dftracer-annotate-python

name	dftracer-annotate-python
description	Python annotation rules for dftracer — decorator usage, initialize/finalize, comp types, class methods, and quick checklist

Python Annotation Rules (dftracer)

Python Rule 1 — Use the decorator for regular functions

from dftracer.logger import dftracer_fn, dft_fn

@dftracer_fn(cat="IO")            # cat groups functions in the trace viewer
def my_read(path: str, size: int) -> bytes:
    ...

• The @dftracer_fn decorator wraps the function with START/END automatically.
• cat (category) is required — use a meaningful group name: "IO", "Compute", "MPI", "Data", "Init", etc.
• dft_fn is an alias for dftracer_fn — prefer dftracer_fn for clarity.
• Apply at every function that qualifies under Rule 0 (see dftracer-annotate-general skill).

Python Rule 2 — Initialize and finalize the tracer

from dftracer.logger import DFTracer

# At program entry (after MPI.Init if using mpi4py)
tracer = DFTracer.initialize_log(
    log_file="traces/my_app",    # prefix for .pfw trace files
    data_dir="/data",            # directory to monitor for I/O events
    process_id=rank,             # Pointer to MPI rank or 0 for single-process
)

# At program exit (before MPI.Finalize if using mpi4py)
DFTracer.finalize_log()

• With mpi4py: call DFTracer.initialize_log AFTER MPI.Init().
• With mpi4py: call DFTracer.finalize_log() BEFORE MPI.Finalize().
• Use process_id=NULL for single-process runs.

Python Rule 3 — Environment-variable initialization (alternative)

export DFTRACER_ENABLE=1
export DFTRACER_LOG_FILE=/tmp/traces/my_app
export DFTRACER_DATA_DIR=/data
python my_app.py

The env var only handles INIT/FINI — decorators are still needed for function-level spans.

Python Rule 4 — Context manager for ad-hoc regions

from dftracer.logger import DFTracer

tracer = DFTracer.get_instance()
with tracer.get_time("IO", "read_loop"):
    for chunk in data:
        process(chunk)

Only use context managers when the code block is too coarse-grained to fit a function decorator.

Python Rule 5 — Classify every annotated function with comp=TYPE

from dftracer.logger import dftracer_fn

@dftracer_fn(cat="IO", comp="io")
def read_checkpoint(path: str) -> dict: ...

@dftracer_fn(cat="COMM", comp="comm")
def broadcast_weights(tensor, comm): ...

@dftracer_fn(cat="CPU", comp="cpu")
def compute_checksum(data: bytes) -> str: ...

@dftracer_fn(cat="MEM", comp="mem")
def copy_batch(src, dst) -> None: ...

Types: "io", "mem", "cpu", "comm" — same taxonomy as C.

If dftracer_fn doesn't accept comp as a keyword argument:

@dftracer_fn(cat="IO")
def read_checkpoint(path: str) -> dict:
    DFTracer.get_instance().update("comp", "io")   # fallback
    ...

Python Rule 6 — Skip trivial functions (Rule 0 applies)

# ✅ Annotate — does real file I/O
@dftracer_fn(cat="IO")
def read_checkpoint(path: str, rank: int) -> dict:
    with open(path, "rb") as f:
        return pickle.load(f)

# ❌ Skip — trivial property
@property
def rank(self) -> int:
    return self._rank

# ❌ Skip — one-liner string helper
def _fmt_path(self, p: str) -> str:
    return str(Path(p).resolve())

Python Rule 7 — Class methods

class DataLoader:
    @dftracer_fn(cat="IO")
    def load(self, path: str, batch_size: int) -> list:
        ...

    @dftracer_fn(cat="IO")
    def write(self, path: str, data: bytes) -> None:
        ...

    def _validate(self, x):      # ❌ skip — trivial helper
        return x is not None

Python Quick checklist

• from dftracer.logger import dftracer_fn imported at top of file
• DFTracer.initialize_log(...) called at program entry (after MPI.Init if applicable)
• DFTracer.finalize_log() called at program exit (before MPI.Finalize if applicable)
• ALL non-trivial functions decorated — skip only pure getters/one-liners
• @dftracer_fn(cat="<CATEGORY>", comp="<type>") on every annotated function
• comp is one of "io", "mem", "cpu", "comm"
• Category names are consistent across the codebase
• For mpi4py: initialize AFTER MPI.Init(), finalize BEFORE MPI.Finalize()