epochs=150
+eval_file=testing.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_femnist_partialmodel.ini" "step_configs/config_femnist_topkacc.ini" "step_configs/config_femnist_wavelet.ini")
+# Learning rates
+lr="0.001"
+# Batch size
+batchsize="16"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="1"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+# Celeba has 63741 samples
+# Reddit has 70642
+# Femnist 734463
+# Shakespeares 3678451, subsampled 678696
+# cifar 50000
+dataset_size=734463
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("97")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')/machine$m
+    echo results are stored in: $log_dir
+    mkdir -p $log_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_xtimes_celeba.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config.ini
+procs_per_machine=16
+machines=6
+global_epochs=120
+eval_file=testing.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_celeba_sharing.ini" "step_configs/config_celeba_partialmodel.ini" "step_configs/config_celeba_topkacc.ini" "step_configs/config_celeba_subsampling.ini" "step_configs/config_celeba_wavelet.ini")
+# Learning rates
+lr="0.001"
+# Batch size
+batchsize="8"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="10"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=63741
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("90" "91" "92" "93" "94")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')/machine$m
+    echo results are stored in: $log_dir
+    mkdir -p $log_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_xtimes_cifar.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config.ini
+procs_per_machine=16
+machines=6
+global_epochs=300
+eval_file=testing.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_cifar_sharing.ini" "step_configs/config_cifar_partialmodel.ini" "step_configs/config_cifar_topkacc.ini" "step_configs/config_cifar_subsampling.ini" "step_configs/config_cifar_wavelet.ini")
+# Learning rates
+lr="0.01"
+# Batch size
+batchsize="8"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="20"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=50000
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("90" "91" "92" "93" "94")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')/machine$m
+    echo results are stored in: $log_dir
+    mkdir -p $log_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#

eval/run_xtimes_femnist.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config.ini
+procs_per_machine=16
+machines=6
+global_epochs=70
+eval_file=testing.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_femnist_sharing.ini" "step_configs/config_femnist_partialmodel.ini" "step_configs/config_femnist_topkacc.ini" "step_configs/config_femnist_subsampling.ini" "step_configs/config_femnist_wavelet.ini")
+# Learning rates
+lr="0.01"
+# Batch size
+batchsize="16"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="10"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=734463
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("90" "91" "92" "93" "94")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')/machine$m
+    echo results are stored in: $log_dir
+    mkdir -p $log_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_xtimes_reddit.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config.ini
+procs_per_machine=16
+machines=6
+global_epochs=50
+eval_file=testing.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_reddit_sharing.ini" "step_configs/config_reddit_partialmodel.ini" "step_configs/config_reddit_topkacc.ini" "step_configs/config_reddit_subsampling.ini" "step_configs/config_reddit_wavelet.ini")
+# Learning rates
+lr="1"
+# Batch size
+batchsize="16"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="10"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=70642
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("90" "91" "92" "93" "94")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')/machine$m
+    echo results are stored in: $log_dir
+    mkdir -p $log_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 200
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/run_xtimes_shakespeare.sh

+#!/bin/bash
+# Documentation
+# This bash file takes three inputs. The first argument (nfs_home) is the path to the nfs home directory.
+# The second one (python_bin) is the path to the python bin folder.
+# The last argument (logs_subfolder) is the path to the logs folder with respect to the nfs home directory.
+#
+# The nfs home directory should contain the code of this framework stored in $nfs_home/decentralizepy and a folder
+# called configs which contains the file 'ip_addr_6Machines.json'
+# The python bin folder needs to include all the dependencies of this project including crudini.
+# The results will be stored in $nfs_home/$logs_subfolder
+# Each of the experiments will be stored in its own folder inside the logs_subfolder. The folder of the experiment
+# starts with the last part of the config name, i.e., for 'config_celeba_topkacc.ini' it will start with topkacc.
+# The name further includes the learning rate, rounds and batchsize as well as the exact date at which the experiment
+# was run.
+# Example: ./run_grid.sh  /mnt/nfs/wigger /mnt/nfs/wigger/anaconda3/envs/sacs39/bin /logs/celeba
+#
+# Additional requirements:
+# Each node needs a folder called 'tmp' in the user's home directory
+#
+# Note:
+# - The script does not change the optimizer. All configs are writen to use SGD.
+# - The script will set '--test_after' and '--train_evaluate_after' such that it happens at the end of a global epoch.
+# - The '--reset_optimizer' option is set to 0, i.e., the optimizer is not reset after a communication round (only
+#   relevant for Adams and other optimizers with internal state)
+#
+# Addapting the script to other datasets:
+# Change the variable 'dataset_size' to reflect the data sets size.
+#
+# Known issues:
+# - If the script is started at the very end of a minute then there is a change that two folders are created as not all
+#   machines may start running the script at the exact same moment.
+
+nfs_home=$1
+python_bin=$2
+logs_subfolder=$3
+decpy_path=$nfs_home/decentralizepy/eval
+cd $decpy_path
+
+env_python=$python_bin/python3
+graph=96_regular.edges
+config_file=~/tmp/config.ini
+procs_per_machine=16
+machines=6
+global_epochs=80
+eval_file=testing.py
+log_level=INFO
+
+ip_machines=$nfs_home/configs/ip_addr_6Machines.json
+
+m=`cat $ip_machines | grep $(/sbin/ifconfig ens785 | grep 'inet ' | awk '{print $2}') | cut -d'"' -f2`
+export PYTHONFAULTHANDLER=1
+
+# Base configs for which the gird search is done
+tests=("step_configs/config_shakespeare_sharing.ini" "step_configs/config_shakespeare_partialmodel.ini" "step_configs/config_shakespeare_topkacc.ini" "step_configs/config_shakespeare_subsampling.ini" "step_configs/config_shakespeare_wavelet.ini")
+# Learning rates
+lr="0.5"
+# Batch size
+batchsize="16"
+# The number of communication rounds per global epoch
+comm_rounds_per_global_epoch="25"
+procs=`expr $procs_per_machine \* $machines`
+echo procs: $procs
+dataset_size=678696
+# Calculating the number of samples that each user/proc will have on average
+samples_per_user=`expr $dataset_size / $procs`
+echo samples per user: $samples_per_user
+
+# random_seeds for which to rerun the experiments
+random_seeds=("90" "91" "92" "93" "94")
+# random_seed = 97
+echo batchsize: $batchsize
+echo communication rounds per global epoch: $comm_rounds_per_global_epoch
+# calculating how many batches there are in a global epoch for each user/proc
+batches_per_epoch=$(($samples_per_user / $batchsize))
+echo batches per global epoch: $batches_per_epoch
+# the number of iterations in 25 global epochs
+iterations=$($env_python -c "from math import floor; print($batches_per_epoch * $global_epochs) if $comm_rounds_per_global_epoch >= $batches_per_epoch else print($global_epochs * $comm_rounds_per_global_epoch)")
+echo iterations: $iterations
+# calculating the number of batches each user/proc uses per communication step (The actual number may be a float, which we round down)
+batches_per_comm_round=$($env_python -c "from math import floor; x = floor($batches_per_epoch / $comm_rounds_per_global_epoch); print(1 if x==0 else x)")
+# since the batches per communication round were rounded down we need to change the number of iterations to reflect that
+new_iterations=$($env_python -c "from math import floor; tmp = floor($batches_per_epoch / $comm_rounds_per_global_epoch); x = 1 if tmp == 0 else tmp; y = floor((($batches_per_epoch / $comm_rounds_per_global_epoch)/x)*$iterations); print($iterations if y<$iterations else y)")
+echo batches per communication round: $batches_per_comm_round
+echo corrected iterations: $new_iterations
+test_after=$(($new_iterations / $global_epochs))
+echo test after: $test_after
+for i in "${tests[@]}"
+do
+  for seed in "${random_seeds[@]}"
+  do
+    echo $i
+    IFS='_' read -ra NAMES <<< $i
+    IFS='.' read -ra NAME <<< ${NAMES[-1]}
+    log_dir=$nfs_home$logs_subfolder/${NAME[0]}:lr=$lr:r=$comm_rounds_per_global_epoch:b=$batchsize:$(date '+%Y-%m-%dT%H:%M')/machine$m
+    echo results are stored in: $log_dir
+    mkdir -p $log_dir
+    cp $i $config_file
+    # changing the config files to reflect the values of the current grid search state
+    $python_bin/crudini --set $config_file COMMUNICATION addresses_filepath $ip_machines
+    $python_bin/crudini --set $config_file OPTIMIZER_PARAMS lr $lr
+    $python_bin/crudini --set $config_file TRAIN_PARAMS rounds $batches_per_comm_round
+    $python_bin/crudini --set $config_file TRAIN_PARAMS batch_size $batchsize
+    $python_bin/crudini --set $config_file DATASET random_seed $seed
+    $env_python $eval_file -ro 0 -tea $test_after -ld $log_dir -mid $m -ps $procs_per_machine -ms $machines -is $new_iterations -gf $graph -ta $test_after -cf $config_file -ll $log_level
+    echo $i is done
+    sleep 500
+    echo end of sleep
+    done
+done
+#
\ No newline at end of file

eval/step_configs/config_celeba_fft.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_grow.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_manualadapt.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_partialmodel.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]
@@ -31,3 +31,4 @@ addresses_filepath = ip_addr_6Machines.json
 [SHARING]
 sharing_package = decentralizepy.sharing.PartialModel
 sharing_class = PartialModel
+alpha = 0.1
\ No newline at end of file

eval/step_configs/config_celeba_randomalpha.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_randomalphainc.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_roundrobin.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_sharing.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_subsampling.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]

eval/step_configs/config_celeba_synchronous.ini

+[DATASET]
+dataset_package = decentralizepy.datasets.Celeba
+dataset_class = Celeba
+model_class = CNN
+images_dir = /mnt/nfs/shared/leaf/data/celeba/data/raw/img_align_celeba
+train_dir = /mnt/nfs/shared/leaf/data/celeba/per_user_data/train
+test_dir = /mnt/nfs/shared/leaf/data/celeba/data/test
+; python list of fractions below
+sizes = 
+
+[OPTIMIZER_PARAMS]
+optimizer_package = torch.optim
+optimizer_class = SGD
+lr = 0.001
+
+[TRAIN_PARAMS]
+training_package = decentralizepy.training.Training
+training_class = Training
+rounds = 4
+full_epochs = False
+batch_size = 16
+shuffle = True
+loss_package = torch.nn
+loss_class = CrossEntropyLoss
+
+[COMMUNICATION]
+comm_package = decentralizepy.communication.TCP
+comm_class = TCP
+addresses_filepath = ip_addr_6Machines.json
+
+[SHARING]
+sharing_package = decentralizepy.sharing.Synchronous
+sharing_class = Synchronous
\ No newline at end of file

eval/step_configs/config_celeba_topkacc.ini

@@ -10,7 +10,7 @@ sizes =
 
 [OPTIMIZER_PARAMS]
 optimizer_package = torch.optim
-optimizer_class = Adam
+optimizer_class = SGD
 lr = 0.001
 
 [TRAIN_PARAMS]
@@ -33,3 +33,4 @@ sharing_package = decentralizepy.sharing.PartialModel
 sharing_class = PartialModel
 alpha = 0.1
 accumulation = True
+accumulate_averaging_changes = True
\ No newline at end of file