Addition of loggers and dist strategy #180

r-sarma · 2024-07-02T10:36:51Z

Summary

Distributed Strategy and loggers are added

Related issue : #160

matbun

Nice integration, just a few comments in addition to the ones left inline:

train.py is not needed and should be replaced with a command like:

itwinai exec-pipeline --config pipeline.yaml --pipe-key pipeline

There are some new changes in main, especially the TrainingConfiguration has been improved to provide more values by default (although it can support any new user-defined fields).

matbun · 2024-07-02T12:11:56Z

use-cases/xtclim/src/trainer.py

+        BCE = bce_loss
+        KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+        return BCE + beta*KLD
+

    @monitor_exec
    def execute(self):


it would be nice to separate the boilerplate code (e.g., distributed strategy setup, loggers setup) from the actual training code, if possible. Ideally, the use cases should not worry about the setup and should only override the train method from the TorchTrainer class. In other words, use cases can get the best out of the TorchTrainer when reusing its execute method and overriding only the train method

As we had discussed, in this case, the preprocessing part first writes the files to disk and then the trainer reads the data from disk. Hence execute is not used, rather @monitor_exec is employed.

I am not sure I fully understood this

I have similar comments as for the execute method in the XTClimPredictor

use-cases/xtclim/src/trainer.py

matbun · 2024-07-02T12:15:13Z

use-cases/xtclim/src/trainer.py

+        Parameters:
+        bce_loss: recontruction loss
+        mu: the mean from the latent vector
+        logvar: log variance from the latent vector
+        beta: weight over the KL-Divergence


this part of the doctstring is not compliant with the Google docstring format

It should look more like this: https://itwinai.readthedocs.io/latest/_modules/itwinai/torch/trainer.html#TorchTrainer

Parameters -> Args

indent argument descriptions

provide arg types in parentheses

matbun · 2024-07-02T12:26:51Z

use-cases/xtclim/src/trainer.py

            batch_size: int,
-            lr: float
+            lr: float,


lr and batch_size should be embedded in config (needs to be added to the constructor). The training config contains all the hyperparams and user-defined params

I mean that in the trainer constructur there should be an argument called config which accepts a dictionary object, which in turn contains a set of hyperparameters (lr, batch_size, etc.).
This way we simplify the trainer constructor, grouping all the hyperparams under config. See this: https://itwinai.readthedocs.io/latest/python-api/itwinai.torch.modules.html#module-itwinai.torch.trainer

this seems to be unresolved yet. Also, try as much as possible to reuse the existing field names from the training configuration: https://itwinai.readthedocs.io/latest/python-api/itwinai.torch.modules.html#itwinai.torch.config.TrainingConfiguration
Example, in this case lr would become optim_lr .

It is also possible to create a custom configuration for the use case to include use case hyperparams, as we did for Virgo:

itwinai/use-cases/virgo/trainer.py

Line 36 in af68d90

class VirgoTrainingConfiguration(TrainingConfiguration):

use-cases/xtclim/src/trainer.py

matbun · 2024-07-02T12:27:37Z

use-cases/xtclim/src/trainer.py

            # initialize the model
-            cvae_model = model.ConvVAE().to(device)
+            cvae_model = model.ConvVAE()
            optimizer = optim.Adam(cvae_model.parameters(), lr=self.lr)


lr should be retrieved from self.config

it should be self.config.optim_lr

https://itwinai.readthedocs.io/latest/python-api/itwinai.torch.modules.html#itwinai.torch.config.TrainingConfiguration.optim_lr

matbun · 2024-07-02T12:34:23Z

I've noticed an interesting pattern: Inside the trainer there is a loop, training a new model for each season.
I wonder how we could represent it in the pipeline... Maybe it is not possible to use the pipeline in this case.

Perhaps it would be easier to create a new XTClimTrainer and train it for each season. It could receive the season as a constructor argument. This would simplify the training code inside the trainer. Although this is not mandatory, it would make the trainer more modular, allowing to separate the instantiation of model, optimizer, dataloader in the dedicated methods: create_model_loss_optimizer and create_dataloaders respectively.
I mean something like this, in the train.py file:

# Do some pre-processing
...

# Training
for season in seasons:
  net = MyModel()
  config = TrainingConfiguration(season=season, batch_size=32, optimizer='sgd', optim_lr=0.001, ...)
  trainer = XTClimTrainer(config=config, model=net)
  trainer.execute()

  # Do some prediction
  ...

In this case, we would not use the itwinai Pipeline.

Each trainer instance would create a new logger context (thus a new logging run), and a new distributed ML context (if distributed ML is needed)...

r-sarma · 2024-07-29T13:33:34Z

Nice integration, just a few comments in addition to the ones left inline:

train.py is not needed and should be replaced with a command like:
itwinai exec-pipeline --config pipeline.yaml --pipe-key pipeline
There are some new changes in main, especially the TrainingConfiguration has been improved to provide more values by default (although it can support any new user-defined fields).

r-sarma · 2024-07-29T13:36:38Z

Nice integration, just a few comments in addition to the ones left inline:
train.py is not needed and should be replaced with a command like:
itwinai exec-pipeline --config pipeline.yaml --pipe-key pipeline
There are some new changes in main, especially the TrainingConfiguration has been improved to provide more values by default (although it can support any new user-defined fields).

'train.py' now takes care of the launch of each season separately. In the current implementation, we can still maintain the pipelines, while running each season separately.

r-sarma · 2024-07-29T13:42:02Z

I've noticed an interesting pattern: Inside the trainer there is a loop, training a new model for each season. I wonder how we could represent it in the pipeline... Maybe it is not possible to use the pipeline in this case.

Perhaps it would be easier to create a new XTClimTrainer and train it for each season. It could receive the season as a constructor argument. This would simplify the training code inside the trainer. Although this is not mandatory, it would make the trainer more modular, allowing to separate the instantiation of model, optimizer, dataloader in the dedicated methods: create_model_loss_optimizer and create_dataloaders respectively. I mean something like this, in the train.py file:
# Do some pre-processing
...

# Training
for season in seasons:
  net = MyModel()
  config = TrainingConfiguration(season=season, batch_size=32, optimizer='sgd', optim_lr=0.001, ...)
  trainer = XTClimTrainer(config=config, model=net)
  trainer.execute()

  # Do some prediction
  ...
In this case, we would not use the itwinai Pipeline.

Each trainer instance would create a new logger context (thus a new logging run), and a new distributed ML context (if distributed ML is needed)...

This has now been implemented in a slightly different manner. The train.py now reads the configuration file, which contains the list of the seasons. Then it loops over the seasons, and dynamically adjust the seasons value to launch a pipeline iteratively for each season.

matbun · 2024-10-16T08:27:04Z

I would suggest to rebase onto main before proceeding given the latest changes

r-sarma · 2024-10-16T09:51:58Z

Branch has been rebased onto main.

matbun · 2024-08-27T11:53:10Z

use-cases/xtclim/src/trainer.py

            batch_size: int,
-            lr: float
+            lr: float,


I mean that in the trainer constructur there should be an argument called config which accepts a dictionary object, which in turn contains a set of hyperparameters (lr, batch_size, etc.).
This way we simplify the trainer constructor, grouping all the hyperparams under config. See this: https://itwinai.readthedocs.io/latest/python-api/itwinai.torch.modules.html#module-itwinai.torch.trainer

use-cases/xtclim/src/trainer.py

matbun · 2024-08-27T11:55:03Z

use-cases/xtclim/src/trainer.py

+        Parameters:
+        bce_loss: recontruction loss
+        mu: the mean from the latent vector
+        logvar: log variance from the latent vector
+        beta: weight over the KL-Divergence


It should look more like this: https://itwinai.readthedocs.io/latest/_modules/itwinai/torch/trainer.html#TorchTrainer

Parameters -> Args

indent argument descriptions

provide arg types in parentheses

use-cases/xtclim/src/trainer.py

matbun · 2024-08-27T11:58:42Z

use-cases/xtclim/src/trainer.py

            # initialize the model
-            cvae_model = model.ConvVAE().to(device)
+            cvae_model = model.ConvVAE()
            optimizer = optim.Adam(cvae_model.parameters(), lr=self.lr)


it should be self.config.optim_lr

https://itwinai.readthedocs.io/latest/python-api/itwinai.torch.modules.html#itwinai.torch.config.TrainingConfiguration.optim_lr

matbun · 2024-12-04T13:53:30Z

use-cases/xtclim/src/trainer.py

            batch_size: int,
-            lr: float
+            lr: float,


this seems to be unresolved yet. Also, try as much as possible to reuse the existing field names from the training configuration: https://itwinai.readthedocs.io/latest/python-api/itwinai.torch.modules.html#itwinai.torch.config.TrainingConfiguration
Example, in this case lr would become optim_lr .

It is also possible to create a custom configuration for the use case to include use case hyperparams, as we did for Virgo:

itwinai/use-cases/virgo/trainer.py

Line 36 in af68d90

class VirgoTrainingConfiguration(TrainingConfiguration):

matbun · 2024-12-04T13:56:23Z

use-cases/xtclim/src/trainer.py

+        BCE = bce_loss
+        KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+        return BCE + beta*KLD
+

    @monitor_exec
    def execute(self):


I have similar comments as for the execute method in the XTClimPredictor

matbun · 2024-12-04T13:57:19Z

use-cases/xtclim/train.py

+def read_config(file_path):
+    with open(file_path, 'r') as f:
+        config = yaml.safe_load(f)
+    return config


this could be replaced by itwinai.utils.load_yaml function

matbun · 2024-12-04T13:58:04Z

use-cases/xtclim/train.py

+        config['pipeline']['init_args']['steps']['training-step']['init_args']['seasons'] = season
+        model_uri = f"outputs/cvae_model_{season}1d_1memb.pth"
+        config['pipeline']['init_args']['steps']['evaluation-step']['init_args']['model_uri'] = model_uri
+        config['pipeline']['init_args']['steps']['evaluation-step']['init_args']['seasons'] = season


See comments in hpo.py on dynamic override of config fields

matbun · 2024-12-04T13:59:37Z

use-cases/xtclim/train.py

+        )
+        pipeline = pipe_parser.parse_pipeline()
+        print(f"Running pipeline for season: {season}")
+        pipeline.execute()


the comment in hpo.py on breaking down pipeline into preprocessing steps (potentially always the same across seasons) and training steps, to avoid re-doing the preprocessing. However, I am not an expert of this use case and this may not be feasible for some reason

jarlsondre · 2024-12-11T10:31:26Z

use-cases/xtclim/src/anomaly.py

+                 self.logger.create_logger_context()
+
+            for scenario in ['585', '245']:


These are magic "numbers". Make them into constants or at least some variable that tells us what they mean.

jarlsondre · 2024-12-11T10:31:37Z

use-cases/xtclim/src/anomaly.py

jarlsondre · 2024-12-11T10:31:54Z

use-cases/xtclim/src/anomaly.py

newlines (see earlier dicussion)

jarlsondre · 2024-12-11T10:32:20Z

use-cases/xtclim/src/anomaly.py

+                                                       projset, self.device, criterion,
+                                                       pixel_wise_criterion)


newlines (see earlier dicussions) and num parameters per line

jarlsondre · 2024-12-11T10:32:29Z

use-cases/xtclim/src/anomaly.py

+                                                       projset, self.device, criterion,
+                                                       pixel_wise_criterion)


Same as above

jarlsondre · 2024-12-11T10:32:44Z

use-cases/xtclim/src/anomaly.py

+                                                       projset, self.device, criterion,
+                                                       pixel_wise_criterion)
+                    tot_proj_losses = list(map(add, tot_proj_losses, proj_losses))


use of "map" (see earlier discussions)

jarlsondre · 2024-12-11T10:32:53Z

use-cases/xtclim/src/anomaly.py

Space between operators

jarlsondre · 2024-12-11T10:33:19Z

use-cases/xtclim/src/anomaly.py

+
+                # save the losses time series
+                pd.DataFrame(tot_proj_losses).to_csv(f"outputs/proj{scenario}_losses_{season}1d_allssp.csv")


pathlib (see earlier discussions)

jarlsondre · 2024-12-11T10:34:08Z

use-cases/xtclim/src/anomaly.py

+                # save the losses time series
+                pd.DataFrame(tot_proj_losses).to_csv(f"outputs/proj{scenario}_losses_{season}1d_allssp.csv")
+                print(f'SSP{scenario} Projection average loss:', proj_avg_losses, 'for', season[:-1])


Why do you use an f-string but then also use commas to separate statements? You should stick to one: either use "{}" for all, or commas for all.

jarlsondre · 2024-12-11T10:34:25Z

use-cases/xtclim/src/anomaly.py

+                     'Average projection loss',
+                     kind='metric')


This probably fits on one line + newline stuff discussed earlier

jarlsondre · 2024-12-11T10:35:53Z

use-cases/xtclim/src/trainer.py

+        Returns:
+            - total loss (torch.Tensor)
+        """
+        BCE = bce_loss


This can be removed?

jarlsondre · 2024-12-11T10:36:13Z

use-cases/xtclim/src/trainer.py

+            - bce_loss (torch.Tensor): recontruction loss
+            - mu (torch.Tensor): the mean from the latent vector
+            - logvar (torch.Tensor): log variance from the latent vector
+            - beta (torch.Tensor): weight over the KL-Divergence
+
+        Returns:
+            - total loss (torch.Tensor)


Make sure to follow Google style guide for Python docstrings.

jarlsondre · 2024-12-11T10:36:48Z

use-cases/xtclim/src/trainer.py

+        train_time = pd.read_csv(f"input/dates_train_{season}data_{self.config.n_memb}memb.csv")
+        train_data = np.load(
+            f"input/preprocessed_1d_train_{season}data_{self.config.n_memb}memb.npy"
+        )


pathlib (see earlier discussion)

jarlsondre · 2024-12-11T10:37:11Z

use-cases/xtclim/src/trainer.py

+        trainloader = self.strategy.create_dataloader(trainset, batch_size=self.config.batch_size,
+                shuffle=True, pin_memory=True)


newlines wrt parenthesis (see earlier discussions)

jarlsondre · 2024-12-11T10:37:23Z

use-cases/xtclim/src/trainer.py

+        test_time = pd.read_csv(f"input/dates_test_{season}data_{self.config.n_memb}memb.csv")
+        test_data = np.load(f"input/preprocessed_1d_test_{season}data_{self.config.n_memb}memb.npy")


pathlib (see earlier discussions)

jarlsondre · 2024-12-11T10:37:36Z

use-cases/xtclim/src/trainer.py

+        testloader = self.strategy.create_dataloader(testset, batch_size=self.config.batch_size,
+                shuffle=False, pin_memory=True)


newlines and parentheses

jarlsondre · 2024-12-11T10:38:21Z

use-cases/xtclim/src/trainer.py

+            self.log(train_epoch_loss,
+                    'epoch_train_loss',
+                    kind='metric'
+                    )
+            self.log(valid_epoch_loss,
+                    'epoch_valid_loss',
+                    kind='metric'
+                    )


newline after opening parenthesis (might fit on one line?)

jarlsondre · 2024-12-11T10:38:50Z

use-cases/xtclim/src/trainer.py

+            #    save_ex(recon_images[0], epoch, season)
+
+            # decreasing learning rate
+            if (epoch + 1) % 20 == 0:


20 is a magic number. Put it in a constant or some variable

jarlsondre · 2024-12-11T10:39:09Z

use-cases/xtclim/src/trainer.py

+
+            # decreasing learning rate
+            if (epoch + 1) % 20 == 0:
+                self.config.lr = self.config.lr / 5


5 is a magic number. Put it in a constant or some variable

jarlsondre · 2024-12-11T10:39:46Z

use-cases/xtclim/src/trainer.py

+                epoch > 1
+                and (self.config.old_valid_loss - valid_epoch_loss) / self.config.old_valid_loss < self.config.stop_delta


This can probably be extracted to two variables for readability

jarlsondre · 2024-12-11T10:40:30Z

use-cases/xtclim/src/trainer.py

+                    }
+                    # save checkpoint only if it is better than
+                    # the previous ones
+                    checkpoint_filename = f"outputs/cvae_model_{season}1d_{self.config.n_memb}memb.pth"


jarlsondre · 2024-12-11T10:40:46Z

use-cases/xtclim/src/trainer.py

+                    self.log(checkpoint_filename,
+                            os.path.basename(checkpoint_filename),
+                            kind='artifact')


parentheses and pathlib

jarlsondre · 2024-12-11T10:41:13Z

use-cases/xtclim/src/trainer.py

+        pd.DataFrame(train_loss).to_csv(
+            f"outputs/train_loss_indiv_{season}1d_{self.config.n_memb}memb.csv"
+        )
+        pd.DataFrame(valid_loss).to_csv(
+            f"outputs/test_loss_indiv_{season}1d_{self.config.n_memb}memb.csv"
+        )


jarlsondre · 2024-12-11T10:41:36Z

use-cases/xtclim/src/trainer.py

+            {"loss": train_epoch_loss,
+             "valid_loss": valid_epoch_loss}


newlines and parentheses

jarlsondre · 2024-12-11T10:42:21Z

use-cases/xtclim/startscript

+echo "DEBUG: TIME: $(date)"
+sysN="$(uname -n | cut -f2- -d.)"
+sysN="${sysN%%[0-9]*}"
+echo "Running on system: $sysN"
+echo "DEBUG: EXECUTE: $EXEC"
+echo "DEBUG: SLURM_SUBMIT_DIR: $SLURM_SUBMIT_DIR"
+echo "DEBUG: SLURM_JOB_ID: $SLURM_JOB_ID"
+echo "DEBUG: SLURM_JOB_NODELIST: $SLURM_JOB_NODELIST"
+echo "DEBUG: SLURM_NNODES: $SLURM_NNODES"
+echo "DEBUG: SLURM_NTASKS: $SLURM_NTASKS"
+echo "DEBUG: SLURM_TASKS_PER_NODE: $SLURM_TASKS_PER_NODE"
+echo "DEBUG: SLURM_SUBMIT_HOST: $SLURM_SUBMIT_HOST"
+echo "DEBUG: SLURMD_NODENAME: $SLURMD_NODENAME"
+echo "DEBUG: CUDA_VISIBLE_DEVICES: $CUDA_VISIBLE_DEVICES"
+if [ "$DEBUG" = true ] ; then
+  echo "DEBUG: NCCL_DEBUG=INFO"
+  export NCCL_DEBUG=INFO
+fi
+echo


There is a variable called "DEBUG", yet the debugging statements are printed regardless of its value. Seems counterintuitive to me.

jarlsondre · 2024-12-11T10:42:56Z

use-cases/xtclim/startscript

+COMMAND="train.py"
+EXEC="$COMMAND"


Why do the same thing twice, here?

jarlsondre · 2024-12-11T10:43:14Z

use-cases/xtclim/startscript

+if [ "$SLURM_CPUS_PER_TASK" > 0 ] ; then
+  export OMP_NUM_THREADS=$SLURM_CPUS_PER_TASK
+fi
+export CUDA_VISIBLE_DEVICES="0,1,2,3"


I believe this is only used for Horovod

jarlsondre · 2024-12-11T10:43:56Z

use-cases/xtclim/startscript

+elif [ "$DIST_MODE" == "horovod" ] ; then
+  echo "HOROVOD training"
+  srun --cpu-bind=none python -u $EXEC


Do you not also have to set "ntasks" for this to properly distribute?

jarlsondre · 2024-12-11T10:44:21Z

use-cases/xtclim/train.py

-         help='Configuration file to the pipeline to execute.'
-    )
-    args = parser.parse_args()
+def read_config(file_path):


Add type hints

jarlsondre · 2024-12-11T10:44:40Z

use-cases/xtclim/train.py

+        pipe_parser = ConfigParser(
+            config=config,
+        )


This can be one line

r-sarma requested a review from matbun July 2, 2024 10:36

r-sarma linked an issue Jul 2, 2024 that may be closed by this pull request

Distributed ML for CERFACS #160

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matbun requested changes Jul 2, 2024

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r-sarma closed this Jul 29, 2024

r-sarma reopened this Jul 29, 2024

r-sarma force-pushed the cerfacs-integration branch from 2c26e1c to afa2765 Compare October 16, 2024 09:33

r-sarma linked an issue Dec 3, 2024 that may be closed by this pull request

Distributed Torch Predictor #173

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matbun self-requested a review December 4, 2024 12:48

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matbun requested a review from jarlsondre December 4, 2024 14:03

r-sarma and others added 14 commits December 10, 2024 12:50

Addition of loggers and dist strategy

9a0eff0

seasons launch iteratively and fixes

e89d103

Distributed inference for CERFACS and src

1d9ce4f

Linter errors

ea624b2

Update inference.py

57a6a47

Update config.yaml

2e25944

Update test_mnist.py

3e31f8e

Update config.yaml

c9a97af

Update inference.py

af542ad

Update inference.py

87ef6b8

Update inference.py

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Update inference.py

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Update config.yaml

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jarlsondre reviewed Dec 11, 2024

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use-cases/xtclim/src/trainer.py

Returns:

- total loss (torch.Tensor)

"""

BCE = bce_loss

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This can be removed?

jarlsondre reviewed Dec 11, 2024

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use-cases/xtclim/train.py

Comment on lines +31 to +33

pipe_parser = ConfigParser(

config=config,

)

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This can be one line

		self.logger.create_logger_context()

		for scenario in ['585', '245']:


		# save the losses time series
		pd.DataFrame(tot_proj_losses).to_csv(f"outputs/proj{scenario}_losses_{season}1d_allssp.csv")

		trainloader = self.strategy.create_dataloader(trainset, batch_size=self.config.batch_size,
		shuffle=True, pin_memory=True)

		test_time = pd.read_csv(f"input/dates_test_{season}data_{self.config.n_memb}memb.csv")
		test_data = np.load(f"input/preprocessed_1d_test_{season}data_{self.config.n_memb}memb.npy")

		testloader = self.strategy.create_dataloader(testset, batch_size=self.config.batch_size,
		shuffle=False, pin_memory=True)

		epoch > 1
		and (self.config.old_valid_loss - valid_epoch_loss) / self.config.old_valid_loss < self.config.stop_delta

Addition of loggers and dist strategy #180

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Addition of loggers and dist strategy #180

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r-sarma commented Jul 2, 2024

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matbun commented Jul 2, 2024 • edited Loading

r-sarma commented Jul 29, 2024

r-sarma commented Jul 29, 2024

r-sarma commented Jul 29, 2024

matbun commented Oct 16, 2024

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matbun commented Jul 2, 2024 •

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