Option to provide seed to random generators to ensure reproducibility (#1572)

* Option to provide seed to random generators to ensure reproducibility

I added small function in utilities which imports torch, numpy, python
random and sets seed for all of the libraries to ensure reproducibility
of results.

* Apply recommendations from core contributors on seeding

1. Moved the seeding code to another file
2. Make deterministic as a parameter for trainer class
3. Add assertions for seeding numpy
4. Added warnings
5. torch.manual_seed should be enough for seeding torch

* Revert "Apply recommendations from core contributors on seeding"

This reverts commit a213c8e6882eec8a9e7408b9418926d2db7c5461.

* Revert "Revert "Apply recommendations from core contributors on seeding""

This reverts commit 59b2da53c62878de7aab0aa3feb3115e105eea06.

* Change in test, for correct seeding

* Allow seed equal to 0

* Allow seed to be uint32.max

* Added deterministic to benchmarks

* Cuda manual seed as in benchmark seeding

* Seeding should be done before model initialization

* cuda manual_seed is not necessary

* Fixing seed test_cpu_lbfgs

On some seeds seems like lbfgs doesn't converge.
So I fixed the seed during testing.

* rebasing issue with old reproducibility.py

* Improved documentation and ability to seed before initializing Train
class

* Change in docs

* Removed seed from trainer, update for documentation

* Typo in the docs

* Added seed_everything to _all_

* Fixing old changes

* Model initialization should be earlier then Trainer

* Update pytorch_lightning/trainer/__init__.py

From Example to testcode

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* Fixing according to the contributors suggestions

* Moving horovod deterministic to Trainer class

* deterministic flag affects horovod docs update

* Improved static typing

* Added deterministic to test runners of horovod

It is failing on some versions, not very predictable

* static seeds for horovod tests

* Change for reset_seed function in tests

* Seeding horovod using reset_seed from tutils

* Update pytorch_lightning/trainer/__init__.py

* chlog

* Update trainer.py

* change "testcode" to "Example" in trainer init documentation

* Update pytorch_lightning/trainer/seed.py, first line in comment

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Jirka <jirka.borovec@seznam.cz>
Co-authored-by: William Falcon <waf2107@columbia.edu>

tests/base/utils.py

@@ -5,7 +5,7 @@ import numpy as np
 import torch
 
 # from pl_examples import LightningTemplateModel
-from pytorch_lightning import Trainer
+from pytorch_lightning import Trainer, seed_everything
 from pytorch_lightning.callbacks import ModelCheckpoint
 from pytorch_lightning.loggers import TensorBoardLogger
 from tests import TEMP_PATH, RANDOM_PORTS, RANDOM_SEEDS
@@ -188,8 +188,7 @@ def assert_ok_model_acc(trainer, key='test_acc', thr=0.5):
 
 def reset_seed():
     seed = RANDOM_SEEDS.pop()
-    torch.manual_seed(seed)
-    np.random.seed(seed)
+    seed_everything(seed)
 
 
 def set_random_master_port():