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Home Page: https://docs.bench-ai.com
License: Other
Pytorch directly integrated to the cloud all through Bench AI!
Home Page: https://docs.bench-ai.com
License: Other
The Accelerate backend should not be required to use the application. Accelerate is not compatible with many ML libraries.
Write tests for RawFile located in src/benchkit/data/file_saver
Write a unit test for function get_local_dataloader in src/benchkit/data/helpers
Make Sure Pytorch is not essential to using the library, allows users to train models using things like Jax, TensorFlow, etc.
Create Design Document before tackling this issue
Write a unit test for function remove_all_temps in src/benchkit/data/helpers
Write a unit test for function create_dataset_dir in src/benchkit/data/helpers
Write tests for BooleanFile located in src/benchkit/data/file_saver
Write a unit test for function compress_directory in src/benchkit/data/helpers
Write tests for NumpyFile located in src/benchkit/data/file_saver
Write tests for the Iterable Chunk class located in src/benchkit/data/datasets/
Users should be able to train and deploy their models through Tensorflow, mainly they should be able to use the IterableChunk class with TensorFlow
Write a unit test for function test_dataloading in src/benchkit/data/helpers
Write tests for TextFile located in src/benchkit/data/file_saver
Refactor and test the save_file_and_label method located in src/benchkit/data/helpers
Write a unit test for function get_folder_size in src/benchkit/data/helpers
The Yaml File should structure their projects with the proper dependencies, integrations, and template code based on the settings provided.
The Yaml File will hold configuration details on the project, that can be used to streamline the model training process.
In the file, you will detail:
The same configuration should be able to be done without a YAML simply through an object
Unicode error while creating a new project in windows
Write a unit test for function get_directory_size in src/benchkit/data/helpers
Write tests for JsonFile located in src/benchkit/data/file_saver
Everything done through the CLI should be doable through an API object.
Make design documentation before implementing.
Write a unit test for function get_dir_size in src/benchkit/data/helpers
if only one page of experiments is present the user is not able to access the logs for that experiment. Need to make sure they can access the logs in this scenario as well.
Write a unit test for function get_total_file_count in src/benchkit/data/helpers
Users should be able to track experiments that do not take place on the managed cloud
Add integrations and templates that make it easier to train LLMs on Bench AI, should integrate well with YAML file.
Write a unit test for function create_dataset_zips in src/benchkit/data/helpers
Speedup the Processor Dataset using multi processing/threading
Write a unit test for function get_dataloader in src/benchkit/data/helpers
Make sure there are restriction put in place to make sure presigned urls arent given after a certain numbe and so file sizes have a minimum
show ds does not show dataset properly
Write tests for TorchFile located in src/benchkit/data/file_saver
Add the ability to record the graphs using tensor-board and pull the graphs to local using bench kit
Write tests for NumericFile located in src/benchkit/data/file_saver
Write a unit test for function iterate_directory in src/benchkit/data/helpers
Make bench-kit not use docker
Write Tests for the ProcessorDataset class, located at src/benchkit/data/datasets
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