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Signed-off-by: Niels Bantilan <niels.bantilan@gmail.com>
Signed-off-by: Niels Bantilan <niels.bantilan@gmail.com>
Signed-off-by: Niels Bantilan <niels.bantilan@gmail.com>
Signed-off-by: Niels Bantilan <niels.bantilan@gmail.com>
Signed-off-by: Niels Bantilan <niels.bantilan@gmail.com>
Signed-off-by: Niels Bantilan <niels.bantilan@gmail.com>
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kumare3
approved these changes
Mar 4, 2026
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This pull request adds several new example applications to the
examples/apps/panel_appdirectory, demonstrating Flyte's capabilities for async workflows, distributed training, caching/retries, agent-based orchestration, and integration with external APIs. Additionally, it fixes a serialization bug in the scaling metric logic and updates related unit tests for correct protobuf mapping.New example applications:
sample_distributed_random_forest.pydemonstrating distributed training of a random forest model using async tasks and Flyte's file/directory abstractions.sample_mnist_training.pyshowing async MNIST data loading, model training, and evaluation with PyTorch, including tensor serialization via Flyte's file API.sample_langgraph_gemini_agent.pyintegrating LangGraph and Gemini for weather forecast agent workflows, including tool invocation and external API calls.sample_pbj_agent.pyimplementing a dependency-respecting agent workflow for a "PBJ sandwich" plan, with concurrent async execution of steps.sample_async.py,sample_caching_and_retries.py, andsample_hello_world.pyas simple async, caching/retries, and basic workflow examples. [1] [2] [3]Bug fixes and test improvements:
_get_scaling_metricby mappingvaltotarget_valuefor both concurrency and request rate metrics.test_app_serde.pyto verify correct protobuf field mapping for scaling metrics, replacing previous bug documentation with positive assertions.