From b46e7471a48c6011dbcdf712d34c88adec1fe4b3 Mon Sep 17 00:00:00 2001 From: pollfly <75068813+pollfly@users.noreply.github.com> Date: Tue, 6 Feb 2024 16:06:43 +0200 Subject: [PATCH] Small edits (#769) --- docs/clearml_data/clearml_data_sdk.md | 2 +- docs/clearml_sdk/task_sdk.md | 18 +++++++++++------- docs/getting_started/ds/best_practices.md | 6 +++--- docs/getting_started/ds/ds_first_steps.md | 2 +- docs/hyper_datasets.md | 4 ++-- docs/model_registry.md | 12 ++++++++---- 6 files changed, 26 insertions(+), 18 deletions(-) diff --git a/docs/clearml_data/clearml_data_sdk.md b/docs/clearml_data/clearml_data_sdk.md index 5ee072a7..c174c055 100644 --- a/docs/clearml_data/clearml_data_sdk.md +++ b/docs/clearml_data/clearml_data_sdk.md @@ -190,7 +190,7 @@ dataset.add_external_files( ) ``` -There is an option to add a set of files based on wildcard matching of a single string or a list of wildcards, using the +You can add a set of files based on wildcard matching of a single string or a list of wildcards using the `wildcard` parameter. Specify whether to match the wildcard files recursively using the `recursive` parameter. ```python diff --git a/docs/clearml_sdk/task_sdk.md b/docs/clearml_sdk/task_sdk.md index 1e831cd8..e12deeaf 100644 --- a/docs/clearml_sdk/task_sdk.md +++ b/docs/clearml_sdk/task_sdk.md @@ -71,12 +71,16 @@ After invoking `Task.init` in a script, ClearML starts its automagical logging, * [TensorFlow](../integrations/tensorflow.md) * [Keras](../integrations/keras.md) * [PyTorch](../integrations/pytorch.md) - * [scikit-learn](../integrations/scikit_learn.md) - * [XGBoost](../integrations/xgboost.md) - * [FastAI](../integrations/fastai.md) + * [AutoKeras](../integrations/autokeras.md) + * [CatBoost](../integrations/catboost.md) + * [Fast.ai](../integrations/fastai.md) + * [LightGBM](../integrations/lightgbm.md) * [MegEngine](../integrations/megengine.md) - * [CatBoost](../integrations/catboost.md) * [MONAI](../integrations/monai.md) + * [scikit-learn](../integrations/scikit_learn.md) (only using joblib) + * [XGBoost](../integrations/xgboost.md) (only using joblib) + * [YOLOv8](../integrations/yolov8.md) + * [YOLOv5](../integrations/yolov5.md) * **Metrics, scalars, plots, debug images** reported through supported frameworks, including: * [Matplotlib](../integrations/matplotlib.md) @@ -172,8 +176,8 @@ It's possible to always create a new task by passing `reuse_last_task_id=False`. See full `Task.init` reference [here](../references/sdk/task.md#taskinit). ### Continuing Task Execution -You can continue the execution of a previously run task using the `continue_last_task` parameter of the `Task.init` -method. This will retain all of its previous artifacts / models / logs. +You can continue the execution of a previously run task using the `continue_last_task` parameter of `Task.init()`. +This will retain all of its previous artifacts / models / logs. The task will continue reporting its outputs based on the iteration in which it had left off. For example: a task's last train/loss scalar reported was for iteration 100, when continued, the next report will be as iteration 101. @@ -432,7 +436,7 @@ A compelling workflow is: 1. Run code on a development machine for a few iterations, or just set up the environment. 1. Move the execution to a beefier remote machine for the actual training. -Use the [`Task.execute_remotely`](../references/sdk/task.md#execute_remotely) method to implement this workflow. This method +Use [`Task.execute_remotely()`](../references/sdk/task.md#execute_remotely) to implement this workflow. This method stops the current manual execution, and then re-runs it on a remote machine. For example: diff --git a/docs/getting_started/ds/best_practices.md b/docs/getting_started/ds/best_practices.md index 7219b8e5..b4783cc7 100644 --- a/docs/getting_started/ds/best_practices.md +++ b/docs/getting_started/ds/best_practices.md @@ -3,8 +3,8 @@ title: Best Practices --- This section talks about what made us design ClearML the way we did and how it reflects on AI workflows. -While ClearML was designed to fit into any workflow, we do feel that working as we describe below brings a lot of advantages from organizing one's workflow -and furthermore, preparing it to scale in the long term. +While ClearML was designed to fit into any workflow, the practices described below brings a lot of advantages from organizing one's workflow +to preparing it to scale in the long term. :::important The below is only our opinion. ClearML was designed to fit into any workflow whether it conforms to our way or not! @@ -22,7 +22,7 @@ During early stages of model development, while code is still being modified hea the model and ensure that you choose a model that makes sense, and the training procedure works. Can be used to provide initial models for testing. The abovementioned setups might be folded into each other and that's great! If you have a GPU machine for each researcher, that's awesome! -The goal of this phase is to get a code, dataset, and environment setup, so you can start digging to find the best model! +The goal of this phase is to get a code, dataset, and environment set up, so you can start digging to find the best model! - [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out [Getting Started](ds_first_steps.md)). This helps visualizing the results and tracking progress. diff --git a/docs/getting_started/ds/ds_first_steps.md b/docs/getting_started/ds/ds_first_steps.md index 0d6b6926..197f1e0f 100644 --- a/docs/getting_started/ds/ds_first_steps.md +++ b/docs/getting_started/ds/ds_first_steps.md @@ -133,6 +133,6 @@ Sit back, relax, and watch your models converge :) or continue to see what else ## YouTube Playlist -Or watch the Getting Started Playlist on ClearML's YouTube Channel! +Or watch the **Getting Started** playlist on ClearML's YouTube Channel! [![Watch the video](https://img.youtube.com/vi/bjWwZAzDxTY/hqdefault.jpg)](https://www.youtube.com/watch?v=bjWwZAzDxTY&list=PLMdIlCuMqSTnoC45ME5_JnsJX0zWqDdlO&index=2) diff --git a/docs/hyper_datasets.md b/docs/hyper_datasets.md index d2ce9b9c..0b6d0bd5 100644 --- a/docs/hyper_datasets.md +++ b/docs/hyper_datasets.md @@ -23,7 +23,7 @@ through parameterized data access and metadata version control. Hyper-Datasets is a data management system specifically tailored for handling unstructured data, like text, audio, or visual data. You can create, manage, and version your datasets. Datasets can be set up to inherit from other datasets, so -data lineages can be created, and users can track when and how their data changes. In the ClearML Enterprise's WebApp, +data lineages can be created, and users can track when and how their data changes. In the ClearML Enterprise's [WebApp](hyperdatasets/webapp/webapp_datasets.md), you can view a dataset's version history, as well as its contents, including annotations, metadata, masks, and other information. @@ -32,7 +32,7 @@ information. The basic premise of Hyper-Datasets is that a user-formed query is a full representation of the dataset used by the ML/DL process. Hyper-Datasets decouple metadata from raw data files, allowing you to manipulate metadata through sophisticated queries and parameters that can be tracked through the experiment manager. You can clone experiments using different -data manipulations--or **DataViews**--without changing any of the hard coded values, making these manipulations part of +data manipulations--or [**DataViews**](hyperdatasets/dataviews.md)--without changing any of the hard coded values, making these manipulations part of the experiment. ClearML **Enterprise**'s Hyper-Datasets supports rapid prototyping, creating new opportunities such as: diff --git a/docs/model_registry.md b/docs/model_registry.md index 71180cf3..e15e02c0 100644 --- a/docs/model_registry.md +++ b/docs/model_registry.md @@ -43,12 +43,16 @@ Automatic logging is supported for the following frameworks: * [TensorFlow](integrations/tensorflow.md) * [Keras](integrations/keras.md) * [PyTorch](integrations/pytorch.md) +* [AutoKeras](integrations/autokeras.md) +* [CatBoost](integrations/catboost.md) +* [Fast.ai](integrations/fastai.md) +* [LightGBM](integrations/lightgbm.md) +* [MegEngine](integrations/megengine.md) +* [MONAI](integrations/monai.md) * [scikit-learn](integrations/scikit_learn.md) (only using joblib) * [XGBoost](integrations/xgboost.md) (only using joblib) -* [Fast.ai](integrations/fastai.md) -* [MegEngine](integrations/megengine.md) -* [CatBoost](integrations/catboost.md) -* [MONAI](integrations/monai.md) +* [YOLOv8](integrations/yolov8.md) +* [YOLOv5](integrations/yolov5.md) You may want more control over which models are logged. Use the `auto_connect_framework` parameter of [`Task.init()`](references/sdk/task.md#taskinit) to control automatic logging of frameworks.