kandi background

ignite | Highlevel library | Machine Learning library

 by   pytorch Python Version: v0.4.8 License: BSD-3-Clause

 by   pytorch Python Version: v0.4.8 License: BSD-3-Clause

Download this library from

kandi X-RAY | ignite Summary

ignite is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. ignite has no bugs, it has build file available, it has a Permissive License and it has high support. However ignite has 3 vulnerabilities. You can install using 'pip install ignite' or download it from GitHub, PyPI.
PyTorch-Ignite is a NumFOCUS Affiliated Project, operated and maintained by volunteers in the PyTorch community in their capacities as individuals (and not as representatives of their employers). See the "About us" page for a list of core contributors. For usage questions and issues, please see the various channels here. For all other questions and inquiries, please send an email to contact@pytorch-ignite.ai.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • ignite has a highly active ecosystem.
  • It has 3927 star(s) with 548 fork(s). There are 61 watchers for this library.
  • There were 1 major release(s) in the last 6 months.
  • There are 114 open issues and 914 have been closed. On average issues are closed in 33 days. There are 37 open pull requests and 0 closed requests.
  • It has a positive sentiment in the developer community.
  • The latest version of ignite is v0.4.8
ignite Support
Best in #Machine Learning
Average in #Machine Learning
ignite Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • ignite has 0 bugs and 0 code smells.
ignite Quality
Best in #Machine Learning
Average in #Machine Learning
ignite Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • ignite has 3 vulnerability issues reported (1 critical, 0 high, 1 medium, 1 low).
  • ignite code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
ignite Security
Best in #Machine Learning
Average in #Machine Learning
ignite Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • ignite is licensed under the BSD-3-Clause License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
ignite License
Best in #Machine Learning
Average in #Machine Learning
ignite License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • ignite releases are available to install and integrate.
  • Deployable package is available in PyPI.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • ignite saves you 14511 person hours of effort in developing the same functionality from scratch.
  • It has 40301 lines of code, 3680 functions and 274 files.
  • It has high code complexity. Code complexity directly impacts maintainability of the code.
ignite Reuse
Best in #Machine Learning
Average in #Machine Learning
ignite Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

kandi has reviewed ignite and discovered the below as its top functions. This is intended to give you an instant insight into ignite implemented functionality, and help decide if they suit your requirements.

  • Main entry point .
  • Creates a trainer .
  • Spawn a new computation on the given backend .
  • Setup a logger .
  • Creates a LRS scheduler with given warmup_start_start_start_start_start_start_start_start_start_start_start_end_start_start_start_start_start_start_start_start_start_start_start_start_start_start_start_start_end_start_start_start_start_start_start_start_start_start_start_start_start_start_start_end_1 and end_start_start_start_start_start_start_start_start_start_start_start_start_
  • Create a dataloader for the given dataset .
  • Train a training model .
  • Plots the learning rate .
  • Set common training handlers .
  • Generate a list of event results .

ignite Key Features

Less code than pure PyTorch while ensuring maximum control and simplicity

Library approach and no program's control inversion - Use ignite where and when you need

Extensible API for metrics, experiment managers, and other components

ignite Examples and Code Snippets

  • Simplified training and validation loop
  • Execute any number of functions whenever you wish
  • Built-in events filtering
  • Stack events to share some actions
  • Custom events to go beyond standard events
  • Out-of-the-box metrics
  • Installation
  • Nightly releases
  • Using pre-built images
  • Citing Ignite
  • Apache Ignite "Update of composite key column is not supported"
  • Ignite Server Node with CacheJdbcPojoStore / Delay Client Node
  • Can't change AllowOverwrite data streamer option for .NET apache ignite thin client
  • Isolated Ignite clusters on same set of machines
  • What is a "closure" in Julia?
  • Apache Ignite 'Failed to prepare update plan' when executing SQL query on cache
  • Apache Ignite unexpectedly deletes IgniteSet
  • Apache ignite docker 'image' not compatible on Apple M1 Max
  • How to get Header Authorization on code igniter 4?
  • apache ignite java thin client expired entry listener

Simplified training and validation loop

from ignite.engine import Engine, Events, create_supervised_evaluator
from ignite.metrics import Accuracy


# Setup training engine:
def train_step(engine, batch):
    # Users can do whatever they need on a single iteration
    # Eg. forward/backward pass for any number of models, optimizers, etc
    # ...

trainer = Engine(train_step)

# Setup single model evaluation engine
evaluator = create_supervised_evaluator(model, metrics={"accuracy": Accuracy()})

def validation():
    state = evaluator.run(validation_data_loader)
    # print computed metrics
    print(trainer.state.epoch, state.metrics)

# Run model's validation at the end of each epoch
trainer.add_event_handler(Events.EPOCH_COMPLETED, validation)

# Start the training
trainer.run(training_data_loader, max_epochs=100)

Community Discussions

Trending Discussions on ignite
  • Apache Ignite "Update of composite key column is not supported"
  • Apache ignite startup failure
  • Ignite Server Node with CacheJdbcPojoStore / Delay Client Node
  • Can't change AllowOverwrite data streamer option for .NET apache ignite thin client
  • Isolated Ignite clusters on same set of machines
  • What is a "closure" in Julia?
  • Why does Apache Ignite use more memory than configured
  • Apache Ignite 'Failed to prepare update plan' when executing SQL query on cache
  • Apache Ignite unexpectedly deletes IgniteSet
  • Apache ignite docker 'image' not compatible on Apple M1 Max
Trending Discussions on ignite

QUESTION

Apache Ignite "Update of composite key column is not supported"

Asked 2022-Mar-28 at 11:06

I have a cache with an AffinityKey as Key and want to insert an entry. If i try to insert the entry using a SQL-Insert statement i get an exception stating "Update of composite key column is not supported" which i guess is because the AffinityKey is a composite key. On https://ignite.apache.org/docs/latest/SQL/indexes#indexing-nested-objects i have read that if you are using indexed nested objects you will no longer be able to use insert or update statements but it is not mentioned on https://ignite.apache.org/docs/latest/data-modeling/affinity-collocation for composite keys.

Does using composite keys also cause insert and update statements to no longer be usable or am i missing something?

Example:

// creating cache using QueryEntities
cache = ignite.CreateCache<AffinityKey, MyClass>(new CacheClientConfiguration(cacheConfiguration)
  {
    Name = "MyCache",
    QueryEntities = new[]
    {
      new QueryEntity(typeof(AffinityKey), typeof(MyClass)){ TableName = "MyTable" }
    },
  });

// insert statement
cache.Query(
  new SqlFieldsQuery(
    "INSERT INTO " +
      "MyTable ( " +
        "_key, " +
        $"{nameof(MyClass.Text)} ) " +
      "VALUES ( " +
        "?, " +
        "? )"
  )
  {
    Arguments = new object[]
    {
      new AffinityKey("Key1", "AffinityKey1"),
      "TextValue"
    },
  });

Solution: As described below by @Alexandr Shapkin, setting "IGNITE_SQL_ALLOW_KEY_VAL_UPDATES" to true makes it possible to update entries via SQL that have been added using a composite key.

For anyone interested https://ignite.apache.org/docs/latest/setup#setting-ignite-system-properties shows how to list available ignite properties/environment variables with a brief description.

ANSWER

Answered 2022-Mar-25 at 23:59

You are mixing the concepts.

In Apache Ignite you can access or insert your data multiple ways. One would be to use Cache API, like cache#get, cache#put, another option would be - using SQL, like INSERT, SELECT, etc.

Since eventually everything is just a key-value pair undeneath, Ignite provide a special SQL _key and _val properties to access the corresponded values.

The thing is - if you don't need to use Cache API and SQL interchangeably and are OK using only, say, SQL, you don't set _key at all.

On the other hand, if you need to use both APIs, say, defined with QueryEntity just like in your example, you need to specify _key field properly.

Consider this example:

IgniteCache<AffinityKey<Long>, Person> colPersonCache = Ignition.ignite().cache(PERSON_CACHE);

Person p1 = new Person(org1, "John", "Doe", 2000, "John Doe has Master Degree.");

colPersonCache.put(p1.key(), p1);

...

it's fine to do this:

    colPersonCache.query(new SqlFieldsQuery(
        "INSERT INTO Person (id, orgId, firstName, lastName, salary, resume) " +
            "VALUES ( ?, ?, ?, ?, ?, ?)")
        .setArgs(6, 2, "first", "last", 1, "Master Degree")).getAll();

But in that case, you won't be able to locate the record using cache API:

// the following line returns null, but you can use SQL to locate the record
colPersonCache.get(new AffinityKey<Long>(6L,2L)); 

But if you use _key:

colPersonCache.query(new SqlFieldsQuery(
            "INSERT INTO Person (_key, id, orgId, firstName, lastName, salary, resume) " +
                "VALUES (?, ?, ?, ?, ?, ?, ?)")
            .setArgs(new AffinityKey<Long>(6L,2L), 6, 2, "first", "last", 1, "Master Degree")).getAll();

Cache API now does return the value:

colPersonCache.get(new AffinityKey<Long>(6L,2L)); 

//Person [id=6, orgId=2, lastName=last, firstName=first, salary=1.0, resume=Master Degree]

Note.

There is a special restriction to avoid possible key desync, and in order to make the latest query (and the original in the question) work, you have to set the following enviroment property:

System.setProperty(IgniteSystemProperties.IGNITE_SQL_ALLOW_KEY_VAL_UPDATES, "true");

Source https://stackoverflow.com/questions/71619576

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ignite

Few pointers to get you started:.
Quick Start Guide: Essentials of getting a project up and running
Concepts of the library: Engine, Events & Handlers, State, Metrics
Full-featured template examples (coming soon)

Support

Stable API documentation and an overview of the library: https://pytorch.org/ignite/Development version API documentation: https://pytorch.org/ignite/master/FAQ, "Questions on Github" and "Questions on Discuss.PyTorch".Project's Roadmap

DOWNLOAD this Library from

Build your Application

Share this kandi XRay Report

Reuse Pre-built Kits with ignite
Reuse Solution Kits and Libraries Curated by Popular Use Cases

Save this library and start creating your kit