Why can't I get reproducible results in even though I set the random seeds?

[ahmad-alismail]

Hello, I am trying to use this framework for text labeling with all existed methods with BERT embedding. However, each time I run the code I get different result. I set both nump.random and tensorflow.set_random_seed, but for some reasons I can't get reproducible results:

# Seed value
# Apparently you may use different seed values at each stage
seed_value= 42

# 1. Set `PYTHONHASHSEED` environment variable at a fixed value
import os
os.environ['PYTHONHASHSEED']=str(seed_value)

# 2. Set `python` built-in pseudo-random generator at a fixed value
import random
random.seed(seed_value)

# 3. Set `numpy` pseudo-random generator at a fixed value
import numpy as np
np.random.seed(seed_value)

# 4. Set `tensorflow` pseudo-random generator at a fixed value
import tensorflow as tf
tf.random.set_seed(seed_value)

Environment

• OS [e.g. Mac OS, Linux]: Mac OS
• Python Version: 3.6 - Google-colab ( training on GPU)

Thanks in Advance!

[BrikerMan]

I haven't tried this use-case. Let me test & come back to you.

[wangberry]

I got the same error! Did you solve it?

[BrikerMan]

I got the same error! Did you solve it? Not yet. Which corpus you are using to train. Can you share a colab with this issue so that I can follow up.

[wangberry]

I got the same error! Did you solve it? Not yet. Which corpus you are using to train. Can you share a colab with this issue so that I can follow up.

Unreproducibility should not be caused by the corpus. Maybe this can help: https://github.com/NVIDIA/framework-determinism

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