diff --git a/docs/clearml_agent.md b/docs/clearml_agent.md index a150cf61..6619cc7a 100644 --- a/docs/clearml_agent.md +++ b/docs/clearml_agent.md @@ -643,7 +643,7 @@ It's possible to add the Docker container as the base Docker image to a Task (ex - Using the **ClearML Web UI** - See [Base Docker image](webapp/webapp_exp_tuning.md#base-docker-image) on the "Tuning Experiments" page. -- In the **ClearML** configuration file - Use the **ClearML** configuration file [agent.default_docker](configs/clearml_conf.md#agentdefault_docker) +- In the ClearML configuration file - Use the ClearML configuration file [agent.default_docker](configs/clearml_conf.md#agentdefault_docker) options. ## Google Colab diff --git a/docs/clearml_sdk.md b/docs/clearml_sdk.md index dca1ec52..9b9d2c12 100644 --- a/docs/clearml_sdk.md +++ b/docs/clearml_sdk.md @@ -3,7 +3,7 @@ title: ClearML SDK --- The **ClearML Python Package** supports the [automatic logging](fundamentals/logger.md#automatic-reporting) -that documents the experiment for you, and an extensive set of powerful features and functionality you can use to improve experimentation, other workflows, and get more out of **ClearML**. +that documents the experiment for you, and an extensive set of powerful features and functionality you can use to improve experimentation, other workflows, and get more out of ClearML. The **ClearML Python Package** collects data from scripts including the Git repository (branch, commit ID, and uncommitted changes), working directory and entry point, hyperparameters, initial weights model, model snapshots (checkpoints), output model, other artifacts, metrics, logs, other reported data (from libraries and visualization toolkits), and debug samples. @@ -13,9 +13,9 @@ In conjunction with the **ClearML Hosted Service** (or self-hosted **ClearML Ser * [Task](references/sdk/task.md) - The `task` module contains the `task.Task` class which is the code template for all `Task` features and functionality, including collecting data from scripts, storing that data in a `Task` object, automatic bindings with frameworks (TensorFlow/TensorBoard, PyTorch, Keras, Fastai, scikit-learn), libraries (Pandas, Plotly, AutoKeras), and visualization tools (Matplotlib, Seaborn), and a robust set of methods for Task execution, cloning, connecting parameter dictionaries, configurations, models, working with storage, and more. -* [Logger](references/sdk/logger.md) - The `logger` module contains the `logger.Logger` class which is the **ClearML** console log and metric statistics interface, and contains methods for explicit reporting, setting an upload destination in storage for debug samples, logger cache control, and TensorBoard support in addition to **ClearML** automatic TensorBoard logging. +* [Logger](references/sdk/logger.md) - The `logger` module contains the `logger.Logger` class which is the ClearML console log and metric statistics interface, and contains methods for explicit reporting, setting an upload destination in storage for debug samples, logger cache control, and TensorBoard support in addition to ClearML automatic TensorBoard logging. -* [Model](references/sdk/model_model.md) - The `model` module contains three classes: `model.Model` which represents an existing model in **ClearML** that can be loaded and connected to a Task, `model.InputModel` which represents an existing model that you can load into **ClearML**, and `model.OutputModel` which represents the experiment output model that is always connected to the Task. +* [Model](references/sdk/model_model.md) - The `model` module contains three classes: `model.Model` which represents an existing model in ClearML that can be loaded and connected to a Task, `model.InputModel` which represents an existing model that you can load into ClearML, and `model.OutputModel` which represents the experiment output model that is always connected to the Task. * [Automation](references/sdk/automation_controller_pipelinecontroller.md) - The `automation` module contains classes supporting hyperparameter optimization, including Optuna, HpBandSter, grid searching, random searching, you own customized search strategies, and resource budgeting for searches; the AWS autoscaler; pipeline controllers; and Task monitoring. @@ -25,5 +25,5 @@ In conjunction with the **ClearML Hosted Service** (or self-hosted **ClearML Ser ## Examples -**ClearML** example scripts in the [examples folder](https://github.com/allegroai/clearml/tree/master/examples) of the `clearml` GitHub repository. They are pre-loaded in the **ClearML Hosted Service**, and can be viewed, cloned, and edited in the **ClearML Web UI**, `ClearML Examples` project. The examples are each explained in this documentation's [examples section](guides/main.md). +ClearML example scripts in the [examples folder](https://github.com/allegroai/clearml/tree/master/examples) of the `clearml` GitHub repository. They are pre-loaded in the **ClearML Hosted Service**, and can be viewed, cloned, and edited in the **ClearML Web UI**, `ClearML Examples` project. The examples are each explained in this documentation's [examples section](guides/main.md). diff --git a/docs/deploying_clearml/clearml_config_for_clearml_server.md b/docs/deploying_clearml/clearml_config_for_clearml_server.md index 0dbcb4d5..572641eb 100644 --- a/docs/deploying_clearml/clearml_config_for_clearml_server.md +++ b/docs/deploying_clearml/clearml_config_for_clearml_server.md @@ -56,18 +56,18 @@ by setting [configuration options](../configs/clearml_conf.md). 1. Get ClearML credentials. Open the ClearML Web UI in a browser. On the **SETTINGS > WORKSPACE** page, click **Create new credentials** **>** **Copy to clipboard**. - 1. At the command prompt `Paste copied configuration here:`, copy and paste the **ClearML** credentials. + 1. At the command prompt `Paste copied configuration here:`, copy and paste the ClearML credentials. The setup wizard confirms the credentials. Detected credentials key="********************" secret="*******" - 1. Enter the **ClearML Server** web server URL, or press **Enter** to accept the default which is detected from the + 1. Enter the ClearML Server web server URL, or press **Enter** to accept the default which is detected from the credentials. WEB Host configured to: [https://app.] - 1. Enter the **ClearML Server** API server URL, or press **Enter** to accept the default value which is based on the previous response: + 1. Enter the ClearML Server API server URL, or press **Enter** to accept the default value which is based on the previous response: API Host configured to: [https://api.] @@ -110,7 +110,7 @@ These may be localhost, the domain, or a sub-domain of the domain. **To add ClearML settings to an existing ClearML configuration file:** -1. Open the **ClearML** configuration file for editing. Depending upon the operating system, it is: +1. Open the ClearML configuration file for editing. Depending upon the operating system, it is: * Linux - `~/clearml.conf` * macOS - `$HOME/clearml.conf` @@ -131,4 +131,4 @@ These may be localhost, the domain, or a sub-domain of the domain. ``` -1. Save the **ClearML** configuration file. **ClearML** is now configured for the **ClearML Server**. \ No newline at end of file +1. Save the ClearML configuration file. ClearML is now configured for the **ClearML Server**. \ No newline at end of file diff --git a/docs/deploying_clearml/clearml_server_aws_ec2_ami.md b/docs/deploying_clearml/clearml_server_aws_ec2_ami.md index d90a89e6..66f2644e 100644 --- a/docs/deploying_clearml/clearml_server_aws_ec2_ami.md +++ b/docs/deploying_clearml/clearml_server_aws_ec2_ami.md @@ -90,7 +90,7 @@ Once deployed, **ClearML Server** exposes the following services: ### Logging in to the Web-App (UI) -**To log in to the **ClearML** Web-App (UI):** +**To log in to the ClearML Web-App (UI):** * If **ClearML Server** was launched from an AWS Community AMI, enter any name. * If **ClearML Server** was launched through the AWS Marketplace, enter the preconfigured default login credentials, which diff --git a/docs/deploying_clearml/clearml_server_config.md b/docs/deploying_clearml/clearml_server_config.md index ba464df3..ee57408a 100644 --- a/docs/deploying_clearml/clearml_server_config.md +++ b/docs/deploying_clearml/clearml_server_config.md @@ -235,7 +235,7 @@ To open external access to the Elasticsearch, MongoDB, and Redis ports: ### Web Login Authentication Web login authentication can be configured in the **ClearML Server** in order to permit only users provided -with credentials to access the **ClearML** system. Those credentials are a username and password. +with credentials to access the ClearML system. Those credentials are a username and password. Without web login authentication, **ClearML Server** does not restrict access (by default). diff --git a/docs/deploying_clearml/clearml_server_gcp.md b/docs/deploying_clearml/clearml_server_gcp.md index 702cf3f7..6a4fca03 100644 --- a/docs/deploying_clearml/clearml_server_gcp.md +++ b/docs/deploying_clearml/clearml_server_gcp.md @@ -2,7 +2,7 @@ title: Google Cloud Platform --- -Deploy **ClearML Server** on the Google Cloud Platform (GCP) using one of the pre-built GCP Custom Images. **ClearML** +Deploy **ClearML Server** on the Google Cloud Platform (GCP) using one of the pre-built GCP Custom Images. ClearML provides custom images for each released version of **ClearML Server**. For a list of the pre-built custom images, see [ClearML Server GCP Custom Image](#clearml-server-gcp-custom-image). diff --git a/docs/deploying_clearml/clearml_server_kubernetes_helm.md b/docs/deploying_clearml/clearml_server_kubernetes_helm.md index 17df5ee0..e224ff4c 100644 --- a/docs/deploying_clearml/clearml_server_kubernetes_helm.md +++ b/docs/deploying_clearml/clearml_server_kubernetes_helm.md @@ -129,13 +129,13 @@ We recommend using the container ports (``8080``, ``8008``, and ``8081``), or a For example, `clearml.app.mydomainname.com` points to your node on port `30080`. - * The record to access the **ClearML** API: + * The record to access the ClearML API: *api..* For example, `clearml.api.mydomainname.com` points to your node on port `30008`. - * The record to access the **ClearML** file server: + * The record to access the ClearML file server: *files..* diff --git a/docs/fundamentals/artifacts.md b/docs/fundamentals/artifacts.md index 1c36124b..f94b4129 100644 --- a/docs/fundamentals/artifacts.md +++ b/docs/fundamentals/artifacts.md @@ -74,7 +74,7 @@ This property makes Models a standalone entry that can be used as an artifactory When models are saved using certain frameworks (for instance, by calling the `torch.save()` method), ClearML automatically logs the models and all snapshot paths. -![image](../img/fundamentals_artifacts_logging_models.png) +![image](../img/examples_model_update_model.png) See automatic model logging examples: * [TF](../guides/frameworks/tensorflow/tensorflow_mnist.md) diff --git a/docs/fundamentals/hpo.md b/docs/fundamentals/hpo.md index 1a4afcfd..543081a0 100644 --- a/docs/fundamentals/hpo.md +++ b/docs/fundamentals/hpo.md @@ -46,7 +46,7 @@ The diagram above demonstrates the typical flow of hyperparameter optimization w ### Supported Optimizers -The `HyperParameterOptimizer` class contains **ClearML**’s hyperparameter optimization modules. Its modular design enables +The `HyperParameterOptimizer` class contains ClearML’s hyperparameter optimization modules. Its modular design enables using different optimizers, including existing software frameworks, enabling simple, accurate, and fast hyperparameter optimization. diff --git a/docs/getting_started/architecture.md b/docs/getting_started/architecture.md index cd6afd90..66b1886a 100644 --- a/docs/getting_started/architecture.md +++ b/docs/getting_started/architecture.md @@ -2,7 +2,7 @@ title: ClearML Modules --- -- **ClearML Python Package** (clearml) for integrating **ClearML** into your existing code-base. +- **ClearML Python Package** (clearml) for integrating ClearML into your existing code-base. - **ClearML Server** (clearml-server) storing experiment, model, and workflow data, and supporting the Web UI experiment manager. It is also the control plane for the MLOps. - **ClearML Agent** (clearml-agent) The MLOps orchestration agent. Enabling experiment and workflow reproducibility, and scalability. - **ClearML Data** (clearml-data) data management and versioning on top of file-systems/object-storage. diff --git a/docs/getting_started/ds/ds_second_steps.md b/docs/getting_started/ds/ds_second_steps.md index 2abe252b..0198d00b 100644 --- a/docs/getting_started/ds/ds_second_steps.md +++ b/docs/getting_started/ds/ds_second_steps.md @@ -150,7 +150,7 @@ Once everything is neatly logged and displayed, using the [comparison tool](../. The experiment table is a powerful tool for creating dashboards and views of your own projects, your team's projects, or the entire development. -![image](../../img/webapp_exp_table_01.png) +![image](../../img/webapp_experiment_table.png) ### Creating Leaderboards diff --git a/docs/guides/advanced/execute_remotely.md b/docs/guides/advanced/execute_remotely.md index fda6d585..e78faf14 100644 --- a/docs/guides/advanced/execute_remotely.md +++ b/docs/guides/advanced/execute_remotely.md @@ -28,12 +28,12 @@ moved to be executed by a stronger machine. During the execution of the example script, the code does the following: * Uses ClearML's automatic and explicit logging. -* Creates an experiment named `remote_execution pytorch mnist train`, which is associated with the `examples` project. +* Creates an experiment named `Remote_execution PyTorch MNIST train`, which is associated with the `examples` project. ## Scalars -In the example script's `train` function, the following code explicitly reports scalars to **ClearML**: +In the example script's `train` function, the following code explicitly reports scalars to ClearML: ```python Logger.current_logger().report_scalar( @@ -55,24 +55,24 @@ Logger.current_logger().report_scalar( These scalars can be visualized in plots, which appear in the ClearML web UI, in the experiment's page **>** **RESULTS** **>** **SCALARS**. -![image](../../img/examples_pytorch_mnist_07.png) +![Experiment Scalars](../../img/examples_pytorch_mnist_07.png) ## Hyperparameters ClearML automatically logs command line options defined with `argparse`. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**. -![image](../../img/examples_pytorch_mnist_01.png) +![Experiment hyperparameters](../../img/examples_pytorch_mnist_01.png) ## Console Text printed to the console for training progress, as well as all other console output, appear in **RESULTS** **>** **CONSOLE**. -![image](../../img/examples_pytorch_mnist_06.png) +![Experiment console log](../../img/examples_pytorch_mnist_06.png) ## Artifacts Models created by the experiment appear in the experiment’s **ARTIFACTS** tab. ClearML automatically logs and tracks models and any snapshots created using PyTorch. -![image](../../img/examples_remote_execution_artifacts.png) +![Experiment artifacts](../../img/examples_remote_execution_artifacts.png) diff --git a/docs/guides/advanced/multiple_tasks_single_process.md b/docs/guides/advanced/multiple_tasks_single_process.md index 744b4b75..cb968888 100644 --- a/docs/guides/advanced/multiple_tasks_single_process.md +++ b/docs/guides/advanced/multiple_tasks_single_process.md @@ -13,13 +13,13 @@ When the script is executed, it should return something like this: ```text ClearML Task: created new task id=5c4d2d3674a94e35b10f04d9d2180l62 -ClearML results page: https://app.community.clear.ml/projects/6835eb7316554c2b933b69638470fe02/experiments/5c4d2d3674a94e35b10f04d9d2180l62/output/log +ClearML results page: https://app.clear.ml/projects/6835eb7316554c2b933b69638470fe02/experiments/5c4d2d3674a94e35b10f04d9d2180l62/output/log ... ClearML Task: created new task id=28a84c17a6204b438e1e7a094a234a7f -ClearML results page: https://app.community.clear.ml/projects/7894eb7316554c4b933a79638473fe02/experiments/28a84c17a6204b438e1e7a094a234a7f/output/log +ClearML results page: https://app.clear.ml/projects/7894eb7316554c4b933a79638473fe02/experiments/28a84c17a6204b438e1e7a094a234a7f/output/log ... ClearML Task: created new task id=6d1e253ba0234d32a38sg85013185g46 -ClearML results page: https://app.community.clear.ml/projects/7895eb7316554c4b933a69638470fe02/experiments/6d1e253ba0234d32a38sg85013185g46/output/log +ClearML results page: https://app.clear.ml/projects/7895eb7316554c4b933a69638470fe02/experiments/6d1e253ba0234d32a38sg85013185g46/output/log ``` Notice that three separate tasks with distinct IDs are created, and a link is provided to view the results of each one. \ No newline at end of file diff --git a/docs/guides/automation/manual_random_param_search_example.md b/docs/guides/automation/manual_random_param_search_example.md index 717d421f..4deed460 100644 --- a/docs/guides/automation/manual_random_param_search_example.md +++ b/docs/guides/automation/manual_random_param_search_example.md @@ -12,7 +12,7 @@ This example accomplishes the automated random parameter search by doing the fol script. This experiment must be executed first, so it will be stored in the server, and then it can be accessed, cloned, and modified by another Task. 1. Creating a parameter dictionary, which is connected to the Task by calling [Task.connect](../../references/sdk/task.md#connect) - so that the parameters are logged by **ClearML**. + so that the parameters are logged by ClearML. 1. Adding the random search hyperparameters and parameters defining the search (e.g., the experiment name, and number of times to run the experiment). 1. Creating a Task object referencing the template experiment, `Keras HP optimization base`. See [Task.get_task](../../references/sdk/task.md#taskget_task). diff --git a/docs/guides/clearml-task/clearml_task_tutorial.md b/docs/guides/clearml-task/clearml_task_tutorial.md index 28a53706..038aa57b 100644 --- a/docs/guides/clearml-task/clearml_task_tutorial.md +++ b/docs/guides/clearml-task/clearml_task_tutorial.md @@ -43,7 +43,7 @@ Your output should look something like this: ```console New task created id=2f96ee95b05d4693b360d0fcbb26b727 Task id=2f96ee95b05d4693b360d0fcbb26b727 sent for execution on queue default -Execution log at: https://app.community.clear.ml/projects/552d5399112d47029c146d5248570295/experiments/2f96ee95b05d4693b360d0fcbb26b727/output/log +Execution log at: https://app.clear.ml/projects/552d5399112d47029c146d5248570295/experiments/2f96ee95b05d4693b360d0fcbb26b727/output/log ``` diff --git a/docs/guides/distributed/distributed_pytorch_example.md b/docs/guides/distributed/distributed_pytorch_example.md index e5e390f7..ef17fc85 100644 --- a/docs/guides/distributed/distributed_pytorch_example.md +++ b/docs/guides/distributed/distributed_pytorch_example.md @@ -3,7 +3,7 @@ title: PyTorch Distributed --- The [pytorch_distributed_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_distributed_example.py) -script demonstrates integrating **ClearML** into code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html) +script demonstrates integrating ClearML into code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html) (`torch.distributed`). The script initializes a main Task and spawns subprocesses, each for an instance of that Task. @@ -34,7 +34,7 @@ Task.current_task().upload_artifact( All of these artifacts appear in the main Task under **ARTIFACTS** **>** **OTHER**. -![image](../../img/examples_pytorch_distributed_example_09.png) +![Experiment artifacts](../../img/examples_pytorch_distributed_example_09.png) ## Scalars @@ -54,11 +54,11 @@ Task.current_task().get_logger().report_scalar( The single scalar plot for loss appears in **RESULTS** **>** **SCALARS**. -![image](../../img/examples_pytorch_distributed_example_08.png) +![Experiment scalars](../../img/examples_pytorch_distributed_example_08.png) ## Hyperparameters -**ClearML** automatically logs the argparse command line options. Since the [`Task.connect`](../../references/sdk/task.md#connect) +ClearML automatically logs the argparse command line options. Since the [`Task.connect`](../../references/sdk/task.md#connect) method is called on [`Task.current_task`](../../references/sdk/task.md#taskcurrent_task), they are logged in the main Task. A different hyperparameter key is used in each subprocess, so they do not overwrite each other in the main Task. @@ -69,12 +69,12 @@ Task.current_task().connect(param) All the hyperparameters appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS**. -![image](../../img/examples_pytorch_distributed_example_01.png) +![Experiment hyperparameters Args](../../img/examples_pytorch_distributed_example_01.png) -![image](../../img/examples_pytorch_distributed_example_01a.png) +![Experiment hyperparameters General ](../../img/examples_pytorch_distributed_example_01a.png) ## Console Output to the console, including the text messages printed from the main Task object and each subprocess appear in **RESULTS** **>** **CONSOLE**. -![image](../../img/examples_pytorch_distributed_example_06.png) \ No newline at end of file +![Experiment console log](../../img/examples_pytorch_distributed_example_06.png) \ No newline at end of file diff --git a/docs/guides/frameworks/autokeras/autokeras_imdb_example.md b/docs/guides/frameworks/autokeras/autokeras_imdb_example.md index dada22c3..291b4064 100644 --- a/docs/guides/frameworks/autokeras/autokeras_imdb_example.md +++ b/docs/guides/frameworks/autokeras/autokeras_imdb_example.md @@ -20,7 +20,7 @@ which are titled **:monitor: machine**. ## Hyperparameters -**ClearML** automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**. +ClearML automatically logs TensorFlow Definitions. They appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **TF_DEFINE**. ![image](../../../img/examples_keras_16.png) diff --git a/docs/guides/frameworks/autokeras/integration_autokeras.md b/docs/guides/frameworks/autokeras/integration_autokeras.md index ddf3356c..ffdad367 100644 --- a/docs/guides/frameworks/autokeras/integration_autokeras.md +++ b/docs/guides/frameworks/autokeras/integration_autokeras.md @@ -1,11 +1,11 @@ --- title: AutoKeras Integration --- -Integrate **ClearML** into code that uses [autokeras](https://github.com/keras-team/autokeras). Initialize a **ClearML** -Task in a code, and **ClearML** automatically logs scalars, plots, and images reported to TensorBoard, Matplotlib, Plotly, +Integrate ClearML into code that uses [autokeras](https://github.com/keras-team/autokeras). Initialize a ClearML +Task in a code, and ClearML automatically logs scalars, plots, and images reported to TensorBoard, Matplotlib, Plotly, and Seaborn, and all other automatic logging, and explicit reporting added to the code (see [Logging](../../../fundamentals/logger.md)). -**ClearML** allows to: +ClearML allows to: * Visualize experiment results in the **ClearML Web UI**. * Track and upload models. @@ -13,7 +13,7 @@ and Seaborn, and all other automatic logging, and explicit reporting added to th * Rerun experiments, reproduce experiments on any target machine, and tune experiments. * Compare experiments. -See the [AutoKeras](autokeras_imdb_example.md) example, which shows **ClearML** automatically logging: +See the [AutoKeras](autokeras_imdb_example.md) example, which shows ClearML automatically logging: * Scalars * Hyperparameters * The console log @@ -22,7 +22,7 @@ See the [AutoKeras](autokeras_imdb_example.md) example, which shows **ClearML** Once these are logged, they can be visualized in the **ClearML Web UI**. :::note -If you are not already using **ClearML**, see [Getting Started](/getting_started/ds/best_practices.md). +If you are not already using ClearML, see [Getting Started](/getting_started/ds/best_practices.md). ::: ## Adding ClearML to Code @@ -36,6 +36,6 @@ task = Task.init(project_name="myProject", task_name="myExperiment") When the code runs, it initializes a Task in **ClearML Server**. A hyperlink to the experiment's log is output to the console. CLEARML Task: created new task id=c1f1dc6cf2ee4ec88cd1f6184344ca4e - CLEARML results page: https://app.community.clear.ml/projects/1c7a45633c554b8294fa6dcc3b1f2d4d/experiments/c1f1dc6cf2ee4ec88cd1f6184344ca4e/output/log + CLEARML results page: https://app.clear.ml/projects/1c7a45633c554b8294fa6dcc3b1f2d4d/experiments/c1f1dc6cf2ee4ec88cd1f6184344ca4e/output/log -Later in the code, define callbacks using TensorBoard, and **ClearML** logs TensorBoard scalars, histograms, and images. \ No newline at end of file +Later in the code, define callbacks using TensorBoard, and ClearML logs TensorBoard scalars, histograms, and images. \ No newline at end of file diff --git a/docs/guides/frameworks/fastai/fastai_with_tensorboard.md b/docs/guides/frameworks/fastai/fastai_with_tensorboard.md index 1a0cab21..66c454dc 100644 --- a/docs/guides/frameworks/fastai/fastai_with_tensorboard.md +++ b/docs/guides/frameworks/fastai/fastai_with_tensorboard.md @@ -2,16 +2,16 @@ title: FastAI --- The [fastai_with_tensorboard_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/fastai-v1/fastai_with_tensorboard_example/fastai_with_tensorboard_example.py) -example demonstrates the integration of **ClearML** into code that uses fastai v1 and TensorBoard. +example demonstrates the integration of ClearML into code that uses fastai v1 and TensorBoard. The example code does the following: 1. Trains a simple deep neural network on the fastai built-in MNIST dataset (see the [fast.ai](https://fastai1.fast.ai) documentation). -1. Uses the fastai `LearnerTensorboardWriter` callback, and **ClearML** automatically logs TensorBoard through the callback. +1. Uses the fastai `LearnerTensorboardWriter` callback, and ClearML automatically logs TensorBoard through the callback. 1. During script execution, creates an experiment named `fastai with tensorboard callback`, which is associated with the `examples` project. ## Scalars -**ClearML** automatically logs the histogram output to TensorBoard. They appear in **RESULTS** **>** **PLOTS**. +ClearML automatically logs the histogram output to TensorBoard. They appear in **RESULTS** **>** **PLOTS**. ![image](../../../img/examples_reporting_fastai_01.png) diff --git a/docs/guides/frameworks/keras/jupyter.md b/docs/guides/frameworks/keras/jupyter.md index 0070dcc5..6053044f 100644 --- a/docs/guides/frameworks/keras/jupyter.md +++ b/docs/guides/frameworks/keras/jupyter.md @@ -3,7 +3,7 @@ title: Keras with Matplotlib - Jupyter Notebook --- The [jupyter.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter.ipynb) example -demonstrates **ClearML**'s automatic logging of code running in a Jupyter Notebook that uses Keras and Matplotlib. +demonstrates ClearML's automatic logging of code running in a Jupyter Notebook that uses Keras and Matplotlib. The example does the following: 1. Trains a simple deep neural network on the Keras built-in [MNIST](https://keras.io/api/datasets/mnist/#load_data-function) @@ -39,7 +39,7 @@ The example calls Matplotlib methods to log debug sample images. They appear in ## Hyperparameters -**ClearML** automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task, by +ClearML automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task, by calling the [Task.connect](../../../references/sdk/task.md#connect) method. ```python diff --git a/docs/guides/frameworks/keras/keras_tensorboard.md b/docs/guides/frameworks/keras/keras_tensorboard.md index d27a97b2..32822c2c 100644 --- a/docs/guides/frameworks/keras/keras_tensorboard.md +++ b/docs/guides/frameworks/keras/keras_tensorboard.md @@ -2,7 +2,7 @@ title: Keras with TensorBoard --- -The example below demonstrates the integration of **ClearML** into code which uses Keras and TensorBoard. +The example below demonstrates the integration of ClearML into code which uses Keras and TensorBoard. View it in [script](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/keras_tensorboard.py) or in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter_keras_TB_example.ipynb). @@ -35,7 +35,7 @@ Histograms for layer density appear in **RESULTS** **>** **PLOTS**. ## Hyperparameters -**ClearML** automatically logs command line options generated with `argparse`, and TensorFlow Definitions. +ClearML automatically logs command line options generated with `argparse`, and TensorFlow Definitions. Command line options appear in **CONFIGURATIONS** **>** **HYPER PARAMETERS** **>** **Args**. diff --git a/docs/guides/frameworks/matplotlib/matplotlib_example.md b/docs/guides/frameworks/matplotlib/matplotlib_example.md index fa89c8f3..8ebc5f42 100644 --- a/docs/guides/frameworks/matplotlib/matplotlib_example.md +++ b/docs/guides/frameworks/matplotlib/matplotlib_example.md @@ -2,8 +2,8 @@ title: Matplotlib --- -The example below demonstrates integrating **ClearML** into code that uses `matplotlib` to plot scatter diagrams, and -show images. **ClearML** automatically logs the diagrams and images. +The example below demonstrates integrating ClearML into code that uses `matplotlib` to plot scatter diagrams, and +show images. ClearML automatically logs the diagrams and images. View the example in [script](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/matplotlib_example.py) or in [Jupyter Notebook](https://github.com/allegroai/clearml/blob/master/examples/frameworks/matplotlib/jupyter_matplotlib_example.ipynb). diff --git a/docs/guides/frameworks/pytorch/notebooks/audio/audio_classification_UrbanSound8K.md b/docs/guides/frameworks/pytorch/notebooks/audio/audio_classification_UrbanSound8K.md index a25dd5f6..99e60488 100644 --- a/docs/guides/frameworks/pytorch/notebooks/audio/audio_classification_UrbanSound8K.md +++ b/docs/guides/frameworks/pytorch/notebooks/audio/audio_classification_UrbanSound8K.md @@ -2,7 +2,7 @@ title: Audio Classification - Jupyter Notebooks --- -The example [audio_classification_UrbanSound8K.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_classifier_UrbanSound8K.ipynb) demonstrates integrating **ClearML** into a Jupyter Notebook which uses PyTorch, TensorBoard, and TorchVision to train a neural network on the UrbanSound8K dataset for audio classification. The example calls TensorBoard methods in training and testing to report scalars, audio debug samples, and spectrogram visualizations. The spectrogram visualizations are plotted by calling Matplotlib methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `audio classifier` which is associated with the `Audio Example` project. +The example [audio_classification_UrbanSound8K.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_classifier_UrbanSound8K.ipynb) demonstrates integrating ClearML into a Jupyter Notebook which uses PyTorch, TensorBoard, and TorchVision to train a neural network on the UrbanSound8K dataset for audio classification. The example calls TensorBoard methods in training and testing to report scalars, audio debug samples, and spectrogram visualizations. The spectrogram visualizations are plotted by calling Matplotlib methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `audio classification UrbanSound8K` which is associated with the `Audio Example` project. ## Scalars @@ -30,7 +30,7 @@ By doubling clicking a thumbnail, you can view a spectrogram plot in the image v ## Hyperparameters -**ClearML** automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using +ClearML automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using a call to the [Task.connect](../../../../../references/sdk/task.md#connect) method. configuration_dict = {'number_of_epochs': 10, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001} diff --git a/docs/guides/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.md b/docs/guides/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.md index 56e950f6..8910b3fa 100644 --- a/docs/guides/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.md +++ b/docs/guides/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.md @@ -3,17 +3,17 @@ title: Audio Preprocessing - Jupyter Notebook --- The example [audio_preprocessing_example.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/audio/audio_preprocessing_example.ipynb) -demonstrates integrating **ClearML** into a Jupyter Notebook which uses PyTorch and preprocesses audio samples. **ClearML** automatically logs spectrogram visualizations reported by calling Matplotlib methods, and audio samples reported by calling TensorBoard methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `data pre-processing`, which is associated with the `Audio Example` project. +demonstrates integrating ClearML into a Jupyter Notebook which uses PyTorch and preprocesses audio samples. ClearML automatically logs spectrogram visualizations reported by calling Matplotlib methods, and audio samples reported by calling TensorBoard methods. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `data pre-processing`, which is associated with the `Audio Example` project. ## Plots -**ClearML** automatically logs the waveform which the example reports by calling a Matplotlib method. These appear in **RESULTS** **>** **PLOTS**. +ClearML automatically logs the waveform which the example reports by calling a Matplotlib method. These appear in **RESULTS** **>** **PLOTS**. ![image](../../../../../img/examples_audio_preprocessing_example_08.png) ## Debug Samples -**ClearML** automatically logs the audio samples which the example reports by calling TensorBoard methods, and the spectrogram visualizations reported by calling Matplotlib methods. They appear in **RESULTS** **>** **DEBUG SAMPLES**. +ClearML automatically logs the audio samples which the example reports by calling TensorBoard methods, and the spectrogram visualizations reported by calling Matplotlib methods. They appear in **RESULTS** **>** **DEBUG SAMPLES**. ### Audio Samples diff --git a/docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md b/docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md index 8dcb064e..acc0a2a5 100644 --- a/docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md +++ b/docs/guides/frameworks/pytorch/notebooks/image/hyperparameter_search.md @@ -3,15 +3,15 @@ title: Image Hyperparameter Optimization - Jupyter Notebook --- [hyperparameter_search.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/hyperparameter_search.ipynb) -demonstrates integrating **ClearML** into a Jupyter Notebook which performs automated hyperparameter optimization. This -is an example of **ClearML** automation. It creates a **ClearML** +demonstrates integrating ClearML into a Jupyter Notebook which performs automated hyperparameter optimization. This +is an example of ClearML automation. It creates a ClearML [HyperParameterOptimizer](../../../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) object, which is a search controller. The search controller's search strategy optimizer is [OptimizerBOHB](../../../../../references/sdk/hpo_hpbandster_bandster_optimizerbohb.md) -The example maximizes total accuracy by finding an optimal batch size, base learning rate, and dropout. **ClearML** +The example maximizes total accuracy by finding an optimal batch size, base learning rate, and dropout. ClearML automatically logs the optimization's top performing experiments. The experiment whose hyperparameters are optimized is named `image_classification_CIFAR10`. It is created by running another -**ClearML** example, [image_classification_CIFAR10.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.ipynb), which must run before `hyperparameter_search.ipynb`. +ClearML example, [image_classification_CIFAR10.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.ipynb), which must run before `hyperparameter_search.ipynb`. When `hyperparameter_search.py` runs, it creates an experiment named `Hyper-Parameter Optimization` which is associated with the `Hyper-Parameter Search` project. @@ -24,7 +24,7 @@ The optimizer Task, `Hyper-Parameter Optimization`, and the experiments appear i Scalars for total accuracy and remaining budget by iteration, and a plot of total accuracy by iteration appear in **RESULTS** **>** **SCALARS**. Remaining budget indicates the percentage of total iterations for all jobs left before that total is reached. -These scalars are reported automatically by **ClearML** from `HyperParameterOptimizer` when it runs. +These scalars are reported automatically by ClearML from `HyperParameterOptimizer` when it runs. ![image](../../../../../img/examples_hyperparameter_search_04.png) @@ -32,7 +32,7 @@ These scalars are reported automatically by **ClearML** from `HyperParameterOpti A plot for the optimization of total accuracy by job appears in **RESULTS** **>** **SCALARS**. -This is also reported automatically by **ClearML** when `HyperParameterOptimizer` runs. +This is also reported automatically by ClearML when `HyperParameterOptimizer` runs. ![image](../../../../../img/examples_hyperparameter_search_05.png) @@ -79,7 +79,7 @@ All console output from `Hyper-Parameter Optimization` appears in **RESULTS** ta ## Experiments Comparison -**ClearML** automatically logs each job, meaning each experiment that executes with a set of hyperparameters, separately. Each appears as an individual experiment in the **ClearML Web UI**, where the Task name is `image_classification_CIFAR10` and the hyperparameters appended. +ClearML automatically logs each job, meaning each experiment that executes with a set of hyperparameters, separately. Each appears as an individual experiment in the **ClearML Web UI**, where the Task name is `image_classification_CIFAR10` and the hyperparameters appended. For example: diff --git a/docs/guides/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.md b/docs/guides/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.md index d4c6604b..c8209e43 100644 --- a/docs/guides/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.md +++ b/docs/guides/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.md @@ -3,8 +3,8 @@ title: Image Classification - Jupyter Notebook --- The example [image_classification_CIFAR10.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/image/image_classification_CIFAR10.ipynb) -demonstrates integrating **ClearML** into a Jupyter Notebook, which uses PyTorch, TensorBoard, and TorchVision to train a -neural network on the CIFAR10 dataset for image classification. **ClearML** automatically logs the example script's +demonstrates integrating ClearML into a Jupyter Notebook, which uses PyTorch, TensorBoard, and TorchVision to train a +neural network on the CIFAR10 dataset for image classification. ClearML automatically logs the example script's calls to TensorBoard methods in training and testing which report scalars and image debug samples, as well as the model and console log. In the example, we also demonstrate connecting parameters to a Task and logging them. When the script runs, it creates an experiment named `image_classification_CIFAR10` which is associated with the `Image Example` project. @@ -29,7 +29,7 @@ By doubling clicking a thumbnail, you can view a spectrogram plot in the image v ## Hyperparameters -**ClearML** automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using +ClearML automatically logs TensorFlow Definitions. A parameter dictionary is logged by connecting it to the Task using a call to the [Task.connect](../../../../../references/sdk/task.md#connect) method. configuration_dict = {'number_of_epochs': 3, 'batch_size': 4, 'dropout': 0.25, 'base_lr': 0.001} diff --git a/docs/guides/frameworks/pytorch/notebooks/table/download_and_preprocessing.md b/docs/guides/frameworks/pytorch/notebooks/table/download_and_preprocessing.md index f2a63023..d63b31fe 100644 --- a/docs/guides/frameworks/pytorch/notebooks/table/download_and_preprocessing.md +++ b/docs/guides/frameworks/pytorch/notebooks/table/download_and_preprocessing.md @@ -2,7 +2,7 @@ title: Tabular Data Downloading and Preprocessing - Jupyter Notebook --- -The [download_and_preprocessing.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/download_and_preprocessing.ipynb) example demonstrates **ClearML** storing preprocessed tabular data as artifacts, and explicitly reporting the tabular data in the **ClearML Web UI**. When the script runs, it creates an experiment named `tabular preprocessing` which is associated with the `Table Example` project. +The [download_and_preprocessing.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/download_and_preprocessing.ipynb) example demonstrates ClearML storing preprocessed tabular data as artifacts, and explicitly reporting the tabular data in the **ClearML Web UI**. When the script runs, it creates an experiment named `tabular preprocessing` which is associated with the `Table Example` project. This tabular data is prepared for another script, [train_tabular_predictor.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/table/train_tabular_predictor.ipynb), which trains a network with it. diff --git a/docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline.md b/docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline.md index 71987840..39370124 100644 --- a/docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline.md +++ b/docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline.md @@ -83,7 +83,7 @@ configuration_dict = { configuration_dict = task.connect(configuration_dict) # enabling configuration override by clearml ``` -**ClearML** tracks and reports each instance of the preprocessing Task. +ClearML tracks and reports each instance of the preprocessing Task. The raw data appears as a table in **RESULTS** **>** **PLOTS**. @@ -157,9 +157,9 @@ configuration_dict = { configuration_dict = task.connect(configuration_dict) # enabling configuration override by clearml ``` -**ClearML** tracks and reports the training step with each instance of the newly cloned and executed training Task. +ClearML tracks and reports the training step with each instance of the newly cloned and executed training Task. -**ClearML** automatically logs training loss and learning. They appear in **RESULTS** **>** **SCALARS**. +ClearML automatically logs training loss and learning. They appear in **RESULTS** **>** **SCALARS**. The following images show one of the two training Tasks. diff --git a/docs/guides/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.md b/docs/guides/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.md index 6a2ea284..6fcddf75 100644 --- a/docs/guides/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.md +++ b/docs/guides/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.md @@ -3,8 +3,8 @@ title: Text Classification - Jupyter Notebook --- The example [text_classification_AG_NEWS.ipynb](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/notebooks/text/text_classification_AG_NEWS.ipynb) -demonstrates using Jupyter Notebook for **ClearML**, and the integration of **ClearML** into code which trains a network -to classify text in the `torchtext` [AG_NEWS](https://pytorch.org/text/stable/datasets.html#ag-news) dataset, and then applies the model to predict the classification of sample text. **ClearML** automatically logs the scalar and console output by calling TensorBoard methods. In the example, we explicitly log parameters with the Task. When the script runs, it creates an experiment named `text classifier` which is associated with the `Text Example` project. +demonstrates using Jupyter Notebook for ClearML, and the integration of ClearML into code which trains a network +to classify text in the `torchtext` [AG_NEWS](https://pytorch.org/text/stable/datasets.html#ag-news) dataset, and then applies the model to predict the classification of sample text. ClearML automatically logs the scalar and console output by calling TensorBoard methods. In the example, we explicitly log parameters with the Task. When the script runs, it creates an experiment named `text classifier` which is associated with the `Text Example` project. ## Scalars @@ -14,7 +14,7 @@ Accuracy, learning rate, and training loss appear in **RESULTS** **>** **SCALARS ## Hyperparameters -**ClearML** automatically logs the command line options, because the example code uses `argparse`. A parameter dictionary +ClearML automatically logs the command line options, because the example code uses `argparse`. A parameter dictionary is logged by connecting it to the Task using a call to the [Task.connect](../../../../../references/sdk/task.md#connect) method. diff --git a/docs/guides/frameworks/pytorch/pytorch_abseil.md b/docs/guides/frameworks/pytorch/pytorch_abseil.md index 3967617b..d41e14a4 100644 --- a/docs/guides/frameworks/pytorch/pytorch_abseil.md +++ b/docs/guides/frameworks/pytorch/pytorch_abseil.md @@ -15,7 +15,7 @@ The example script does the following: ## Scalars -In the example script's `train` function, the following code explicitly reports scalars to **ClearML**: +In the example script's `train` function, the following code explicitly reports scalars to ClearML: ```python Logger.current_logger().report_scalar( diff --git a/docs/guides/frameworks/pytorch/pytorch_distributed_example.md b/docs/guides/frameworks/pytorch/pytorch_distributed_example.md index 89493d30..48cbb8f3 100644 --- a/docs/guides/frameworks/pytorch/pytorch_distributed_example.md +++ b/docs/guides/frameworks/pytorch/pytorch_distributed_example.md @@ -3,7 +3,7 @@ title: PyTorch Distributed --- The [pytorch_distributed_example.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_distributed_example.py) -script demonstrates integrating **ClearML** into a code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html) +script demonstrates integrating ClearML into a code that uses the [PyTorch Distributed Communications Package](https://pytorch.org/docs/stable/distributed.html) (`torch.distributed`). The script does the following: diff --git a/docs/guides/frameworks/pytorch/pytorch_matplotlib.md b/docs/guides/frameworks/pytorch/pytorch_matplotlib.md index 8df1c659..79b7c1ae 100644 --- a/docs/guides/frameworks/pytorch/pytorch_matplotlib.md +++ b/docs/guides/frameworks/pytorch/pytorch_matplotlib.md @@ -3,7 +3,7 @@ title: PyTorch with Matplotlib --- The [pytorch_matplotlib.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/pytorch/pytorch_matplotlib.py) -example demonstrates the integration of **ClearML** into code that uses PyTorch and Matplotlib. +example demonstrates the integration of ClearML into code that uses PyTorch and Matplotlib. The example does the following: * Creates an experiment named `pytorch with matplotlib example`, in the `examples` project. diff --git a/docs/guides/frameworks/pytorch/pytorch_mnist.md b/docs/guides/frameworks/pytorch/pytorch_mnist.md index 9c545eeb..73fc4795 100644 --- a/docs/guides/frameworks/pytorch/pytorch_mnist.md +++ b/docs/guides/frameworks/pytorch/pytorch_mnist.md @@ -14,7 +14,7 @@ The example script does the following: ## Scalars -In the example script's `train` function, the following code explicitly reports scalars to **ClearML**: +In the example script's `train` function, the following code explicitly reports scalars to ClearML: ```python Logger.current_logger().report_scalar( diff --git a/docs/guides/frameworks/pytorch/pytorch_tensorboardx.md b/docs/guides/frameworks/pytorch/pytorch_tensorboardx.md index 7029402e..f002f420 100644 --- a/docs/guides/frameworks/pytorch/pytorch_tensorboardx.md +++ b/docs/guides/frameworks/pytorch/pytorch_tensorboardx.md @@ -43,4 +43,4 @@ models and any snapshots created using PyTorch. Clicking on the model name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view the model’s details and access the model. -![image](../../../img/examples_pytorch_tensorboardx_05.png) +![image](../../../img/examples_pytorch_tensorboardx_model.png) diff --git a/docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md b/docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md index 7fb29b8d..32b141c4 100644 --- a/docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md +++ b/docs/guides/frameworks/pytorch_ignite/integration_pytorch_ignite.md @@ -42,7 +42,7 @@ View the scalars in the experiment's page in the **ClearML Web UI**, in **RESULT ## Model Snapshots -**ClearML** automatically captures the model logged with Torch, and saves it as an artifact. +ClearML automatically captures the model logged with Torch, and saves it as an artifact. View saved snapshots in the experiment's **ARTIFACTS** tab. diff --git a/docs/guides/frameworks/tensorboardx/tensorboardx.md b/docs/guides/frameworks/tensorboardx/tensorboardx.md index 43acc8a3..87a9ae27 100644 --- a/docs/guides/frameworks/tensorboardx/tensorboardx.md +++ b/docs/guides/frameworks/tensorboardx/tensorboardx.md @@ -41,5 +41,5 @@ models and any snapshots created using PyTorch. Clicking on the model’s name takes you to the [model’s page](../../../webapp/webapp_model_viewing.md), where you can view the model’s details and access the model. -![image](../../../img/examples_pytorch_tensorboardx_05.png) +![image](../../../img/examples_pytorch_tensorboardx_model.png) diff --git a/docs/guides/frameworks/tensorflow/integration_keras_tuner.md b/docs/guides/frameworks/tensorflow/integration_keras_tuner.md index 24fba700..60f3f8a9 100644 --- a/docs/guides/frameworks/tensorflow/integration_keras_tuner.md +++ b/docs/guides/frameworks/tensorflow/integration_keras_tuner.md @@ -2,20 +2,20 @@ title: Keras Tuner Integration --- -Integrate **ClearML** into code that uses [Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner). By +Integrate ClearML into code that uses [Keras Tuner](https://www.tensorflow.org/tutorials/keras/keras_tuner). By specifying `ClearMLTunerLogger` (see [kerastuner.py](https://github.com/allegroai/clearml/blob/master/clearml/external/kerastuner.py)) -as the Keras Tuner logger, **ClearML** automatically logs scalars and hyperparameter optimization. +as the Keras Tuner logger, ClearML automatically logs scalars and hyperparameter optimization. ## ClearMLTunerLogger Take a look at [keras_tuner_cifar.py](https://github.com/allegroai/clearml/blob/master/examples/frameworks/kerastuner/keras_tuner_cifar.py) -example script, which demonstrates the integration of **ClearML** in a code that uses Keras Tuner. +example script, which demonstrates the integration of ClearML in a code that uses Keras Tuner. The script does the following: 1. Creates a `Hyperband` object, which uses Keras Tuner's `Hyperband` tuner. It finds the best hyperparameters to train a network on a CIFAR10 dataset. 1. When the `Hyperband` object is created, instantiates a `ClearMLTunerLogger` object and assigns it to the `Hyperband` logger. -The `ClearMLTunerLogger` class provides the required binding for **ClearML** automatic logging. +The `ClearMLTunerLogger` class provides the required binding for ClearML automatic logging. ```python tuner = kt.Hyperband( @@ -35,21 +35,21 @@ When the script runs, it logs: ## Scalars -**ClearML** logs the scalars from training each network. They appear in the project's page in the **ClearML web UI**, under +ClearML logs the scalars from training each network. They appear in the project's page in the **ClearML web UI**, under **RESULTS** **>** **SCALARS**. ![image](../../../img/integration_keras_tuner_06.png) ## Summary of Hyperparameter Optimization -**ClearML** automatically logs the parameters of each experiment run in the hyperparameter search. They appear in tabular +ClearML automatically logs the parameters of each experiment run in the hyperparameter search. They appear in tabular form in **RESULTS** **>** **PLOTS**. ![image](../../../img/integration_keras_tuner_07.png) ## Artifacts -**ClearML** automatically stores the output model. It appears in **ARTIFACTS** **>** **Output Model**. +ClearML automatically stores the output model. It appears in **ARTIFACTS** **>** **Output Model**. ![image](../../../img/integration_keras_tuner_03.png) @@ -65,7 +65,7 @@ The model configuration is stored with the model. ### Hyperparameters -**ClearML** automatically logs the TensorFlow Definitions, which appear in **RESULTS** **>** **CONFIGURATION** **>** **HYPER PARAMETERS**. +ClearML automatically logs the TensorFlow Definitions, which appear in **RESULTS** **>** **CONFIGURATION** **>** **HYPER PARAMETERS**. ![image](../../../img/integration_keras_tuner_01.png) diff --git a/docs/guides/ide/google_colab.md b/docs/guides/ide/google_colab.md index 4ac5d46c..a3dfeb2f 100644 --- a/docs/guides/ide/google_colab.md +++ b/docs/guides/ide/google_colab.md @@ -39,9 +39,9 @@ and running, users can send Tasks to be executed on the Google Colab's HW. from clearml import Task Task.set_credentials( - api_host="https://api.community.clear.ml", - web_host="https://app.community.clear.ml", - files_host="https://files.community.clear.ml", + api_host="https://api.clear.ml", + web_host="https://app.clear.ml", + files_host="https://files.clear.ml", key='6ZHX9UQMYL874A1NE8', secret='=2h6#%@Y&m*tC!VLEXq&JI7QhZPKuJfbaYD4!uUk(t7=9ENv' ) diff --git a/docs/guides/ide/integration_pycharm.md b/docs/guides/ide/integration_pycharm.md index 7a532e60..3a5132da 100644 --- a/docs/guides/ide/integration_pycharm.md +++ b/docs/guides/ide/integration_pycharm.md @@ -27,13 +27,13 @@ the settings in the ClearML configuration file. **To set ClearML configuration parameters:** -1. In PyCharm, open **Settings** **>** **Tools** **>** **ClearML**. +1. In PyCharm, open **Settings** **>** **Tools** **>** ClearML. 1. Configure your ClearML server information: 1. API server (for example: ``http://localhost:8008``) 1. Web server (for example: ``http://localhost:8080``) 1. File server (for example: ``http://localhost:8081``) -1. Add **ClearML** user credentials key/secret. +1. Add ClearML user credentials key/secret. ![image](../../img/ide_pycharm_config_params.png) diff --git a/docs/guides/ide/remote_jupyter_tutorial.md b/docs/guides/ide/remote_jupyter_tutorial.md index 1f9bffb4..ab5bb2a5 100644 --- a/docs/guides/ide/remote_jupyter_tutorial.md +++ b/docs/guides/ide/remote_jupyter_tutorial.md @@ -70,7 +70,7 @@ Waiting for remote machine allocation [id=35c0af81ae6541589dbae1efb747f388] ...Status [in_progress] Remote machine allocated Setting remote environment [Task id=35c0af81ae6541589dbae1efb747f388] -Setup process details: https://app.community.clear.ml/projects/60893b87b0b642679fde00db96e90359/experiments/35c0af81ae6541589dbae1efb747f388/output/log +Setup process details: https://app.clear.ml/projects/60893b87b0b642679fde00db96e90359/experiments/35c0af81ae6541589dbae1efb747f388/output/log Waiting for environment setup to complete [usually about 20-30 seconds] ``` diff --git a/docs/guides/main.md b/docs/guides/main.md index 33b77c77..b66eee39 100644 --- a/docs/guides/main.md +++ b/docs/guides/main.md @@ -5,7 +5,7 @@ slug: /guides --- -To help learn and use **ClearML**, we provide example scripts that demonstrates how to use ClearML's various features. +To help learn and use ClearML, we provide example scripts that demonstrates how to use ClearML's various features. Examples scripts are in the [examples](https://github.com/allegroai/clearml/tree/master/examples) folder of the GitHub `clearml` repository. They are also pre-loaded in the **ClearML Server**: diff --git a/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md b/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md index 2227b585..7e0d97b1 100644 --- a/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md +++ b/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt.md @@ -3,7 +3,7 @@ title: Hyperparameter Optimization --- The [hyper_parameter_optimizer.py](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py) -example script demonstrates hyperparameter optimization, which is automated by using **ClearML** +example script demonstrates hyperparameter optimization, which is automated by using ClearML ## Set the Search Strategy for Optimization @@ -18,13 +18,13 @@ The following search strategies can be used: BOHB performs robust and efficient hyperparameter optimization at scale by combining the speed of Hyperband searches with the guidance and guarantees of convergence of Bayesian Optimization. - **ClearML** implements BOHB for automation with HpBandSter's [bohb.py](https://github.com/automl/HpBandSter/blob/master/hpbandster/optimizers/bohb.py). + ClearML implements BOHB for automation with HpBandSter's [bohb.py](https://github.com/automl/HpBandSter/blob/master/hpbandster/optimizers/bohb.py). For more information about HpBandSter BOHB, see the [HpBandSter](https://automl.github.io/HpBandSter/build/html/index.html) documentation. * Random uniform sampling of hyperparameter strategy - [automation.RandomSearch](../../../references/sdk/hpo_optimization_randomsearch.md) * Full grid sampling strategy of every hyperparameter combination - Grid search [automation.GridSearch](../../../references/sdk/hpo_optimization_gridsearch.md). -* Custom - Use a custom class and inherit from the **ClearML** automation base strategy class, automation.optimization.SearchStrategy. +* Custom - Use a custom class and inherit from the ClearML automation base strategy class, automation.optimization.SearchStrategy. The search strategy class that is chosen will be passed to the [automation.HyperParameterOptimizer](../../../references/sdk/hpo_optimization_hyperparameteroptimizer.md) object later. @@ -122,7 +122,7 @@ an_optimizer = HyperParameterOptimizer( base_task_id=args['template_task_id'], ``` -Set the hyperparameter ranges to sample, instantiating them as **ClearML** automation objects using [automation.UniformIntegerParameterRange](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md) +Set the hyperparameter ranges to sample, instantiating them as ClearML automation objects using [automation.UniformIntegerParameterRange](../../../references/sdk/hpo_parameters_uniformintegerparameterrange.md) and [automation.DiscreteParameterRange](../../../references/sdk/hpo_parameters_discreteparameterrange.md). ```python diff --git a/docs/guides/reporting/3d_plots_reporting.md b/docs/guides/reporting/3d_plots_reporting.md index bd4bd852..0c8de0e9 100644 --- a/docs/guides/reporting/3d_plots_reporting.md +++ b/docs/guides/reporting/3d_plots_reporting.md @@ -7,7 +7,7 @@ example demonstrates reporting a series as a surface plot and as a 3D scatter pl When the script runs, it creates an experiment named `3D plot reporting`, which is associated with the `examples` project. -**ClearML** reports these plots in the **ClearML Web UI** **>** experiment page **>** **RESULTS** tab **>** **PLOTS** sub-tab. +ClearML reports these plots in the **ClearML Web UI** **>** experiment page **>** **RESULTS** tab **>** **PLOTS** sub-tab. ## Surface Plot diff --git a/docs/guides/reporting/artifacts.md b/docs/guides/reporting/artifacts.md index a2890e0d..ca6c055e 100644 --- a/docs/guides/reporting/artifacts.md +++ b/docs/guides/reporting/artifacts.md @@ -14,9 +14,9 @@ These artifacts include: Artifacts can be uploaded and dynamically tracked, or uploaded without tracking. -Configure **ClearML** for uploading artifacts to any of the supported types of storage, which include local and shared folders, +Configure ClearML for uploading artifacts to any of the supported types of storage, which include local and shared folders, S3 buckets, Google Cloud Storage, and Azure Storage ([debug sample storage](../../references/sdk/logger.md#set_default_upload_destination) -is different). Configure **ClearML** in any of the following ways: +is different). Configure ClearML in any of the following ways: * In the configuration file, set [default_output_uri](../../configs/clearml_conf.md#sdkdevelopment). * In code, when [initializing a Task](../../references/sdk/task.md#taskinit), use the `output_uri` parameter. @@ -24,14 +24,14 @@ is different). Configure **ClearML** in any of the following ways: When the script runs, it creates an experiment named `artifacts example`, which is associated with the `examples` project. -**ClearML** reports artifacts in the **ClearML Web UI** **>** experiment details **>** **ARTIFACTS** tab. +ClearML reports artifacts in the **ClearML Web UI** **>** experiment details **>** **ARTIFACTS** tab. ![image](../../img/examples_reporting_03.png) ## Dynamically Tracked Artifacts -Currently, **ClearML** supports uploading and dynamically tracking Pandas DataFrames. Use the [Task.register_artifact](../../references/sdk/task.md#register_artifact) -method. If the Pandas DataFrame changes, **ClearML** uploads the changes. The updated artifact is associated with the experiment. +Currently, ClearML supports uploading and dynamically tracking Pandas DataFrames. Use the [Task.register_artifact](../../references/sdk/task.md#register_artifact) +method. If the Pandas DataFrame changes, ClearML uploads the changes. The updated artifact is associated with the experiment. For example: @@ -51,7 +51,7 @@ task.register_artifact('train', df, metadata={'counting': 'legs', 'max legs': 69 ``` By changing the artifact, and calling the [Task.get_registered_artifacts](../../references/sdk/task.md#get_registered_artifacts) -method to retrieve it, we can see that **ClearML** tracked the change. +method to retrieve it, we can see that ClearML tracked the change. ```python # change the artifact object @@ -62,7 +62,7 @@ Task.current_task().get_registered_artifacts()['train'].sample(frac=0.5, replace ## Artifacts Without Tracking -**ClearML** supports several types of objects that can be uploaded and are not tracked. Use the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact) +ClearML supports several types of objects that can be uploaded and are not tracked. Use the [Task.upload_artifact](../../references/sdk/task.md#upload_artifact) method. Artifacts without tracking include: diff --git a/docs/guides/reporting/hyper_parameters.md b/docs/guides/reporting/hyper_parameters.md index e6eb69af..58856e06 100644 --- a/docs/guides/reporting/hyper_parameters.md +++ b/docs/guides/reporting/hyper_parameters.md @@ -4,7 +4,7 @@ title: Hyperparameters Reporting The [hyper_parameters.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/hyper_parameters.py) example script demonstrates: -* **ClearML**'s automatic logging of `argparse` command line options and TensorFlow Definitions +* ClearML's automatic logging of `argparse` command line options and TensorFlow Definitions * Logging user-defined hyperparameters with a parameter dictionary and connecting the dictionary to a Task. Hyperparameters appear in the **web UI** in the experiment's page, under **CONFIGURATIONS** **>** **HYPER PARAMETERS**. @@ -15,7 +15,7 @@ When the script runs, it creates an experiment named `hyper-parameters example`, ## Argparse Command Line Options -If a code uses argparse and initializes a Task, **ClearML** automatically logs the argparse arguments. +If a code uses argparse and initializes a Task, ClearML automatically logs the argparse arguments. ```python parser = ArgumentParser() @@ -34,7 +34,7 @@ Command line options appears in **HYPER PARAMETERS** **>** **Args**. ## TensorFlow Definitions -**ClearML** automatically logs TensorFlow Definitions, whether they are defined before or after the Task is initialized. +ClearML automatically logs TensorFlow Definitions, whether they are defined before or after the Task is initialized. ```python flags.DEFINE_string('echo', None, 'Text to echo.') @@ -55,7 +55,7 @@ TensorFlow Definitions appear in **HYPER PARAMETERS** **>** **TF_DEFINE**. ## Parameter Dictionaries Connect a parameter dictionary to a Task by calling the [Task.connect](../../references/sdk/task.md#connect) -method, and **ClearML** logs the parameters. **ClearML** also tracks changes to the parameters. +method, and ClearML logs the parameters. ClearML also tracks changes to the parameters. ```python parameters = { diff --git a/docs/guides/reporting/manual_matplotlib_reporting.md b/docs/guides/reporting/manual_matplotlib_reporting.md index bc902ca4..0237fe98 100644 --- a/docs/guides/reporting/manual_matplotlib_reporting.md +++ b/docs/guides/reporting/manual_matplotlib_reporting.md @@ -3,7 +3,7 @@ title: Manual Matplotlib Reporting --- The [matplotlib_manual_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/matplotlib_manual_reporting.py) -example demonstrates reporting using Matplotlib and Seaborn with **ClearML**. +example demonstrates reporting using Matplotlib and Seaborn with ClearML. When the script runs, it creates an experiment named `Manual Matplotlib example`, which is associated with the `examples` project. diff --git a/docs/guides/reporting/media_reporting.md b/docs/guides/reporting/media_reporting.md index 73eec08f..4c5a14e6 100644 --- a/docs/guides/reporting/media_reporting.md +++ b/docs/guides/reporting/media_reporting.md @@ -9,11 +9,11 @@ method to upload from: * BytesIO stream * URL of media already uploaded to some storage -**ClearML** uploads media to the bucket specified in the **ClearML** configuration file or **ClearML** can be configured for image storage, see [Logger.set_default_upload_destination](../../references/sdk/logger.md#set_default_upload_destination) -(storage for [artifacts](../../fundamentals/artifacts.md#setting-upload-destination) is different). Set credentials for storage in the **ClearML** +ClearML uploads media to the bucket specified in the ClearML configuration file or ClearML can be configured for image storage, see [Logger.set_default_upload_destination](../../references/sdk/logger.md#set_default_upload_destination) +(storage for [artifacts](../../fundamentals/artifacts.md#setting-upload-destination) is different). Set credentials for storage in the ClearML [configuration file](../../configs/clearml_conf.md). -**ClearML** reports media in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **DEBUG SAMPLES** +ClearML reports media in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **DEBUG SAMPLES** sub-tab. When the script runs, it creates an experiment named `audio and video reporting`, which is associated with the `examples` diff --git a/docs/guides/reporting/pandas_reporting.md b/docs/guides/reporting/pandas_reporting.md index a6fe8cfc..d9ae7812 100644 --- a/docs/guides/reporting/pandas_reporting.md +++ b/docs/guides/reporting/pandas_reporting.md @@ -4,10 +4,10 @@ title: Tables Reporting (Pandas and CSV Files) The [pandas_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/pandas_reporting.py) example demonstrates reporting tabular data from Pandas DataFrames and CSV files as tables. -**ClearML** reports these tables in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **PLOTS** +ClearML reports these tables in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **PLOTS** sub-tab. -When the script runs, it creates an experiment named `pandas table reporting`, which is associated with the `examples` project. +When the script runs, it creates an experiment named `table reporting`, which is associated with the `examples` project. ## Reporting Pandas DataFrames as Tables diff --git a/docs/guides/reporting/plotly_reporting.md b/docs/guides/reporting/plotly_reporting.md index 261d0c5c..d622b41b 100644 --- a/docs/guides/reporting/plotly_reporting.md +++ b/docs/guides/reporting/plotly_reporting.md @@ -3,9 +3,9 @@ title: Plotly Reporting --- The [plotly_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/plotly_reporting.py) example -demonstrates **ClearML**'s Plotly integration and reporting. +demonstrates ClearML's Plotly integration and reporting. -Report Plotly plots in **ClearML** by calling the [`Logger.report_plotly`](../../references/sdk/logger.md#report_plotly) method, and passing a complex +Report Plotly plots in ClearML by calling the [`Logger.report_plotly`](../../references/sdk/logger.md#report_plotly) method, and passing a complex Plotly figure, using the `figure` parameter. In this example, the Plotly figure is created using `plotly.express.scatter` (see [Scatter Plots in Python](https://plotly.com/python/line-and-scatter/) @@ -33,7 +33,7 @@ task.get_logger().report_plotly( When the script runs, it creates an experiment named `plotly reporting`, which is associated with the examples project. -**ClearML** reports Plotly plots in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **PLOTS** +ClearML reports Plotly plots in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **PLOTS** sub-tab. ![image](../../img/examples_reporting_13.png) \ No newline at end of file diff --git a/docs/guides/reporting/scalar_reporting.md b/docs/guides/reporting/scalar_reporting.md index fa9c5289..cc8f38f2 100644 --- a/docs/guides/reporting/scalar_reporting.md +++ b/docs/guides/reporting/scalar_reporting.md @@ -3,7 +3,7 @@ title: Scalars Reporting --- The [scalar_reporting.py](https://github.com/allegroai/clearml/blob/master/examples/reporting/scalar_reporting.py) script -demonstrates explicit scalar reporting. **ClearML** reports scalars in the **ClearML Web UI** **>** experiment details **>** +demonstrates explicit scalar reporting. ClearML reports scalars in the **ClearML Web UI** **>** experiment details **>** **RESULTS** tab **>** **SCALARS** sub-tab. When the script runs, it creates an experiment named `scalar reporting`, which is associated with the `examples` project. diff --git a/docs/guides/reporting/scatter_hist_confusion_mat_reporting.md b/docs/guides/reporting/scatter_hist_confusion_mat_reporting.md index 34ad968c..5166811b 100644 --- a/docs/guides/reporting/scatter_hist_confusion_mat_reporting.md +++ b/docs/guides/reporting/scatter_hist_confusion_mat_reporting.md @@ -8,7 +8,7 @@ example demonstrates reporting series data in the following 2D formats: * [Confusion matrices](#confusion-matrices) * [Scatter plots](#2d-scatter-plots) -**ClearML** reports these tables in the **ClearML Web UI**, experiment details **>** **RESULTS** tab **>** **PLOTS** sub-tab. +ClearML reports these tables in the **ClearML Web UI**, experiment details **>** **RESULTS** tab **>** **PLOTS** sub-tab. When the script runs, it creates an experiment named `2D plots reporting`, which is associated with the `examples` project. diff --git a/docs/guides/reporting/text_reporting.md b/docs/guides/reporting/text_reporting.md index ae70bdc6..0f182210 100644 --- a/docs/guides/reporting/text_reporting.md +++ b/docs/guides/reporting/text_reporting.md @@ -6,7 +6,7 @@ The [text_reporting.py](https://github.com/allegroai/clearml/blob/master/example demonstrates reporting explicit text, by calling the [Logger.report_text](../../references/sdk/logger.md#report_text) method. -**ClearML** reports these tables in the **ClearML Web UI**, experiment details, **RESULTS** tab, **CONSOLE** sub-tab. +ClearML reports these tables in the **ClearML Web UI**, experiment details, **RESULTS** tab, **CONSOLE** sub-tab. When the script runs, it creates an experiment named `text reporting`, which is associated with the `examples` project. diff --git a/docs/guides/services/aws_autoscaler.md b/docs/guides/services/aws_autoscaler.md index b1847c3e..41f0ec9d 100644 --- a/docs/guides/services/aws_autoscaler.md +++ b/docs/guides/services/aws_autoscaler.md @@ -134,9 +134,9 @@ and the script prints a hyperlink to the Task's log. ```console CLEARML Task: created new task id=d0ee5309a9a3471d8802f2561da60dfa CLEARML Monitor: GPU monitoring failed getting GPU reading, switching off GPU monitoring -CLEARML results page: https://app.community.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log +CLEARML results page: https://app.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log Running AWS auto-scaler as a service -Execution log https://app.community.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log +Execution log https://app.clear.ml/projects/142a598b5d234bebb37a57d692f5689f/experiments/d0ee5309a9a3471d8802f2561da60dfa/output/log ``` ### Remote Execution diff --git a/docs/guides/services/cleanup_service.md b/docs/guides/services/cleanup_service.md index e6d8bcda..9250830a 100644 --- a/docs/guides/services/cleanup_service.md +++ b/docs/guides/services/cleanup_service.md @@ -34,7 +34,7 @@ A new task called `Cleanup Service` is created in the `DevOps` project on your C look similar to: ```console ClearML Task: created new task id=8126c0af800f4903be07421aa344d7b3 -ClearML results page: https://app.community.clear.ml/projects/608e9039/experiments/81261aa34d7b3/output/log +ClearML results page: https://app.clear.ml/projects/608e9039/experiments/81261aa34d7b3/output/log Cleanup service started Starting cleanup Deleting 100 tasks diff --git a/docs/hyperdatasets/task.md b/docs/hyperdatasets/task.md index 0a1a9131..589e4c5f 100644 --- a/docs/hyperdatasets/task.md +++ b/docs/hyperdatasets/task.md @@ -2,7 +2,7 @@ title: Tasks --- -Hyper-Datasets extend the **ClearML** [**Task**](../fundamentals/task.md) with [Dataviews](dataviews.md) +Hyper-Datasets extend the ClearML [**Task**](../fundamentals/task.md) with [Dataviews](dataviews.md) ## Usage diff --git a/docs/hyperdatasets/webapp/webapp_datasets_frames.md b/docs/hyperdatasets/webapp/webapp_datasets_frames.md index 87aca6f8..d527f596 100644 --- a/docs/hyperdatasets/webapp/webapp_datasets_frames.md +++ b/docs/hyperdatasets/webapp/webapp_datasets_frames.md @@ -7,7 +7,7 @@ of frames and enables viewing SingleFrames and FramesGroups, and edit SingleFram Before opening the frame viewer, you can filter the frames by applying [simple](#simple-frame-filtering) or [advanced](#advanced-frame-filtering) filtering logic. -![Dataset page](../../img/hyperdatasets/frames_01.png) +![Dataset 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video), and a player (for audio) (see [reporting media](../guides/reporting/media_reporting.md)). -The **ClearML** experiment comparison provides [comparison features](#comparison-features) making it easy to compare experiments. +The ClearML experiment comparison provides [comparison features](#comparison-features) making it easy to compare experiments. ## Selecting Experiments to Compare @@ -166,7 +166,7 @@ This image shows scalars grouped by metric and variant. ## Plots -Visualize the comparison of any data that **ClearML** automatically captures or that is explicitly reported in experiments, +Visualize the comparison of any data that ClearML automatically captures or that is explicitly reported in experiments, in the **PLOTS** tab. **To compare plots:** @@ -277,23 +277,15 @@ Show differences in other experiments in reference to a new base experiment. To * Click on Switch base experiment on the top right of the experiment that will be the new base. * Click on Pan the new base experiment and drag it all the way to the left -![image](../img/webapp_compare_22.png) - - - ### Dynamic Ordering of the Compared Experiments To reorder the experiments being compared, press Pan on the top right of the experiment that - needs to be moved, and drag the experiment to its new position. - -![image](../img/webapp_compare_21.png) +needs to be moved, and drag the experiment to its new position. ### Removing an Experiment from the Comparison Remove an experiment from the comparison, by pressing Minus on the top right of the experiment that needs to be removed. -![image](../img/webapp_compare_23.png) - ### Sharing Experiments To share a comparison table, copy the full URL from the address bar and send it to a teammate to collaborate. They will diff --git a/docs/webapp/webapp_exp_table.md b/docs/webapp/webapp_exp_table.md index 3eb35f6c..7bcc9311 100644 --- a/docs/webapp/webapp_exp_table.md +++ b/docs/webapp/webapp_exp_table.md @@ -12,7 +12,7 @@ To assist in focusing on active experimentation, experiments and models can be a in the active experiments and models tables. See [Archiving](webapp_archiving.md). ::: -![Experiment table](../img/webapp_exp_table_01.png) +![Experiment table](../img/webapp_experiment_table.png) ## Experiments Table Columns @@ -20,7 +20,7 @@ The experiments table default and customizable columns are described in the foll | Column | Description | Type | |---|---|---| -| **TYPE** | Type of experiment. **ClearML** supports multiple [task types](../fundamentals/task.md#task-types) for experimentation, and a variety of workflows and use cases. | Default | +| **TYPE** | Type of experiment. ClearML supports multiple [task types](../fundamentals/task.md#task-types) for experimentation, and a variety of workflows and use cases. | Default | | **NAME** | Experiment name. | Default | | **TAGS** | Descriptive, user-defined, color-coded tags assigned to experiments. Use tags to classify experiments, and filter the list. See [tagging experiments](webapp_exp_track_visual.md#tagging-experiments). | Default | | **STATUS** | Experiment state (status). See a list of the [task states and state transitions](../fundamentals/task.md#task-states). | Default | @@ -60,7 +60,7 @@ Use experiments table customization for various use cases, including: * Tracking hyperparameters - Track hyperparameters by adding them as columns, and applying filters and sorting. Changes are persistent (cached in the browser), and represented in the URL so customized settings can be saved in a browser -bookmark and shared with other **ClearML** users to collaborate. +bookmark and shared with other ClearML users to collaborate. :::note The following experiments-table customizations are saved on a **per project** basis: @@ -156,7 +156,7 @@ operations on the selected experiments. Actions can be performed only on the exp of experiments that can be affected by each action. The same information can be found in the bottom menu, in a tooltip that appears when hovering over an action icon. -![Experiment table batch operations](../img/webapp_exp_table_batch_operations.png) +![Experiment table batch operations](../img/webapp_experiment_table_context_menu.png) ## Selecting Multiple Experiments diff --git a/docs/webapp/webapp_exp_track_visual.md b/docs/webapp/webapp_exp_track_visual.md index f614bacf..3bf77135 100644 --- a/docs/webapp/webapp_exp_track_visual.md +++ b/docs/webapp/webapp_exp_track_visual.md @@ -2,7 +2,7 @@ title: Tracking Experiments and Visualizing Results --- -While an experiment is running, and any time after it finishes, track it and visualize the results in the **ClearML Web UI**, +While an experiment is running, and any time after it finishes, track it and visualize the results in the ClearML Web UI, including: * [Execution details](#execution-details) - Code, the base Docker image used for [ClearML Agent](../clearml_agent.md), output destination for artifacts, and the logging level. @@ -56,65 +56,55 @@ table is not visible when the full screen details view is open. Perform experime -## Execution Details -In the EXECUTION tab of an experiment's detail page, there are records of: +## Execution +An experiment's **EXECUTION** tab of lists the following: * Source code -* ClearML Agent configuration -* Output details * Uncommitted changes * Installed Python packages +* Container details +* Output details +In full-screen mode, the source code and output details are grouped in the **DETAILS** section. -### Source Code, ClearML Agent Configuration, and Output Details +### Source Code -The source code details of the EXECUTION tab of an experiment include: +The Source Code section of an experiment's **EXECUTION** tab includes: * The experiment's repository * Commit ID * Script path * Working directory -Additionally, there is information about the ClearML Agent configuration. The ClearML Agent base image is a pre-configured -Docker that ClearML Agent will use to remotely execute this experiment (see [Building Docker containers](../clearml_agent.md#exporting-a-task-into-a-standalone-docker-container)). - -The output details include: -* The output destination used for storing model checkpoints (snapshots) and artifacts (see also, [default_output_uri](../configs/clearml_conf.md#config_default_output_uri) - in the configuration file, and [output_uri](../references/sdk/task.md#taskinit) - in `Task.init` parameters). - -* The logging level for the experiment, which uses the standard Python [logging levels](https://docs.python.org/3/howto/logging.html#logging-levels). - - -
-View a screenshot -
- -![Execution details section](../img/webapp_tracking_18.png) - -
-
- +![Source code section](../img/webapp_exp_source_code.png) ### Uncommitted Changes -
-View a screenshot -
+ClearML displays the git diff of the experiment in the Uncommitted Changes section. -![Uncommitted changes section](../img/webapp_tracking_19.png) +![Uncommitted changes section](../img/webapp_exp_uncommitted_changes.png) -
-
+### Installed Packages + +The Installed Packages section lists the experiment's installed Python packages and their versions. + +![Installed packages section](../img/webapp_exp_installed_packages.png) + +### Container +The Container section list the following information: +* Image - a pre-configured Docker that ClearML Agent will use to remotely execute this experiment (see [Building Docker containers](../clearml_agent.md#exporting-a-task-into-a-standalone-docker-container)) +* Arguments - add Docker arguments +* Setup shell script - a bash script to be executed inside the Docker before setting up the experiment's environment -### Installed Python Packages and Their Versions -
-View a screenshot -
+![Container section](../img/webapp_exp_container.png) -![Installed packages section](../img/webapp_tracking_20.png) +### Output +The Output details include: +* The output destination used for storing model checkpoints (snapshots) and artifacts (see also, [default_output_uri](../configs/clearml_conf.md#config_default_output_uri) + in the configuration file, and `output_uri` in [`Task.init`](../references/sdk/task.md#taskinit) parameters). -
-
+* The logging level for the experiment, which uses the standard Python [logging levels](https://docs.python.org/3/howto/logging.html#logging-levels). + +![Execution details section](../img/webapp_exp_output.png) ## Configuration @@ -124,66 +114,37 @@ All parameters and configuration objects appear in the **CONFIGURATION** tab. ### Hyperparameters :::important -In older versions of **ClearML Server**, the **CONFIGURATION** tab was named **HYPER PARAMETERS**, and it contained all parameters. The renamed tab contains a **HYPER PARAMETER** section, and subsections for hyperparameter groups. +In older versions of ClearML Server, the **CONFIGURATION** tab was named **HYPER PARAMETERS**, and it contained all parameters. The renamed tab contains a **HYPER PARAMETER** section, and subsections for hyperparameter groups. ::: Hyperparameters are grouped by their type and appear in **CONFIGURATION** **>** **HYPER PARAMETERS**. #### Command Line Arguments -The **Args** parameter group shows automatically logged `argparse` arguments, and all older experiments parameters, except TensorFlow Definitions. Hover over a parameter, and the type, description, and default value appear, if they were provided. - -
-View a screenshot -
+The **Args** parameter group shows automatically logged `argparse` arguments, and all older experiments parameters, except +TensorFlow Definitions. Hover over a parameter, and the type, description, and default value appear, if they were provided. ![Command line arguments configuration group](../img/webapp_tracking_22.png) -
-
- - #### Environment Variables If the `CLEARML_LOG_ENVIRONMENT` variable was set, the **Environment** group will show environment variables (see [this FAQ](../faq.md#track-env-vars)). -
-View a screenshot -
- ![Environment variables configuration group](../img/webapp_tracking_23.png) -
-
- - #### Custom Parameter Groups Custom parameter groups show parameter dictionaries if the parameters were connected to the Task, using the `Task.connect` method, with a `name` argument provided. -
-View a screenshot -
- ![Custom parameters group](../img/webapp_tracking_25.png) -
-
- #### TensorFlow Definitions The **TF_DEFINE** parameter group shows automatic TensorFlow logging. -
-View a screenshot -
- ![TF_DEFINE parameter group](../img/webapp_tracking_26.png) -
-
- Once an experiment is run and stored in **ClearML Server**, any of these hyperparameters can be [modified](webapp_exp_tuning.md#modifying-experiments). ### User Properties @@ -191,45 +152,24 @@ Once an experiment is run and stored in **ClearML Server**, any of these hyperpa User properties allow to store any descriptive information in a key-value pair format. They are editable in any experiment, except experiments whose status is *Published* (read-only). -
-View a screenshot -
- ![User properties section](../img/webapp_tracking_21.png) -
-
- - ### Configuration Objects ClearML tracks experiment (Task) model configuration objects, which appear in **Configuration Objects** **>** **General**. These objects include those that are automatically tracked, and those connected to a Task in code (see [Task.connect_configuration](../references/sdk/task.md#connect_configuration)). -ClearML supports providing a name for a Task model configuration object (see the [name](../references/sdk/task.md#connect_configuration) -parameter in `Task.connect_configuration`). + +![Configuration objects](../img/webapp_tracking_24.png) + +ClearML supports providing a name for a Task model configuration object (see the `name` +parameter in [`Task.connect_configuration`](../references/sdk/task.md#connect_configuration)). + +![Custom configuration objects](../img/webapp_tracking_28.png) :::important In older versions of **ClearML Server**, the Task model configuration appeared in the **ARTIFACTS** tab, **MODEL CONFIGURATION** section. Task model configurations now appear in the **Configuration Objects** section, in the **CONFIGURATION** tab. ::: -
-View a screenshot - Configuration objects -
- -![Configuration objects](../img/webapp_tracking_24.png) - -
-
-
- -
-View a screenshot - Custom configuration object -
- -![Custom configuration objects](../img/webapp_tracking_28.png) - -
-
## Artifacts @@ -256,56 +196,22 @@ including design, label enumeration, and general information, go to the **MODELS if it is in a local file. -
-View a screenshot -
- ![Models in Artifacts tab](../img/webapp_exp_artifacts_01.png) -
-
- - ### Other Artifacts -**To retrieve another artifact:** +Other artifacts, which are uploaded but not dynamically tracked after the upload, appear in the **OTHER** section. +They include the file path, file size, and hash. -1. In the **ARTIFACTS** tab **>** **DATA AUDIT** or **OTHER** **>** Select an artifact **>** Either: +**To retrieve Other artifacts:** - * Download the artifact , if it is stored in remote storage. - * Copy its location to the clipboard Copy Clipboard, +In the **ARTIFACTS** tab **>** **OTHER** **>** Select an artifact **>** Either: +* Download the artifact , if it is stored in remote storage. +* Copy its location to the clipboard Copy Clipboard, if it is in a local file. -#### Data Audit - -Artifacts which are uploaded and dynamically tracked by ClearML appear in the **DATA AUDIT** section. They include the file path, file size, hash, and metadata stored with the artifact. - - -
-View a screenshot -
- -![Data audit section](../img/webapp_tracking_29.png) - -
-
- -#### Other - -Other artifacts, which are uploaded but not dynamically tracked after the upload, appear in the **OTHER** section. They include the file path, file size, and hash. - -
-View a screenshot -
- ![Other artifacts section](../img/webapp_tracking_30.png) -
-
- - - - ## General Information General experiment details appear in the **INFO** tab. This includes information describing the stored experiment: @@ -326,19 +232,8 @@ General experiment details appear in the **INFO** tab. This includes information * Processor * Python version - -
-View a screenshot -
- ![Info tab](../img/webapp_tracking_31.png) -
-
- - - - ## Experiment Results @@ -414,15 +309,8 @@ Non-time-series plots appear in **RESULTS** **>** **PLOTS**. These include data tools, and ClearML explicit reporting. These may include 2D and 3D plots, tables (Pandas and CSV files), and Plotly plots. Individual plots can be shown / hidden or filtered by title. -
-View a screenshot -
- ![Plots tab](../img/webapp_tracking_35.png) -
-
- #### Plot Controls @@ -460,7 +348,11 @@ These controls allow you to better analyze the results. Hover over a plot, and t ### Debug Samples -View debug samples by metric at any iteration. The most recent iteration appears first. Use the viewer / player to inspect images, audio, video samples and do any of the following: +View debug samples by metric at any iteration. The most recent iteration appears first. + +![Debug Samples tab](../img/webapp_tracking_43.png) + +Use the viewer / player to inspect images, audio, video samples and do any of the following: * Move to the same sample in a different iteration (move the iteration slider). * Show the next or previous iteration's sample. @@ -468,28 +360,8 @@ View debug samples by metric at any iteration. The most recent iteration appears * Zoom. * View the sample's iteration number, width, height, and coordinates. - -
-View a screenshot - Debug samples -
- -![Debug Samples tab](../img/webapp_tracking_43.png) - -
-
- -
- -
-View a screenshot - Viewer -
- ![Debug Samples image viewer](../img/webapp_tracking_44.png) -
-
-
- **To view debug samples:** 1. Click the **DEBUG SAMPLES** tab. The most recent iteration appears at the top. diff --git a/docs/webapp/webapp_exp_tuning.md b/docs/webapp/webapp_exp_tuning.md index 8fd2a16f..035af7a2 100644 --- a/docs/webapp/webapp_exp_tuning.md +++ b/docs/webapp/webapp_exp_tuning.md @@ -98,9 +98,9 @@ and formats for specifying locations include: :::note Set Output Destination for Artifacts Also set the output destination for artifacts in code (see the `output_uri` parameter of the [Task.init](../references/sdk/task.md#taskinit) -method), and in the **ClearML** configuration file +method), and in the ClearML configuration file for all experiments (see [default_output_uri](../configs/clearml_conf.md#config_default_output_uri) -on the **ClearML** Configuration Reference page). +on the ClearML Configuration Reference page). ::: #### Log Level diff --git a/docs/webapp/webapp_home.md b/docs/webapp/webapp_home.md index a29fe722..e0254330 100644 --- a/docs/webapp/webapp_home.md +++ b/docs/webapp/webapp_home.md @@ -8,7 +8,7 @@ The **Dashboard** provides the following options: * Create new projects * Open the [**Workers & Queues**](webapp_workers_queues.md) tab to monitor resource utilization and to manage queues. -![image](../img/0100.png) +![Dashboard](../img/webapp_dashboard.png) **To select a project, experiment, or model:** diff --git a/docs/webapp/webapp_overview.md b/docs/webapp/webapp_overview.md index 2f58270e..70eefa6c 100644 --- a/docs/webapp/webapp_overview.md +++ b/docs/webapp/webapp_overview.md @@ -9,7 +9,7 @@ The **ClearML Web UI** is the graphical user interface for the ClearML platform, * Profile management * Direct access to the ClearML community (Slack Channel, Youtube, and GitHub). -![image](../img/webapp_screenshots.gif) +![image](../img/gif/webapp_exp_table_cust.gif) The ClearML Web UI is composed of the following pages: diff --git a/docs/webapp/webapp_projects_page.md b/docs/webapp/webapp_projects_page.md index 8094c2eb..31222d45 100644 --- a/docs/webapp/webapp_projects_page.md +++ b/docs/webapp/webapp_projects_page.md @@ -9,13 +9,11 @@ projects in your workspace. Projects that contain nested subprojects are identif An exception is the **All Experiments** folder, which shows all projects’ and subprojects’ contents in a single, flat list. -![Projects page](../img/webapp_projects_page.png) +![Projects page](../img/webapp_project_page.png) If a project has any subprojects, clicking its folder will open its own project page. Access the projects’ top-level contents (i.e. experiments, models etc.) via the folder with the bracketed (`[ ]`) project name. -![Subprojects in projects](../img/webapp_projects_inside.png) - If a project does not contain any subprojects, clicking on its folder will open its experiment table (or [Project Overview](webapp_project_overview.md) page when relevant). @@ -23,7 +21,7 @@ page when relevant). Project folders display summarized project information. -![Project card](../img/webapp_projects_card.png) +![Project card](../img/webapp_project_card.png) When relevant, the folder has a tab which displays the number of subprojects in the project. Click the tab to view a list of subprojects. Click on a subproject's name to navigate to it.