Iris commands
An overview of the iris API.
Last updated
An overview of the iris API.
Last updated
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Also see the API
By default, these commands will send a request to the backend, then print a table with the response to the command line. If you would like to receive the response as a JSON object, simply add the tag --json after the command
Dispatches knowledge distillation jobs to the TitanML platform. You can look at all of the iris distil arguments by using this command: iris distil --help.
Dispatches fine-tuning jobs to the TitanML platform.
For the above two commands, both model and dataset can be any of:
A HuggingFace model/dataset
A UUID generated by iris upload
The filepath of a model/dataset folder (which will then be uploaded as part of the job).
The following arguments apply to both iris distil and iris finetune:
--model
-m
TEXT
The model to optimize. [default: None] [required]
--dataset
-d
TEXT
The dataset to optimize the model with. [default: None] [required]
--task
-t
[sequence_classification | question_answering | token_classification]
The task to optimize the model for.[default: None] [required]
--subset
-ss
TEXT
The subset of the dataset to use
--name
-n
TEXT
The name to use for this job. Visible in the titan web interface.
--file
-f
PATH
The .yaml file containing experiment parameters.
--short-run
-s
flag
Truncates the run after 1 batch and 1 epoch. Will provide poor results, but useful to check that the model and dataset choices are valid.
--num-labels
-nl
INTEGER
Number of labels. Required for task sequence_classification [default: None]
--text-fields
-tf
TEXT
Text fields. Required for task sequence_classification [default: None]
--has-negative
-hn
flag
Has negative. Required for question_answering
--label-name
-ln
int:TEXT
The label names used for token classification
--help
flag
Show this message and exit.
You can use these additional arguments to specify hyperparameters when running a fine-tuning job:
--batch-size
-bs
INTEGER
The batch size to use for training. Default is 16.
--learning-rate
-lr
FLOAT
The learning rate to use for training. Default is 2e-5.
--num-epochs
-ne
INTEGER
Number of epochs to finetune for. Default is 1.
Gets objects from the TitanAPI. You can look at all of the Iris get commands by using this command: iris get --help
Retrieves the status of an experiment. You can look at all of the Iris status commands by using this command: iris status --help
Uploads local models and datasets by local filepath. You can look at all of the Iris upload commands by using iris upload --help.
Deletes a model or dataset (i.e. an artefact), or an entire experiment, from the TitanML store.
Downloads a finished, Titan-optimised model directly onto your machine in the ONNX format.
Downloads a finished, Titan-optimised model as a Docker image (usage is equivalent to docker pull).
Runs inference by requesting the Triton Inference Server.
Converts a model to the safetensors format, including those models which the transformers save method can't convert.
More explanation , on how to launch a distillation or fine-tuning job.
