Friday, March 1, 2024

Organizing ML Monorepo With Pants


Have you ever ever copy-pasted chunks of utility code between tasks, leading to a number of variations of the identical code residing in several repositories? Or, maybe, you needed to make pull requests to tens of tasks after the title of the GCP bucket by which you retailer your information was up to date?

Conditions described above come up approach too usually in ML groups, and their penalties range from a single developer’s annoyance to the crew’s lack of ability to ship their code as wanted. Fortunately, there’s a treatment.

Let’s dive into the world of monorepos, an structure extensively adopted in main tech corporations like Google, and the way they’ll improve your ML workflows. A monorepo affords a plethora of benefits which, regardless of some drawbacks, make it a compelling selection for managing complicated machine studying ecosystems. 

We are going to briefly debate monorepos’ deserves and demerits, study why it’s a wonderful structure selection for machine studying groups, and peek into how BigTech is utilizing it. Lastly, we’ll see easy methods to harness the ability of the Pants construct system to arrange your machine studying monorepo into a strong CI/CD construct system. 

Strap in as we embark on this journey to streamline your ML undertaking administration.

What’s a monorepo?

What is ML monorepo? (short for monolithic repository)
Machine studying monorepo | Supply: Writer

A monorepo (brief for monolithic repository) is a software program growth technique the place code for a lot of tasks is saved in the identical repository. The thought might be as broad as all of the corporate code written in quite a lot of programming languages saved collectively (did any person say Google?) or as slim as a few Python tasks developed by a small crew thrown right into a single repository. 

On this weblog publish, we concentrate on repositories storing machine studying code.

Monorepos vs. polyrepos

Monorepos are in stark distinction to the polyrepos strategy, the place every particular person undertaking or element has its personal separate repository. Lots has been mentioned in regards to the benefits and drawbacks of each approaches, and we gained’t go down this rabbit gap too deep. Let’s simply put the fundamentals on the desk.

The monorepo structure affords the next benefits:

Monorepo architecture
Monorepo structure | Supply: Writer
  • Single CI/CD pipeline, that means no hidden deployment information unfold throughout particular person contributors to totally different repositories;
  • Atomic commits, given that every one tasks reside in the identical repository, builders could make cross-project modifications that span throughout a number of tasks however are merged as a single commit;
  • Straightforward sharing of utilities and templates throughout tasks;
  • Straightforward unification of coding requirements and approaches;
  • Higher code discoverability.

Naturally, there aren’t any free lunches. We have to pay for the above goodies, and the value comes within the type of:

  • Scalability challenges: Because the codebase grows, managing a monorepo can change into more and more troublesome. At a extremely massive scale, you’ll want highly effective instruments and servers to deal with operations like cloning, pulling, and pushing modifications, which might take a big period of time and assets.
  • Complexity: A monorepo might be extra complicated to handle, notably with regard to dependencies and versioning. A change in a shared element may doubtlessly influence many tasks, so additional warning is required to keep away from breaking modifications.
  • Visibility and entry management: With everybody understanding of the identical repository, it may be troublesome to regulate who has entry to what. Whereas not a drawback as such, it may pose issues of a authorized nature in instances the place code is topic to a really strict NDA.

The choice as as to if the benefits a monorepo affords are value paying the value is to be decided by every group or crew individually. Nevertheless, until you’re working at a prohibitively massive scale or are coping with top-secret missions, I’d argue that – not less than in the case of my space of experience, the machine studying tasks – a monorepo is an effective structure selection generally. 

Let’s speak about why that’s.

Machine studying with monorepos 

There are not less than six the reason why monorepos are notably appropriate for machine studying tasks.

  • 1
    Knowledge pipeline integration
  • 2
    Consistency throughout experiments
  • 3
    Simplified mannequin versioning
  • 4
    Cross-functional collaboration
  • 5
    Atomic modifications
  • 6
    Unification of coding requirements

Knowledge pipeline integration

Machine studying tasks usually contain information pipelines that preprocess, rework, and feed information into the mannequin. These pipelines is likely to be tightly built-in with the ML code. Protecting the information pipelines and ML code in the identical repo helps preserve this tight integration and streamline the workflow.

Consistency throughout experiments

Machine studying growth includes a number of experimentation. Having all experiments in a monorepo ensures constant surroundings setups and reduces the chance of discrepancies between totally different experiments attributable to various code or information variations.

Simplified mannequin versioning

In a monorepo, the code and mannequin variations are in sync as a result of they’re checked into the identical repository. This makes it simpler to handle and hint mannequin variations, which might be particularly essential in tasks the place ML reproducibility is essential. 

Simply take the commit SHA at any given time limit, and it provides the knowledge on the state of all fashions and providers.

Cross-functional collaboration

Machine studying tasks usually contain collaboration between information scientists, ML engineers, and software program engineers. A monorepo facilitates this cross-functional collaboration by offering a single supply of reality for all project-related code and assets.

Atomic modifications

Within the context of ML, a mannequin’s efficiency can depend upon numerous interconnected components like information preprocessing, function extraction, mannequin structure, and post-processing. A monorepo permits for atomic modifications – a change to a number of of those elements might be dedicated as one, making certain that interdependencies are at all times in sync.

Unification of coding requirements

Lastly, machine studying groups usually embody members with out a software program engineering background. These mathematicians, statisticians, and econometricians are brainy people with sensible concepts and the abilities to coach fashions that remedy enterprise issues. Nevertheless, writing code that’s clear, simple to learn, and preserve may not at all times be their strongest aspect. 

A monorepo helps by mechanically checking and imposing coding requirements throughout all tasks, which not solely ensures excessive code high quality but in addition helps the much less engineering-inclined crew members study and develop.

How they do it in business: well-known monorepos

Within the software program growth panorama, a number of the largest and most profitable corporations on the planet use monorepos. Listed here are a number of notable examples.

  • Google: Google has lengthy been a staunch advocate for the monorepo strategy. Their whole codebase, estimated to comprise 2 billion traces of code, is contained in a single, huge repository. They even printed a paper about it.
  • Meta: Meta additionally employs a monorepo for his or her huge codebase. They created a model management system referred to as “Mercurial” to deal with the dimensions and complexity of their monorepo.
  • Twitter: Twitter has been managing their monorepo for a very long time utilizing Pants, the construct system we’ll speak about subsequent!

Many different corporations equivalent to Microsoft, Uber, Airbnb, and Stripe are utilizing the monorepo strategy not less than for some components of their codebases, too.

Sufficient of the speculation! Let’s check out easy methods to truly construct a machine studying monorepo. As a result of simply throwing what was separate repositories into one folder doesn’t do the job.

The way to arrange ML monorepo with Python?

All through this part, we’ll base our dialogue on a pattern machine studying repository I’ve created for this text. It’s a easy monorepo holding only one undertaking, or module: a hand-written digits classifier referred to as mnist, after the well-known dataset it makes use of. 

All you might want to know proper now’s that within the monorepo’s root there’s a listing referred to as mnist, and in it, there’s some Python code for coaching the mannequin, the corresponding unit assessments, and a Dockerfile to run coaching in a container.

ML monorepo: mnist directory

We will likely be utilizing this small instance to maintain issues easy, however in a bigger monorepo, mnist could be simply one of many many undertaking folders within the repo’s root, every of which is able to comprise supply code, assessments, dockerfiles, and requirement recordsdata as a minimum.

Construct system: Why do you want one and the way to decide on it?

The Why?

Take into consideration all of the actions, apart from writing code, that the totally different groups creating totally different tasks throughout the monorepo take as a part of their growth workflow. They’d run linters in opposition to their code to make sure adherence to type requirements, run unit assessments, construct artifacts equivalent to docker containers and Python wheels, push them to exterior artifact repositories, and deploy them to manufacturing. 

Take testing. 

You’ve made a change in a utility perform you preserve, ran the assessments, and all’s inexperienced. However how are you going to be certain your change just isn’t breaking code for different groups that is likely to be importing your utility? You need to run their check suite, too, in fact. 

However to do that, you might want to know precisely the place the code you modified is getting used. Because the codebase grows, discovering this out manually doesn’t scale effectively. In fact, in its place, you possibly can at all times execute all of the assessments, however once more: that strategy doesn’t scale very effectively.

Setting up ML monorepo and why do you need a system (testing)
Why do you want a construct system: testing | Supply: Writer

One other instance, manufacturing deployment

Whether or not you deploy weekly, each day, or constantly, when the time comes, you’d construct all of the providers within the monorepo and push them to manufacturing. However hey, do you might want to construct all of them on every event? That may very well be time-consuming and costly at scale. 

Some tasks may not have been up to date for weeks. However, the shared utility code they use may need obtained updates. How will we determine what to construct? Once more, it’s all about dependencies. Ideally, we might solely construct providers which have been affected by the latest modifications.

Setting up ML monorepo and why do you need a system (deployment)
Why do you want a construct system: deployment | Supply: Writer

All of this may be dealt with with a easy shell script with a small codebase, however because it scales and tasks begin sharing code, challenges emerge, lots of which revolve round dependency administration. 

Choosing the right system

The entire above just isn’t an issue anymore for those who spend money on a correct construct system. A construct system’s major job is to construct code. And it ought to achieve this in a intelligent approach: the developer ought to solely want to inform it what to construct (“construct docker pictures affected by my newest commit”, or “run solely these assessments that cowl code which makes use of the strategy I’ve up to date”), however the how must be left for the system to determine.

There are a few nice open-source construct programs on the market. Since most machine studying is completed in Python, let’s concentrate on those with the most effective Python assist. The 2 hottest selections on this regard are Bazel and Pants

Bazel is an open-source model of Google’s inside construct system, Blaze. Pants can also be closely impressed by Blaze and it goals for related technical design objectives as Bazel. An reader will discover a good comparability of Pants vs. Bazel on this weblog publish (however consider it comes from the Pants devs). The desk on the backside of monorepo.instruments affords yet one more comparability.

Each programs are nice, and it isn’t my intention to declare a “higher” answer right here. That being mentioned, Pants is commonly described as simpler to arrange, extra approachable, and well-optimized for Python, which makes it an ideal match for machine studying monorepos. 

In my private expertise, the decisive issue that made me go together with Pants was its energetic and useful group. Every time you’ve got questions or doubts, simply publish on the group Slack channel, and a bunch of supportive people will assist you out quickly.

Introducing Pants 

Alright, time to get to the meat of it! We are going to go step-by-step, introducing totally different Pants’ functionalities and easy methods to implement them. Once more, you possibly can try the related pattern repo right here.

Setup

Pants is installable with pip. On this tutorial, we’ll use the latest steady model as of this writing, 2.15.1.

pip set up pantsbuild.pants==2.15.1

Pants is configurable by a worldwide grasp config file named pants.toml. In it, we are able to configure Pants’ personal habits in addition to the settings of downstream instruments it depends on, equivalent to pytest or mypy.

Let’s begin with a naked minimal pants.toml:

[GLOBAL]
pants_version = "2.15.1"
backend_packages = [
    "pants.backend.python",
]

[source]
root_patterns = ["/"]

[python]
interpreter_constraints = ["==3.9.*"]

Within the international part, we outline the Pants model and the backend packages we want. These packages are Pants’ engines that assist totally different options. For starters, we solely embody the Python backend.

Within the supply part, we set the supply to the repository’s root. Since model 2.15, to verify that is picked up, we additionally want so as to add an empty BUILD_ROOT file on the repository’s root.

Lastly, within the Python part, we select the Python model to make use of. Pants will browse our system in quest of a model that matches the situations specified right here, so be sure to have this model put in.

That’s a begin! Subsequent, let’s check out any construct system’s coronary heart: the BUILD recordsdata.

Construct recordsdata

Construct recordsdata are configuration recordsdata used to outline targets (what to construct) and their dependencies (what they should work) in a declarative approach. 

You may have a number of construct recordsdata at totally different ranges of the listing tree. The extra there are, the extra granular the management over dependency administration. In actual fact, Google has a construct file in nearly each listing of their repo. 

In our instance, we’ll use three construct recordsdata:

  • mnist/BUILD – within the undertaking listing, this construct file will outline the python necessities for the undertaking and the docker container to construct;
  • mnist/src/BUILD – within the supply code listing, this construct file will outline python sources, that’s, recordsdata to be lined by python-specific checks;
  • mnist/assessments/BUILD – within the assessments listing, this construct file will outline which recordsdata to run with Pytest and what dependencies are wanted for these assessments to run.

Let’s check out the mnist/src/BUILD:

python_sources(
    title="python",
    resolve="mnist",
    sources=["**/*.py"],
)

On the identical time, mnist/BUILD seems like this:

python_requirements(
    title="reqs",
    supply="necessities.txt",
    resolve="mnist",
)

The 2 entries within the construct recordsdata are known as targets. First, now we have a Python sources goal, which we aptly name python, though the title may very well be something. We outline our Python sources as all .py recordsdata within the listing. That is relative to the construct file’s location, that’s: even when we had Python recordsdata exterior of the mnist/src listing, these sources solely seize the contents of the mnist/src folder. There may be additionally a resolve filed; we’ll speak about it in a second.

Subsequent, now we have the Python necessities goal. It tells Pants the place to seek out the necessities wanted to execute our Python code (once more, relative to the construct file’s location, which is within the mnist undertaking’s root on this case).

That is all we have to get began. To verify the construct file definition is appropriate, let’s run:

pants tailor --check update-build-files --check ::

As anticipated, we get: “No required modifications to BUILD recordsdata discovered.” because the output. Good!

Let’s spend a bit extra time on this command. In a nutshell, a naked pants tailor can mechanically create construct recordsdata. Nevertheless, it generally tends so as to add too many for one’s wants, which is why I have a tendency so as to add them manually, adopted by the command above that checks their correctness. 

The double semicolon on the finish is a Pants notation that tells it to run the command over the whole monorepo. Alternatively, we may have changed it with mnist: to run solely in opposition to the mnist module. 

Dependencies and lockfiles

To do environment friendly dependency administration, pants depends on lockfiles. Lockfiles report the precise variations and sources of all dependencies utilized by every undertaking. This consists of each direct and transitive dependencies. 

By capturing this info, lockfiles make sure that the identical variations of dependencies are used persistently throughout totally different environments and builds. In different phrases, they function a snapshot of the dependency graph, making certain reproducibility and consistency throughout builds.

To generate a lockfile for our mnist module, we want the next addition to pants.toml:

[python]
interpreter_constraints = ["==3.9.*"]
enable_resolves = true
default_resolve = "mnist"

[python.resolves]
mnist = "mnist/mnist.lock"

We allow the resolves (Pants time period for lockfiles’ environments) and outline one for mnist passing a file path. We additionally select it because the default one. That is the resolve now we have handed to Python sources and Python necessities goal earlier than: that is how they know what dependencies are wanted. We are able to now run:

to get:

Accomplished: Generate lockfile for mnist
Wrote lockfile for the resolve `mnist` to mnist/mnist.lock

This has created a file at mnist/mnist.lock. This file must be checked with git for those who intend to make use of Pants in your distant CI/CD. And naturally, it must be up to date each time you replace the necessities.txt file. 

With extra tasks within the monorepo, you’d slightly generate the lockfiles selectively for the undertaking that wants it, e.g. pants generate-lockfiles mnist: .

That’s it for the setup! Now let’s use Pants to do one thing helpful for us.

Unifying code type with Pants

Pants natively helps plenty of Python linters and code formatting instruments equivalent to Black, yapf, Docformatter, Autoflake, Flake8, isort, Pyupgrade, or Bandit. They’re all utilized in the identical approach; in our instance, let’s implement Black and Docformatter.

To take action, we add applicable two backends to pants.toml:

[GLOBAL]
pants_version = "2.15.1"
colours = true
backend_packages = [
    "pants.backend.python",
    "pants.backend.python.lint.docformatter",
    "pants.backend.python.lint.black",
]

We may configure each instruments if we wished to by including extra sections beneath within the toml file, however let’s follow the defaults now.

To make use of the formatters, we have to execute what’s referred to as a Pants objective. On this case, two objectives are related.

First, the lint objective will run each instruments (within the order by which they’re listed in backend packages, so Docformatter first, Black second) within the verify mode.

pants lint ::

Accomplished: Format with docformatter - docformatter made no modifications.
Accomplished: Format with Black - black made no modifications.

✓ black succeeded.
✓ docformatter succeeded.

It seems like our code adheres to the requirements of each formatters! Nevertheless, if that was not the case, we may execute the fmt (brief for “format”) objective that adapts the code appropriately:

In follow, you may wish to use greater than these two formatters. On this case, chances are you’ll must replace every formatter’s config to make sure that it’s appropriate with the others. As an illustration, in case you are utilizing Black with its default config as now we have completed right here, it can count on code traces to not exceed 88 characters. 

However for those who then wish to add isort to mechanically type your imports, they may conflict: isort truncates traces after 79 characters. To make isort appropriate with Black, you would wish to incorporate the next part within the toml file:

[isort]
args = [
    "-l=88",
 ]

All formatters might be configured in the identical approach in pants.toml by passing the arguments to their underlying instrument.

Testing with Pants

Let’s run some assessments! To do that, we want two steps.

First, we add the suitable sections to pants.toml:

[test]
output = "all"
report = false
use_coverage = true

[coverage-py]
global_report = true

[pytest]
args = ["-vv", "-s", "-W ignore::DeprecationWarning", "--no-header"]

These settings guarantee that because the assessments are run, a check protection report is produced. We additionally move a few customized pytest choices to adapt its output.

Subsequent, we have to return to our mnist/assessments/BUILD file and add a Python assessments goal:

python_tests(
    title="assessments",
    resolve="mnist",
    sources=["test_*.py"],
)

We name it assessments and specify the resolve (i.e. lockfile) to make use of. Sources are the places the place pytest will likely be let in to search for assessments to run; right here, we explicitly move all .py recordsdata prefixed with “test_”. 

Now we are able to run:

To get:

✓ mnist/assessments/test_data.py:../assessments succeeded in 3.83s.
✓ mnist/assessments/test_model.py:../assessments succeeded in 2.26s.

Identify                               Stmts   Miss  Cowl
------------------------------------------------------
__global_coverage__/no-op-exe.py       0      0   100%
mnist/src/information.py                     14      0   100%
mnist/src/mannequin.py                    15      0   100%
mnist/assessments/test_data.py              21      1    95%
mnist/assessments/test_model.py             20      1    95%
------------------------------------------------------
TOTAL                                 70      2    97%

As you possibly can see, it took round three seconds to run this check suite. Now, if we re-run it once more, we’ll get the outcomes instantly:

✓ mnist/assessments/test_data.py:../assessments succeeded in 3.83s (memoized).
✓ mnist/assessments/test_model.py:../assessments succeeded in 2.26s (memoized).

Discover how Pants tells us these outcomes are memoized, or cached. Since no modifications have been made to the assessments, the code being examined, or the necessities,  there isn’t any want to truly re-run the assessments – their outcomes are assured to be the identical, so they’re simply served from the cache. 

Checking static typing with Pants

Let’s add another code high quality verify. Pants enable utilizing mypy to verify static typing in Python. All we have to do is add the mypy backend in pants.toml: “pants.backend.python.typecheck.mypy”.

You may also wish to configure mypy to make its output extra readable and informative by additionally including the next config part:

[mypy]
args = [
    "--ignore-missing-imports",
    "--local-partial-types",
    "--pretty",
    "--color-output",
    "--error-summary",
    "--show-error-codes",
    "--show-error-context",
]

With this, we are able to run pants verify :: to get:

Accomplished: Typecheck utilizing MyPy - mypy - mypy succeeded.
Success: no points discovered in 6 supply recordsdata

✓ mypy succeeded.


Delivery ML fashions with Pants

Let’s speak delivery. Most machine studying tasks contain a number of docker containers, for instance, processing coaching information, coaching a mannequin, or serving it by way of an API utilizing Flask or FastAPI. In our toy undertaking, we even have a container for mannequin coaching.

Pants assist computerized constructing and pushing of docker pictures. Let’s see the way it works.

First, we add the docker backend in pants.toml: pants.backend.docker. We can even configure our docker, passing it plenty of surroundings variables and a construct arg which is able to turn out to be useful in a second:

[docker]
build_args = ["SHORT_SHA"]
env_vars = ["DOCKER_CONFIG=%(env.HOME)s/.docker", "HOME", "USER", "PATH"]

Now, in the mnist/BUILD file, we'll add two extra targets: a recordsdata goal and a docker picture goal.

recordsdata(
    title="module_files",
    sources=["**/*"],
)

docker_image(
    title="train_mnist",
    dependencies=["mnist:module_files"],
    registries=["docker.io"],
    repository="michaloleszak/mnist",
    image_tags=["latest", "{build_args.SHORT_SHA}"],
)

We name the docker goal “train_mnist”. As a dependency, we have to move it the listing of recordsdata to be included within the container. Probably the most handy approach to do that is to outline this listing as a separated recordsdata goal. Right here, we merely embody all of the recordsdata within the mnist undertaking in a goal referred to as module_files, and move it as a dependency to the docker picture goal.

Naturally, if that just some subset of recordsdata will likely be wanted by the container, it’s a good suggestion to move solely them as a dependency. It’s important as a result of these dependencies are utilized by Pants to deduce whether or not a container has been affected by a change and desires a rebuild. Right here, with module_files together with all recordsdata, if any file within the mnist folder modifications (even a readme!), Pants will see the train_mnist docker picture as affected by this alteration.

Lastly, we are able to additionally set the exterior registry and repository to which the picture might be pushed, and the tags with which it is going to be pushed: right here, I will likely be pushing the picture to my private dockerhub repo, at all times with two tags: “newest”, and the brief commit SHA which will likely be handed as a construct arg.

With this, we are able to construct a picture. Only one thing more: since Pants is working in its remoted environments, it can not learn env vars from the host. Therefore, to construct or push the picture that requires the SHORT_SHA variable, we have to move it along with the Pants command.

We are able to construct the picture like this:

SHORT_SHA=$(git rev-parse --short HEAD) pants bundle mnist:train_mnist 

to get:

Accomplished: Constructing docker picture docker.io/michaloleszak/mnist:newest +1 extra tag.
Constructed docker pictures: 
  * docker.io/michaloleszak/mnist:newest
  * docker.io/michaloleszak/mnist:0185754

A fast verify reveals that the photographs have certainly been constructed:

docker pictures 


REPOSITORY            TAG       IMAGE ID       CREATED              SIZE
michaloleszak/mnist   0185754   d86dca9fb037   A couple of minute in the past   3.71GB
michaloleszak/mnist   newest    d86dca9fb037   A couple of minute in the past   3.71GB

We are able to additionally construct and push pictures in a single go utilizing Pants. All it takes is changing the bundle command with the publish command.

SHORT_SHA=$(git rev-parse --short HEAD) pants publish mnist:train_mnist 

This constructed the photographs and pushed them to my dockerhub, the place they’ve certainly landed.

Pants in CI/CD

The identical instructions now we have simply manually run regionally might be executed as components of a CI/CD pipeline. You may run them by way of providers equivalent to GitHub Actions or Google CloudBuild, for example as a PR verify earlier than a function department is allowed to be merged to the primary department, or after the merge, to validate it’s inexperienced and construct & push containers.

In our toy repo, I’ve applied a pre-push commit hook that runs Pants instructions on git push and solely lets it by if all of them move. In it, we’re working the next instructions:

pants tailor --check update-build-files --check ::
pants lint ::
pants --changed-since=major --changed-dependees=transitive verify
pants check ::

You may see some new flags for pants verify, that’s the typing verify with mypy. They make sure that the verify is just run on recordsdata which have modified in comparison with the primary department and their transitive dependencies. That is helpful since mypy tends to take a while to run. Limiting its scope to what’s truly wanted accelerates the method.

How would a docker construct & push look in a CI/CD pipeline? Considerably like this:

pants --changed-since=HEAD^ --changed-dependees=transitive --filter-target-type=docker_image publish

We use the publish command as earlier than, however with three extra arguments:

  • –changed-since=HEAD^ and –changed-dependees=transitive guarantee that solely the containers affected by the modifications in comparison with the earlier commit are constructed; that is helpful for executing on the primary department after the merge.
  • –filter-target-type=docker_image makes certain that the one issues Pants does is construct and push docker; it’s because the pants publish command can check with targets apart from docker: for instance, it may be used to publish helm charts to OCI registries. 

The identical goes for pants bundle: on prime of constructing docker pictures, it may well additionally create a Python bundle; for that purpose, it’s an excellent follow to move the –filter-target-type choice.

Conclusion

Monorepos are as a rule an incredible structure selection for machine studying groups. Managing them at scale, nonetheless, requires funding in a correct construct system. One such system is Pants: it’s simple to arrange and use and affords native assist for a lot of Python and Docker options that machine studying groups usually use. 

On prime of that, it’s an open-source undertaking with a big and useful group. I hope after studying this text you’ll go forward and check out it out. Even for those who don’t presently have a monolithic repository, Pants can nonetheless streamline and facilitate many facets of your each day work!

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