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https://github.com/vale981/ray
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d8a61f801f
* Create a core set of algorithms tests to run nightly. * Run release tests under tf, tf2, and torch frameworks. * Fix * Add eager_tracing option for tf2 framework. * make sure core tests can run in parallel. * cql * Report progress while running nightly/weekly tests. * Innclude SAC in nightly lineup. * Revert changes to learning_tests * rebrand to performance test. * update build_pipeline.py with new performance_tests name. * Record stats. * bug fix, need to populate experiments dict. * Alphabetize yaml files. * Allow specifying frameworks. And do not run tf2 by default. * remove some debugging code. * fix * Undo testing changes. * Do not run CQL regression for now. * LINT. Co-authored-by: sven1977 <svenmika1977@gmail.com>
95 lines
2.4 KiB
YAML
95 lines
2.4 KiB
YAML
# Heavy learning tests (Atari and HalfCheetah) for major algos.
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- name: learning_tests
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cluster:
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app_config: app_config.yaml
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compute_template: 8gpus_64cpus.yaml
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run:
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timeout: 14400
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script: python learning_tests/run.py
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smoke_test:
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run:
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timeout: 1200
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# 2-GPU learning tests (CartPole and RepeatAfterMeEnv) for major algos.
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- name: multi_gpu_learning_tests
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cluster:
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app_config: app_config.yaml
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compute_template: 8gpus_96cpus.yaml
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run:
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timeout: 7200
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script: python multi_gpu_learning_tests/run.py
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# 2-GPU learning tests (StatelessCartPole) + use_lstm=True for major algos
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# (that support RNN models).
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- name: multi_gpu_with_lstm_learning_tests
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cluster:
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app_config: app_config.yaml
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compute_template: 8gpus_96cpus.yaml
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run:
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timeout: 7200
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script: python multi_gpu_with_lstm_learning_tests/run.py
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# 2-GPU learning tests (StatelessCartPole) + use_attention=True for major
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# algos (that support RNN models).
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- name: multi_gpu_with_attention_learning_tests
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cluster:
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app_config: app_config.yaml
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compute_template: 8gpus_96cpus.yaml
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run:
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timeout: 7200
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script: python multi_gpu_with_attention_learning_tests/run.py
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# We'll have these as per-PR tests soon.
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# - name: example_scripts_on_gpu_tests
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# cluster:
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# app_config: app_config.yaml
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# compute_template: 1gpu_4cpus.yaml
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# run:
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# timeout: 7200
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# script: bash unit_gpu_tests/run.sh
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# IMPALA large machine stress tests (4x Atari).
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- name: stress_tests
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cluster:
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app_config: app_config.yaml
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compute_template: 4gpus_544_cpus.yaml
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run:
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timeout: 5400
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prepare: python wait_cluster.py 6 600
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script: python stress_tests/run_stress_tests.py
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smoke_test:
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run:
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timeout: 2000
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# Tests that exercise auto-scaling and Anyscale connect.
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- name: connect_tests
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cluster:
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app_config: app_config.yaml
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compute_template: auto_scale.yaml
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run:
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use_connect: True
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timeout: 3000
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script: python connect_tests/run_connect_tests.py
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# Nightly performance regression for popular algorithms.
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# These algorithms run nightly for pre-determined amount of time without
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# passing criteria.
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# Performance metrics, such as reward achieved and throughput, are then
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# collected and tracked over time.
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- name: performance_tests
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cluster:
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app_config: app_config.yaml
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compute_template: 12gpus_192cpus.yaml
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run:
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timeout: 7200
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script: python performance_tests/run.py
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