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How To Profile An Experiment

When optimizing the training speed of a model, the first step is to understand where and why training is slow. Once the bottlenecks have been identified, the next step is to do further investigation and experimentation to alleviate those bottlenecks.

To understand the performance profile of a training job, the training code and infrastructure needs to be instrumented. There are many different layers that can be instrumented, from raw throughput all the way down to GPU kernels.

Determined provides two tools out-of-the-box for instrumenting training:

  • System Metrics: measurements of hardware usage

  • Timings: durations of actions taken during training, such as dataloading

System Metrics are useful to see if the software is taking full advantage of the available hardware, particularly around GPU usage, dataloading, and network communication during distributed training. Timings are useful for identifying the section of code to focus on for optimizations. Most commonly, Timings help answers the question of whether the dataloader is the main bottleneck in training.

System Metrics

System Metrics are statistics around hardware usage, such as GPU utilization and network throughput. These metrics are useful for seeing whether training is using the hardware effectively. When the System Metrics reported for an experiment are below what is expected from the hardware, that is a sign that the software may be able to be optimized to make better use of the hardware resources.

Specifically, Determined tracks:

  • GPU utilization

  • GPU free memory

  • Network throughput (sent)

  • Network throughput (received)

  • Disk IOPS

  • Disk throughput (read)

  • Disk throughput (write)

  • Host available memory

  • CPU utilization averaged across cores

For distributed training, these metrics are collected for every agent. The data is broken down per agent. GPU metrics can be further broken down by GPU.

Note

System Metrics record agent-level metrics, so when there are multiple experiments on the same agent, it is difficult to analyze. We suggest that profiling is done with only a single experiment per agent.

Timings

The other type of profiling metric that Determined tracks is Timings. Timings are measurements of how long specific training events take. Examples of training events include retrieving data from the dataloader, moving data betwee host and device, running the forward/backwards pass, and executing callbacks.

These measurements provide a fairly high level picture of where to focus optimization efforts.

Note

Timings are currently only supported for PyTorchTrial.

Specifically, Determined tracks:

  • dataloader_next (retrieving the next item from the dataloader)

  • to_device (transfering input from host to device)

  • train_batch (how long the user-defined train_batch function takes to execute*)

  • step_lr_schedulers (amount time taken to update the LR schedules)

  • from_device (amount of time transfering output from device to host)

  • reduce_metrics (amount of time taken to calculate global metrics in distributed training)

* train_batch is typically the forward pass and the backwards pass, but it is a user-defined function so it could include other steps.