SageMaker Training Compiler FAQ
Important
Amazon Web Services (AWS) announces that there will be no new releases or versions of SageMaker Training Compiler. You can continue to utilize SageMaker Training Compiler through the existing AWS Deep Learning Containers (DLCs) for SageMaker Training. It is important to note that while the existing DLCs remain accessible, they will no longer receive patches or updates from AWS, in accordance with the AWS Deep Learning Containers Framework Support Policy.
Use the following FAQ items to find answers to commonly asked questions about SageMaker Training Compiler.
Q. How do I know SageMaker Training Compiler is working?
If you successfully launched your training job with SageMaker Training Compiler, you receive the following log messages:
-
With
TrainingCompilerConfig(debug=False)Found configuration for Training Compiler Configuring SM Training Compiler... -
With
TrainingCompilerConfig(debug=True)Found configuration for Training Compiler Configuring SM Training Compiler... Training Compiler set to debug mode
Q. Which models does SageMaker Training Compiler accelerate?
SageMaker Training Compiler supports the most popular deep learning models from the Hugging Face
transformers library. With most of the operators that the compiler supports, these
models can be trained faster with SageMaker Training Compiler. Compilable models include but are not limited
to the following: bert-base-cased, bert-base-chinese,
bert-base-uncased, distilbert-base-uncased,
distilbert-base-uncased-finetuned-sst-2-english, gpt2,
roberta-base, roberta-large, t5-base, and
xlm-roberta-base. The compiler works with most DL operators and data
structures and can accelerate many other DL models beyond those that have been
tested.
Q. What happens if I enable SageMaker Training Compiler with a model that isn't tested?
For an untested model, you might need to first modify the training script to be compatible with SageMaker Training Compiler. For more information, see Bring Your Own Deep Learning Model and follow the instructions on how to prepare your training script.
Once you have updated your training script, you can start the training job. The
compiler proceeds to compile the model. However, training speed may not increase and
might even decrease relative to the baseline with an untested model. You might need to
retune training parameters such as batch_size and
learning_rate to achieve any speedup benefits.
If compilation of the untested model fails, the compiler returns an error. See SageMaker Training Compiler Troubleshooting for detailed information about the failure types and error messages.
Q. Will I always get a faster training job with SageMaker Training Compiler?
No, not necessarily. First, SageMaker Training Compiler adds some compilation overhead before the ongoing training process can be accelerated. The optimized training job must run sufficiently long to amortize and make up for this incremental compilation overhead at the beginning of the training job.
Additionally, as with any model training process, training with suboptimal parameters can increase training time. SageMaker Training Compiler can change the characteristics of the training job by, for example, changing the memory footprint of the job. Because of these differences, you might need to retune your training job parameters to speed up training. A reference table specifying the best performing parameters for training jobs with different instance types and models can be found at Tested Models.
Finally, some code in a training script might add additional overhead or disrupt the compiled computation graph and slow training. If working with a customized or untested model, see the instructions at Best Practices to Use SageMaker Training Compiler with PyTorch/XLA.
Q. Can I always use a larger batch size with SageMaker Training Compiler?
Batch size increases in most, but not all, cases. The optimizations made by SageMaker Training Compiler can change the characteristics of your training job, such as the memory footprint. Typically, a Training Compiler job occupies less memory than an uncompiled training job with the native framework, which allows for a larger batch size during training. A larger batch size, and a corresponding adjustment to the learning rate, increases training throughput and can decrease total training time.
However, there could be cases where SageMaker Training Compiler might actually increase memory footprint based on its optimization scheme. The compiler uses an analytical cost model to predict the execution schedule with the lowest cost of execution for any compute-intensive operator. This model could find an optimal schedule that increases memory use. In this case, you won’t be able to increase batch sizes, but your sample throughput is still higher.
Q. Does SageMaker Training Compiler work with other SageMaker training features, such as the SageMaker AI distributed training libraries and SageMaker Debugger?
SageMaker Training Compiler is currently not compatible with SageMaker AI’s distributed training libraries.
SageMaker Training Compiler is compatible with SageMaker Debugger, but Debugger might degrade computational performance by adding overhead.
Q. Does SageMaker Training Compiler support custom containers (bring your own container)?
SageMaker Training Compiler is provided through AWS Deep Learning Containers, and you can extend a
subset of the containers to customize for your use-case. Containers that are extended
from AWS DLCs are supported by SageMaker Training Compiler. For more information, see Supported Frameworks and Using the SageMaker AI Python
SDK and Extending SageMaker AI Framework Deep Learning Containers. If you need further
support, reach out to the SageMaker AI team through AWS Support
