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Description:

hidebound 0.34.2

Overview
A MLOps framework for generating ML assets and metadata.
Hidebound is an ephemeral database and asset framework used for generating,
validating and exporting assets to various data stores. Hidebound enables
developers to ingest arbitrary sets of files and output them as content and
generated metadata, which has been validated according to specifications they
define.
Assets are placed into an ingress directory, typically reserved for Hidebound
projects, and then processed by Hidebound. Hidebound extracts metadata from the
files and directories that make each asset according to their name, location and
file properties. This data comprises the entirety of Hidebound's database at any
one time.
See documentation for details.
Installation for Developers
Docker

Install docker-desktop
Ensure docker-desktop has at least 4 GB of memory allocated to it.
git clone git@github.com:theNewFlesh/hidebound.git
cd hidebound
chmod +x bin/hidebound
bin/hidebound docker-start

If building on a M1 Mac run export DOCKER_DEFAULT_PLATFORM=linux/amd64 first.

The service should take a few minutes to start up.
Run bin/hidebound --help for more help on the command line tool.
ZSH Setup

bin/hidebound must be run from this repository's top level directory.

Therefore, if using zsh, it is recommended that you paste the following line
in your ~/.zshrc file:

alias hidebound="cd [parent dir]/hidebound; bin/hidebound"
Replace [parent dir] with the parent directory of this repository

Consider adding the following line to your ~/.zshrc if you are using a M1 Mac:

export DOCKER_DEFAULT_PLATFORM=linux/amd64

Running the zsh-complete command will enable tab completions of the cli
commands, in the next shell session.
For example:

hidebound [tab] will show you all the cli options, which you can press
tab to cycle through
hidebound docker-[tab] will show you only the cli options that begin with
"docker-"

Installation for Production
Python
pip install hidebound
Docker

Install docker-desktop
docker pull thenewflesh/hidebound:[version]

Dataflow

Data begins as files on disk. Hidebound creates a JSON-compatible dict from
their name traits and file traits and then constructs an internal database table
from them, one dict per row. All the rows are then aggregated by asset, and
converted into JSON blobs. Those blobs are then validated according to their
respective specifications. Files from valid assets are then copied or moved into
Hidebound's content directory, according to their same directory structure and
naming. Metadata is written to JSON files inside Hidebound's metadata directory.
Each file's metadata is written as a JSON file in /hidebound/metadata/file, and
each asset's metadata (the aggregate of its file metadata) is written to
/hidebound/metadata/asset. From their exporters, can export the valid
asset data and its accompanying metadata to various locations, like an AWS S3
bucket.
Workflow
The acronynm to remember for workflows is CRUDES: create, read, update,
delete, export and search. Those operations constitue the main functionality
that Hidebound supports.
Create Asset
For example, an asset could be an image sequence, such as a directory full of
PNG files, all of which have a frame number, have 3 (RGB) channels, and are 1024
pixels wide by 1024 pixels tall. Let's call the specification for this type of
asset "spec001". We create an image sequence of a cat running, and we move it
into the Hidebound projects directory.
Update

We call the update function via Hidebound's web app. Hidebound creates
a new database based upon the recursive listing of all the files within said
directory. This database is displayed to us as a table, with one file per row.
If we choose to group by asset in the app, the table will display one asset per
row. Hidebound extracts metadata from each filename (not any directory name) as
well as from the file itself. That metadata is called file_traits. Using only
information derived from filename and file traits, Hidebound determines which
files are grouped together as a single asset and the specification of that
asset. Asset traits are then derived from this set of files (one or more).
Finally, Hidebound validates each asset according to its determined
specification. All of this data is displayed as a table within the web app.
Importantly, all of the errors in filenames, file traits and asset traits are
included.
Review Graph

If we click on the graph tab, we are greeted by a hierarchical graph of all our
assets in our project directory. Our asset is red, meaning it's invalid. Valid
asset's are green, and all other files and directories, including parent
directories, are cyan.
Diagnose and Repair
We flip back to the data tab. Using table within it, we search (via SQL) for our
asset within Hidebound's freshly created database. We see an error in one of the
filenames, conveniently displayed in red text. The descriptor in one orf our
filenames has capital letters in it. This violates Hidebound's naming
convention, and so we get an error. We go and rename the file appropriately and
call update again. Our asset is now valid. The filenames are correct and we can
see in the height and width columns, that it's 1024 by 1024 and the channels
column says it has three.
Create
Next we click the create button. For each valid asset, Hidebound generates file
and asset metadata as JSON files within the hidebound/metadata directory.
Hidebound also copies or moves, depending on the config write mode, valid files
and directories into the hidebound/content directory. Hidebound/content and
hidebound/metadata are both staging directories used for generating a valid
ephemeral database. We now have a hidebound directory that looks like this
(unmentioned assets are collapsed behind the ellipses):
/tmp/hidebound
├── hidebound_config.yaml
│
├── specifications
│ └── specifications.py
│
├── data
│ ...
│ └── p-cat001
│ └── spec001
│ └── p-cat001_s-spec001_d-running-cat_v001
│ ├── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0001.png
│ ├── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0002.png
│ └── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0003.png
│
├── metadata
├── asset
│ ...
│ └── a9f3727c-cb9b-4eb1-bc84-a6bc3b756cc5.json
│
└── file
...
├── 279873a2-bfd0-4757-abf2-7dc4f771f992.json
├── e50160ae-8678-40b3-b766-ee8311b1f0c9.json
└── ea95bd79-cb8f-4262-8489-efe734c5f65c.json

Export
The hidebound directories contain only valid assets. Thus, we are now free to
export this data to various data stores, such as AWS S3, MongoDB, and Girder.
Exporters are are defined within the exporters subpackage. They expect a
populated hidebound directory and use the files and metadata therein to export
hidebound data. Exporter configurations are stored in the hidebound config,
under the "exporters" key. Currently supported exporters include, disk, s3 and
girder. Below we can see the results of an export to Girder in the Girder web
app.

Delete
Once this export process is complete, we may click the delete button. Hidebound
deletes the hidebound/content and hidebound/metdata directories and all their
contents. If write_mode in the Hidebound configuration is set to "copy", then
this step will merely delete data created by Hidebound. If it is set to "move",
then Hidebound will presumably delete, the only existing copy of out asset data
on the host machine. The delete stage in combination with the removal of assets
from the ingress directory is what makes Hidebound's database ephemeral.
Workflow
/api/workflow is a API endpoint that initializes a database a with a given
config, and then calls each method from a given list. For instance, if you send
this data to /api/workflow:
{config={...}, workflow=['update', 'create', 'export', 'delete']}
A database instance will be created with the given config, and then that
instance will call its update, create, export and delete methods, in that order.
Naming Convention
Hidebound is a highly opinionated framework that relies upon a strict but
composable naming convention in order to extract metadata from filenames. All
files and directories that are part of assets must conform to a naming
convention defined within that asset's specification.
In an over-simplified sense; sentences are constructions of words. Syntax
concerns how each word is formed, grammar concerns how to form words into a
sentence, and semantics concerns what each word means. Similarly, filenames can
be thought of as crude sentences. They are made of several words (ie fields).
These words have distinct semantics (as determines by field indicators). Each
word is constructed according to a syntax (ie indicator + token). All words are
joined together by spaces (ie underscores) in a particular order as determined
by grammar (as defined in each specification).
Syntax

Names consist of a series of fields, each separated by a single underscore
“_”, also called a field separator.
Periods, ".", are the exception to this, as it indicates file extension.
Legal characters include and only include:

Name
Characters
Use

Underscore
_
only for field separation

Period
.
only for file extensions

Lowercase letter
a to z
everything

Number
0 to 9
everything

Hyphen
-
token separator

Fields are comprised of two main parts:

Name
Use

Field indicator
determines metadata key

Field token
a set of 1+ characters that define the field's data

Example Diagrams
In our example filename:
p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0003.png the metadata will be:
{
"project": "cat001",
"specification": "spec001",
"descriptor": "running-cat",
"version": 1,
"coordinate": [0, 5],
"frame": 3,
"extension": "png",
}

The spec001 specification is derived from the second field of this filename:
field field
indicator token
| __|__
| | |
p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0003.png
|_______|
|
field

Part
Value

Field
s-spec001

Field indicator
s-

Field token
spec001

Derived metadata
{specification: spec001}

Special Field Syntax

Projects begin with 3 to 10 letters followed by 1 to 4 numbers
Specifications begin with 3 or 4 letters followed by 3 numbers
Descriptors begin with a letter or number and may also contain hyphens
Descriptors may not begin with the words master, final or last
Versions are triple-padded with zeros and must be greater than 0
Coordinates may contain up to 3 quadruple-padded numbers, separated by hyphens
Coordinates are always evaluated in XYZ order. For example: c0001-0002-0003
produces {x: 1, y: 2, z: 3}.
Each element of a coordinate may be equal to or greater than zero
Frames are quadruple-padded and are greater than or equal to 0
Extensions may only contain upper and lower case letters a to z and numbers 0
to 9

Semantics
Hidebound is highly opionated, especially with regards to its semantics. It
contains exactly seven field types, as indicated by their field indicators.
They are:

Field
Indicator

project
p-

specification
s-

descriptor
d-

version
v

coordinate
c

frame
f

extension
.

Grammar
The grammar is fairly simple:

Names are comprised of an ordered set of fields drawn from the seven above
All names must contain the specification field
All specification must define a field order
All fields of a name under that specification must occcur in its defined
field order

Its is highly encouraged that fields be defined in the following order:
project specification descriptor version coordinate frame extension
The grammatical concept of field order here is one of rough encapsulation:

Projects contain assets
Assets are grouped by specification
A set of assets of the same content is grouped by a descriptor
That set of assets consists of multiple versions of the same content
A single asset may broken into chunks, identified by 1, 2 or 3 coordinates
Each chunk may consist of a series of files seperated by frame number
Each file has an extension

Encouraged Lexical Conventions

Specifications end with a triple padded number so that they may be explicitely
versioned. You redefine an asset specification to something slightly
different, by copying its specification class, adding one to its name and
change the class attributes in some way. That way you always maintain
backwards compatibility with legacy assets.
Descriptors are not a dumping ground for useless terms like wtf, junk, stuff,
wip and test.
Descriptors should not specify information known at the asset specification
level, such as the project name, the generic content of the asset (ie image,
mask, png, etc).
Descriptors should not include information that can be known from the
preceding tokens, such as version, frame or extension.
A descriptor should be applicable to every version of the asset it designates.
Use of hyphens in descriptors is encouraged.
When in doubt, hyphenate and put into the descriptor.

Project Structure
Hidebound does not formally define a project structure. It merely stipulates
that assets must exist under some particular root directory. Each asset
specification does define a directory structure for the files that make up that
asset. Assets are divided into 3 types: file, sequence and complex. File defines
an asset that consists of a single file. Sequence is defined to be a single
directory containing one or more files. Complex is for assets that consist of an
arbitrarily complex layout of directories and files.
The following project structure is recommended:
project
|-- specification
|-- descriptor
|-- asset # either a file or directory of files and directories
|- file

For Example
/tmp/projects
└── p-cat001
├── s-spec002
│ ├── d-calico-jumping
│ │ └── p-cat001_s-spec002_d-calico-jumping_v001
│ │ ├── p-cat001_s-spec002_d-calico-jumping_v001_f0001.png
│ │ ├── p-cat001_s-spec002_d-calico-jumping_v001_f0002.png
│ │ └── p-cat001_s-spec002_d-calico-jumping_v001_f0003.png
│ │
│ └── d-tabby-playing
│ ├── p-cat001_s-spec002_d-tabby-playing_v001
│ │ ├── p-cat001_s-spec002_d-tabby-playing_v001_f0001.png
│ │ ├── p-cat001_s-spec002_d-tabby-playing_v001_f0002.png
│ │ └── p-cat001_s-spec002_d-tabby-playing_v001_f0003.png
│ │
│ └── p-cat001_s-spec002_d-tabby-playing_v002
│ ├── p-cat001_s-spec002_d-tabby-playing_v002_f0001.png
│ ├── p-cat001_s-spec002_d-tabby-playing_v002_f0002.png
│ └── p-cat001_s-spec002_d-tabby-playing_v002_f0003.png
│
└── spec001
└── p-cat001_s-spec001_d-running-cat_v001
├── p-cat001_s-spec001_d-Running-Cat_v001_c0000-0005_f0002.png
├── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0001.png
└── p-cat001_s-spec001_d-running-cat_v001_c0000-0005_f0003.png

Application
The Hidebound web application has five sections: data, graph, config, api and
docs.
Data
The data tab is the workhorse of the Hidebound app.

Its functions are as follows:

Search - Search the updated database's data via SQL
Dropdown - Groups search results by file or asset
Init - Initialized the database with the current config
Update - Initializes and updates the database with the current config
Create - Copies or moves valid assets to hidebound/content directory and
creates JSON files in hidebound/metadata directory
Delete - Deletes hidebound/content and hidebound/metadata directories

Prior to calling update, the application will look like this:

Graph
The graph tab is used for visualizing the state of all the assets within a root
directory.

It's color code is as follows:

Color
Meaning

Cyan
Non-asset file or directory

Green
Valid asset

Red
Invalid asset

Config
The config tab is used for uploading and writing Hidebound's configuration file.

API
The API tab is really a link to Hidebound's REST API documentation.

Docs
The API tab is really a link to Hidebound's github documentation.

Errors
Hidebound is oriented towards developers and technically proficient users. It
displays errors in their entirety within the application.

Configuration
Hidebound is configured via a configuration file or environment variables.
Hidebound configs consist of four main sections:
Base

ingress_directory - the directory hidebound parses for assets that comprise its database
staging_directory - the staging directory valid assets are created in
specification_files - a list of python specification files
include_regex - filepaths in the root that match this are included in the database
exclude_regex - filepaths in the root that match this are excluded from the database
write_mode - whether to copy or move files from root to staging
redact_regex - regular expression which matches config keys whose valuse are to be redacted
redact_hash - whether to redact config values with "REDACTED" or a hash of the value
workflow - order list of steps to be followed in workflow

Dask
Default configuration of Dask distributed framework.

cluster_type - dask cluster type
num_partitions - number of partions for each dataframe
local_num_workers - number of workers on local cluster
local_threads_per_worker - number of threads per worker on local cluster
local_multiprocessing - use multiprocessing for local cluster
gateway_address - gateway server address
gateway_proxy_address - scheduler proxy server address
gateway_public_address - gateway server address, as accessible from a web browser
gateway_auth_type - authentication type
gateway_api_token - api token or password
gateway_api_user - api user
gateway_cluster_options - list of dask gateway cluster options
gateway_shutdown_on_close - whether to shudown cluster upon close
gateway_timeout - gateway client timeout

Exporters
Which exporters to us in the workflow.
Options include:

s3
disk
girder

Webhooks
Webhooks to call after the export phase has completed.

Environment Variables
If HIDEBOUND_CONFIG_FILEPATH is set, Hidebound will ignore all other
environment variables and read the given filepath in as a yaml or json config
file.

Variable
Format
Portion

HIDEBOUND_CONFIG_FILEPATH
str
Entire Hidebound config file

HIDEBOUND_INGRESS_DIRECTORY
str
ingress_directory parameter of config

HIDEBOUND_STAGING_DIRECTORY
str
staging_directory parameter of config

HIDEBOUND_INCLUDE_REGEX
str
include_regex parameter of config

HIDEBOUND_EXCLUDE_REGEX
str
exclude_regex parameter of config

HIDEBOUND_WRITE_MODE
str
write_mode parameter of config

HIDEBOUND_REDACT_REGEX
str
redact_regex parameter of config

HIDEBOUND_REDACT_HASH
str
redact_hash parameter of config

HIDEBOUND_WORKFLOW
yaml
workflow paramater of config

HIDEBOUND_SPECIFICATION_FILES
yaml
specification_files section of config

HIDEBOUND_DASK_CLUSTER_TYPE
str
dask cluster type

HIDEBOUND_DASK_NUM_PARTITIONS
int
number of partions for each dataframe

HIDEBOUND_DASK_LOCAL_NUM_WORKERS
int
number of workers on local cluster

HIDEBOUND_DASK_LOCAL_THREADS_PER_WORKER
int
number of threads per worker on local cluster

HIDEBOUND_DASK_LOCAL_MULTIPROCESSING
str
use multiprocessing for local cluster

HIDEBOUND_DASK_GATEWAY_ADDRESS
str
gateway server address

HIDEBOUND_DASK_GATEWAY_PROXY_ADDRESS
str
scheduler proxy server address

HIDEBOUND_DASK_GATEWAY_PUBLIC_ADDRESS
str
gateway server address, as accessible from a web browser

HIDEBOUND_DASK_GATEWAY_AUTH_TYPE
str
authentication type

HIDEBOUND_DASK_GATEWAY_API_TOKEN
str
api token or password

HIDEBOUND_DASK_GATEWAY_API_USER
str
api user

HIDEBOUND_DASK_GATEWAY_CLUSTER_OPTIONS
yaml
list of dask gateway cluster options

HIDEBOUND_DASK_GATEWAY_SHUTDOWN_ON_CLOSE
str
whether to shudown cluster upon close

HIDEBOUND_TIMEOUT
int
gateway client timeout

HIDEBOUND_EXPORTERS
yaml
exporters section of config

HIDEBOUND_WEBHOOKS
yaml
webhooks section of config

HIDEBOUND_TESTING
str
run in test mode

Config File
Here is a full example config with comments:
ingress_directory: /mnt/storage/projects # where hb looks for assets
staging_directory: /mnt/storage/hidebound # hb staging directory
include_regex: "" # include files that match
exclude_regex: "\\.DS_Store" # exclude files that match
write_mode: copy # copy files from root to staging
# options: copy, move
redact_regex: "(_key|_id|_token|url)$" # regex matched config keys to redact
redact_hash: true # hash redacted values
workflow: # workflow steps
- delete # clear staging directory
- update # create database from ingress files
- create # stage valid assets
- export # export assets in staging
specification_files: # list of spec files
- /mnt/storage/specs/image_specs.py
- /mnt/storage/specs/video_specs.py
dask:
cluster_type: local # Dask cluster type
# options: local, gateway
num_partitions: 16 # number of partions for each dataframe
local_num_workers: 16 # number of workers on local cluster
local_threads_per_worker: 1 # number of threads per worker on local cluster
local_multiprocessing: true # use multiprocessing for local cluster
gateway_address: http://proxy-public/services/dask-gateway # gateway server address
gateway_proxy_address: gateway://traefik-daskhub-dask-gateway.core:80 # scheduler proxy server address
gateway_public_address: https://dask-gateway/services/dask-gateway/ # gateway server address, as accessible from a web browser
gateway_auth_type: jupyterhub # authentication type
gateway_api_token: token123 # api token or password
gateway_api_user: admin # api user
gateway_cluster_options: # list of dask gateway options
- field: image # option field
label: image # option label
option_type: select # options: bool, float, int, mapping, select, string
default: "some-image:latest" # option default value
options: # list of choices if option_type is select
- "some-image:latest" # choice 1
- "some-image:0.1.2" # choice 2
gateway_min_workers: 1 # min dask gateway workers
gateway_max_workers: 8 # max dask gateway workers
gateway_shutdown_on_close: true # whether to shudown cluster upon close
gateway_timeout: 30 # gateway client timeout
exporters: # dict of exporter configs
- name: disk # export to disk
target_directory: /mnt/storage/archive # target location
metadata_types: # options: asset, file, asset-chunk, file-chunk
- asset # only asset and file metadata
- file
dask: # dask settings override
num_workers: 8
local_threads_per_worker: 2
- name: s3 # export to s3
access_key: ABCDEFGHIJKLMNOPQRST # aws access key
secret_key: abcdefghijklmnopqrstuvwxyz1234567890abcd # aws secret key
bucket: prod-data # s3 bucket
region: us-west-2 # bucket region
metadata_types: # options: asset, file, asset-chunk, file-chunk
- asset # drop file metadata
- asset-chunk
- file-chunk
dask: # dask settings override
cluster_type: gateway
num_workers: 64
- name: girder # export to girder
api_key: eyS0nj9qPC5E7yK5l7nhGVPqDOBKPdA3EC60Rs9h # girder api key
root_id: 5ed735c8d8dd6242642406e5 # root resource id
root_type: collection # root resource type
host: http://prod.girder.com # girder server url
port: 8180 # girder server port
metadata_types: # options: asset, file
- asset # only asset metadata
dask: # dask settings override
num_workers: 10
dask: # dask settings override
num_workers: 10
webhooks: # call these after export
- url: https://hooks.slack.com/services/ABCDEFGHI/JKLMNO # slack URL
method: post # post this to slack
timeout: 60 # timeout after 60 seconds
# params: {} # params to post (NA here)
# json: {} # json to post (NA here)
data: # data to post
channel: "#hidebound" # slack data
text: export complete # slack data
username: hidebound # slack data
headers: # request headers
Content-type: application/json

Specification
Asset specifications are defined in python using the base classes found in
specification_base.py. The base classes are defined using the schematics
framework. Hidebound generates a single JSON blob of metadata for each file of
an asset, and then combines blob into a single blob with a list values per key.
Thus every class member defined with schematics is encapsulated with ListType.
Example asset
Suppose we have an image sequence asset that we wish to define a specificqtion
for. Our image sequences consist of a directory containing 1 or 3 channel png
with frame numbers in the filename.
projects
└── cat001
└── raw001
└── p-cat001_s-raw001_d-calico-jumping_v001
├── p-cat001_s-raw001_d-calico-jumping_v001_f0001.png
├── p-cat001_s-raw001_d-calico-jumping_v001_f0002.png
└── p-cat001_s-raw001_d-calico-jumping_v001_f0003.png

Example specification
We would write the following specification for such an asset.
from schematics.types import IntType, ListType, StringType
import hidebound.core.validators as vd # validates traits
import hidebound.core.traits as tr # gets properties of files and file names
from hidebound.core.specification_base import SequenceSpecificationBase

class Raw001(SequenceSpecificationBase):
asset_name_fields = [ # naming convention for asset directory
'project', 'specification', 'descriptor', 'version'
]
filename_fields = [ # naming convention for asset files
'project', 'specification', 'descriptor', 'version', 'frame',
'extension'
]
height = ListType(IntType(), required=True) # heights of png images
width = ListType(IntType(), required=True) # widths of png images
frame = ListType(
IntType(),
required=True,
validators=[vd.is_frame] # validates that frame is between 0 and 9999
)
channels = ListType(
IntType(),
required=True,
validators=[lambda x: vd.is_in(x, [1, 3])] # validates that png is 1 or 3 channel
)
extension = ListType(
StringType(),
required=True,
validators=[
vd.is_extension,
lambda x: vd.is_eq(x, 'png') # validates that image is png
]
)
file_traits = dict(
width=tr.get_image_width, # retrieves image width from file
height=tr.get_image_height, # retrieves image height from file
channels=tr.get_num_image_channels, # retrieves image channel number from file
)

Production CLI
Hidebound comes with a command line interface defined in command.py.
Its usage pattern is: hidebound COMMAND [FLAGS] [-h --help]
Commands

Command
Description

bash-completion
Prints BASH completion code to be written to a _hidebound completion file

config
Prints hidebound config

serve
Runs a hidebound server

zsh-completion
Prints ZSH completion code to be written to a _hidebound completion file

Flags

Command
Flag
Description
Default

serve
--port
Server port
8080

serve
--timeout
Gunicorn timeout
0

serve
--testing
Testing mode
False

serve
--debug
Debug mode (no gunicorn)
False

all
--help
Show help message

Quickstart Guide
This repository contains a suite commands for the whole development process.
This includes everything from testing, to documentation generation and
publishing pip packages.
These commands can be accessed through:

The VSCode task runner
The VSCode task runner side bar
A terminal running on the host OS
A terminal within this repositories docker container

Running the zsh-complete command will enable tab completions of the CLI.
See the zsh setup section for more information.
Command Groups
Development commands are grouped by one of 10 prefixes:

Command
Description

build
Commands for building packages for testing and pip publishing

docker
Common docker commands such as build, start and stop

docs
Commands for generating documentation and code metrics

library
Commands for managing python package dependencies

session
Commands for starting interactive sessions such as jupyter lab and python

state
Command to display the current state of the repo and container

test
Commands for running tests, linter and type annotations

version
Commands for bumping project versions

quickstart
Display this quickstart guide

zsh
Commands for running a zsh session in the container and generating zsh completions

Common Commands
Here are some frequently used commands to get you started:

Command
Description

docker-restart
Restart container

docker-start
Start container

docker-stop
Stop container

docs-full
Generate documentation, coverage report, diagram and code

library-add
Add a given package to a given dependency group

library-graph-dev
Graph dependencies in dev environment

library-remove
Remove a given package from a given dependency group

library-search
Search for pip packages

library-update
Update dev dependencies

session-lab
Run jupyter lab server

state
State of

test-dev
Run all tests

test-lint
Run linting and type checking

zsh
Run ZSH session inside container

zsh-complete
Generate ZSH completion script

Development CLI
bin/hidebound is a command line interface (defined in cli.py) that
works with any version of python 2.7 and above, as it has no dependencies.
Commands generally do not expect any arguments or flags.
Its usage pattern is: bin/hidebound COMMAND [-a --args]=ARGS [-h --help] [--dryrun]
Commands
The following is a complete list of all available development commands:

Command
Description

build-package
Build production version of repo for publishing

build-prod
Publish pip package of repo to PyPi

build-publish
Run production tests first then publish pip package of repo to PyPi

build-test
Build test version of repo for prod testing

docker-build
Build Docker image

docker-build-from-cache
Build Docker image from cached image

docker-build-prod
Build production image

docker-container
Display the Docker container id

docker-destroy
Shutdown container and destroy its image

docker-destroy-prod
Shutdown production container and destroy its image

docker-image
Display the Docker image id

docker-prod
Start production container

docker-pull-dev
Pull development image from Docker registry

docker-pull-prod
Pull production image from Docker registry

docker-push-dev
Push development image to Docker registry

docker-push-dev-latest
Push development image to Docker registry with dev-latest tag

docker-push-prod
Push production image to Docker registry

docker-push-prod-latest
Push production image to Docker registry with prod-latest tag

docker-remove
Remove Docker image

docker-restart
Restart Docker container

docker-start
Start Docker container

docker-stop
Stop Docker container

docs
Generate sphinx documentation

docs-architecture
Generate architecture.svg diagram from all import statements

docs-full
Generate documentation, coverage report, diagram and code

docs-metrics
Generate code metrics report, plots and tables

library-add
Add a given package to a given dependency group

library-graph-dev
Graph dependencies in dev environment

library-graph-prod
Graph dependencies in prod environment

library-install-dev
Install all dependencies into dev environment

library-install-prod
Install all dependencies into prod environment

library-list-dev
List packages in dev environment

library-list-prod
List packages in prod environment

library-lock-dev
Resolve dev.lock file

library-lock-prod
Resolve prod.lock file

library-remove
Remove a given package from a given dependency group

library-search
Search for pip packages

library-sync-dev
Sync dev environment with packages listed in dev.lock

library-sync-prod
Sync prod environment with packages listed in prod.lock

library-update
Update dev dependencies

library-update-pdm
Update PDM

quickstart
Display quickstart guide

session-lab
Run jupyter lab server

session-python
Run python session with dev dependencies

session-server
Runn application server inside Docker container

state
State of repository and Docker container

test-coverage
Generate test coverage report

test-dev
Run all tests

test-fast
Test all code excepts tests marked with SKIP_SLOWS_TESTS decorator

test-lint
Run linting and type checking

test-prod
Run tests across all support python versions

version
Full resolution of repo: dependencies, linting, tests, docs, etc

version-bump-major
Bump pyproject major version

version-bump-minor
Bump pyproject minor version

version-bump-patch
Bump pyproject patch version

version-commit
Tag with version and commit changes to master

zsh
Run ZSH session inside Docker container

zsh-complete
Generate oh-my-zsh completions

zsh-root
Run ZSH session as root inside Docker container

Flags

Short
Long
Description

-a
--args
Additional arguments, this can generally be ignored

-h
--help
Prints command help message to stdout

--dryrun
Prints command that would otherwise be run to stdout