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README: note about RANDSEED

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@ -172,6 +172,8 @@ ARGS | Optional. Any additional arguments to pass to the python program.
**Making predictions:** Set `PATH_CHECKPOINT` to point to a checkpoint file to make predictions with an existing model that you trained earlier instead of training a new one. Data is pulled from the given dataset, same as during training. The first `PREDICT_COUNT` items in the dataset are picked to make a prediction. **Making predictions:** Set `PATH_CHECKPOINT` to point to a checkpoint file to make predictions with an existing model that you trained earlier instead of training a new one. Data is pulled from the given dataset, same as during training. The first `PREDICT_COUNT` items in the dataset are picked to make a prediction.
**`RANDSEED`:** The experiment runs in the series in the conference paper (see above!) uses `RANDSEED=QPnwyRZbLJBaTS7pjo4JZcl8vy9yv1D0SCpbS6olD5cmy`
> [!NOTE] > [!NOTE]
> The dataset pipeline is naturally non-deterministic with respect to the order in which samples are read. Ensuring the ordering of samples is not mangled is only possible when making predictions, and requires a number of environment variables to be set: > The dataset pipeline is naturally non-deterministic with respect to the order in which samples are read. Ensuring the ordering of samples is not mangled is only possible when making predictions, and requires a number of environment variables to be set:
> >