# Population based training does not train **URL:** \ **Category:** Ray Tune\ **Created:** [July 8, 2021, 5:19pm UTC](https://discuss.ray.io/t/population-based-training-does-not-train/2830 "2021-07-08T17:19:29Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![vinay\_ethiraj](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/vinay_ethiraj/32/1272_2.png) [@vinay\_ethiraj](https://discuss.ray.io/u/vinay_ethiraj)\ **Post date:** [July 8, 2021, 5:19pm UTC](https://discuss.ray.io/t/population-based-training-does-not-train/2830/1 "2021-07-08T17:19:30Z")
I am performing a hyperparameter search on roberta-large using transformers trainer.hyperparameter\_search API here is my code snippet ```auto num_cpus=1 num_gpus=1 keep_checkpoints_num=1 perturbation_interval=10 n_trials=16 def compute_perplexity(pred): logits = torch.from_numpy(pred.predictions) labels = torch.from_numpy(pred.label_ids) loss = cross_entropy(logits.view(-1, tokenizer.vocab_size), labels.view(-1)) try: perplexity = math.exp(loss) except OverflowError: perplexity = float("inf") return {'perplexity': perplexity, 'calculated_loss': loss} training_args = TrainingArguments( output_dir="/home/ubuntu/train/results", overwrite_output_dir = True, learning_rate=1e-5, # config do_train=True, do_eval=True, evaluation_strategy="epoch", load_best_model_at_end=True, metric_for_best_model = 'eval_perplexity', greater_is_better = False, num_train_epochs=2, # config max_steps=-1, # config per_device_train_batch_size=32, # config per_device_eval_batch_size=32, # config warmup_steps=0, # config weight_decay=0.1, # config logging_dir="/home/ubuntu/train/logs", logging_steps=10, skip_memory_metrics=True, report_to="none", fp16=True, seed=12, # config ) trainer = Trainer( model_init=get_model, args=training_args, train_dataset=lm_datasets['train'].shard(index=1, num_shards=10), eval_dataset=lm_datasets['valid'].shard(index=1, num_shards=10), tokenizer = tokenizer, data_collator = data_collator, compute_metrics=compute_perplexity ) tune_config = { "max_steps": -1, "per_device_eval_batch_size": 32, } scheduler = PopulationBasedTraining( time_attr="training_iteration", metric="eval_perplexity", mode="min", perturbation_interval=perturbation_interval, require_attrs=True, hyperparam_mutations={ "learning_rate": tune.loguniform(1e-6, 1e-1), "num_train_epochs": tune.randint(1, 11), "per_device_train_batch_size": tune.randint(1, 33), "warmup_steps": tune.randint(1, 10001), "weight_decay": tune.uniform(1e-1, 0.6), "seed": tune.randint(1, 1000), }) def objective_perplexity(metrics): return metrics['eval_perplexity'] best_run = trainer.hyperparameter_search( hp_space=lambda _: tune_config, compute_objective=objective_perplexity, direction='minimize', backend="ray", n_trials=n_trials, resources_per_trial={ "cpu": num_cpus, "gpu": num_gpus }, scheduler=scheduler, keep_checkpoints_num=keep_checkpoints_num, checkpoint_score_attr="training_iteration", stop=None, local_dir="/home/ubuntu/train/ray_results", name="tune-clrp-mlm", log_to_file=True ) ``` output ```auto +------------------------+------------+----------------------+-----------------+--------------------+-------------------------------+--------+----------------+----------------+-------------+ | Trial name | status | loc | learning_rate | num_train_epochs | per_device_train_batch_size | seed | warmup_steps | weight_decay | objective | |------------------------+------------+----------------------+-----------------+--------------------+-------------------------------+--------+----------------+----------------+-------------| | _objective_ea634_00000 | TERMINATED | 104.171.200.69:56320 | 0.00687667 | 1 | 11 | 964 | 3271 | 0.300316 | 8.50084 | | _objective_ea634_00001 | TERMINATED | | 1.03111e-05 | 10 | 23 | 481 | 9442 | 0.126476 | 8.92374 | | _objective_ea634_00002 | TERMINATED | | 0.0430234 | 5 | 4 | 533 | 4588 | 0.568076 | 8.00639 | | _objective_ea634_00003 | TERMINATED | | 1.49914e-06 | 6 | 20 | 787 | 5932 | 0.4181 | 8.77923 | | _objective_ea634_00004 | TERMINATED | | 1.18166e-06 | 7 | 21 | 92 | 504 | 0.12751 | 8.40113 | | _objective_ea634_00005 | TERMINATED | | 0.0105669 | 7 | 5 | 113 | 2870 | 0.386202 | 7.49462 | | _objective_ea634_00006 | TERMINATED | | 2.09139e-05 | 1 | 4 | 951 | 1006 | 0.386697 | 6.14532 | | _objective_ea634_00007 | TERMINATED | | 0.00034102 | 2 | 5 | 838 | 3738 | 0.402889 | 8.15622 | | _objective_ea634_00008 | TERMINATED | | 1.46586e-05 | 3 | 5 | 949 | 2481 | 0.524008 | 8.56621 | | _objective_ea634_00009 | TERMINATED | | 5.89949e-06 | 7 | 30 | 964 | 7410 | 0.143217 | 5.45729 | | _objective_ea634_00010 | TERMINATED | | 1.1827e-06 | 7 | 18 | 460 | 3726 | 0.141262 | 1953.19 | | _objective_ea634_00011 | TERMINATED | | 4.85341e-06 | 1 | 22 | 83 | 1129 | 0.591824 | 8.9625 | | _objective_ea634_00012 | TERMINATED | | 0.0183518 | 5 | 4 | 448 | 7586 | 0.376019 | 1954.01 | | _objective_ea634_00013 | TERMINATED | | 0.00264013 | 7 | 19 | 636 | 209 | 0.48228 | 1871.01 | | _objective_ea634_00014 | TERMINATED | | 0.0101467 | 1 | 1 | 986 | 7279 | 0.435726 | 2231.21 | | _objective_ea634_00015 | TERMINATED | | 4.34358e-05 | 6 | 12 | 694 | 7414 | 0.593281 | 8.38733 | +------------------------+------------+----------------------+-----------------+--------------------+-------------------------------+--------+----------------+----------------+-------------+ ``` All 16 trails run only once and then gets terminated, is the tuning even happening? It looks like it fired out 16 random search and picked the best out of those 16. ========================= Discussion on Slack channel @rliaw What is your `perturbation_interval` set to?It seems like you’re tuning train epochs, and probably the training is finishing before the interval is hit @rliaw BTW, I would recommend posting to [discuss.ray.io](http://discuss.ray.io) @vinay_ethiraj @rliaw Thank you for the reply, I will move the message to [discuss.ray.io](http://discuss.ray.io) ========================= ---
**Author:** ![vinay\_ethiraj](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/vinay_ethiraj/32/1272_2.png) [@vinay\_ethiraj](https://discuss.ray.io/u/vinay_ethiraj)\ **Post date:** [July 8, 2021, 5:20pm UTC](https://discuss.ray.io/t/population-based-training-does-not-train/2830/2 "2021-07-08T17:20:38Z")
> [@vinay\_ethiraj](#): > > `perturbation_interval` Do I decrease the interval value and try tuning again? ---
**Author:** ![rliaw](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/rliaw/32/24_2.png) [@rliaw](https://discuss.ray.io/u/rliaw)\ **Post date:** [July 8, 2021, 5:37pm UTC](https://discuss.ray.io/t/population-based-training-does-not-train/2830/3 "2021-07-08T17:37:30Z")
@vinay_ethiraj yeah, try decreasing the interval to 1? and also, i wouldn’t tune the ‘train\_epochs’ variable. ---
**Author:** ![vinay\_ethiraj](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/vinay_ethiraj/32/1272_2.png) [@vinay\_ethiraj](https://discuss.ray.io/u/vinay_ethiraj)\ **Post date:** [July 8, 2021, 5:42pm UTC](https://discuss.ray.io/t/population-based-training-does-not-train/2830/4 "2021-07-08T17:42:58Z")
will update my findings here, I will move the `num_train_epochs` out of search space. Thank you for the reply 🙂 ---
**Author:** ![vinay\_ethiraj](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/vinay_ethiraj/32/1272_2.png) [@vinay\_ethiraj](https://discuss.ray.io/u/vinay_ethiraj)\ **Post date:** [July 8, 2021, 6:14pm UTC](https://discuss.ray.io/t/population-based-training-does-not-train/2830/5 "2021-07-08T18:14:57Z")
```auto Number of trials: 16/16 (14 PAUSED, 1 PENDING, 1 RUNNING) +------------------------+----------+-------+-----------------+-------------------------------+--------+----------------+----------------+-------------+ | Trial name | status | loc | learning_rate | per_device_train_batch_size | seed | warmup_steps | weight_decay | objective | |------------------------+----------+-------+-----------------+-------------------------------+--------+----------------+----------------+-------------| | _objective_39f1e_00000 | RUNNING | | 5.89949e-06 | 7 | 260 | 3326 | 0.231658 | 9.5388 | | _objective_39f1e_00002 | PAUSED | | 1.46929e-06 | 3 | 385 | 8652 | 0.168605 | 9.37472 | | _objective_39f1e_00003 | PAUSED | | 0.00239877 | 13 | 959 | 4179 | 0.526368 | 8.79337 | | _objective_39f1e_00004 | PAUSED | | 8.89983e-06 | 2 | 353 | 5791 | 0.553634 | 7.36863 | | _objective_39f1e_00005 | PAUSED | | 1.06798e-05 | 1 | 423 | 1137 | 0.442907 | 1996.67 | | _objective_39f1e_00006 | PAUSED | | 0.0101467 | 14 | 111 | 4511 | 0.21168 | 13.1061 | | _objective_39f1e_00007 | PAUSED | | 0.00227648 | 26 | 26 | 4796 | 0.508084 | 8.17222 | | _objective_39f1e_00008 | PAUSED | | 5.26616e-06 | 25 | 578 | 3845 | 0.182184 | 8.51736 | | _objective_39f1e_00009 | PAUSED | | 4.71235e-05 | 6 | 837 | 7372 | 0.412291 | 7.48557 | | _objective_39f1e_00010 | PAUSED | | 7.31494e-06 | 22 | 481 | 3692 | 0.372732 | 8.94498 | | _objective_39f1e_00011 | PAUSED | | 0.000180527 | 7 | 157 | 6766 | 0.597569 | 6.96075 | | _objective_39f1e_00012 | PAUSED | | 2.20231e-05 | 28 | 533 | 4588 | 0.568076 | 8.52336 | | _objective_39f1e_00013 | PAUSED | | 1.49914e-06 | 6 | 161 | 8372 | 0.46626 | 8.61623 | | _objective_39f1e_00014 | PAUSED | | 0.004259 | 26 | 583 | 1783 | 0.210426 | 8.97854 | | _objective_39f1e_00015 | PAUSED | | 0.000318775 | 20 | 571 | 487 | 0.124109 | 7.70769 | | _objective_39f1e_00001 | PENDING | | 0.00414963 | 17 | 902 | 279 | 0.116982 | 1901.44 | +------------------------+----------+-------+-----------------+-------------------------------+--------+----------------+----------------+-------------+ ``` Now it trains beautifully! @rliaw thank you for the valuable inputs 🙂