A simple scheduler for running commands on multiple GPUs.
Project description
simple_gpu_scheduler
A simple scheduler to run your commands on individual GPUs. Following the
KISS principle, this script
simply accepts commands via stdin
and executes them on a specific GPU by
setting the CUDA_VISIBLE_DEVICES
variable.
The commands read are executed using the login shell, thus redirections >
pipes |
and all other kinds of bash magic can be used.
Installation
The package can simply be installed from pypi
$ pip install simple_gpu_scheduler
Example
To show how this generally works, we will create jobs that simply outputs
a job id and the value of CUDA_VISIBLE_DEVICES
:
for i in {0..10}; do echo "echo job_id=$i device=\$CUDA_VISIBLE_DEVICES && sleep 3"; done | simple_gpu_scheduler --gpus 0,1,2
which results in the following output:
Processing `command echo job_id=0 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 2
Processing `command echo job_id=1 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 1
Processing `command echo job_id=2 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 0
job_id=0 device=2
job_id=1 device=1
job_id=2 device=0
--- 3 seconds no output ---
Processing command `echo job_id=3 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 2
Processing command `echo job_id=4 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 1
Processing command `echo job_id=5 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 0
job_id=3 device=2
job_id=4 device=1
job_id=5 device=0
--- 3 seconds no output ---
Processing command `echo job_id=6 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 2
Processing command `echo job_id=7 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 1
Processing command `echo job_id=8 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 0
job_id=6 device=2
job_id=7 device=1
job_id=8 device=0
--- 3 seconds no output ---
Processing command `echo job_id=9 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 2
Processing command `echo job_id=10 device=$CUDA_VISIBLE_DEVICES && sleep 3` on gpu 0
job_id=9 device=2
job_id=10 device=0
This is equivalent to creating a file commands.txt
with the following content:
echo job_id=0 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=1 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=2 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=3 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=4 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=5 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=6 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=7 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=8 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=9 device=$CUDA_VISIBLE_DEVICES && sleep 3
echo job_id=10 device=$CUDA_VISIBLE_DEVICES && sleep 3
and running
simple_gpu_scheduler --gpus 0,1,2 < commands.txt
Simple scheduler for jobs
Combined with some basic command line tools, one can set up a very basic scheduler which waits for new jobs to be "submitted" and executes them in order of submission.
Setup and start scheduler in background or in a separate permanent session
(using for example tmux
):
touch gpu.queue
tail -f -n 0 gpu.queue | simple_gpu_scheduler --gpus 0,1,2
the command tail -f -n 0
follows the end of the gpu.queue file. Thus if there
was anything written into gpu.queue
prior to the execution of the command it
will not be passed to simple_gpu_scheduler
.
Then submitting commands boils down to appending text to the gpu.queue
file:
echo "my_command_with | and stuff > logfile" >> gpu.queue
Hyperparameter search
In order to allow user friendly utilization of the scheduler in the common
scenario of hyperparameter search, a convenience script simple_hypersearch
is
included in the package.
simple_hypersearch -h
usage: simple_hypersearch [-h] [--sampling-mode {shuffled_grid,grid}]
[--n-samples N_SAMPLES] [--seed SEED]
[-p NAME [VALUES ...]]
command_pattern
Convenience tool to generate hyperparameter search commands from a command pattern and parameter ranges.
positional arguments:
command_pattern Command pattern where placeholders with {parameter_name} should be replaced.
optional arguments:
-h, --help show this help message and exit
--sampling-mode {shuffled_grid,grid}
Determine how to sample commands. Either in the grid order [grid]
or in a shuffled order [shuffled_grid, default].
--n-samples N_SAMPLES
Number of samples to draw. If not provided use all possible combinations.
--seed SEED Random seed to ensure reproducability when using randomized order of the grid.
-p NAME [VALUES ...], --parameter NAME [VALUES ...]
Name of parameter followed by values that should be considered for hyperparameter search.
Example: `-p lr 0.01 0.001 0.0001`
Usage example:
simple_hypersearch "my_program --param1 {param1} --param2 {param2}" -p param1 0 1 -p param2 2 3
will generate the output:
my_program --param1 0 --param2 2
my_program --param1 0 --param2 3
my_program --param1 1 --param2 2
my_program --param1 1 --param2 3
This allows to easily perform hyperparameter searches over a grid of values or
uniform samples of the grid (dependent on the setting of sampling-mode
).
The output can directly be piped into simple_gpu_scheduler
or appended to the
"queue file" (see Simple scheduler for jobs).
Here some more concrete examples:
Grid of all possible parameter configurations in random order:
simple_hypersearch "python3 train_dnn.py --lr {lr} --batch_size {bs}" -p lr 0.001 0.0005 0.0001 -p bs 32 64 128 | simple_gpu_scheduler --gpus 0,1,2
5 uniformly sampled parameter configurations:
simple_hypersearch "python3 train_dnn.py --lr {lr} --batch_size {bs}" --n-samples 5 -p lr 0.001 0.0005 0.0001 -p bs 32 64 128 | simple_gpu_scheduler --gpus 0,1,2
TODO
- Multi line jobs (evtl. we would then need a submission script after all)
- Stop, but let commands finish when receiving a defined signal
- Tests would be nice, until now the project is still very small but if it grows tests should be added
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