Using iris upload
Last updated
Last updated
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Remember to ensure you have the latest version of iris installed before running any command! You can check this by running pip install update titan-iris.
Although you can upload models and datasets on the fly by using their respective file paths with iris distil
or iris finetune
, it may be useful to have access to your models and datasets from other machines. iris upload
allows models to be uploaded without immediately using them for a job. iris upload <filepath> <name>
also returns an artefact ID, which is a canonical reference to the uploaded artefact (model or a dataset). You can pass this ID anytime as an argument to iris distil.
There are a few constraints on the types of models and datasets that can be passed to iris upload
. These constraints also apply whenever you run an iris distil
or iris finetune
job with a local directory.
Model and dataset paths must be paths to folders.
The dataset folder must contain the following:
Dataset files which have already been split into training and validation sets, titled train.csv
and val.csv
respectively (only .csv files are accepted!)
A common scheme (i.e. the same column names and number of columns) shared by both the training and validation datasets.
For sequence classification jobs, a label column titled 'Label' which contains integer values.
The model folder must contain the following:
A pytorch.bin
model file (of course!)
A tokeniser file named tokenizer_config.json
.
Model weights must be stored using Either save your model from transformers
using safe_serialization=true
or use iris makesafe
to get a .safetensors
file.
To run iris makesafe
, simply pass the path to the folder containing the model, e.g. iris makesafe iris upload /Users/myaccount/All-code/Kaggle/Models/twitter_sc
. This will add a safetensors file to your model directory.
Below are some examples:
Remember that you can choose to upload a model, a dataset or both from a local file.