ray/release/nightly_tests/dataset/dataset_test.yaml

95 lines
2.5 KiB
YAML

- name: inference
team: core
cluster:
app_config: app_config.yaml
compute_template: inference.yaml
run:
timeout: 600
prepare: python wait_cluster.py 2 600
script: python inference.py
- name: shuffle_data_loader
team: core
cluster:
app_config: shuffle_app_config.yaml
compute_template: shuffle_compute.yaml
run:
timeout: 1800
script: python dataset_shuffle_data_loader.py
- name: parquet_metadata_resolution
team: core
cluster:
app_config: pipelined_training_app.yaml
compute_template: pipelined_training_compute.yaml
run:
timeout: 1200
prepare: python wait_cluster.py 15 1200
script: python parquet_metadata_resolution.py --num-files 915
- name: pipelined_training_50_gb
team: core
cluster:
app_config: pipelined_training_app.yaml
compute_template: pipelined_training_compute.yaml
run:
timeout: 4800
prepare: python wait_cluster.py 15 1200
script: python pipelined_training.py --epochs 1
- name: pipelined_ingestion_1500_gb
team: core
cluster:
app_config: pipelined_ingestion_app.yaml
compute_template: pipelined_ingestion_compute.yaml
run:
timeout: 9600
prepare: python wait_cluster.py 21 2400
script: python pipelined_training.py --epochs 2 --num-windows 2 --num-files 915 --debug
- name: datasets_ingest_train_infer
team: core
cluster:
app_config: ray_sgd_training_app.yaml
compute_template: ray_sgd_training_compute.yaml
run:
timeout: 14400
prepare: python wait_cluster.py 66 2400
script: python ray_sgd_training.py --address auto --use-s3 --num-workers 16 --use-gpu --large-dataset
smoke_test:
cluster:
app_config: ray_sgd_training_app.yaml
compute_template: ray_sgd_training_smoke_compute.yaml
run:
timeout: 3600
prepare: python wait_cluster.py 8 2400
script: python ray_sgd_training.py --address auto --use-s3 --num-workers 8 --use-gpu
- name: datasets_preprocess_ingest
team: core
cluster:
app_config: ray_sgd_training_app.yaml
compute_template: ray_sgd_training_compute_no_gpu.yaml
run:
timeout: 7200
prepare: python wait_cluster.py 21 2400
script: python ray_sgd_training.py --address auto --use-s3 --num-workers 16 --use-gpu --large-dataset --debug
- name: datasets_ingest_400G
team: core
cluster:
app_config: ray_sgd_training_app.yaml
compute_template: dataset_ingest_400G_compute.yaml
run:
timeout: 7200
script: python ray_sgd_runner.py --address auto --use-gpu --num-epochs 1