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128 lines
5.5 KiB
Markdown
128 lines
5.5 KiB
Markdown
---
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title: Accelerate
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---
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Hugging Face's [Accelerate](https://huggingface.co/docs/accelerate/main/en/index) library simplifies DL model training
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and inference. ClearML is a supported tracker in Accelerate: It automatically logs experiment environment information,
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such as required packages and uncommitted changes, and supports reporting scalars, parameters, debug samples, and plots.
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## Setup
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To use Accelerate's ClearML tracker, make sure that `clearml` is [installed and set up](../getting_started/ds/ds_first_steps.md#install-clearml)
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in your environment, and use the `log_with` parameter when instantiating an `Accelerator`:
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```python
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from accelerate import Accelerator
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accelerator = Accelerator(log_with="all") # For all available trackers in the environment
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accelerator = Accelerator(log_with="clearml") # For the ClearML tracker only
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```
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Add `Accelerator.init_trackers()` in the beginning of your experiment. Specify the project where the ClearML task should
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be saved in the `project_name` parameter, and add any experiment hyperparameters to be logged in the `config` parameter.
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The following code initializes the ClearML Task, which will capture all environment details, such as required packages
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and uncommitted code. The logged hyperparameters are displayed in the experiment's **Hyperparameters** tab:
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```python
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hps = {"lr": 0.03, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
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accelerator.init_trackers(project_name="my_project", config=hps)
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```
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:::tip
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You can also pass parameters to [`Task.init()`](../references/sdk/task.md#taskinit) by adding a nested dictionary
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to the `init_kwargs` argument of `Accelerator.init_trackers()`: use `"clearml"` as the key, and a dictionary of
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parameter-name/parameter-value pairs as the value. For example, the following code adds tags to your task:
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```python
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accelerator.init_trackers(
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project_name="my_project",
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configs=hp,
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init_kwargs={"clearml": {"tags": ["tag_a", "tag_b"]}}
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)
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```
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:::
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## Logging
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### Scalars
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To log scalars use `Accelerator.log()`. Specify a dictionary of name-value pairs under `values` and the step number
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under `step`.
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Within the `values` dictionary, the name in each name-value pair is a string representing the title (metric) and series
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(variant) of the scalar. The `eval_`, `test_`, or `train_` name prefixes, will cause the scalar to be logged under the
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corresponding `eval`, `test`, or `train` title, and the remaining part of the name will be logged as the series. If none
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of these prefixes is used, the entire string is used as the series, and the title will default to `train`.
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If `step` is not specified, the scalar is reported as a single-value scalar.
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```python
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accelerator.log(values={"accuracy": accuracy}, step=epoch)
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```
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The scalars are displayed in the experiment's **Scalars** tab.
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### Images and Table Plots
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To add images or table plots to your experiment, you can use the `ClearMLTracker.log_images()` and `ClearMLTracker.log_table()`
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respectively.
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First instantiate the `ClearMLTracker`. If you have run `Accelerator.init_trackers()` in your script, that means a ClearML
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task has already been initialized, so anything logged through the tracker will be logged to the existing task.
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Otherwise, just specify the experiment name under `run_name` and any kwargs to pass to
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[`Task.init()`](../references/sdk/task.md#taskinit):
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```python
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from accelerate.tracking import ClearMLTracker
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clearml_tracker= ClearMLTracker(run_name="Example experiment")
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```
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#### Logging Images
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To log images, use `ClearMLTracker.log_images()`. Specify a dictionary of name-image pairs under `values` and the step
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number under `step`.
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Within the `values` dictionary, the name in each name-image pair should be a string representing the title (metric) and
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series (variant) of the image. The `eval_`, `test_`, or `train_` name prefixes, will cause the image to be logged under
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the corresponding `eval`, `test`, or `train` title, and the remaining part of the name will be logged as the series.
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If none of these prefixes is used, the entire string is used as the series, and the title will default to `train`. The
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image in the name-image pair should be either a numpy array or a `PIL.Image` object.
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```python
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clearml_tracker.log_images(values={"test_cat_siamese":[image_obect]})
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```
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The images will be displayed in the experiment's **Debug Samples**
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#### Logging Plots
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To log plots, use `ClearMLTracker.log_table()`. Specify the data under the `dataframe` or `data` parameters. List the
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name of the columns under `columns`, otherwise the first entry in `data` will be used as the column names.
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The plot will appear in the experiment's **Plots** tab.
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### Additional Logging Options
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To augment its automatic logging, ClearML also provides an explicit logging interface.
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See more information about explicitly logging information to a ClearML Task:
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* [Models](../clearml_sdk/model_sdk.md#manually-logging-models)
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* [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files)
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* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task)
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* [Scalars](../clearml_sdk/task_sdk.md#scalars)
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* [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting)
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See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md).
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## Close Task
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Once you've finished training, make sure to run `Accelerator.end_training()` so that ClearML can mark the task as
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completed.
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```python
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accelerator.end_training()
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```
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