mirror of
https://github.com/chanzuckerberg/cellxgene.git
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5c0b8c6296
* Improve diffexp for tiledb - The rows from the A and B sets are gathered and processed at the same time. In this way the matrix is only accessed once instead of twice for each tile. - There is now a single thread queue that gets shared between all callers of the diffexp. This will slow down work if diffexp gets too busy. - There is a target_workunit amount of work given to each thread. Previously the workunit was (rows selected * width of tile), which could be small. Now multiple column tiles can be combined into one workunit. If the target is too small then thread and other overheads may reduce performance. If target_workunit is too large then the size of the gathered sub matrix may take up too much memory. - add configuration parameters (max_workers, cpu_multiplier, and target_workunit)
108 lines
2.6 KiB
Python
108 lines
2.6 KiB
Python
import yaml
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default_config = """
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# cellxgene configuration
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server:
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verbose: false
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debug: false
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host: "127.0.0.1"
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port : null
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scripts : []
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open_browser: false
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about_legal_tos: null
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about_legal_privacy: null
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force_https: false
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flask_secret_key: null
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generate_cache_control_headers: false
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server_timing_headers: false
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presentation:
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max_categories: 1000
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multi_dataset:
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dataroot: null
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# The index page when in multi-dataset mode:
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# false or null: this returns a 404 code
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# true: loads a test index page, which links to the datasets that are available in the dataroot
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# string/URL: redirect to this URL: flask.redirect(config.multi_dataset__index)
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index: false
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# A list of allowed matrix types. If an empty list, then all matrix types are allowed
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allowed_matrix_types: []
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matrix_cache:
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# The maximum number of datasets that may be opened at one time. The least recently used dataset
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# is evicted from the cache first.
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max_datasets: 5
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# A matrix is automatically removed from the cache after timelimit_s number of seconds.
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# If timelimit_s is set to None, then there is no time limit.
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timelimit_s: 30
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single_dataset:
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datapath: null
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obs_names: null
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var_names: null
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about: null
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title: null
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user_annotations:
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enable: true
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type: local_file_csv
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local_file_csv:
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directory: null
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file: null
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ontology:
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enable: false
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obo_location: null
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embeddings:
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names : []
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enable_reembedding: false
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diffexp:
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enable: true
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lfc_cutoff: 0.01
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top_n: 10
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alg_cxg:
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# The number of threads to use is computed from: min(max_workers, cpu_multipler * cpu_count).
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# Where cpu_count is determined at runtime.
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max_workers: 64
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cpu_multiplier: 4
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# The target number of matrix elements that are evaluated
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# together in one thread.
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target_workunit: 16_000_000
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data_locator:
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s3:
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# s3 region name.
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# if true, then the s3 location is automatically determined from the datapath or dataroot.
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# if false/null, then do not set.
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# if a string, then use that value (e.g. us-east-1).
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region_name: true
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adaptor:
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cxg_adaptor:
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# The key/values under tiledb_ctx will be used to initialize the tiledb Context.
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# If 'vfs.s3.region' is not set, then it will automatically use the setting from
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# data_locator / s3 / region_name.
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tiledb_ctx:
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sm.tile_cache_size: 8589934592
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sm.num_reader_threads: 32
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anndata_adaptor:
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backed: false
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limits:
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column_request_max: 32
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diffexp_cellcount_max: null
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"""
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def get_default_config():
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return yaml.load(default_config, Loader=yaml.Loader)
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