{"id":17838064,"url":"https://github.com/thenewflesh/hidebound","last_synced_at":"2025-03-19T21:30:58.011Z","repository":{"id":37612387,"uuid":"68332107","full_name":"theNewFlesh/hidebound","owner":"theNewFlesh","description":"Hidebound is massive, distributed digital asset management system for ML pipelines on 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\u003ca href=\"https://www.linkedin.com/in/alexandergbraun\" rel=\"nofollow noreferrer\"\u003e\n \u003cimg src=\"https://www.gomezaparicio.com/wp-content/uploads/2012/03/linkedin-logo-1-150x150.png\"\n alt=\"linkedin\" width=\"30px\" height=\"30px\"\n \u003e\n \u003c/a\u003e\n \u003ca href=\"https://github.com/theNewFlesh\" rel=\"nofollow noreferrer\"\u003e\n \u003cimg src=\"https://tadeuzagallo.com/GithubPulse/assets/img/app-icon-github.png\"\n alt=\"github\" width=\"30px\" height=\"30px\"\n \u003e\n \u003c/a\u003e\n \u003ca href=\"https://pypi.org/user/the-new-flesh\" rel=\"nofollow noreferrer\"\u003e\n \u003cimg src=\"https://cdn.iconscout.com/icon/free/png-256/python-2-226051.png\"\n alt=\"pypi\" width=\"30px\" height=\"30px\"\n \u003e\n \u003c/a\u003e\n \u003ca href=\"http://vimeo.com/user3965452\" rel=\"nofollow noreferrer\"\u003e\n \u003cimg src=\"https://cdn.iconscout.com/icon/free/png-512/movie-52-151107.png?f=avif\u0026w=512\"\n alt=\"vimeo\" width=\"30px\" height=\"30px\"\n 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--\u003e\n\n[![](https://img.shields.io/badge/License-MIT-F77E70?style=for-the-badge)](https://github.com/theNewFlesh/hidebound/blob/master/LICENSE)\n[![](https://img.shields.io/pypi/pyversions/hidebound?style=for-the-badge\u0026label=Python\u0026color=A0D17B\u0026logo=python\u0026logoColor=A0D17B)](https://github.com/theNewFlesh/hidebound/blob/master/docker/config/pyproject.toml)\n[![](https://img.shields.io/pypi/v/hidebound?style=for-the-badge\u0026label=PyPI\u0026color=5F95DE\u0026logo=pypi\u0026logoColor=5F95DE)](https://pypi.org/project/hidebound/)\n[![](https://img.shields.io/pypi/dm/hidebound?style=for-the-badge\u0026label=Downloads\u0026color=5F95DE)](https://pepy.tech/project/hidebound)\n\n# Overview\nHidebound is massive, distributed digital asset management system for ML pipelines\non Kubernetes. Raw file data comes in, validated file content and generated metadata\ncome out, known as assets.\n\nHidebound is an ephemeral database and asset framework used for generating,\nvalidating and exporting assets to various data stores. Hidebound enables\ndevelopers to ingest arbitrary sets of files and output them as content and\ngenerated metadata, which has been validated according to specifications they\ndefine.\n\nAssets are placed into an ingress directory, typically reserved for Hidebound\nprojects, and then processed by Hidebound. Hidebound extracts metadata from the\nfiles and directories that make each asset according to their name, location and\nfile properties. This data comprises the entirety of Hidebound's database at any\none time.\n\nIndustries, such as visual effects, have common principles and practices that\nenable them to effectively and efficiently manage petabytes of highly diverse\ndata as it is generated and run through complex data transformation pipelines in\na reliable fashion. Hidebound's goal is to extend this capability to other\nheterogenous, data intensive industries, such as artificial intelligence\n(especially computer vision pipelines) and biotech.\n\nAssets are easily managed and readily consumable by digital asset management\nsystems, such as\n[Autodesk Flow (formerly known as Shotgun)](https://www.autodesk.com/products/flow-production-tracking/overview),\n[Adobe Bridge](https://www.adobe.com/products/bridge.html),\n[Adobe XMP](https://www.adobe.com/products/xmp.html),\n[FTrack](https://www.ftrack.com/en),\n[Girder](https://girder.readthedocs.io/en/latest),\nas well as by common storage platforms such as\n[Amazon S3](https://aws.amazon.com/s3/), [MinIO](https://min.io) and Network\nAttached Storage devices. The guarantees assets provide in terms of uniformity\nof content via asset specification, and uniformity of indexing via asset\nmetadata, empowers developers to create narrow, powerful pipelines that do not\nhave to battle the complexities of heterogenous content.\n\nKubeflow, Dask and Hadoop are good examples of pipeline frameworks that can\neasily benefit through use of Hidebound assets. All of which Hidebound's author\nuses personally and professionally for ML pipelines.\n\nSee [documentation](https://thenewflesh.github.io/hidebound/) for details.\n\n# Installation for Developers\n\n### Docker\n1. Install [docker-desktop](https://docs.docker.com/desktop/)\n2. Ensure docker-desktop has at least 4 GB of memory allocated to it.\n3. `git clone git@github.com:theNewFlesh/hidebound.git`\n4. `cd hidebound`\n5. `chmod +x bin/hidebound`\n6. `bin/hidebound docker-start`\n - If building on a M1 Mac run `export DOCKER_DEFAULT_PLATFORM=linux/amd64` first.\n\nThe service should take a few minutes to start up.\n\nRun `bin/hidebound --help` for more help on the command line tool.\n\n### ZSH Setup\n1. `bin/hidebound` must be run from this repository's top level directory.\n2. Therefore, if using zsh, it is recommended that you paste the following line\n in your ~/.zshrc file:\n - `alias hidebound=\"cd [parent dir]/hidebound; bin/hidebound\"`\n - Replace `[parent dir]` with the parent directory of this repository\n3. Consider adding the following line to your ~/.zshrc if you are using a M1 Mac:\n - `export DOCKER_DEFAULT_PLATFORM=linux/amd64`\n4. Running the `zsh-complete` command will enable tab completions of the cli\n commands, in the next shell session.\n\n For example:\n - `hidebound [tab]` will show you all the cli options, which you can press\n tab to cycle through\n - `hidebound docker-[tab]` will show you only the cli options that begin with\n \"docker-\"\n\n# Installation for Production\n\n### Python\n`pip install hidebound`\n\n### Docker\n1. Install [docker-desktop](https://docs.docker.com/desktop/)\n2. `docker pull thenewflesh/hidebound:[version]`\n\n---\n\n# Dataflow\n![](resources/screenshots/data_flow.png)\n\nData begins as files on disk. Hidebound creates a JSON-compatible dict from\ntheir name traits and file traits and then constructs an internal database table\nfrom them, one dict per row. All the rows are then aggregated by asset, and\nconverted into JSON blobs. Those blobs are then validated according to their\nrespective specifications. Files from valid assets are then copied or moved into\nHidebound's content directory, according to their same directory structure and\nnaming. Metadata is written to JSON files inside Hidebound's metadata directory.\nEach file's metadata is written as a JSON file in /hidebound/metadata/file, and\neach asset's metadata (the aggregate of its file metadata) is written to\n/hidebound/metadata/asset. From their exporters, can export the valid\nasset data and its accompanying metadata to various locations, like an AWS S3\nbucket.\n\n# Workflow\nThe acronynm to remember for workflows is **CRUDES**: create, read, update,\ndelete, export and search. Those operations constitute the main functionality\nthat Hidebound supports.\n\n## *Create Asset*\nFor example, an asset could be an image sequence, such as a directory full of\nPNG files, all of which have a frame number, have 3 (RGB) channels, and are 1024\npixels wide by 1024 pixels tall. Let's call the specification for this type of\nasset \"spec001\". We create an image sequence of a cat running, and we move it\ninto the Hidebound projects directory.\n\n## *Update*\n![](resources/screenshots/update.png)\n\nWe call the update function via Hidebound's web app. Hidebound creates\na new database based upon the recursive listing of all the files within said\ndirectory. This database is displayed to us as a table, with one file per row.\nIf we choose to group by asset in the app, the table will display one asset per\nrow. Hidebound extracts metadata from each filename (not any directory name) as\nwell as from the file itself. That metadata is called file_traits. Using only\ninformation derived from filename and file traits, Hidebound determines which\nfiles are grouped together as a single asset and the specification of that\nasset. Asset traits are then derived from this set of files (one or more).\nFinally, Hidebound validates each asset according to its determined\nspecification. All of this data is displayed as a table within the web app.\nImportantly, all of the errors in filenames, file traits and asset traits are\nincluded.\n\n## *Review Graph*\n![](resources/screenshots/graph.png)\nIf we click on the graph tab, we are greeted by a hierarchical graph of all our\nassets in our project directory. Our asset is red, meaning it's invalid. Valid\nasset's are green, and all other files and directories, including parent\ndirectories, are cyan.\n\n## *Diagnose and Repair*\nWe flip back to the data tab. Using table within it, we search (via SQL) for our\nasset within Hidebound's freshly created database. We see an error in one of the\nfilenames, conveniently displayed in red text. The descriptor in one orf our\nfilenames has capital letters in it. This violates Hidebound's naming\nconvention, and so we get an error. We go and rename the file appropriately and\ncall update again. Our asset is now valid. The filenames are correct and we can\nsee in the height and width columns, that it's 1024 by 1024 and the channels\ncolumn says it has three.\n\n## *Create*\nNext we click the create button. For each valid asset, Hidebound generates file\nand asset metadata as JSON files within the hidebound/metadata directory.\nHidebound also copies or moves, depending on the config write mode, valid files\nand directories into the hidebound/content directory. Hidebound/content and\nhidebound/metadata are both staging directories used for generating a valid\nephemeral database. We now have a hidebound directory that looks like this\n(unmentioned assets are collapsed behind the ellipses):\n```shell\n/tmp/hidebound\n├── hidebound_config.yaml\n│\n├── specifications\n│ └── specifications.py\n│\n├── data\n│ ...\n│ └── p-cat001\n│ └── spec001\n│ └── p-cat001_s-spec001_d-running-cat_v001\n│ ├── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0001.png\n│ ├── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0002.png\n│ └── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0003.png\n│\n├── metadata\n ├── asset\n │ ...\n │ └── a9f3727c-cb9b-4eb1-bc84-a6bc3b756cc5.json\n │\n └── file\n ...\n ├── 279873a2-bfd0-4757-abf2-7dc4f771f992.json\n ├── e50160ae-8678-40b3-b766-ee8311b1f0c9.json\n └── ea95bd79-cb8f-4262-8489-efe734c5f65c.json\n```\n\n## *Export*\nThe hidebound directories contain only valid assets. Thus, we are now free to\nexport this data to various data stores, such as AWS S3, MongoDB, and Girder.\nExporters are are defined within the exporters subpackage. They expect a\npopulated hidebound directory and use the files and metadata therein to export\nhidebound data. Exporter configurations are stored in the hidebound config,\nunder the \"exporters\" key. Currently supported exporters include, disk, s3 and\ngirder. Below we can see the results of an export to Girder in the Girder web\napp.\n\n![](resources/screenshots/girder.png)\n\n## *Delete*\nOnce this export process is complete, we may click the delete button. Hidebound\ndeletes the hidebound/content and hidebound/metdata directories and all their\ncontents. If write_mode in the Hidebound configuration is set to \"copy\", then\nthis step will merely delete data created by Hidebound. If it is set to \"move\",\nthen Hidebound will presumably delete, the only existing copy of out asset data\non the host machine. The delete stage in combination with the removal of assets\nfrom the ingress directory is what makes Hidebound's database ephemeral.\n\n## *Workflow*\n`/api/workflow` is a API endpoint that initializes a database a with a given\nconfig, and then calls each method from a given list. For instance, if you send\nthis data to `/api/workflow`:\n\n```{config={...}, workflow=['update', 'create', 'export', 'delete']}```\n\nA database instance will be created with the given config, and then that\ninstance will call its update, create, export and delete methods, in that order.\n\n# Naming Convention\nHidebound is a highly opinionated framework that relies upon a strict but\ncomposable naming convention in order to extract metadata from filenames. All\nfiles and directories that are part of assets must conform to a naming\nconvention defined within that asset's specification.\n\nIn an over-simplified sense; sentences are constructions of words. Syntax\nconcerns how each word is formed, grammar concerns how to form words into a\nsentence, and semantics concerns what each word means. Similarly, filenames can\nbe thought of as crude sentences. They are made of several words (ie fields).\nThese words have distinct semantics (as determines by field indicators). Each\nword is constructed according to a syntax (ie indicator + token). All words are\njoined together by spaces (ie underscores) in a particular order as determined\nby grammar (as defined in each specification).\n\n## *Syntax*\n- Names consist of a series of fields, each separated by a single underscore\n “_”, also called a field separator.\n- Periods, \".\", are the exception to this, as it indicates file extension.\n- Legal characters include and only include:\n\n| Name | Characters | Use |\n| ---------------- | ---------- | ------------------------- |\n| Underscore | _ | only for field separation |\n| Period | . | only for file extensions |\n| Lowercase letter | a to z | everything |\n| Number | 0 to 9 | everything |\n| Hyphen | - | token separator |\n\nFields are comprised of two main parts:\n\n| Name | Use |\n| ---------------- | --------------------------------------------------- |\n| Field indicator | determines metadata key |\n| Field token | a set of 1+ characters that define the field's data |\n\n---\n## **Example Diagrams**\nIn our example filename:\n`p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0003.png` the metadata will be:\n\n```json\n{\n \"project\": \"cat001\",\n \"specification\": \"spec001\",\n \"descriptor\": \"running-cat\",\n \"version\": 1,\n \"coordinate\": [0, 5],\n \"frame\": 3,\n \"extension\": \"png\",\n}\n```\n\nThe spec001 specification is derived from the second field of this filename:\n```shell\n field field\n indicator token\n | __|__\n | | |\np-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0003.png\n |_______|\n |\n field\n```\n\n| Part | Value |\n| ---------------- | ------------------------ |\n| Field | s-spec001 |\n| Field indicator | s- |\n| Field token | spec001 |\n| Derived metadata | {specification: spec001} |\n\n## *Special Field Syntax*\n\n- Projects begin with 3 to 10 letters followed by 1 to 4 numbers\n- Specifications begin with 3 or 4 letters followed by 3 numbers\n- Descriptors begin with a letter or number and may also contain hyphens\n- Descriptors may not begin with the words master, final or last\n- Versions are triple-padded with zeros and must be greater than 0\n- Coordinates may contain up to 3 quadruple-padded numbers, separated by hyphens\n- Coordinates are always evaluated in XYZ order. For example: `c0001-0002-0003`\n produces `{x: 1, y: 2, z: 3}`.\n- Each element of a coordinate may be equal to or greater than zero\n- Frames are quadruple-padded and are greater than or equal to 0\n- Extensions may only contain upper and lower case letters a to z and numbers 0\n to 9\n\n## *Semantics*\nHidebound is highly opionated, especially with regards to its semantics. It\ncontains exactly seven field types, as indicated by their field indicators.\nThey are:\n\n| Field | Indicator |\n| ------------- | --------- |\n| project | p- |\n| specification | s- |\n| descriptor | d- |\n| version | v |\n| coordinate | c |\n| frame | f |\n| extension | . |\n\n## *Grammar*\nThe grammar is fairly simple:\n\n - Names are comprised of an ordered set of fields drawn from the seven above\n - All names must contain the specification field\n - All specification must define a field order\n - All fields of a name under that specification must occcur in its defined\n field order\n\nIts is highly encouraged that fields be defined in the following order:\n\n`project specification descriptor version coordinate frame extension`\n\nThe grammatical concept of field order here is one of rough encapsulation:\n\n- Projects contain assets\n- Assets are grouped by specification\n- A set of assets of the same content is grouped by a descriptor\n- That set of assets consists of multiple versions of the same content\n- A single asset may broken into chunks, identified by 1, 2 or 3 coordinates\n- Each chunk may consist of a series of files seperated by frame number\n- Each file has an extension\n\n## *Encouraged Lexical Conventions*\n- Specifications end with a triple padded number so that they may be explicitely\n versioned. You redefine an asset specification to something slightly\n different, by copying its specification class, adding one to its name and\n change the class attributes in some way. That way you always maintain\n backwards compatibility with legacy assets.\n- Descriptors are not a dumping ground for useless terms like wtf, junk, stuff,\n wip and test.\n- Descriptors should not specify information known at the asset specification\n level, such as the project name, the generic content of the asset (ie image,\n mask, png, etc).\n- Descriptors should not include information that can be known from the\n preceding tokens, such as version, frame or extension.\n- A descriptor should be applicable to every version of the asset it designates.\n- Use of hyphens in descriptors is encouraged.\n- When in doubt, hyphenate and put into the descriptor.\n\n---\n# Project Structure\nHidebound does not formally define a project structure. It merely stipulates\nthat assets must exist under some particular root directory. Each asset\nspecification does define a directory structure for the files that make up that\nasset. Assets are divided into 3 types: file, sequence and complex. File defines\nan asset that consists of a single file. Sequence is defined to be a single\ndirectory containing one or more files. Complex is for assets that consist of an\narbitrarily complex layout of directories and files.\n\nThe following project structure is recommended:\n\n```shell\nproject\n |-- specification\n |-- descriptor\n |-- asset # either a file or directory of files and directories\n |- file\n```\n\n## For Example\n```shell\n/tmp/projects\n└── p-cat001\n ├── s-spec002\n │ ├── d-calico-jumping\n │ │ └── p-cat001_s-spec002_d-calico-jumping_v001\n │ │ ├── p-cat001_s-spec002_d-calico-jumping_v001_f0001.png\n │ │ ├── p-cat001_s-spec002_d-calico-jumping_v001_f0002.png\n │ │ └── p-cat001_s-spec002_d-calico-jumping_v001_f0003.png\n │ │\n │ └── d-tabby-playing\n │ ├── p-cat001_s-spec002_d-tabby-playing_v001\n │ │ ├── p-cat001_s-spec002_d-tabby-playing_v001_f0001.png\n │ │ ├── p-cat001_s-spec002_d-tabby-playing_v001_f0002.png\n │ │ └── p-cat001_s-spec002_d-tabby-playing_v001_f0003.png\n │ │\n │ └── p-cat001_s-spec002_d-tabby-playing_v002\n │ ├── p-cat001_s-spec002_d-tabby-playing_v002_f0001.png\n │ ├── p-cat001_s-spec002_d-tabby-playing_v002_f0002.png\n │ └── p-cat001_s-spec002_d-tabby-playing_v002_f0003.png\n │\n └── spec001\n └── p-cat001_s-spec001_d-running-cat_v001\n ├── p-cat001_s-spec001_d-Running-Cat_v001_c0000-0005_f0002.png\n ├── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0001.png\n └── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0003.png\n```\n\n# Application\nThe Hidebound web application has five sections: data, graph, config, api and\ndocs.\n\n## Data\nThe data tab is the workhorse of the Hidebound app.\n\n![](resources/screenshots/data.png)\n\nIts functions are as follows:\n\n* Search - Search the updated database's data via SQL\n* Dropdown - Groups search results by file or asset\n* Init - Initialized the database with the current config\n* Update - Initializes and updates the database with the current config\n* Create - Copies or moves valid assets to hidebound/content directory and\n creates JSON files in hidebound/metadata directory\n* Delete - Deletes hidebound/content and hidebound/metadata directories\n\nPrior to calling update, the application will look like this:\n\n![](resources/screenshots/pre_update.png)\n\n## Graph\nThe graph tab is used for visualizing the state of all the assets within a root\ndirectory.\n\n![](resources/screenshots/graph.png)\n\nIt's color code is as follows:\n\n| Color | Meaning |\n| ----- | --------------------------- |\n| Cyan | Non-asset file or directory |\n| Green | Valid asset |\n| Red | Invalid asset |\n\n## Config\nThe config tab is used for uploading and writing Hidebound's configuration file.\n\n![](resources/screenshots/config.png)\n\n## API\nThe API tab is really a link to Hidebound's REST API documentation.\n\n![](resources/screenshots/api.png)\n\n## Docs\nThe API tab is really a link to Hidebound's github documentation.\n\n![](resources/screenshots/docs.png)\n\n## Errors\nHidebound is oriented towards developers and technically proficient users. It\ndisplays errors in their entirety within the application.\n\n![](resources/screenshots/error.png)\n\n# Configuration\nHidebound is configured via a configuration file or environment variables.\n\nHidebound configs consist of four main sections:\n\n## Base\n* ingress_directory - the directory hidebound parses for assets that comprise its database\n* staging_directory - the staging directory valid assets are created in\n* specification_files - a list of python specification files\n* include_regex - filepaths in the root that match this are included in the database\n* exclude_regex - filepaths in the root that match this are excluded from the database\n* write_mode - whether to copy or move files from root to staging\n* redact_regex - regular expression which matches config keys whose valuse are to be redacted\n* redact_hash - whether to redact config values with \"REDACTED\" or a hash of the value\n* workflow - order list of steps to be followed in workflow\n\n## Dask\nDefault configuration of Dask distributed framework.\n\n* cluster_type - dask cluster type\n* num_partitions - number of partitions for each datafme\n* local_num_workers - number of workers on local cluster\n* local_threads_per_worker - number of threads per worker on local cluster\n* local_multiprocessing - use multiprocessing for local cluster\n* gateway_address - gateway server address\n* gateway_proxy_address - **scheduler** proxy server address\n* gateway_public_address - gateway server address, as accessible from a web browser\n* gateway_auth_type - authentication type\n* gateway_api_token - api token or password\n* gateway_api_user - api user\n* gateway_cluster_options - list of dask gateway cluster options\n* gateway_shutdown_on_close - whether to shudown cluster upon close\n* gateway_timeout - gateway client timeout\n\n## Exporters\nWhich exporters to us in the workflow.\nOptions include:\n\n* s3\n* disk\n* girder\n\n## Webhooks\nWebhooks to call after the export phase has completed.\n\n---\n\n## Environment Variables\nIf `HIDEBOUND_CONFIG_FILEPATH` is set, Hidebound will ignore all other\nenvironment variables and read the given filepath in as a yaml or json config\nfile.\n\n| Variable | Format | Portion |\n| ---------------------------------------- | ------ | -------------------------------------------------------- |\n| HIDEBOUND_CONFIG_FILEPATH | str | Entire Hidebound config file |\n| HIDEBOUND_INGRESS_DIRECTORY | str | ingress_directory parameter of config |\n| HIDEBOUND_STAGING_DIRECTORY | str | staging_directory parameter of config |\n| HIDEBOUND_INCLUDE_REGEX | str | include_regex parameter of config |\n| HIDEBOUND_EXCLUDE_REGEX | str | exclude_regex parameter of config |\n| HIDEBOUND_WRITE_MODE | str | write_mode parameter of config |\n| HIDEBOUND_REDACT_REGEX | str | redact_regex parameter of config |\n| HIDEBOUND_REDACT_HASH | str | redact_hash parameter of config |\n| HIDEBOUND_WORKFLOW | yaml | workflow paramater of config |\n| HIDEBOUND_SPECIFICATION_FILES | yaml | specification_files section of config |\n| HIDEBOUND_DASK_CLUSTER_TYPE | str | dask cluster type |\n| HIDEBOUND_DASK_NUM_PARTITIONS | int | number of partitions for each dataframe |\n| HIDEBOUND_DASK_LOCAL_NUM_WORKERS | int | number of workers on local cluster |\n| HIDEBOUND_DASK_LOCAL_THREADS_PER_WORKER | int | number of threads per worker on local cluster |\n| HIDEBOUND_DASK_LOCAL_MULTIPROCESSING | str | use multiprocessing for local cluster |\n| HIDEBOUND_DASK_GATEWAY_ADDRESS | str | gateway server address |\n| HIDEBOUND_DASK_GATEWAY_PROXY_ADDRESS | str | scheduler proxy server address |\n| HIDEBOUND_DASK_GATEWAY_PUBLIC_ADDRESS | str | gateway server address, as accessible from a web browser |\n| HIDEBOUND_DASK_GATEWAY_AUTH_TYPE | str | authentication type |\n| HIDEBOUND_DASK_GATEWAY_API_TOKEN | str | api token or password |\n| HIDEBOUND_DASK_GATEWAY_API_USER | str | api user |\n| HIDEBOUND_DASK_GATEWAY_CLUSTER_OPTIONS | yaml | list of dask gateway cluster options |\n| HIDEBOUND_DASK_GATEWAY_SHUTDOWN_ON_CLOSE | str | whether to shudown cluster upon close |\n| HIDEBOUND_TIMEOUT | int | gateway client timeout |\n| HIDEBOUND_EXPORTERS | yaml | exporters section of config |\n| HIDEBOUND_WEBHOOKS | yaml | webhooks section of config |\n| HIDEBOUND_TESTING | str | run in test mode |\n\n---\n\n## Config File\nHere is a full example config with comments:\n```yaml\ningress_directory: /mnt/storage/projects # where hb looks for assets\nstaging_directory: /mnt/storage/hidebound # hb staging directory\ninclude_regex: \"\" # include files that match\nexclude_regex: \"\\\\.DS_Store\" # exclude files that match\nwrite_mode: copy # copy files from root to staging\n # options: copy, move\nredact_regex: \"(_key|_id|_token|url)$\" # regex matched config keys to redact\nredact_hash: true # hash redacted values\nworkflow: # workflow steps\n - delete # clear staging directory\n - update # create database from ingress files\n - create # stage valid assets\n - export # export assets in staging\nspecification_files: # list of spec files\n - /mnt/storage/specs/image_specs.py\n - /mnt/storage/specs/video_specs.py\ndask:\n cluster_type: local # Dask cluster type\n # options: local, gateway\n num_partitions: 16 # number of partitions for each datafme\n local_num_workers: 16 # number of workers on local cluster\n local_threads_per_worker: 1 # number of threads per worker on local cluster\n local_multiprocessing: true # use multiprocessing for local cluster\n gateway_address: http://proxy-public/services/dask-gateway # gateway server address\n gateway_proxy_address: gateway://traefik-daskhub-dask-gateway.core:80 # scheduler proxy server address\n gateway_public_address: https://dask-gateway/services/dask-gateway/ # gateway server address, as accessible from a web browser\n gateway_auth_type: jupyterhub # authentication type\n gateway_api_token: token123 # api token or password\n gateway_api_user: admin # api user\n gateway_cluster_options: # list of dask gateway options\n - field: image # option field\n label: image # option label\n option_type: select # options: bool, float, int, mapping, select, string\n default: \"some-image:latest\" # option default value\n options: # list of choices if option_type is select\n - \"some-image:latest\" # choice 1\n - \"some-image:0.1.2\" # choice 2\n gateway_min_workers: 1 # min dask gateway workers\n gateway_max_workers: 8 # max dask gateway workers\n gateway_shutdown_on_close: true # whether to shudown cluster upon close\n gateway_timeout: 30 # gateway client timeout\nexporters: # dict of exporter configs\n - name: disk # export to disk\n target_directory: /mnt/storage/archive # target location\n metadata_types: # options: asset, file, asset-chunk, file-chunk\n - asset # only asset and file metadata\n - file\n dask: # dask settings override\n num_workers: 8\n local_threads_per_worker: 2\n - name: s3 # export to s3\n access_key: ABCDEFGHIJKLMNOPQRST # aws access key\n secret_key: abcdefghijklmnopqrstuvwxyz1234567890abcd # aws secret key\n bucket: prod-data # s3 bucket\n region: us-west-2 # bucket region\n metadata_types: # options: asset, file, asset-chunk, file-chunk\n - asset # drop file metadata\n - asset-chunk\n - file-chunk\n dask: # dask settings override\n cluster_type: gateway\n num_workers: 64\n - name: girder # export to girder\n api_key: eyS0nj9qPC5E7yK5l7nhGVPqDOBKPdA3EC60Rs9h # girder api key\n root_id: 5ed735c8d8dd6242642406e5 # root resource id\n root_type: collection # root resource type\n host: http://prod.girder.com # girder server url\n port: 8180 # girder server port\n metadata_types: # options: asset, file\n - asset # only asset metadata\n dask: # dask settings override\n num_workers: 10\n dask: # dask settings override\n num_workers: 10\nwebhooks: # call these after export\n - url: https://hooks.slack.com/services/ABCDEFGHI/JKLMNO # slack URL\n method: post # post this to slack\n timeout: 60 # timeout after 60 seconds\n # params: {} # params to post (NA here)\n # json: {} # json to post (NA here)\n data: # data to post\n channel: \"#hidebound\" # slack data\n text: export complete # slack data\n username: hidebound # slack data\n headers: # request headers\n Content-type: application/json\n```\n\n# Specification\nAsset specifications are defined in python using the base classes found in\nspecification_base.py. The base classes are defined using the schematics\nframework. Hidebound generates a single JSON blob of metadata for each file of\nan asset, and then combines blob into a single blob with a list values per key.\nThus every class member defined with schematics is encapsulated with ListType.\n\n## Example asset\n\nSuppose we have an image sequence asset that we wish to define a specificqtion\nfor. Our image sequences consist of a directory containing 1 or 3 channel png\nwith frame numbers in the filename.\n\n```shell\nprojects\n └── cat001\n └── raw001\n └── p-cat001_s-raw001_d-calico-jumping_v001\n ├── p-cat001_s-raw001_d-calico-jumping_v001_f0001.png\n ├── p-cat001_s-raw001_d-calico-jumping_v001_f0002.png\n └── p-cat001_s-raw001_d-calico-jumping_v001_f0003.png\n```\n\n## Example specification\n\nWe would write the following specification for such an asset.\n\n```python\nfrom schematics.types import IntType, ListType, StringType\nimport hidebound.core.validators as vd # validates traits\nimport hidebound.core.traits as tr # gets properties of files and file names\nfrom hidebound.core.specification_base import SequenceSpecificationBase\n\nclass Raw001(SequenceSpecificationBase):\n asset_name_fields = [ # naming convention for asset directory\n 'project', 'specification', 'descriptor', 'version'\n ]\n filename_fields = [ # naming convention for asset files\n 'project', 'specification', 'descriptor', 'version', 'frame',\n 'extension'\n ]\n height = ListType(IntType(), required=True) # heights of png images\n width = ListType(IntType(), required=True) # widths of png images\n frame = ListType(\n IntType(),\n required=True,\n validators=[vd.is_frame] # validates that frame is between 0 and 9999\n )\n channels = ListType(\n IntType(),\n required=True,\n validators=[lambda x: vd.is_in(x, [1, 3])] # validates that png is 1 or 3 channel\n )\n extension = ListType(\n StringType(),\n required=True,\n validators=[\n vd.is_extension,\n lambda x: vd.is_eq(x, 'png') # validates that image is png\n ]\n )\n file_traits = dict(\n width=tr.get_image_width, # retrieves image width from file\n height=tr.get_image_height, # retrieves image height from file\n channels=tr.get_num_image_channels, # retrieves image channel number from file\n )\n```\n\n# Production CLI\nHidebound comes with a command line interface defined in command.py.\n\nIts usage pattern is: `hidebound COMMAND [FLAGS] [-h --help]`\n\n## Commands\n\n| Command | Description |\n| --------------- | ------------------------------------------------------------------------- |\n| bash-completion | Prints BASH completion code to be written to a _hidebound completion file |\n| config | Prints hidebound config |\n| serve | Runs a hidebound server |\n| zsh-completion | Prints ZSH completion code to be written to a _hidebound completion file |\n\n## Flags\n\n| Command | Flag | Description | Default |\n| ------- | --------- | ------------------------ | ------- |\n| serve | --port | Server port | 8080 |\n| serve | --timeout | Gunicorn timeout | 0 |\n| serve | --testing | Testing mode | False |\n| serve | --debug | Debug mode (no gunicorn) | False |\n| all | --help | Show help message | \u003cp\u003e\u003c/p\u003e |\n\n---\n\n# Quickstart Guide\nThis repository contains a suite commands for the whole development process.\nThis includes everything from testing, to documentation generation and\npublishing pip packages.\n\nThese commands can be accessed through:\n\n - The VSCode task runner\n - The VSCode task runner side bar\n - A terminal running on the host OS\n - A terminal within this repositories docker container\n\nRunning the `zsh-complete` command will enable tab completions of the CLI.\nSee the zsh setup section for more information.\n\n### Command Groups\n\nDevelopment commands are grouped by one of 10 prefixes:\n\n| Command | Description |\n| ---------- | ---------------------------------------------------------------------------------- |\n| build | Commands for building packages for testing and pip publishing |\n| docker | Common docker commands such as build, start and stop |\n| docs | Commands for generating documentation and code metrics |\n| library | Commands for managing python package dependencies |\n| session | Commands for starting interactive sessions such as jupyter lab and python |\n| state | Command to display the current state of the repo and container |\n| test | Commands for running tests, linter and type annotations |\n| version | Commands for bumping project versions |\n| quickstart | Display this quickstart guide |\n| zsh | Commands for running a zsh session in the container and generating zsh completions |\n\n### Common Commands\n\nHere are some frequently used commands to get you started:\n\n| Command | Description |\n| ----------------- | --------------------------------------------------------- |\n| docker-restart | Restart container |\n| docker-start | Start container |\n| docker-stop | Stop container |\n| docs-full | Generate documentation, coverage report, diagram and code |\n| library-add | Add a given package to a given dependency group |\n| library-graph-dev | Graph dependencies in dev environment |\n| library-remove | Remove a given package from a given dependency group |\n| library-search | Search for pip packages |\n| library-update | Update dev dependencies |\n| session-lab | Run jupyter lab server |\n| state | State of |\n| test-dev | Run all tests |\n| test-lint | Run linting and type checking |\n| zsh | Run ZSH session inside container |\n| zsh-complete | Generate ZSH completion script |\n\n---\n\n# Development CLI\nbin/hidebound is a command line interface (defined in cli.py) that\nworks with any version of python 2.7 and above, as it has no dependencies.\nCommands generally do not expect any arguments or flags.\n\nIts usage pattern is: `bin/hidebound COMMAND [-a --args]=ARGS [-h --help] [--dryrun]`\n\n### Commands\nThe following is a complete list of all available development commands:\n\n| Command | Description |\n| ----------------------- | ------------------------------------------------------------------- |\n| build-package | Build production version of repo for publishing |\n| build-prod | Publish pip package of repo to PyPi |\n| build-publish | Run production tests first then publish pip package of repo to PyPi |\n| build-test | Build test version of repo for prod testing |\n| docker-build | Build Docker image |\n| docker-build-from-cache | Build Docker image from registry cache |\n| docker-build-no-cache | Build Docker image without cache |\n| docker-build-prod | Build production image |\n| docker-container | Display the Docker container id |\n| docker-destroy | Shutdown container and destroy its image |\n| docker-destroy-prod | Shutdown production container and destroy its image |\n| docker-image | Display the Docker image id |\n| docker-prod | Start production container |\n| docker-pull-dev | Pull development image from Docker registry |\n| docker-pull-prod | Pull production image from Docker registry |\n| docker-push-dev | Push development image to Docker registry |\n| docker-push-dev-latest | Push development image to Docker registry with dev-latest tag |\n| docker-push-prod | Push production image to Docker registry |\n| docker-push-prod-latest | Push production image to Docker registry with prod-latest tag |\n| docker-remove | Remove Docker image |\n| docker-restart | Restart Docker container |\n| docker-start | Start Docker container |\n| docker-stop | Stop Docker container |\n| docs | Generate sphinx documentation |\n| docs-architecture | Generate architecture.svg diagram from all import statements |\n| docs-full | Generate documentation, coverage report, diagram and code |\n| docs-metrics | Generate code metrics report, plots and tables |\n| library-add | Add a given package to a given dependency group |\n| library-graph-dev | Graph dependencies in dev environment |\n| library-graph-prod | Graph dependencies in prod environment |\n| library-install-dev | Install all dependencies into dev environment |\n| library-install-prod | Install all dependencies into prod environment |\n| library-list-dev | List packages in dev environment |\n| library-list-prod | List packages in prod environment |\n| library-lock-dev | Resolve dev.lock file |\n| library-lock-prod | Resolve prod.lock file |\n| library-remove | Remove a given package from a given dependency group |\n| library-search | Search for pip packages |\n| library-sync-dev | Sync dev environment with packages listed in dev.lock |\n| library-sync-prod | Sync prod environment with packages listed in prod.lock |\n| library-update | Update dev dependencies |\n| library-update-pdm | Update PDM |\n| quickstart | Display quickstart guide |\n| session-lab | Run jupyter lab server |\n| session-python | Run python session with dev dependencies |\n| session-server | Runn application server inside Docker container |\n| state | State of repository and Docker container |\n| test-coverage | Generate test coverage report |\n| test-dev | Run all tests |\n| test-fast | Test all code excepts tests marked with SKIP_SLOWS_TESTS decorator |\n| test-lint | Run linting and type checking |\n| test-prod | Run tests across all support python versions |\n| version | Full resolution of repo: dependencies, linting, tests, docs, etc |\n| version-bump-major | Bump pyproject major version |\n| version-bump-minor | Bump pyproject minor version |\n| version-bump-patch | Bump pyproject patch version |\n| version-commit | Tag with version and commit changes to master |\n| zsh | Run ZSH session inside Docker container |\n| zsh-complete | Generate oh-my-zsh completions |\n| zsh-root | Run ZSH session as root inside Docker container |\n\n### Flags\n\n| Short | Long | Description |\n| ----- | --------- | ---------------------------------------------------- |\n| -a | --args | Additional arguments, this can generally be ignored |\n| -h | --help | Prints command help message to stdout |\n| | --dryrun | Prints command that would otherwise be run to stdout |\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthenewflesh%2Fhidebound","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthenewflesh%2Fhidebound","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthenewflesh%2Fhidebound/lists"}