Saravanan666/pypi-org huggingface dataset:
Package: lmao
DainikDainikis a client library for NimbleBox.ai LMAO (Logging, Monitoring, Alerts and Observability) service.UsagefromdainikimportDainik# initialise the client objectdk=Dainik()# initialise the rundk.init("california",config={"model_name":"qual","config":{"batch_size":32,"n_steps":n_steps,"optimizer":"adam",}},)forepochinrange(10):log={"loss":1/epoch,"accuracy":epoch,}dk.log(log,step=epoch)# optionally log metrics directlydk.metrics.f1_score(x=[[1,2,3]],y=[[1,2,1]],step=epoch)# log files for tracking as well, wildcards work as well!dk.save_files([f"./checkpoint-{epoch}/*"])dk.end()# send signal to sleepDevDue to the current structure the source code for this is only available for NimbleBox.ai engineers. This is spun out of a much larger monorepo which contains the logic for server and client together which helps us work on this faster.
Package: lmaps
Lightweight Management and Provisioning Service===============================================lMaPS provides a simple way the use schema defined "instances" that canbe passed to an underlying configuration management system.  This willprovide the ability to allow desperate systems to use a common namespaceapplication scheme to interact directly with configuration managementsystems.## Installation```pip install lmaps```## Overview![System Architecture](.readmeimg/sysarch.png)## Example use cases### Using lMaPS to reconfigure a host using using ansibleIn this example, an ansible playbook can be written with the expectationthat a vars file containing the proper data structure will have thecorrect information to turn up a given service.Let's assume the following playbook /my-playbook.yaml:```- name: An example playbookhosts: localhostconnection: localvars:some_structure:- message: This is one- message: This is two- message: This is threetasks:- debug:msg: '{{ item.message }}'with_items: '{{ some_structure }}'```Using this, a unit can be defined to call it via the `ansible_playbook`unit handler.We define the unit on the host as /etc/lmaps/units/some_example.yaml:```---meta:name: some_exampledata_type: YamlKeyListstore_key: some_structurestore_root: /some/dir/to/store/instanceshandler:name: ansible_some_exampletype: ansible_playbookargs:playbook: /my-playbook.yamlparams:additionalProperties: falseproperties:message:type: string```Now when the daemon is run (`lmaps -D`), we can create instances.With no instances defined, ansible will use the default vars declaredin the playbook, but if we created the following as hello_world.yaml:```message: Hello world!```and created it via:`lmaps --create --file hello_world.yaml --unit some_example`then when applied, the loop will get the following output:[![asciicast](https://asciinema.org/a/AZC5wiglgxG5Vt83a1YBsOhyP.png)](https://asciinema.org/a/AZC5wiglgxG5Vt83a1YBsOhyP)
Package: lmax-python-sdk
lmax-python-sdkUnofficial LMAX Python SDKThis is a Python SDK for interacting with the LMAX trading platform. It provides a set of modules to interact with different aspects of the platform. Since the OpenAPI generator is messy and I needed to interact with LMAX rather implemented a very lightweight and simple SDK.ModulesClientThe client module, located inlmax_sdk/client.py, provides the LMAXClient class for interacting with the LMAX API. It uses theclient_key_idfor authentication.ValidationThe validation module, located inlmax_sdk/validation.py, provides various validation functions and constants. Contains differentenumtypes for categorical input arguments.AccountThe account module, located inlmax_sdk/account, provides functionality related to account data and trading.Broker Market DataThe broker allows to access the real-time and historical LMAX data for close prices and orderbook level data.Public DataNot implemented.Example UsageHere are some examples of how to use these modules. For more depth take a look at thedocs page.import os
import lmax_sdk

lmax_client = lmax_sdk.account.account_data.LMAXAccount(
    client_key_id=os.getenv("LMAX_DEMO_API_KEY"),
    secret=os.getenv("LMAX_DEMO_API_SECRET"),
    base_url=lmax_sdk.ClientBaseURLType.ACCOUNT_LONDON_DEMO,
)

lmax_client.get_instrument_data_symbol("EUR/USD")

[{'instrument_id': 'eur-usd',
  'security_id': '4001',
  'symbol': 'EUR/USD',
  'currency': 'USD',
  'unit_of_measure': 'EUR',
  'quantity_increment': '1000.0000',
  'margin': '1.00',
  'minimum_position_size': '1000.0000',
  'maximum_position_size': '500000000.0000',
  'trading_days': ['MONDAY', 'TUESDAY', 'WEDNESDAY', 'THURSDAY', 'FRIDAY'],
  'open_time': '17:05',
  'close_time': '17:00',
  'time_zone': 'America/New_York',
  'minimum_commission': '0.00000',
  'aggressive_commission_rate': '0.25000',
  'passive_commission_rate': '0.25000',
  'price_increment': '0.000010',
  'minimum_price': '0.000000',
  'maximum_price': '99999.000000',
  'short_swap_points': '-0.510',
  'long_swap_points': '0.950',
  'short_swap_cost': '-0.51000',
  'long_swap_cost': '-0.95000',
  'swap_cost_units': '10000.0000',
  'trade_date': '2024-02-08',
  'value_date_from': '2024-02-12',
  'value_date_to': '2024-02-13',
  'asset_class': 'CURRENCY'}]
Package: lmbr-reader
Real Python Feed ReaderThe Real Python Feed Reader is a basicweb feedreader that can download the latest Real Python tutorials from theReal Python feed.For more information see the tutorialHow to Publish an Open-Source Python Package to PyPIon Real Python.InstallationYou can install the Real Python Feed Reader fromPyPI:pip install realpython-readerThe reader is supported on Python 2.7, as well as Python 3.4 and above.How to useThe Real Python Feed Reader is a command line application, namedrealpython. To see a list of thelatest Real Python tutorialssimply call the program:$ realpython
The latest tutorials from Real Python (https://realpython.com/)
 0 How to Publish an Open-Source Python Package to PyPI
 1 Python "while" Loops (Indefinite Iteration)
 2 Writing Comments in Python (Guide)
 3 Setting Up Python for Machine Learning on Windows
 4 Python Community Interview With Michael Kennedy
 5 Practical Text Classification With Python and Keras
 6 Getting Started With Testing in Python
 7 Python, Boto3, and AWS S3: Demystified
 8 Python's range() Function (Guide)
 9 Python Community Interview With Mike Grouchy
10 How to Round Numbers in Python
11 Building and Documenting Python REST APIs With Flask and Connexion – Part 2
12 Splitting, Concatenating, and Joining Strings in Python
13 Image Segmentation Using Color Spaces in OpenCV + Python
14 Python Community Interview With Mahdi Yusuf
15 Absolute vs Relative Imports in Python
16 Top 10 Must-Watch PyCon Talks
17 Logging in Python
18 The Best Python Books
19 Conditional Statements in PythonTo read one particular tutorial, call the program with the numerical ID of the tutorial as a parameter:$ realpython 0
# How to Publish an Open-Source Python Package to PyPI

Python is famous for coming with batteries included. Sophisticated
capabilities are available in the standard library. You can find modules for
working with sockets, parsing CSV, JSON, and XML files, and working with
files and file paths.

However great the packages included with Python are, there are many
fantastic projects available outside the standard library. These are most
often hosted at the Python Packaging Index (PyPI), historically known as the
Cheese Shop. At PyPI, you can find everything from Hello World to advanced
deep learning libraries.

[... The full text of the article ...]You can also call the Real Python Feed Reader in your own Python code, by importing from thereaderpackage:>>> from reader import feed
>>> feed.get_titles()
['How to Publish an Open-Source Python Package to PyPI', ...]
Package: lmbr-second
Real Python Feed ReaderThe Real Python Feed Reader is a basicweb feedreader that can download the latest Real Python tutorials from theReal Python feed.For more information see the tutorialHow to Publish an Open-Source Python Package to PyPIon Real Python.InstallationYou can install the Real Python Feed Reader fromPyPI:pip install realpython-readerThe reader is supported on Python 2.7, as well as Python 3.4 and above.How to useThe Real Python Feed Reader is a command line application, namedrealpython. To see a list of thelatest Real Python tutorialssimply call the program:$ realpython
The latest tutorials from Real Python (https://realpython.com/)
 0 How to Publish an Open-Source Python Package to PyPI
 1 Python "while" Loops (Indefinite Iteration)
 2 Writing Comments in Python (Guide)
 3 Setting Up Python for Machine Learning on Windows
 4 Python Community Interview With Michael Kennedy
 5 Practical Text Classification With Python and Keras
 6 Getting Started With Testing in Python
 7 Python, Boto3, and AWS S3: Demystified
 8 Python's range() Function (Guide)
 9 Python Community Interview With Mike Grouchy
10 How to Round Numbers in Python
11 Building and Documenting Python REST APIs With Flask and Connexion – Part 2
12 Splitting, Concatenating, and Joining Strings in Python
13 Image Segmentation Using Color Spaces in OpenCV + Python
14 Python Community Interview With Mahdi Yusuf
15 Absolute vs Relative Imports in Python
16 Top 10 Must-Watch PyCon Talks
17 Logging in Python
18 The Best Python Books
19 Conditional Statements in PythonTo read one particular tutorial, call the program with the numerical ID of the tutorial as a parameter:$ realpython 0
# How to Publish an Open-Source Python Package to PyPI

Python is famous for coming with batteries included. Sophisticated
capabilities are available in the standard library. You can find modules for
working with sockets, parsing CSV, JSON, and XML files, and working with
files and file paths.

However great the packages included with Python are, there are many
fantastic projects available outside the standard library. These are most
often hosted at the Python Packaging Index (PyPI), historically known as the
Cheese Shop. At PyPI, you can find everything from Hello World to advanced
deep learning libraries.

[... The full text of the article ...]You can also call the Real Python Feed Reader in your own Python code, by importing from thereaderpackage:>>> from reader import feed
>>> feed.get_titles()
['How to Publish an Open-Source Python Package to PyPI', ...]
Package: lm-checkpoints
lm-checkpointsSimple library for dealing with language model checkpoints.Install usingpip install lm-checkpoints.ExampleSay you want to compute some metrics for all model checkpoints of Pythia 160m, but only seed 0.from lm_checkpoints import PythiaCheckpoints

for ckpt in PythiaCheckpoints(size=160,seed=[0]):
    # Do something with ckpt.model or ckpt.tokenizerOr if you only want to load steps0, 1, 2, 4, 8, 16for all available seeds:from lm_checkpoints import PythiaCheckpoints

for ckpt in PythiaCheckpoints(size=160,step=[0, 1, 2, 4, 8, 16]):
    # Do something with ckpt.model or ckpt.tokenizerAlternatively, you may want to load all final checkpoints of MultiBERTs:from lm_checkpoints import MultiBERTCheckpoints

for ckpt in MultiBERTCheckpoints.final_checkpoints():
    # Do something with ckpt.model or ckpt.tokenizerIn case you don't want the checkpoints to fill up your space, useclean_cache=Trueto delete older checkpoints:from lm_checkpoints import PythiaCheckpoints

for ckpt in PythiaCheckpoints(size=14,clean_cache=True):
    # Do something with ckpt.model or ckpt.tokenizer
Package: lmclient-core
lmclient面向于大规模异步请求 OpenAI 接口设计的客户端，使用场景 self-instruct, 大规模翻译等Features支持大规模异步请求 openai 接口支持进度条支持限制每分钟最大请求次数支持限制异步容量 （类似于线程池的大小）支持磁盘缓存100% type hints非常易用支持 OpenAI, Azure, Minimax, MinimaxPro, 智谱, 百度文心, 腾讯混元支持 FunctionCall安装方式支持 python3.8 及以上pipinstalllmclient-core使用方法CompletionEnginefromlmclientimportCompletionEngine,OpenAIChat,OpenAIChatParametersmodel=OpenAIChat('gpt-3.5-turbo',parameters=OpenAIChatParameters(temperature=0))# 控制每分钟最大请求次数为 20， 异步容量为 5client=CompletionEngine(model,async_capacity=5,max_requests_per_minute=20)prompts=['Hello, my name is','can you please tell me your name?',[{'role':'user','content':'hello, who are you?'}],'what is your name?',]outputs=client.async_run(prompts=prompts)foroutputinoutputs:print(output.reply)ChatEnginefromlmclientimportChatEngine,OpenAIChatmodel=OpenAIChat('gpt-3.5-turbo')chat_engine=ChatEngine(model)print(chat_engine.chat('你好，我是 chat_engine'))print(chat_engine.chat('我上一句话是什么？'))
Package: lmc-runner
lmc-runnerRun LMC assembly code from the command line.InstallationUsingpip:$pipinstalllmc-runnerUsageHelp$lmc-runner-hlmc-runner [-h] namepositional arguments:name        LMC assembly code file nameoptional arguments:-h, --help  show this help message and exitRun LMC file$lmc-runner[name]Example with an LMC assembly code file namedexample.txt$lmc-runnerexample.txt
Package: lmctl
LMCTL is a command-line client that provides commands for interacting with IBM Telco Network Cloud Manager (TNCM) Orchestrator (TNCO/ALM) environments.In addition it includes an opinionated pattern for managing xNF/Network Service designs during the CICD lifecycle, as file based projects, to produce packages suitable for production release.Quick InstallAssumes you already have Python3.6+ and have decided if LMCTL should be installed in avirtual environment(recommended) or globally.Install the latest from Pypi:python3 -m pip install lmctlVerify LMCTL is ready to use:lmctl --versionInitiate a config file:lmctl create config

lmctl get config --print-pathDocumentationDocumentation available in the source code:https://github.com/IBM/lmctl/blob/main/docs/index.mdLinksCode:https://github.com/IBM/lmctlReleases:https://github.com/IBM/lmctl/releases
Package: lm-dataformat
LM_DataformatUtilities for storing data for LM training.Basic UsageTo write:ar = Archive('output_dir')

for x in something():
  # do other stuff
  ar.add_data(somedocument, meta={
    'example': stuff,
    'someothermetadata': [othermetadata, otherrandomstuff],
    'otherotherstuff': True
  })

# remember to commit at the end!
ar.commit()To read:rdr = Reader('input_dir_or_file')

for doc in rdr.stream_data():
  # do something with the document
Package: lm-dataloader
Installation:pipinstalllm_dataloaderDesign PhilosophyA library to unify lm dataloading at large scaleSimple interface, any tokenizer can be integratedMinimal changes needed from small -> large scale (many multiple GPU nodes)follows fairseq / megatron's 'mmap' dataformat, but with improvements. Those being:Easily combine multiple datasetsunified into a single 'file' (which is actually a directory containing a .bin / .idx file)index files that are built on the fly are hidden files, leaving less mess in the directory.More straightforward interface, better documentation.Inspectable with a command line toolCan load from urlsCan load from S3 bucketsCan load from GCS bucketsCan tokenizeon the flyinstead of preprocessingExample usageTo tokenize a dataset contained in a .jsonl file (where the text to be tokenized can be accessed under the 'text' key):importlm_dataloaderaslmdlfromtransformersimportGPT2TokenizerFastjsonl_path="test.jsonl"output="my_dataset.lmd"tokenizer=GPT2TokenizerFast.from_pretrained('gpt2')lmdl.encode(jsonl_path,tokenize_fn=tokenizer.encode,tokenizer_vocab_size=len(tokenizer),output_prefix=output,eod_token=tokenizer.eos_token_id,)This will create a dataset at "my_dataset.lmd" which can be loaded as an indexed torch dataset like so:fromlm_dataloaderimportLMDatasettokenizer=GPT2TokenizerFast.from_pretrained('gpt2')seq_length=tokenizer.model_max_length# or whatever the sequence length of your model isdataset=LMDataset("my_dataset.lmd",seq_length=seq_length)# peek at 0th indexprint(dataset[0])Command line utilitiesThere are also command line utilities provided to inspect / merge datasets, e.g:lm-dataloaderinspectmy_dataset.lmdLaunches an interactive terminal to inspect the data in my_dataset.lmdAnd:lm-dataloadermergemy_dataset.lmd,my_dataset_2.lmdnew_dataset.lmdMerges the datasets at "my_dataset.lmd" and "my_dataset_2.lmd" into a new file at "new_dataset.lmd".
Package: lmdbm
lmdbmThis is a Python DBM interface style wrapper aroundLMDB(Lightning Memory-Mapped Database).
It uses the existing lower level Python bindingspy-lmdb.
This is especially useful on Windows, where otherwisedbm.dumbis the defaultdbmdatabase.Installpip install lmdbmExamplefromlmdbmimportLmdbwithLmdb.open("test.db","c")asdb:db[b"key"]=b"value"db.update({b"key1":b"value1",b"key2":b"value2"})# batch insert, uses a single transactionUse inheritance to store Python objects using json serializationimportjsonfromlmdbmimportLmdbclassJsonLmdb(Lmdb):def_pre_key(self,value):returnvalue.encode("utf-8")def_post_key(self,value):returnvalue.decode("utf-8")def_pre_value(self,value):returnjson.dumps(value).encode("utf-8")def_post_value(self,value):returnjson.loads(value.decode("utf-8"))withJsonLmdb.open("test.db","c")asdb:db["key"]={"some":"object"}obj=db["key"]print(obj["some"])# prints "object"WarningAs oflmdb==1.2.1the docs say that callinglmdb.Environment.set_mapsizefrom multiple processes "may cause catastrophic loss of data". Iflmdbmis used in write mode from multiple processes, setautogrow=Falseand map_size to a large enough value:Lmdb.open(..., map_size=2**30, autogrow=False).BenchmarksSeebenchmark.pyandrequirements-bench.txt. Other storage engines which could be tested:wiredtiger,berkeleydb.
Storage engines not benchmarked:
-tinydb(because it doesn't have built-in str/bytes keys)continuous writes in seconds (best of 3)itemslmdbmlmdbm-batchpysossqlitedictsqlitedict-batchdbm.dumbsemidbmvedisvedis-batchunqliteunqlite-batch100.0000.0150.0000.0310.0000.0160.0000.0000.0000.0000.0001000.0940.0000.0000.2650.0160.1880.0000.0000.0000.0000.00010001.6840.0160.0153.8850.1242.3870.0160.0150.0150.0160.0001000016.8950.0930.26545.3341.32625.3500.1560.0930.0940.0940.093100000227.1061.0302.698461.63812.964238.4001.6231.3881.4671.4661.35710000003482.52013.10427.8155851.239133.3962432.94516.41115.69315.70914.50814.103random reads in seconds (best of 3)itemslmdbmlmdbm-batchpysossqlitedictsqlitedict-batchdbm.dumbsemidbmvedisvedis-batchunqliteunqlite-batch100.0000.0000.0000.0000.0000.0000.0001000.0000.0000.0310.0000.0000.0000.00010000.0160.0150.2500.1090.0160.0150.000100000.1090.1562.5581.1230.1710.1090.1091000001.0142.13727.76911.4192.0901.1701.170100000010.39024.258447.613870.58022.838214.486211.319
Package: lmdbm-lockable
lmdbmThis is a Python DBM interface style wrapper aroundLMDB(Lightning Memory-Mapped Database).
It uses the existing lower level Python bindingspy-lmdb.
This is especially useful on Windows, where otherwisedbm.dumbis the defaultdbmdatabase.Installpip install lmdbmExamplefromlmdbmimportLmdbwithLmdb.open("test.db","c")asdb:db[b"key"]=b"value"db.update({b"key1":b"value1",b"key2":b"value2"})# batch insert, uses a single transactionUse inheritance to store Python objects using json serializationimportjsonfromlmdbmimportLmdbclassJsonLmdb(Lmdb):def_pre_key(self,value):returnvalue.encode("utf-8")def_post_key(self,value):returnvalue.decode("utf-8")def_pre_value(self,value):returnjson.dumps(value).encode("utf-8")def_post_value(self,value):returnjson.loads(value.decode("utf-8"))withJsonLmdb.open("test.db","c")asdb:db["key"]={"some":"object"}obj=db["key"]print(obj["some"])# prints "object"WarningAs oflmdb==1.2.1the docs say that callinglmdb.Environment.set_mapsizefrom multiple processes "may cause catastrophic loss of data". Iflmdbmis used in write mode from multiple processes, setautogrow=Falseand map_size to a large enough value:Lmdb.open(..., map_size=2**30, autogrow=False).BenchmarksSeebenchmark.pyandrequirements-bench.txt. Other storage engines which could be tested:wiredtiger,berkeleydb.
Storage engines not benchmarked:
-tinydb(because it doesn't have built-in str/bytes keys)continuous writes in seconds (best of 3)itemslmdbmlmdbm-batchpysossqlitedictsqlitedict-batchdbm.dumbsemidbmvedisvedis-batchunqliteunqlite-batch100.0000.0150.0000.0310.0000.0160.0000.0000.0000.0000.0001000.0940.0000.0000.2650.0160.1880.0000.0000.0000.0000.00010001.6840.0160.0153.8850.1242.3870.0160.0150.0150.0160.0001000016.8950.0930.26545.3341.32625.3500.1560.0930.0940.0940.093100000227.1061.0302.698461.63812.964238.4001.6231.3881.4671.4661.35710000003482.52013.10427.8155851.239133.3962432.94516.41115.69315.70914.50814.103random reads in seconds (best of 3)itemslmdbmlmdbm-batchpysossqlitedictsqlitedict-batchdbm.dumbsemidbmvedisvedis-batchunqliteunqlite-batch100.0000.0000.0000.0000.0000.0000.0001000.0000.0000.0310.0000.0000.0000.00010000.0160.0150.2500.1090.0160.0150.000100000.1090.1562.5581.1230.1710.1090.1091000001.0142.13727.76911.4192.0901.1701.170100000010.39024.258447.613870.58022.838214.486211.319
Package: lm-deezer-bf-dec
LM_DEEZER_BF_DEC[!IMPORTANT]
Rust is needed for installing this libraryDirect install[!WARNING]
May not work due different architectureUsing pippipinstalllm_deezer_bf_decBuild by yourselfCreate a python virtual environmentpython3.12-mvenvvenvActivate itsourcevenv/bin/activateInstall maturinpipinstallmaturin[patchelf]Install Rust[!NOTE]
If you have already installed RUST you can jump this stepcurl--proto'=https'--tlsv1.2-sSfhttps://sh.rustup.rs|shBuild itmaturinbuild-r--sdistInstall itpipinstalltarget/wheels/<file>.tar.gz
Package: lm-detect
lm-detectZero-Shot Machine-Generated Text Detectionlm-detect provides tooling for automatically detecting AI-written text.Installationpipinstalllm-detectUsage>>>fromtransformersimportAutoTokenizer,AutoModel>>>tokenizer=AutoTokenizer.from_pretrained("gpt2")>>>model=AutoModel.from_pretrained("gpt2")>>>fromlm_detectimportPerplexity>>>ppl=Perplexity(model,tokenizer)>>>ppl("Lorem ipsum dolor sit amet")6.536835670471191Authorslm-detect is written byAndré Storhaugandr3.storhaug@gmail.comLicenseThis project is licensed under theMIT License- see theLICENSEfile for details.
Package: lmdiag
Python Library providing Diagnostic Plots for Lineare Regression Models.(Likeplot.lmin R.)I built this, because I missed the diagnostics plots of R for a university project. There are some substitutions in Python for individual charts, but they are spread over different libraries and sometimes don’t show the exact same. My implementation tries to copycat the R-plots, but I didn’t reimplement the R-code: The charts are just based on available documentation.InstallationAvailable in PyPi:https://pypi.org/project/lmdiag/Using pip:pip install lmdiagUsing pipenv:pipenv install lmdiagUsageThe plots need afitted Linear Regression Modelcreated by statsmodels as input.FittedModel from Linearmodels <https://bashtage.github.io/linearmodels/doc/index.html>should also work, however, that’s not tested very well.Example(See also the more extensiveExample Notebook)import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
import lmdiag

%matplotlib inline  # In Jupyter

# Generate sample model
np.random.seed(20)
predictor = np.random.normal(size=30, loc=20, scale=3)
response = 5 + 5 * predictor + np.random.normal(size=30)
X = sm.add_constant(predictor)
lm = sm.OLS(response, X).fit()

# Plot chart matrix (and enlarge figure)
plt.figure(figsize=(10,7))
lmdiag.plot(lm);MethodsDraw matrix of all plots:lmdiag.plot(lm)Draw individual plots:lmdiag.resid_fit(lm)lmdiag.q_q(lm)lmdiag.scale_loc(lm)lmdiag.resid_lev(lm)Print useful descriptions for interpretations:lmdiag.info()(for all plots)lmdiag.info('<methodname>')(for individual plot)DevelopmentDisclaimerThis is my very first public python library. Don’t expect everything to work smoothly. I’m happy to receive useful feedback or pull requests.‘Programming Language :: Python :: 3.6’,CertificationPackaging and Upload to PyPipipenv run rstcheck README.rst(check syntax)rm-rf./dist(delete old builds)python setup.py sdistpython setup.py bdist_wheeltwine upload dist/*Then new release on github…