mirror of
https://github.com/vale981/ray
synced 2025-03-06 02:21:39 -05:00
[Nightly tests] Migrate rest of core tests (#23085)
MIgrate the rest of core tests
This commit is contained in:
SangBin Cho
GitHub
parent
04ea180dfb
commit
2b38fe89e2
2 changed files with 370 additions and 44 deletions
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@ -57,31 +57,31 @@ class SmokeTest(ReleaseTest):
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CORE_NIGHTLY_TESTS = {
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"~/ray/release/nightly_tests/nightly_tests.yaml": [
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# "shuffle_10gb",
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# "shuffle_50gb",
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# "shuffle_50gb_large_partition",
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# "shuffle_100gb",
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# "non_streaming_shuffle_100gb",
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# "non_streaming_shuffle_50gb_large_partition",
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# "non_streaming_shuffle_50gb",
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# SmokeTest("dask_on_ray_large_scale_test_no_spilling"),
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# SmokeTest("dask_on_ray_large_scale_test_spilling"),
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# "stress_test_placement_group",
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# "shuffle_1tb_1000_partition",
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# "non_streaming_shuffle_1tb_1000_partition",
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# "shuffle_1tb_5000_partitions",
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# TODO(sang): It doesn't even work without spilling
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# as it hits the scalability limit.
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# "non_streaming_shuffle_1tb_5000_partitions",
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# "decision_tree_autoscaling",
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# "decision_tree_autoscaling_20_runs",
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# "autoscaling_shuffle_1tb_1000_partitions",
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# SmokeTest("stress_test_many_tasks"),
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# SmokeTest("stress_test_dead_actors"),
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# SmokeTest("threaded_actors_stress_test"),
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# "pg_long_running_performance_test",
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],
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# "~/ray/release/nightly_tests/nightly_tests.yaml": [
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# "shuffle_10gb",
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# "shuffle_50gb",
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# "shuffle_50gb_large_partition",
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# "shuffle_100gb",
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# "non_streaming_shuffle_100gb",
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# "non_streaming_shuffle_50gb_large_partition",
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# "non_streaming_shuffle_50gb",
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# SmokeTest("dask_on_ray_large_scale_test_no_spilling"),
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# SmokeTest("dask_on_ray_large_scale_test_spilling"),
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# "stress_test_placement_group",
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# "shuffle_1tb_1000_partition",
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# "non_streaming_shuffle_1tb_1000_partition",
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# "shuffle_1tb_5000_partitions",
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# TODO(sang): It doesn't even work without spilling
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# as it hits the scalability limit.
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# "non_streaming_shuffle_1tb_5000_partitions",
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# "decision_tree_autoscaling",
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# "decision_tree_autoscaling_20_runs",
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# "autoscaling_shuffle_1tb_1000_partitions",
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# SmokeTest("stress_test_many_tasks"),
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# SmokeTest("stress_test_dead_actors"),
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# SmokeTest("threaded_actors_stress_test"),
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# "pg_long_running_performance_test",
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# ],
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# "~/ray/benchmarks/benchmark_tests.yaml": [
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# "single_node",
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# "object_store",
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@ -89,21 +89,21 @@ CORE_NIGHTLY_TESTS = {
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# "many_tasks_smoke_test",
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# "many_pgs_smoke_test",
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# ],
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"~/ray/release/nightly_tests/dataset/dataset_test.yaml": [
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"inference",
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"shuffle_data_loader",
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"parquet_metadata_resolution",
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"pipelined_training_50_gb",
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"pipelined_ingestion_1500_gb",
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"datasets_preprocess_ingest",
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"datasets_ingest_400G",
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SmokeTest("datasets_ingest_train_infer"),
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],
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"~/ray/release/nightly_tests/chaos_test.yaml": [
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"chaos_many_actors",
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"chaos_many_tasks_no_object_store",
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"chaos_pipelined_ingestion_1500_gb_15_windows",
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],
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# "~/ray/release/nightly_tests/dataset/dataset_test.yaml": [
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# "inference",
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# "shuffle_data_loader",
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# "parquet_metadata_resolution",
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# "pipelined_training_50_gb",
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# "pipelined_ingestion_1500_gb",
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# "datasets_preprocess_ingest",
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# "datasets_ingest_400G",
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# SmokeTest("datasets_ingest_train_infer"),
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# ],
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# "~/ray/release/nightly_tests/chaos_test.yaml": [
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# "chaos_many_actors",
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# "chaos_many_tasks_no_object_store",
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# "chaos_pipelined_ingestion_1500_gb_15_windows",
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# ],
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# "~/ray/release/microbenchmark/microbenchmark.yaml": [
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# "microbenchmark",
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# ],
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@ -137,10 +137,10 @@ CORE_DAILY_TESTS = {
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# "stress_test_many_tasks",
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# "stress_test_dead_actors",
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# ],
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"~/ray/release/nightly_tests/chaos_test.yaml": [
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"chaos_dask_on_ray_large_scale_test_no_spilling",
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"chaos_dask_on_ray_large_scale_test_spilling",
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],
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# "~/ray/release/nightly_tests/chaos_test.yaml": [
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# "chaos_dask_on_ray_large_scale_test_no_spilling",
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# "chaos_dask_on_ray_large_scale_test_spilling",
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# ],
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}
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CORE_SCALABILITY_TESTS_DAILY = {
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@ -2391,6 +2391,204 @@
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# file_manager: sdk
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# stable: false
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###############
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# Dataset tests
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###############
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- name: inference
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group: core-dataset-tests
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working_dir: dataset
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legacy:
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test_name: inference
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test_suite: dataset_test
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frequency: multi
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team: core
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cluster:
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cluster_env: app_config.yaml
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cluster_compute: inference.yaml
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run:
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timeout: 600
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script: python inference.py
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wait_for_nodes:
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num_nodes: 2
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timeout: 600
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type: sdk_command
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file_manager: sdk
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- name: shuffle_data_loader
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group: core-dataset-tests
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working_dir: dataset
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legacy:
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test_name: shuffle_data_loader
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test_suite: dataset_test
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frequency: multi
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team: core
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cluster:
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cluster_env: shuffle_app_config.yaml
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cluster_compute: shuffle_compute.yaml
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run:
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timeout: 1800
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script: python dataset_shuffle_data_loader.py
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type: sdk_command
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file_manager: sdk
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- name: parquet_metadata_resolution
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group: core-dataset-tests
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working_dir: dataset
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legacy:
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test_name: parquet_metadata_resolution
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test_suite: dataset_test
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frequency: multi
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team: core
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cluster:
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cluster_env: pipelined_training_app.yaml
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cluster_compute: pipelined_training_compute.yaml
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run:
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timeout: 1200
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script: python parquet_metadata_resolution.py --num-files 915
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wait_for_nodes:
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num_nodes: 15
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timeout: 1200
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type: sdk_command
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file_manager: sdk
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- name: pipelined_training_50_gb
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group: core-dataset-tests
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working_dir: dataset
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legacy:
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test_name: pipelined_training_50_gb
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test_suite: dataset_test
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frequency: multi
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team: core
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cluster:
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cluster_env: pipelined_training_app.yaml
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cluster_compute: pipelined_training_compute.yaml
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run:
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timeout: 4800
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script: python pipelined_training.py --epochs 1
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wait_for_nodes:
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num_nodes: 15
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timeout: 1200
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type: sdk_command
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file_manager: sdk
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- name: pipelined_ingestion_1500_gb
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group: core-dataset-tests
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working_dir: dataset
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legacy:
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test_name: pipelined_ingestion_1500_gb
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test_suite: dataset_test
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frequency: multi
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team: core
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cluster:
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cluster_env: pipelined_ingestion_app.yaml
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cluster_compute: pipelined_ingestion_compute.yaml
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run:
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timeout: 9600
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script: python pipelined_training.py --epochs 2 --num-windows 2 --num-files 915
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--debug
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wait_for_nodes:
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num_nodes: 21
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timeout: 2400
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type: sdk_command
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file_manager: sdk
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- name: datasets_ingest_train_infer
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group: core-dataset-tests
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working_dir: dataset
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legacy:
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test_name: datasets_ingest_train_infer
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test_suite: dataset_test
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frequency: multi
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team: core
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cluster:
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cluster_env: ray_sgd_training_app.yaml
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cluster_compute: ray_sgd_training_compute.yaml
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run:
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timeout: 14400
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script: python ray_sgd_training.py --address auto --use-s3 --num-workers 16 --use-gpu
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--large-dataset
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wait_for_nodes:
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num_nodes: 66
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timeout: 2400
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type: sdk_command
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file_manager: sdk
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smoke_test:
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cluster:
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app_config: ray_sgd_training_app.yaml
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compute_template: ray_sgd_training_smoke_compute.yaml
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run:
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timeout: 3600
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script: python ray_sgd_training.py --address auto --use-s3 --num-workers 8 --use-gpu
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wait_for_nodes:
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num_nodes: 8
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timeout: 2400
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- name: datasets_preprocess_ingest
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group: core-dataset-tests
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working_dir: dataset
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legacy:
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test_name: datasets_preprocess_ingest
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test_suite: dataset_test
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frequency: multi
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team: core
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cluster:
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cluster_env: ray_sgd_training_app.yaml
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cluster_compute: ray_sgd_training_compute_no_gpu.yaml
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run:
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timeout: 7200
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script: python ray_sgd_training.py --address auto --use-s3 --num-workers 16 --use-gpu
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--large-dataset --debug
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wait_for_nodes:
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num_nodes: 21
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timeout: 2400
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type: sdk_command
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file_manager: sdk
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- name: datasets_ingest_400G
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group: core-dataset-tests
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working_dir: dataset
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legacy:
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test_name: datasets_ingest_400G
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test_suite: dataset_test
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frequency: multi
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team: core
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cluster:
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cluster_env: ray_sgd_training_app.yaml
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cluster_compute: dataset_ingest_400G_compute.yaml
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run:
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timeout: 7200
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script: python ray_sgd_runner.py --address auto --use-gpu --num-epochs 1
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type: sdk_command
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file_manager: sdk
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################
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# Core K8s tests
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################
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@ -2472,3 +2670,131 @@
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file_manager: job
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stable: false
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##################
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# Core Chaos tests
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##################
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- name: chaos_many_tasks_no_object_store
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group: core-dataset-tests
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working_dir: nightly_tests
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legacy:
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test_name: chaos_many_tasks_no_object_store
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test_suite: chaos_test
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frequency: multi
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team: core
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cluster:
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cluster_env: chaos_test/app_config.yaml
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cluster_compute: chaos_test/compute_template.yaml
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run:
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timeout: 3600
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wait_for_nodes:
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num_nodes: 10
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timeout: 600
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prepare: python setup_chaos.py --no-start
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script: python chaos_test/test_chaos_basic.py --workload=tasks
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type: sdk_command
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file_manager: sdk
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- name: chaos_many_actors
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group: core-dataset-tests
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working_dir: nightly_tests
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legacy:
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test_name: chaos_many_actors
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test_suite: chaos_test
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frequency: multi
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team: core
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cluster:
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cluster_env: chaos_test/app_config.yaml
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cluster_compute: chaos_test/compute_template.yaml
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run:
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timeout: 3600
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wait_for_nodes:
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num_nodes: 10
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timeout: 600
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prepare: python setup_chaos.py --no-start
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script: python chaos_test/test_chaos_basic.py --workload=actors
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type: sdk_command
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file_manager: sdk
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- name: chaos_dask_on_ray_large_scale_test_no_spilling
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group: core-dataset-tests
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working_dir: nightly_tests
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legacy:
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test_name: chaos_dask_on_ray_large_scale_test_no_spilling
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test_suite: chaos_test
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frequency: nightly
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team: core
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cluster:
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cluster_env: chaos_test/dask_on_ray_app_config_reconstruction.yaml
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cluster_compute: dask_on_ray/dask_on_ray_stress_compute.yaml
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run:
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timeout: 7200
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wait_for_nodes:
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num_nodes: 21
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timeout: 600
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prepare: python setup_chaos.py --node-kill-interval 100
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script: python dask_on_ray/large_scale_test.py --num_workers 20 --worker_obj_store_size_in_gb
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20 --error_rate 0 --data_save_path /tmp/ray
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type: sdk_command
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file_manager: sdk
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- name: chaos_dask_on_ray_large_scale_test_spilling
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group: core-dataset-tests
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working_dir: nightly_tests
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legacy:
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test_name: chaos_dask_on_ray_large_scale_test_spilling
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test_suite: chaos_test
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frequency: nightly
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team: core
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cluster:
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cluster_env: chaos_test/dask_on_ray_app_config_reconstruction.yaml
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cluster_compute: dask_on_ray/dask_on_ray_stress_compute.yaml
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run:
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timeout: 7200
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wait_for_nodes:
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num_nodes: 21
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timeout: 600
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prepare: python setup_chaos.py --node-kill-interval 100
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script: python dask_on_ray/large_scale_test.py --num_workers 150 --worker_obj_store_size_in_gb
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70 --error_rate 0 --data_save_path /tmp/ray
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type: sdk_command
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file_manager: sdk
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- name: chaos_pipelined_ingestion_1500_gb_15_windows
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group: core-dataset-tests
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working_dir: nightly_tests
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legacy:
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test_name: chaos_pipelined_ingestion_1500_gb_15_windows
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test_suite: chaos_test
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frequency: multi
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team: core
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cluster:
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cluster_env: dataset/pipelined_ingestion_app.yaml
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cluster_compute: dataset/pipelined_ingestion_compute.yaml
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run:
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timeout: 7200
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wait_for_nodes:
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num_nodes: 21
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timeout: 2400
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prepare: ' python setup_chaos.py --node-kill-interval 300'
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script: python dataset/pipelined_training.py --epochs 1 --num-windows 15 --num-files
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915 --debug
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type: sdk_command
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file_manager: sdk
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