GitLab

* Add run_mim_no_trainer.py draft from #20412

Add parse_args method and copy over other dependencies

Add Method call for sending telemetry

Initialize Accelerator

Make one log on every process

Set seed and Handle repository creation

Initialize dataset and Set validation split

Create Config

Adapt Config

Update Config

Create Feature Extractor

Create model

Set column names

Create transforms

Create mask generator

Create method to preprocess images

Shuffle datasets if needed and set transforms

Create Dataloaders

Add optimizer

Add learning rate scheduler

Prepare everything with our accelerator

Tie weights for TPU training

Recalculate training steps and training epochs

Set accelerator checkpointing steps

Initialize trackers and store configuration

Set total batch size

Fix typo: mlm -> mim

Log info at the start of training

Load in the weights and states from previous save

update the progress_bar if load from checkpoint

Define train loop

Add evaluation loop to training

Add to parse_args method

Push repo to hub

Save accelerator state

End training and save model and feature extractor

Remove unused imports

Fix trailing whitespace

* Update code based on comments, Rename feature_extractor to image_processor

* Fix linting

* Add argument for learning rate

* Add argument for setting number of training epochs

* Remove incorrect logger argument

* Convert max_train_steps to int for tqdm

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Co-authored-by: Saad Mahmud <shuvro.mahmud79@gmail.com>

Migration to Evaluate for metric is not quite complete

Save the tokenizer and image preprocessor after training a model with the contrastive image-text example (#23035)

Save tokenizer and image preprocessor

Bump flask in /examples/research_projects/decision_transformer

Bumps [flask](https://github.com/pallets/flask) from 2.0.3 to 2.3.2.
- [Release notes](https://github.com/pallets/flask/releases)
- [Changelog](https://github.com/pallets/flask/blob/main/CHANGES.rst)
- [Commits](https://github.com/pallets/flask/compare/2.0.3...2.3.2

)

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updated-dependencies:
- dependency-name: flask
  dependency-type: direct:production
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* Avoid invalid escape sequences, use raw strings

* Integrate PR feedback

minor refactoring to allow compatible datasets to work.

Remove accelerate from tf

* add: tokenizer training script for TF TPU LM training.

* add: script for preparing the TFRecord shards.

* add: sequence of execution to readme.

* remove limit from the tfrecord shard name.

* Add initial train_model.py

* Add basic training arguments and model init

* Get up to the point of writing the data collator

* Pushing progress so far!

* Complete first draft of model training code

* feat: grouping of texts efficiently.
Co-authored-by: Matt <rocketknight1@gmail.com>

* Add proper masking collator and get training loop working

* fix: things.

* Read sample counts from filenames

* Read sample counts from filenames

* Draft README

* Improve TPU warning

* Use distribute instead of distribute.experimental

* Apply suggestions from code review
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Modularize loading and add MLM probability as arg

* minor refactoring to better use the cli args.

* readme fillup.

* include tpu and inference sections in the readme.

* table of contents.

* parallelize maps.

* polish readme.

* change script name to run_mlm.py

* address PR feedback (round I).

---------
Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Replace -100s in predictions by the pad token

* Style

* Try to catch them all

* Update run_speech_recognition_ctc.py

Make sure all processes wait until data is saved before loading the processor from the output_dit

* Make sure all processes wait until data is saved before loading the processor from the output_dit

* Update run_speech_recognition_ctc.py

* Update run_speech_recognition_seq2seq.py

Add id2label and label2id to config in run_xnil

Bump redis in /examples/research_projects/decision_transformer

Bumps [redis](https://github.com/redis/redis-py) from 4.5.3 to 4.5.4.
- [Release notes](https://github.com/redis/redis-py/releases)
- [Changelog](https://github.com/redis/redis-py/blob/master/CHANGES)
- [Commits](https://github.com/redis/redis-py/compare/v4.5.3...v4.5.4

)

---
updated-dependencies:
- dependency-name: redis
  dependency-type: direct:production
...
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Bump redis in /examples/research_projects/decision_transformer

Bumps [redis](https://github.com/redis/redis-py) from 4.1.4 to 4.5.3.
- [Release notes](https://github.com/redis/redis-py/releases)
- [Changelog](https://github.com/redis/redis-py/blob/master/CHANGES)
- [Commits](https://github.com/redis/redis-py/compare/v4.1.4...v4.5.3

)

---
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- dependency-name: redis
  dependency-type: direct:production
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* Add initial remote hardware auto-setup docs

* Fix a few typos and clarify some language

* Add missing dependency

* Update self-hosted launch script with Sylvain's comments.

* Formatting.

* Trigger CI

* Style

* Pin tensorflow-text to go with tensorflow

* Make it more convenient to pin TensorFlow

* setup don't like f-strings

allow only test_file in pytorch and flax summarization

* add low_cpu_mem_usage option in run_clm.py example which will benefit LLM loading
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* update all the example and README under language-modeling
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

---------
Signed-off-by: Wang, Yi A <yi.a.wang@intel.com>

* Add specaugment to run_speech_recognition_seq2seq.py

* Remove useless argument: text_column

* Fix quality

* Update return_attention_mask condition

* Update specaugment arguments only for whisper models

* Remove SpecAugment arguments from ModelArguments, only leave default values for simplicity

* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* Update apply_spec_augment only for whisper models

* Apply suggestions from code review
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* Rename return_attention_mask to forward_attention_mask to avoid confusion with wav2vec2 models

---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* Stop requiring Torch for our TF examples!

* Slight tweak to logging in the example itself

* Initial commit

* stash commit

* Add model checkpointing and pushing

* Fix model name inference

* Update README

* Update README

* Remove a couple of Torch references

* Update copyright date

* make fixup

* Update PushToHubCallback args!

* Remove the torch summary

* Add strategy.scope

* Add check for different embedding types in examples

* Correctly update summarization example

* Override the decoding parameters of Seq2SeqTrainer

* Fix quality

* Fix max_length parameter

* Fix quality

* Remove redundant parameter max_length

* Separate the preprocess of train and validation to use different max_target_length

* [Examples] Generalise run audio classification for log-mel models

* batch feature extractor

* make style

* Respect documentation on passive log level

* Fix test and set log level in examples

* Add doc

pin pytorch lightning requirement

Bump werkzeug in /examples/research_projects/decision_transformer

Bumps [werkzeug](https://github.com/pallets/werkzeug) from 2.0.3 to 2.2.3.
- [Release notes](https://github.com/pallets/werkzeug/releases)
- [Changelog](https://github.com/pallets/werkzeug/blob/main/CHANGES.rst)
- [Commits](https://github.com/pallets/werkzeug/compare/2.0.3...2.2.3

)

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updated-dependencies:
- dependency-name: werkzeug
  dependency-type: direct:production
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