I'm using clear-ml agent right now. I just upload the task inside a project. I've used arg parse as well however as of yet, I have not been able find writable hyperparameters in the UI. Is there any tutorial video you can recommend that deals with this or something? I was following https://www.youtube.com/watch?v=Y5tPfUm9Ghg&t=1100s this one on youtube but I can't seem to recreate his steps as he sifts through his code.
You can only edit the cloned copy, not the experiment created from the local run
from sklearn.datasets import load_iris
import tensorflow as tf
import numpy as np
from clearml import Task, Logger
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', metavar='N', default=64, type=int)
args = parser.parse_args()
parsed_args = vars(args)
task = Task.init(project_name="My Workshop Examples", task_name="scikit-learn joblib example")
iris = load_iris()
data = iris.data
target = iris.target
labels = np.unique(target)
epochs = parsed_args['epochs']
task.connect(args)
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(64, input_dim=4, activation='relu'),
tf.keras.layers.Dense(len(labels), activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(data, target, epochs=epochs)
print(model.evaluate(data, target))
if name == "main":
main()
Here's the script I'm testing with.
Here's the script I run it with.
clearml-task --project ClearML-Learn --name EpochsConnectReturns --requirements requirements.txt --script demo.py
Try to clone the task (right click on the task and choose “clone”) and you will get a new task in draft mode, that you can configure ( https://clear.ml/docs/latest/docs/getting_started/mlops/mlops_first_steps#clone-an-experiment )
Hi VexedCat68
You can use argparse and all the parameters will be log automagically to the hyperparameters section like in https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py#L56 example, or just connecting any dict like in https://github.com/allegroai/clearml/blob/master/examples/frameworks/ignite/cifar_ignite.py#L23 example
In any case, when sending something to be executed remotely , you can change the values as you see fit. On the other hand, when running locally , it will always use the values explicitly passed in the local run command
Also my execution just completed and as of yet, I can only see the hyperparameters as a report. not in a configurable form. I've just started with ClearML and am having these issues.
Basically when I have to re run the experiment with different hyperparameters, I should clone the previous experiment and change the hyperparameters then before putting it in the queue?
Quick follow up question. Once I parse args, should they be directly available for i even enque the project for the first time or will i be able to access hyperparameters after running it once?
VexedCat68 there are two possibilities:
This is a new experiment, and you've just run the code locally - an experiment entry will be created in the UI, and the values you used for the args will be in the hyperparams section The experiment already exists in the UI (as a result of running it locally, or even a previous experiment which was already executed remotely) - you can clone it, and than edit the hyper parameters values in the UI. Then, you send this experiment to be executed remotely - when it will run, the values you've just updated in the UI will be the values returned by the argparse parse_args() method
My draft is View Only but the cloned toy task one is in normal Draft mode.
I checked and it seems when i an example from git, it works as it should. but when I try to run my own script, the draft is in read only mode.
It seems that is the case. Thank you for all your help guys.
Basically when I have to re run the experiment with different hyperparameters, I should clone the previous experiment and change the hyperparameters then before putting it in the queue?
You can also reset it instead of cloning