pydftracer 1.0.6

I/O profiler for deep learning python apps. Specifically for dlio_benchmark.

DFTracer v1.0.4

A multi-level profiler for capturing application functions and low-level system I/O calls from deep learning workloads.

Requirements for profiler

1. Python > 3.7
2. pybind11

Requirements for analyzer

1. bokeh>=2.4.2
2. pybind11
3. zindex_py
4. pandas>=2.0.3
5. dask>=2023.5.0
6. distributed
7. numpy>=1.24.3
8. pyarrow>=12.0.1
9. rich>=13.6.0
10. python-intervals>=1.10.0.post1
11. matplotlib>=3.7.3

Installation

Users can easily install DFTracer using pip. This is the way most Python packages are installed. This method would work for both native Python environments and Conda environments.

From PyPI

pip install pydftracer

From Github

DFT_VERSION=develop
pip install git+https://github.com/hariharan-devarajan/dftracer.git@${DFT_VERSION}

From source

git clone [e-mail]:hariharan-devarajan/dftracer.git
cd dftracer
# You can skip this for installing the dev branch.
# for latest stable version use master branch.
git checkout tags/<Release> -b <Release>
pip install .

For more build instructions check here.

Usage

from dftracer.logger import dftracer, dft_fn
log_inst = dftracer.initialize_log(logfile=None, data_dir=None, process_id=-1)
dft_fn = dft_fn("COMPUTE")

# Example of using function decorators
@dft_fn.log
def log_events(index):
    sleep(1)

# Example of function spawning and implicit I/O calls
def posix_calls(val):
    index, is_spawn = val
    path = f"{cwd}/data/demofile{index}.txt"
    f = open(path, "w+")
    f.write("Now the file has more content!")
    f.close()
    if is_spawn:
        print(f"Calling spawn on {index} with pid {os.getpid()}")
        log_inst.finalize() # This need to be called to correctly finalize DFTracer.
    else:
        print(f"Not calling spawn on {index} with pid {os.getpid()}")

# NPZ calls internally calls POSIX calls.
def npz_calls(index):
    # print(f"{cwd}/data/demofile2.npz")
    path = f"{cwd}/data/demofile{index}.npz"
    if os.path.exists(path):
        os.remove(path)
    records = np.random.randint(255, size=(8, 8, 1024), dtype=np.uint8)
    record_labels = [0] * 1024
    np.savez(path, x=records, y=record_labels)

def main():
    log_events(0)
    npz_calls(1)
    with get_context('spawn').Pool(1, initializer=init) as pool:
        pool.map(posix_calls, ((2, True),))
    log_inst.finalize()

if __name__ == "__main__":
    main()

For this example, as the dftracer.initialize_log do not pass logfile or data_dir, we need to set DFTRACER_LOG_FILE and DFTRACER_DATA_DIR. By default the DFTracer mode is set to FUNCTION. Example of running this configurations are:

# The process id, app_name and .pfw will be appended by the profiler for each app and process.
# The name of the final log file is ~/log_file-<APP_NAME>-<PID>.pfw
DFTRACER_LOG_FILE=~/log_file
# Colon separated paths for including for profiler
DFTRACER_DATA_DIR=/dev/shm/:/p/gpfs1/$USER/dataset:$PWD/data
# Enable profiler
DFTRACER_ENABLE=1

For more example check Examples.

Citation and Reference

The original SC'24 paper describes the design and implementation of DFTracer code. Please cite this paper and the code if you use DFTracer for your research.

@inproceedings{devarajan_dftracer_2024,
	address = {Atlanta, GA},
	title = {{DFTracer}: {An} {Analysis}-{Friendly} {Data} {Flow} {Tracer} for {AI}-{Driven} {Workflows}},
	shorttitle = {{DFTracer}},
	urldate = {2024-07-31},
	booktitle = {{SC24}: {International} {Conference} for {High} {Performance} {Computing}, {Networking}, {Storage} and {Analysis}},
	publisher = {IEEE},
	author = {Devarajan, Hariharan and Pottier, Loic and Velusamy, Kaushik and Zheng, Huihuo and Yildirim, Izzet and Kogiou, Olga and Yu, Weikuan and Kougkas, Anthony and Sun, Xian-He and Yeom, Jae Seung and Mohror, Kathryn},
	month = nov,
	year = {2024},
}

@misc{devarajan_dftracer_code_2024,
    type = {Github},
    title = {Github {DFTracer}},
    shorttitle = {{DFTracer}},
    url = {https://github.com/hariharan-devarajan/dftracer.git},
    urldate = {2024-07-31},
    journal = {DFTracer: A multi-level dataflow tracer for capture I/O calls from worklows.},
    author = {Devarajan, Hariharan and Pottier, Loic and Velusamy, Kaushik and Zheng, Huihuo and Yildirim, Izzet and Kogiou, Olga and Yu, Weikuan and Kougkas, Anthony and Sun, Xian-He and Yeom, Jae Seung and Mohror, Kathryn},
    month = jun,
    year = {2024},
}

Acknowledgments

This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344; and under the auspices of the National Cancer Institute (NCI) by Frederick National Laboratory for Cancer Research (FNLCR) under Contract 75N91019D00024. This research used resources of the Argonne Leadership Computing Facility, a U.S. Department of Energy (DOE) Office of Science user facility at Argonne National Laboratory and is based on research supported by the U.S. DOE Office of Science-Advanced Scientific Computing Research Program, under Contract No. DE-AC02-06CH11357. Office of Advanced Scientific Computing Research under the DOE Early Career Research Program. Also, This material is based upon work partially supported by LLNL LDRD 23-ERD-045 and 24-SI-005. LLNL-CONF-857447.

License

MIT License LICENSE