AWS Machine Learning
JULY 26, 2023
We compile the UNet for one batch (by using input tensors with one batch), then use the torch_neuronx.DataParallel API to load this single batch model onto each core. Load the UNet model onto two Neuron cores using the torch_neuronx.DataParallel API. If you have a custom inference script, you need to provide that instead.
Spearline
JULY 21, 2022
And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Flip the script With testingRTC, you only need to write scripts once, you can then run them multiple times and scale them up or down as you see fit. Happy days!
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AWS Machine Learning
FEBRUARY 6, 2023
The former question addresses model selection across model architectures, while the latter question concerns benchmarking trained models against a test dataset. This post provides details on how to implement large-scale Amazon SageMaker benchmarking and model selection tasks. swin-large-patch4-window7-224 195.4M efficientnet-b5 29.0M
AWS Machine Learning
APRIL 12, 2023
The code to invoke the pipeline script is available in the Studio notebooks, and we can change the hyperparameters and input/output when invoking the pipeline. This is quite different from our earlier method where we had all the parameters hard coded within the scripts and all the processes were inextricably linked.
AWS Machine Learning
JANUARY 10, 2023
We first benchmark the performance of our model on a single instance to identify the TPS it can handle per our acceptable latency requirements. Note that the model container also includes any custom inference code or scripts that you have passed for inference. Any issues related to end-to-end latency can then be isolated separately.
AWS Machine Learning
OCTOBER 31, 2022
To get started, follow Modify a PyTorch Training Script to adapt SMPs’ APIs in your training script. In this section, we only call out a few main steps with code snippets from the ready-to-use training script train_gpt_simple.py. The notebook uses the script data_prep_512.py Benchmarking performance.
AWS Machine Learning
AUGUST 18, 2022
The team’s early benchmarking results show 7.3 The baseline model used in these benchmarking is a multi-layer perceptron neural network with seven dense fully connected layers and over 200 parameters. The following table summarizes the benchmarking result on ml.p3.16xlarge SageMaker training instances. Number of Instances.
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