ClearML/clearml-docs
1
0
Fork 0
mirror of https://github.com/clearml/clearml-docs synced 2025-06-26 18:17:44 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

Small edits (#779)

Browse Source
This commit is contained in:
pollfly
2024-02-15 15:28:26 +02:00
committed by GitHub
parent 15ac5c2ce6
commit 6fb11e8e0d
18 changed files with 40 additions and 40 deletions
Download Patch File Download Diff File Expand all files Collapse all files

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

@@ -27,9 +27,9 @@ The goal of this phase is to get a code, dataset, and environment set up, so you
- [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.
- [ClearML Agent](../../clearml_agent.md) helps moving your work to other machines without the hassle of rebuilding the environment every time,
while also creating an easy queue interface that easily lets you just drop your experiments to be executed one by one
while also creating an easy queue interface that easily lets you drop your experiments to be executed one by one
(great for ensuring that the GPUs are churning during the weekend).
- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, just like you'd develop on your local laptop!
- [ClearML Session](../../apps/clearml_session.md) helps with developing on remote machines, in the same way that you'd develop on your local laptop!
## Train Remotely
@@ -66,7 +66,7 @@ improving your results later on!
## Visibility Matters
While it's possible to track experiments with one tool, and pipeline them with another, having
While you can track experiments with one tool, and pipeline them with another, having
everything under the same roof has its benefits!
Being able to track experiment progress and compare experiments, and, based on that, send experiments to execution on remote

14
docs/getting_started/ds/ds_second_steps.md
View File

@@ -12,8 +12,8 @@ Every previously executed experiment is stored as a Task.
A Task's project and name can be changed after the experiment has been executed.
A Task is also automatically assigned an auto-generated unique identifier (UUID string) that cannot be changed and always locates the same Task in the system.
It's possible to retrieve a Task object programmatically by querying the system based on either the Task ID,
or project and name combination. It's also possible to query tasks based on their properties, like tags (see [Querying Tasks](../../clearml_sdk/task_sdk.md#querying--searching-tasks)).
Retrieve a Task object programmatically by querying the system based on either the Task ID,
or project and name combination. You can also query tasks based on their properties, like tags (see [Querying Tasks](../../clearml_sdk/task_sdk.md#querying--searching-tasks)).
```python
prev_task = Task.get_task(task_id='123456deadbeef')
@@ -28,7 +28,7 @@ on model performance, saving and comparing these between experiments is sometime
ClearML supports logging `argparse` module arguments out of the box, so once ClearML is integrated into the code, it automatically logs all parameters provided to the argument parser.
It's also possible to log parameter dictionaries (very useful when parsing an external config file and storing as a dict object),
You can also log parameter dictionaries (very useful when parsing an external config file and storing as a dict object),
whole configuration files, or even custom objects or [Hydra](https://hydra.cc/docs/intro/) configurations!
```python
@@ -139,9 +139,9 @@ This feature lets you easily get a full genealogy of every trained and used mode
Full metrics logging is the key to finding the best performing model!
By default, everything that's reported to TensorBoard and Matplotlib is automatically captured and logged.
Since not all metrics are tracked that way, it's also possible to manually report metrics using a [`Logger`](../../fundamentals/logger.md) object.
Since not all metrics are tracked that way, you can also manually report metrics using a [`Logger`](../../fundamentals/logger.md) object.
It's possible to log everything, from time series data to confusion matrices to HTML, Audio and Video, to custom plotly graphs! Everything goes!
You can log everything, from time series data to confusion matrices to HTML, Audio and Video, to custom plotly graphs! Everything goes!
![image](../../img/report_plotly.png)
@@ -157,7 +157,7 @@ The experiment table is a powerful tool for creating dashboards and views of you
### Creating Leaderboards
Customize the [experiments table](../../webapp/webapp_exp_table.md) to fit your own needs, adding desired views of parameters, metrics and tags.
It's possible to filter and sort based on parameters and metrics, so creating custom views is simple and flexible.
You can filter and sort based on parameters and metrics, so creating custom views is simple and flexible.
Create a dashboard for a project, presenting the latest Models and their accuracy scores, for immediate insights.
@@ -166,7 +166,7 @@ This is helpful to monitor your projects' progress, and to share it across the o
Any page is sharable by copying the URL from the address bar, allowing you to bookmark leaderboards or to send an exact view of a specific experiment or a comparison page.
It's also possible to tag Tasks for visibility and filtering allowing you to add more information on the execution of the experiment.
You can also tag Tasks for visibility and filtering allowing you to add more information on the execution of the experiment.
Later you can search based on task name in the search bar, and filter experiments based on their tags, parameters, status, and more.
## What's Next?

4
docs/getting_started/mlops/mlops_first_steps.md
View File

@@ -26,7 +26,7 @@ required python packages, and execute and monitor the process.
## Set up an Agent
1. Let's install the agent!
1. Install the agent:
```bash
pip install clearml-agent
@@ -42,7 +42,7 @@ required python packages, and execute and monitor the process.
If you've already created credentials, you can copy-paste the default agent section from [here](https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf#L15) (this is optional. If the section is not provided the default values will be used)
:::
1. Start the agent's daemon and assign it to a [queue](../../fundamentals/agents_and_queues.md#what-is-a-queue).
1. Start the agent's daemon and assign it to a [queue](../../fundamentals/agents_and_queues.md#what-is-a-queue):
```bash
clearml-agent daemon --queue default