Saravanan666/pypi-org huggingface dataset:
Package: sagemaker-knockout
🥊 sagemaker-knockoutKnock the SageMaker instance out when it's not active!❓ Why?Are you enjoying big powerful, pricey SageMaker instances to experiment with your data, but often forget to shut it down after you're done? 💸💸💸😳sagemaker-knockoutwill knock your machine out when it's inactive! 👾⚙️ How does it work?It tracks three metrics to detect the activity. If any of them report as active, machine will not shut down.Jupyter connections- if Jupyter server has incoming connections opened, it means you have an open console or notebook tab (with laptop opened). We do not want to shut the machine down in that caseGPU usage- if you've left your laptop closed, but are running GPU intensive operations, we'll detect that (threshold is set to 5%)GPU usage- if you've left your laptop closed, but are running CPU intensive operations, we'll detect that (threshold is set to 10%)🧠 SetupIn order to make sure you don't need to remember to run this program to shut down your machine 😅, the most covenient setup is to change the SageMaker's lifecycle configuration.Add this snippet of code to "Start notebook" script:#!/bin/bash# make the script fail if anything failsset-e# set up the language so we don't support just ASCIIexportLC_ALL=en_US.utf-8exportLANG=en_US.utf-8echo"Setting up 🥊 sagemaker-knockout..."pip3installsagemaker-knockout
python3-msagemaker_knockoutrun--daemonize--max-inactive-minutes60sleep3python3-msagemaker_knockoutcheck-daemonAs you can guess, we are running this program in the background (daemonizing) and you can change the inactivity period after which the shutdown process kicks in. Feel free to change it to any number that suits you (yes, I do like long lunch breaks 🍔).
Package: sagemaker-mxnet-inference
SageMaker MXNet Inference Toolkit is an open-source library for serving MXNet models on Amazon SageMaker.
This library provides default pre-processing, predict and postprocessing for certain MXNet model types and
utilizes theSageMaker Inference Toolkitfor starting up the model server, which is responsible for handling inference requests.For training, seeSageMaker MXNet Training Toolkit.For the Dockerfiles used for building SageMaker MXNet Containers, seeAWS Deep Learning Containers.For information on using MXNet on Amazon SageMaker, please refer to theSageMaker Python SDK documentation.ContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.LicenseSageMaker MXNet Containers is licensed under the Apache 2.0 License.
It is copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
The license is available at:http://aws.amazon.com/apache2.0/
Package: sagemaker-mxnet-training
SageMaker MXNet Training Toolkit is an open-source library for using MXNet to train models on Amazon SageMaker.
For inference, seeSageMaker MXNet Inference Toolkit.
For the Dockerfiles used for building SageMaker MXNet Containers, seeAWS Deep Learning Containers.
For information on running MXNet jobs on Amazon SageMaker, please refer to theSageMaker Python SDK documentation.ContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.TestingSet up a virtual environment for testing.One of the multiple ways to setup a virtual environment# use a package virtualenv
# create a virtualenv
virtualenv -p python3 <name of env>
# activate the virtualenv
source <name of env>/bin/activateInstall requirementspip install --upgrade .[test]Local TestTo run specific testtox -- -k test/unit/test_training.py::test_train_for_distributed_schedulerTo run an entire filetox -- test/unit/test_training.pyTo run all tests within a folder [e.g. integration/local/]Note: To run integration tests locally, one needs to build an image. To trigger image build, use-Bflag.tox -- test/integration/localYou can also run them in parallel:tox -- -n auto test/integration/localTo run for specific interpreter [Python environment], use the-eflagtox -e py37 -- test/unit/test_training.pyRemote TestMake sure to provide AWS account ID, Region, Docker base name & Tag.
Docker Registry is composed of (aws_id, region)
Image URI is composed of (docker_registry, docker_base_name, tag)Resulting Image URI is composed as:{aws_id}.dkr.ecr.{region}.amazonaws.com/{docker_base_name}:{tag}tox -- --aws-id <aws_id> --region <region> --docker-base-name <docker_base_name> --tag <tag> test/integration/sagemakerFor more details, referconftest.pyLicenseSageMaker MXNet Training Toolkit is licensed under the Apache 2.0 License.
It is copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
The license is available at:http://aws.amazon.com/apache2.0/
Package: sagemaker-pytorch-inference
SageMaker PyTorch Inference Toolkit is an open-source library for serving PyTorch models on Amazon SageMaker.
This library provides default pre-processing, predict and postprocessing for certain PyTorch model types and
is responsible for starting up the TorchServe model server on SageMaker, which is responsible for handling inference requests.For training, seeSageMaker PyTorch Training Toolkit.For the Dockerfiles used for building SageMaker PyTorch Containers, seeAWS Deep Learning Containers.For information on running PyTorch jobs on Amazon SageMaker, please refer to theSageMaker Python SDK documentation.For notebook examples:SageMaker Notebook
Examples.ContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull
requests to us.LicenseSageMaker PyTorch Serving Container is licensed under the Apache 2.0 License. It is copyright 2018 Amazon
.com, Inc. or its affiliates. All Rights Reserved. The license is available at:http://aws.amazon.com/apache2.0/
Package: sagemaker-pytorch-inference-test
SageMaker PyTorch Inference Toolkit is an open-source library for serving PyTorch models on Amazon SageMaker.
This library provides default pre-processing, predict and postprocessing for certain PyTorch model types and
utilizes theSageMaker Inference Toolkitfor starting up the model server, which is responsible for handling inference requests.For training, seeSageMaker PyTorch Training Toolkit.For the Dockerfiles used for building SageMaker PyTorch Containers, seeAWS Deep Learning Containers.For information on running PyTorch jobs on Amazon SageMaker, please refer to theSageMaker Python SDK documentation.For notebook examples:SageMaker Notebook
Examples.ContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull
requests to us.LicenseSageMaker PyTorch Serving Container is licensed under the Apache 2.0 License. It is copyright 2018 Amazon
.com, Inc. or its affiliates. All Rights Reserved. The license is available at:http://aws.amazon.com/apache2.0/
Package: sagemaker-pytorch-training
SageMaker PyTorch Training Toolkit is an open-source library for using PyTorch to train models on Amazon SageMaker.This toolkit depends and extends the baseSageMaker Training Toolkitwith PyTorch specific support.For inference, seeSageMaker PyTorch Inference Toolkit.For the Dockerfiles used for building SageMaker PyTorch Containers, seeAWS Deep Learning Containers.For information on running PyTorch jobs on Amazon SageMaker, please refer to theSageMaker Python SDK documentation.For notebook examples:SageMaker Notebook
Examples.ContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull
requests to us.LicenseSageMaker PyTorch Training Toolkit is licensed under the Apache 2.0 License. It is copyright 2018 Amazon
.com, Inc. or its affiliates. All Rights Reserved. The license is available at:http://aws.amazon.com/apache2.0/
Package: sagemaker-ssh-helper
SageMaker SSH Helper is a library that allows you to "SSH into SageMaker", i.e., securely connect to Amazon SageMaker training jobs, processing jobs, and realtime inference endpoints as well as SageMaker Studio notebook containers for fast interactive experimentation, remote debugging, and advanced troubleshooting.For the documentation, see the repohttps://github.com/aws-samples/sagemaker-ssh-helper/.
Package: sagemath
This is a dummy package that checks that Sage is installed in the system. Please see www.sagemath.org for instructions on how to download and install the actual software.SageMath is a free open-source mathematics software system licensed under the GPL. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more. Access their combined power through a common, Python-based language or directly via interfaces or wrappers.
Package: sagemath-bliss
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this pip-installable source distributionThis pip-installable source distributionsagemath-blissis a small
optional distribution for use withsagemath-standard.It provides a Cython interface to theblisslibrary for the purpose
of computing graph (iso/auto)morphisms.
Package: sagemath-categories
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this experimental pip-installable source distributionThis pip-installable source distributionsagemath-categoriesis an
experimental distribution of a small part of the Sage Library.  Use at your own
risk.  It provides a small subset of the modules of the Sage library
(“sagelib”,sagemath-standard).  It is a superset of thesagemath-objects(providing Sage objects, the element/parent framework, categories, the coercion
system and the related metaclasses), making various additional categories
available without introducing dependencies on additional mathematical
libraries.DependenciesWhen building from source, development packages ofgmp,mpfr, andmpcare needed.DocumentationCategoriesStructureCoercionClasses, Metaclasses
Package: sagemath-coxeter3
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this pip-installable source distributionThis pip-installable source distributionsagemath-coxeter3is a small
optional distribution for use withsagemath-standard.It provides a Cython interface to thecoxeter3library.
Package: sagemath-environment
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this experimental pip-installable source distributionThis pip-installable source distributionsagemath-environmentis an
experimental distribution of a small part of the Sage Library.  Use at your own
risk.  It provides a small, fundamental subset of the modules of the Sage
library (“sagelib”,sagemath-standard), providing the connection to the
system and software environment.  It also includes thesagescript for
launching the Sage REPL and accessing various developer tools (seesage
–help).
Package: sagemath-mcqd
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this pip-installable source distributionThis pip-installable source distributionsagemath-mcqdis a small
optional distribution for use withsagemath-standard.It provides a Cython interface to themcqdlibrary,
providing a fast exact algorithm for finding a maximum clique in
an undirected graph.
Package: sagemath-meataxe
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this pip-installable source distributionThis pip-installable source distributionsagemath-meataxeis a small
optional distribution for use withsagemath-standard.This distribution provides the SageMath modulessage.libs.meataxeandsage.matrix.matrix_gfpn_dense.It provides a specialized implementation of matrices over the finite field F_q, where
q <= 255, using theSharedMeatAxe <http://users.minet.uni-jena.de/~king/SharedMeatAxe/>library.
Package: sagemath-objects
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this experimental pip-installable source distributionThis pip-installable source distributionsagemath-objectsis an experimental
distribution of a small part of the Sage Library.  Use at your own risk.  It
provides a small, fundamental subset of the modules of the Sage library
(“sagelib”,sagemath-standard), making Sage objects, the element/parent
framework, categories, the coercion system and the related metaclasses
available.DependenciesWhen building from source, development packages ofgmp,mpfr, andmpcare needed.DocumentationCategoriesStructureCoercionClasses, Metaclasses
Package: sagemath-polyhedra
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2020 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via Sage-the-distribution (https://www.sagemath.org/download-source.html).  Sage-the-distribution first builds a large number of open source packages from source (unless it finds suitable versions installed in the system) and then installs the Sage Library (sagelib, implemented in Python and Cython).About this experimental pip-installable source distributionThis pip-installable source distributionsagemath-categoriesis an experimental distribution of a small part of the Sage Library.  Use at your own risk.  It provides a small subset of the modules of the Sage library (“sagelib”,sagemath-standard).  It is a superset of thesagemath-objects(providing Sage objects, the element/parent framework, categories, the coercion system and the related metaclasses), making various additional categories available without introducing dependencies on additional mathematical libraries.DocumentationCategoriesStructureCoercionClasses, Metaclasses
Package: sagemath-repl
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this experimental pip-installable source distributionThis pip-installable source distributionsagemath-replis an experimental
distribution of a small part of the Sage Library.  Use at your own risk.  It
provides a small, fundamental subset of the modules of the Sage library
(“sagelib”,sagemath-standard), providing the IPython kernel, Sage preparser,
and doctester.
Package: sagemath-sirocco
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this pip-installable source distributionThis pip-installable source distributionsagemath-siroccois a small
optional distribution for use withsagemath-standard.It provides a Cython interface to thesiroccolibrary for the purpose
of compute topologically certified root continuation of bivariate polynomials.
Package: sagemath-standard
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2020 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via Sage-the-distribution (https://www.sagemath.org/download-source.html).  Sage-the-distribution first builds a large number of open source packages from source (unless it finds suitable versions installed in the system) and then installs the Sage Library (sagelib, implemented in Python and Cython).About this experimental pip-installable source distributionThis pip-installable source distributionsagemath-standardis an experimental distribution of the Sage Library.  Use at your own risk.Buildingsagemath-standardhas a large number of system packages as prerequisites.
Seehttps://doc.sagemath.org/html/en/installation/source.html#linux-recommended-installationfor partial lists for various systems.The connection to the system environment is facilitated through thehttps://pypi.org/project/sage-conf/distribution package.A modularization effort is in progress with the goal of making it possible to install parts of the Sage Library with fewer prerequisites.https://github.com/sagemath/sage/issues/29705
Package: sagemath-tdlib
About SageMath“Creating a Viable Open Source Alternative toMagma, Maple, Mathematica, and MATLAB”Copyright (C) 2005-2023 The Sage Development Teamhttps://www.sagemath.orgSageMath fully supports all major Linux distributions, recent versions of
macOS, and Windows (using Cygwin or Windows Subsystem for Linux).The traditional and recommended way to install SageMath is from source via
Sage-the-distribution (https://www.sagemath.org/download-source.html).
Sage-the-distribution first builds a large number of open source packages from
source (unless it finds suitable versions installed in the system) and then
installs the Sage Library (sagelib, implemented in Python and Cython).About this pip-installable source distributionThis pip-installable source distributionsagemath-tdlibis a small
optional distribution for use withsagemath-standard.It provides a Cython interface to thetdliblibrary, providing
algorithms concerning tree decompositions.
Package: sagemill
Sagemill lets you to execute your notebook asSageMakerjobs in the similar
way topapermillOverall featuresparameterize notebooksexecute notebooks as SageMaker Trainig/Processing JobInstallatioin$ pip install sagemillPrerequsitesPython>=3.5Required IAM policyecrcreate-repositoryget-authorization-tokenstsget-caller-identitypermission to run Sagemaker jobswrite to cloudwatch logsaccess to some S3 bucketsstart training/proceessing jobsetc.Example notebooksThese notebook are assumed to beconda_python3in SageMaker Notebook
instance If you run it on different environments, installcondaand
runpip install sagemakerTraining job with tensorflowTraining job with your own docker
imageProcessing job with
SKLearnProcessorProcessing job with your own docker
image