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License: MIT License
Template for hosting a dataset and remotely training models on the data
License: MIT License
When a worker trains a model, it creates a lot of logs with important information and potentially important error messages. Log all outputs to a unique file tied to the training job instead of stdout on the worker.
Right now there is no way for a user to specify a loss function. Unlike optimizers which can be specified with a string loss functions must be loaded directly from a python object. Determine a safe and usable way for researchers to specify their own loss functions on upload
Currently, datasets can contain images of any size. On load the images should be modified to be the same size as to work with all models.
Similar to this Server Issue which also handles Docker worker scaling.
Right now, there is a single Docker worker which handles all training tasks. This is not optimal, as the single worker is hard-coded into the docker-compose.yml
file and will not handle large request loads. Figure out the best way to dynamically scale the number of workers available.
Right now all training is done via Tensorflow2 and Keras. Add support for Pytorch Lightning.
Currently, the Docker container used for the microservice only has access to the CPU and memory. Enabling the container to interact with the system GPU can lead to large speed ups in training tasks.
Right now the server only supports a train/validation split with the entire dataset, and no test set is generated when the dataset is loaded.
Create a new environment variable in .env
for train/test split, and then use this new test set to provide additional statistics on model training.
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