diff --git a/docs/guides/frameworks/tensorflow/tensorflow_mnist.md b/docs/guides/frameworks/tensorflow/tensorflow_mnist.md index 108d3b52..b13aa133 100644 --- a/docs/guides/frameworks/tensorflow/tensorflow_mnist.md +++ b/docs/guides/frameworks/tensorflow/tensorflow_mnist.md @@ -1,6 +1,5 @@ --- -title: TensorFlow -displayed_sidebar: mainSidebar +title: TensorFlow MNIST --- The [tensorflow_mnist.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/tensorflow_mnist.py) diff --git a/docs/img/gif/tensorflow.gif b/docs/img/gif/tensorflow.gif new file mode 100644 index 00000000..72770880 Binary files /dev/null and b/docs/img/gif/tensorflow.gif differ diff --git a/docs/integrations/tensorflow.md b/docs/integrations/tensorflow.md new file mode 100644 index 00000000..2f8b6a4b --- /dev/null +++ b/docs/integrations/tensorflow.md @@ -0,0 +1,134 @@ +--- +title: TensorFlow +--- + +:::tip +If you are not already using ClearML, see [Getting Started](../getting_started/ds/ds_first_steps.md) for setup +instructions. +::: + +ClearML integrates with [TensorFlow](https://www.tensorflow.org/) out-of-the-box, automatically logging its models, +definitions, scalars, as well as TensorBoard outputs. + +All you have to do is simply add two lines of code to your TensorFlow script: + +```python +from clearml import Task +task = Task.init(task_name="", project_name="") +``` + +And that’s it! This creates a [ClearML Task](../fundamentals/task.md) which captures: +* Source code and uncommitted changes +* Installed packages +* TensorFlow definitions +* TensorFlow model files +* [TensorBoard](https://www.tensorflow.org/tensorboard) outputs (see example [here](https://clear.ml/docs/latest/docs/guides/frameworks/tensorflow/tensorboard_toy/)) +* Scalars (loss, learning rates) +* Console output +* General details such as machine details, runtime, creation date etc. +* And more + +You can view all the task details in the [WebApp](../webapp/webapp_overview.md). + +![WebApp Gif](../img/gif/tensorflow.gif) + +## Automatic Logging Control +By default, when ClearML is integrated into your TensorFlow script, it captures TensorFlow definitions, models, and +scalars. But, you may want to have more control over what your experiment logs. + +To control a task's framework logging, use the `auto_connect_frameworks` parameter of [`Task.init()`](../references/sdk/task.md#taskinit). +Completely disable all automatic logging by setting the parameter to `False`. For finer grained control of logged +frameworks, input a dictionary, with framework-boolean pairs. + +For example: + +```python +auto_connect_frameworks={ + 'matplotlib': True, 'tensorflow': False, 'tensorboard': False, 'pytorch': True, + 'xgboost': False, 'scikit': True, 'fastai': True, 'lightgbm': False, + 'hydra': True, 'detect_repository': True, 'tfdefines': True, 'joblib': True, + 'megengine': True, 'jsonargparse': True, 'catboost': True +} +``` + +You can also input wildcards as dictionary values, so ClearML will log a model created by a framework only if its local +path matches at least one wildcard. + +For example, in the code below, ClearML will log TensorFlow models only if their paths have the `.pt` extension. The +unspecified frameworks' values default to true so all their models are automatically logged. + +```python +auto_connect_frameworks={'tensorflow' : '*.pt'} +``` + +## Manual Logging +To augment its automatic logging, ClearML also provides an explicit logging interface. + +See more information about explicitly logging information to a ClearML Task: +* [Models](../clearml_sdk/model_sdk.md#manually-logging-models) +* [Configuration](../clearml_sdk/task_sdk.md#configuration) (e.g. parameters, configuration files) +* [Artifacts](../clearml_sdk/task_sdk.md#artifacts) (e.g. output files or python objects created by a task) +* [Scalars](../clearml_sdk/task_sdk.md#scalars) +* [Text/Plots/Debug Samples](../fundamentals/logger.md#manual-reporting) + +See [Explicit Reporting Tutorial](../guides/reporting/explicit_reporting.md). + +## Examples + +Take a look at ClearML’s TensorFlow examples. The examples use TensorFlow and ClearML in different configurations with +additional tools, like Abseil and TensorBoard: + +* [TensorFlow MNIST](../guides/frameworks/tensorflow/tensorflow_mnist.md) - Demonstrates ClearML's automatic logging of +model checkpoints, TensorFlow definitions, and scalars logged using TensorFlow methods +* [TensorBoard PR Curve](../guides/frameworks/tensorflow/tensorboard_pr_curve.md) - Demonstrates ClearML’s automatic +logging of TensorBoard output and TensorFlow definitions. +* [TensorBoard Toy](../guides/frameworks/tensorflow/tensorboard_toy.md) - Demonstrates ClearML’s automatic logging of +TensorBoard scalars, histograms, images, and text, as well as all console output and TensorFlow Definitions. +* [Absl flags](https://github.com/allegroai/clearml/blob/master/examples/frameworks/tensorflow/absl_flags.py) - Demonstrates +ClearML’s automatic logging of parameters defined using `absl.flags` + +## Remote Execution +ClearML logs all the information required to reproduce an experiment on a different machine (installed packages, +uncommitted changes etc.). The [ClearML Agent](../clearml_agent) listens to designated queues and when a task is enqueued, +the agent pulls it, recreates its execution environment, and runs it, reporting its scalars, plots, etc. to the +experiment manager. + +Deploy a ClearML Agent onto any machine (e.g. a cloud VM, a local GPU machine, your own laptop) by simply running the +following command on it: + +```commandline +clearml-agent daemon --queue [--docker] +``` + +Use the ClearML [Autoscalers](../cloud_autoscaling/autoscaling_overview.md), to help you manage cloud workloads in the +cloud of your choice (AWS, GCP, Azure) and automatically deploy ClearML agents: the autoscaler automatically spins up +and shuts down instances as needed, according to a resource budget that you set. + +### Cloning, Editing, and Enqueuing + +![Cloning, editing, enqueuing gif](../img/gif/integrations_yolov5.gif) + +Use ClearML's web interface to edit task details, like configuration parameters or input models, then execute the task +with the new configuration on a remote machine: + +* Clone the experiment +* Edit the hyperparameters and/or other details +* Enqueue the task + +The ClearML Agent executing the task will use the new values to [override any hard coded values](../clearml_agent). + +### Executing a Task Remotely + +You can set a task to be executed remotely programmatically by adding [`Task.execute_remotely()`](../references/sdk/task.md#execute_remotely) +to your script. This method stops the current local execution of the task, and then enqueues it to a specified queue to +re-run it on a remote machine. + +```python +# If executed locally, process will terminate, and a copy will be executed by an agent instead +task.execute_remotely(queue_name='default', exit_process=True) +``` + +## Hyperparameter Optimization +Use ClearML’s [`HyperParameterOptimizer`](../references/sdk/hpo_optimization_hyperparameteroptimizer.md) class to find +the hyperparameter values that yield the best performing models. See [Hyperparameter Optimization](../fundamentals/hpo.md) +for more information. diff --git a/sidebars.js b/sidebars.js index da52e959..d343a9c5 100644 --- a/sidebars.js +++ b/sidebars.js @@ -68,7 +68,7 @@ module.exports = { 'integrations/python_fire', 'guides/frameworks/pytorch/pytorch_mnist', 'integrations/ignite', 'guides/frameworks/pytorch_lightning/pytorch_lightning_example', 'guides/frameworks/scikit-learn/sklearn_joblib_example', - 'guides/frameworks/pytorch/pytorch_tensorboard', 'guides/frameworks/tensorboardx/tensorboardx', 'guides/frameworks/tensorflow/tensorflow_mnist', + 'guides/frameworks/pytorch/pytorch_tensorboard', 'guides/frameworks/tensorboardx/tensorboardx', 'integrations/tensorflow', 'integrations/seaborn', 'integrations/xgboost', 'integrations/yolov5', 'integrations/yolov8' ] },