Using Checkpoints¶
Determined provides APIs for downloading checkpoints and loading them into memory in a Python process.
This guide discusses:
Querying model checkpoints from trials and experiments.
Loading model checkpoints in Python.
Storing additional user-defined metadata in a checkpoint.
Using the Determined CLI to download checkpoints to disk.
Querying Checkpoints¶
The ExperimentReference class is a
reference to an experiment. It is retrieved via the
Determined class. The reference
contains the
top_checkpoint()
method. Without arguments, the method will check the experiment
configuration searcher field for the metric and
smaller_is_better values. These values are used to sort the
experiment’s checkpoints by validation performance. The searcher
settings in the following snippet from an experiment configuration file
will result in checkpoints being sorted by the loss metric in ascending
order.
searcher:
metric: "loss"
smaller_is_better: true
The following snippet of Python code can be run after the
specified experiment has generated a checkpoint. It returns an
instance of Checkpoint
representing the checkpoint that has the best validation metric.
from determined.experimental import Determined
checkpoint = Determined().get_experiment(id).top_checkpoint()
Checkpoints can be sorted by any metric using the sort_by keyword
argument, which defines which metric to use, and smaller_is_better,
which defines whether to sort the checkpoints in ascending or descending
order with respect to the specified metric.
from determined.experimental import Determined
checkpoint = Determined().get_experiment(id).top_checkpoint(sort_by="accuracy", smaller_is_better=False)
You may also query multiple checkpoints at the same time using the
top_n_checkpoints()
method. Only the single best checkpoint from each trial is considered;
out of those, the checkpoints with the best validation metric values are
returned in sorted order, with the best one first. For example, the
following snippet returns the top five checkpoints from distinct trials
of a specified experiment.
from determined.experimental import Determined
checkpoints = Determined.get_experiment(id).top_n_checkpoints(5)
This method also accepts sort_by and smaller_is_better
arguments.
TrialReference is used for
fine-grained control over checkpoint selection within a trial. It
contains a
top_checkpoint() method,
which mirrors
top_checkpoint() for
an experiment. It also contains
select_checkpoint(), which
offers three ways to query checkpoints:
best: Returns the best checkpoint based on validation metrics as discussed above. When usingbest,smaller_is_betterandsort_byare also accepted.latest: Returns the most recent checkpoint for the trial.uuid: Returns the checkpoint with the specified UUID.
The following snippet showcases how to use the different modes for selecting checkpoints.
from determined.experimental import Determined
trial = Determined.get_trial(id)
best_checkpoint = trial.top_checkpoint()
most_accurate_checkpoint = trial.select_checkpoint(
best=True,
sort_by="accuracy",
smaller_is_better=False
)
most_recent_checkpoint = trial.select_checkpoint(latest=True)
specific_checkpoint = trial.select_checkpoint(uuid="uuid-for-checkpoint")
Using the Checkpoint Class¶
The Checkpoint class can both
download the checkpoint from persistent storage and load it into memory in a
Python process.
The download() method
downloads a checkpoint from persistent storage to a directory on the
local file system. By default, checkpoints are downloaded to
checkpoints/<checkpoint-uuid>/ (relative to the current working
directory). The download()
method accepts path as an optional parameter, which changes the
checkpoint download location.
from determined.experimental import Determined
checkpoint = Determined().get_experiment(id).top_checkpoint()
checkpoint_path = checkpoint.download()
specific_path = checkpoint.download(path="specific-checkpoint-path")
The load() method downloads the
checkpoint, if it does not already exist locally, and then loads it into memory
in a Python process, as shown in the following snippet.
from determined.experimental import Determined
checkpoint = Determined.get_experiment(id).top_checkpoint()
model = checkpoint.load()
TensorFlow checkpoints are saved in either the saved_model or h5
formats and are loaded as trackable objects (see documentation for
tf.compat.v1.saved_model.load_v2
for details).
PyTorch checkpoints are saved using pickle and loaded as determined.pytorch.PyTorchTrial objects (see the PyTorch documentation for details).
User-Defined Checkpoint Metadata¶
You can add arbitrary user-defined metadata to a checkpoint via the Python API. This feature is useful for storing post-training metrics, labels, information related to deployment, etc.
from determined.experimental import Determined
checkpoint = Determined().get_experiment(id).top_checkpoint()
checkpoint.add_metadata({"environment": "production"})
# Metadata will be stored in Determined and accessible on the checkpoint object.
print(checkpoint.metadata)
You may store an arbitrarily nested dictionary using the add_metadata()
method. If the top level key already exists the entire tree beneath it will
be overwritten.
from determined.experimental import Determined
checkpoint = Determined().get_experiment(id).top_checkpoint()
checkpoint.add_metadata({"metrics": {"loss": 0.12}})
checkpoint.add_metadata({"metrics": {"acc": 0.92}})
print(checkpoint.metadata) # Output: {"metrics": {"acc": 0.92}}
You may remove metadata via the
remove_metadata() method. The method
accepts a list of top level keys. The entire tree beneath the keys passed will
be deleted.
from determined.experimental import Determined
checkpoint = Determined().get_experiment(id).top_checkpoint()
checkpoint.remove_metadata(["metrics"])
Download Checkpoints via the CLI¶
Determined offers the following CLI commands for downloading checkpoints locally:
det checkpoint downloaddet trial downloaddet experiment download
The det checkpoint download command downloads a checkpoint for the given
UUID as shown below:
# Download a specific checkpoint.
det checkpoint download 46985143-af68-4d48-ab91-a6447052ca49
The command should display output resembling the following upon successfully downloading the checkpoint.
Local checkpoint path:
checkpoints/46985143-af68-4d48-ab91-a6447052ca49
Batch | Checkpoint UUID | Validation Metrics
-----------+--------------------------------------+---------------------------------------------
1000 | 46985143-af68-4d48-ab91-a6447052ca49 | {
| | "num_inputs": 0,
| | "validation_metrics": {
| | "loss": 7.906739711761475,
| | "accuracy": 0.9646000266075134,
| | "global_step": 1000,
| | "average_loss": 0.12492649257183075
| | }
| | }
The det trial download command downloads checkpoints for a specified trial.
Similar to the TrialReference API, the
det trial download command accepts --best, --latest, and --uuid
options.
# Download best checkpoint.
det trial download <trial_id> --best
# Download best checkpoint to a particular directory.
det trial download <trial_id> --best --output-dir local_checkpoint
The command should display output resembling the following upon successfully downloading the checkpoint.
Local checkpoint path:
checkpoints/46985143-af68-4d48-ab91-a6447052ca49
Batch | Checkpoint UUID | Validation Metrics
-----------+--------------------------------------+---------------------------------------------
1000 | 46985143-af68-4d48-ab91-a6447052ca49 | {
| | "num_inputs": 0,
| | "validation_metrics": {
| | "loss": 7.906739711761475,
| | "accuracy": 0.9646000266075134,
| | "global_step": 1000,
| | "average_loss": 0.12492649257183075
| | }
| | }
The --latest and --uuid options are used as follows:
# Download the most recent checkpoint.
det trial download <trial_id> --latest
# Download a specific checkpoint.
det trial download <trial_id> --uuid <uuid-for-checkpoint>
Finally, the det experiment download command provides a similar experience
to using the ExperimentReference Python API.
# Download the best checkpoint for a given experiment.
det experiment download <experiment_id>
# Download the best 3 checkpoints for a given experiment.
det experiment download <experiment_id> --top-n 3
The command should display output resembling the following upon successfully downloading the checkpoints.
Local checkpoint path:
checkpoints/8d45f621-8652-4268-8445-6ae9a735e453
Batch | Checkpoint UUID | Validation Metrics
-----------+--------------------------------------+------------------------------------------
400 | 8d45f621-8652-4268-8445-6ae9a735e453 | {
| | "num_inputs": 56,
| | "validation_metrics": {
| | "val_loss": 0.26509127765893936,
| | "val_categorical_accuracy": 1
| | }
| | }
Local checkpoint path:
checkpoints/62131ba1-983c-49a8-98ef-36207611d71f
Batch | Checkpoint UUID | Validation Metrics
-----------+--------------------------------------+------------------------------------------
1600 | 62131ba1-983c-49a8-98ef-36207611d71f | {
| | "num_inputs": 50,
| | "validation_metrics": {
| | "val_loss": 0.04411194706335664,
| | "val_categorical_accuracy": 1
| | }
| | }
Local checkpoint path:
checkpoints/a36d2a61-a384-44f7-a84b-8b30b09cb618
Batch | Checkpoint UUID | Validation Metrics
-----------+--------------------------------------+------------------------------------------
400 | a36d2a61-a384-44f7-a84b-8b30b09cb618 | {
| | "num_inputs": 46,
| | "validation_metrics": {
| | "val_loss": 0.07265569269657135,
| | "val_categorical_accuracy": 1
| | }
| | }
Loading From a Local Path¶
Checkpoint contains a static method,
load_from_path(), that loads
a checkpoint from a path on the local file system.
Suppose a checkpoint is downloaded using a command like this:
det trial download <trial_id> --best --output-dir local_checkpoint
The checkpoint can then be loaded in Python with this code:
from determined.experimental import Checkpoint
model = Checkpoint.load_from_path("local_checkpoint")
Next Steps¶
determined.experimental: The reference documentation for this API.