AWS Machine Learning

FEBRUARY 13, 2024

Note the following calculations: The size of the global batch is (number of nodes in a cluster) * (number of GPUs per node) * (per batch shard) A batch shard (small batch) is a subset of the dataset assigned to each GPU (worker) per iteration BigBasket used the SMDDP library to reduce their overall training time.

Engineering 100

Engineering Gamification Scripts Big data 100

AWS Machine Learning

OCTOBER 4, 2022

Every request can be batched to ensure full utilization of the accelerator, especially for small requests that may not fully utilize the compute node. In our study, through a parametric sweep of batch size and queries from multi-threaded clients, the throughput vs. latency curve is determined.

Benchmark 67

Benchmark Engineering Metrics Technology 67

AWS Machine Learning

SEPTEMBER 30, 2022

The main reason SophosAI chose SageMaker is the ability to benefit from the fully managed distributed training on multi-node CPU instances by simply specifying more than one instance. SageMaker automatically splits the data across nodes, aggregates the results across peer nodes, and generates a single model. Number of Samples.

Scripts 80

Scripts Engineering Metrics Big data 80

AWS Machine Learning

AUGUST 4, 2023

For Inference output node, enter the destination node corresponding to the transforms applied to your training data. In our example, the URI is s3://sagemaker-us-east-1-43257985977/data_wrangler_flows/example-2023-05-30T12-20-18.tar.gz. For Inference artifact name, enter the name of your inference artifact (with.tar.gz

Engineering 73

Engineering APIs Management 73

AWS Machine Learning

SEPTEMBER 19, 2023

47495| 1686627154| | 18| Acura TLX Advance| 2023| New| NA|52245.00|52245| The @remote decorator runs the local Python code as a single or multi-node SageMaker training job. primary node exiting. .| 2023| New| NA|55345.00| NA| 1686627154| | 14| Acura TLX A-Spec| 2023| New| NA|50195.00|50195| 24995.00| 22334.0|

Engineering 93

Engineering Sales Accountability Best practices 93

AWS Machine Learning

SEPTEMBER 23, 2022

The revenue needs to be forecasted at every node in the hierarchy with a forecasting horizon of 12 months into the future. We use a context window with monthly revenue data from the past 18 months, selected via HPO in the backtest window from July 2018 to June 2019. Monthly historical data is available.

APIs 82

APIs Metrics Automotive Engineering 82

AWS Machine Learning

JANUARY 30, 2023

Dataset Size Number of Examples Number of Features Problem Type lending club loan ~10 G 1, 439, 141 955 Binary classification code ~10 G 18, 268, 221 9 Multi-class classification (number of classes in target: 10) NYC taxi ~0.5 The data statistics are presented as follows.

Benchmark 80

Benchmark Construction Metrics Big data 80