DFTracer trace files not generated and missing hydra_log folder with profiling and logging enabled
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Description
I attempted to enable DFTracer profiling and hydra logging in the mlpstorage training workflow, but I was unable to locate the expected output trace/log files.
Steps Taken:
Installed DFTracer:
pip install pydftracer
Exported environment variable:
export DFTRACER_ENABLE=1
Ran the training command with DFTracer:
mlpstorage training run \
--hosts <ip> --num-client-hosts 1 \
--client-host-memory-in-gb 256 \
--num-accelerators 4 \
--accelerator-type h100 \
--model unet3d \
--data-dir /mnt/mlperf/june25/data/ \
--results-dir /mnt/mlperf/june25/result_july7/run9 \
--param dataset.num_files_train=500 \
dataset.num_subfolders_train=70 \
reader.read_threads=8 \
reader.odirect=True \
reader.prefetch_size=0 \
workflow.profiling=True \
workflow.profiling=dftracer \
--checkpoint-folder /mnt/chkpt
Despite the setup, I was not able to find the DFTracer trace files in the results directory or elsewhere.
Tried alternate logging settings (iostat + hydra):
mlpstorage training run \
--hosts <ip> --num-client-hosts 1 \
--client-host-memory-in-gb 256 \
--num-accelerators 4 \
--accelerator-type h100 \
--model unet3d \
--data-dir /mnt/mlperf/june25/data/ \
--results-dir /mnt/mlperf/june25/result_july8/run1 \
--param dataset.num_files_train=500 \
dataset.num_subfolders_train=70 \
reader.read_threads=8 \
reader.odirect=True \
reader.prefetch_size=0 \
workflow.profiling=True \
workflow.profiling=iostat \
workflow.hydra_logging=enabled \
workflow.job_logging=enabled \
--checkpoint-folder /mnt/chkpt
However, I couldn't find the expected hydra_log folder or logs in the results directory either.
Questions:
- Is there any additional step required to make sure DFTracer generates trace files?
- Where should the hydra_log directory be located when logging is enabled?
- Are there known issues with workflow.profiling=dftracer or workflow.hydra_logging=enabled flags?
Please let me know if further logs or config dumps would be helpful.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start at the mlpstorage training run entry point and reproduce the two commands from the issue with workflow.profiling=dftracer and the Hydra logging flags. Inspect the configured results directories and runtime configuration to determine where trace files and the hydra_log folder are expected. Done means identifying whether the flags work, documenting the output locations, or isolating a reproducible configuration problem.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- observability, performance
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100