ClearML/clearml-docs
1
0
Fork 0
mirror of https://github.com/clearml/clearml-docs synced 2025-02-07 05:20:07 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Small edits (#769)

Browse Source
This commit is contained in:
pollfly 2024-02-06 16:06:43 +02:00 committed by GitHub
parent f017ca99b3
commit b46e7471a4
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
6 changed files with 26 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
docs/clearml_data/clearml_data_sdk.md
View File

@ -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

18
docs/clearml_sdk/task_sdk.md
View File

@ -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:

6
docs/getting_started/ds/best_practices.md
View File

@ -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.

2
docs/getting_started/ds/ds_first_steps.md
View File

@ -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)

4
docs/hyper_datasets.md
View File

@ -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:

12
docs/model_registry.md
View File

@ -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.