Default system monitoring and customized framework profiling with different profiling options (original) (raw)

This section gives information about the supported profiling configuration classes, as well as an example configuration. You can use the following profiling configuration classes to manage the framework profiling options:

• DetailedProfilingConfig – Specify a target step or time range to profile framework operations using the native framework profilers (TensorFlow profiler and PyTorch profiler). For example, if using TensorFlow, the Debugger hooks enable the TensorFlow profiler to collect TensorFlow-specific framework metrics. Detailed profiling enables you to profile all framework operators at a pre-step (before the first step), within steps, and between steps of a training job.

Note

Detailed profiling might significantly increase GPU memory consumption. We do not recommend enabling detailed profiling for more than a couple of steps.

• DataloaderProfilingConfig – Specify a target step or time range to profile deep learning framework data loader processes. Debugger collects every data loader event of the frameworks.

Note

Data loader profiling might lower the training performance while collecting information from data loaders. We don't recommend enabling data loader profiling for more than a couple of steps.
Debugger is preconfigured to annotate data loader processes only for the AWS deep learning containers. Debugger cannot profile data loader processes from any other custom or external training containers.

• PythonProfilingConfig – Specify a target step or time range to profile Python functions. You can also choose between two Python profilers: cProfile and Pyinstrument.
  • cProfile – The standard Python profiler. cProfile collects information for every Python operator called during training. With cProfile, Debugger saves cumulative time and annotation for each function call, providing complete detail about Python functions. In deep learning, for example, the most frequently called functions might be the convolutional filters and backward pass operators, and cProfile profiles every single of them. For the cProfile option, you can further select a timer option: total time, CPU time, and off-CPU time. While you can profile every function call executing on processors (both CPU and GPU) in CPU time, you can also identify I/O or network bottlenecks with the off-CPU time option. The default is total time, and Debugger profiles both CPU and off-CPU time. With cProfile, you are able to drill down to every single functions when analyzing the profile data.
  • Pyinstrument – Pyinstrument is a low-overhead Python profiler that works based on sampling. With the Pyinstrument option, Debugger samples profiling events every millisecond. Because Pyinstrument measures elapsed wall-clock time instead of CPU time, the Pyinstrument option can be a better choice over the cProfile option for reducing profiling noise (filtering out irrelevant function calls that are cumulatively fast) and capturing operators that are actually compute intensive (cumulatively slow) for training your model. With Pyinstrument, you are able to see a tree of function calls and better understand the structure and root cause of the slowness.

Note

Enabling Python profiling might slow down the overall training time. cProfile profiles the most frequently called Python operators at every call, so the processing time on profiling increases with respect to the number of calls. For Pyinstrument, the cumulative profiling time increases with respect to time because of its sampling mechanism.

The following example configuration shows the full structure when you use the different profiling options with specified values.

import time
from sagemaker.debugger import (ProfilerConfig, 
                                FrameworkProfile, 
                                DetailedProfilingConfig, 
                                DataloaderProfilingConfig, 
                                PythonProfilingConfig,
                                PythonProfiler, cProfileTimer)

profiler_config=ProfilerConfig(
    system_monitor_interval_millis=500,
    framework_profile_params=FrameworkProfile(
        detailed_profiling_config=DetailedProfilingConfig(
            start_step=5, 
            num_steps=1
        ),
        dataloader_profiling_config=DataloaderProfilingConfig(
            start_step=7, 
            num_steps=1
        ),
        python_profiling_config=PythonProfilingConfig(
            start_step=9, 
            num_steps=1, 
            python_profiler=PythonProfiler.CPROFILE, 
            cprofile_timer=cProfileTimer.TOTAL_TIME
        )
    )
)

For more information about available profiling options, see DetailedProfilingConfig, DataloaderProfilingConfig, and PythonProfilingConfig in the Amazon SageMaker Python SDK.