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Rewrite fundamentals sections (#252)

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pollfly
2022-05-18 11:49:31 +03:00
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4
docs/getting_started/ds/best_practices.md
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@@ -24,7 +24,7 @@ During early stages of model development, while code is still being modified hea
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 we can start digging to find the best model!
- [ClearML SDK](../../clearml_sdk.md) should be integrated into your code (check out our [getting started](ds_first_steps.md)).
- [ClearML SDK](../../clearml_sdk/clearml_sdk.md) should be integrated into your code (check out our [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 allows you to just drop your experiments to be executed one by one
@@ -43,7 +43,7 @@ yields the best performing model for our task!
Visualization and comparisons dashboards keep your sanity at bay! In this stage we usually have a docker container with all the binaries
that we need.
- [ClearML SDK](../../clearml_sdk.md) ensures that all the metrics, parameters and Models are automatically logged and can later be
- [ClearML SDK](../../clearml_sdk/clearml_sdk.md) ensures that all the metrics, parameters and Models are automatically logged and can later be
accessed, [compared](../../webapp/webapp_exp_comparing.md) and [tracked](../../webapp/webapp_exp_track_visual.md).
- [ClearML Agent](../../clearml_agent.md) does the heavy lifting. It reproduces the execution environment, clones your code,
applies code patches, manages parameters (Including overriding them on the fly), executes the code and queues multiple tasks

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docs/getting_started/ds/ds_first_steps.md
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@@ -81,7 +81,7 @@ Now you can integrate ClearML into your code!
In ClearML, experiments are organized as [Tasks](../../fundamentals/task.md).
ClearML will automatically log your experiment and code, including outputs and parameters from popular ML frameworks,
once you integrate the ClearML [SDK](../../clearml_sdk.md) with your code. To control what ClearML automatically logs, see this [FAQ](../../faq.md#controlling_logging).
once you integrate the ClearML [SDK](../../clearml_sdk/clearml_sdk.md) with your code. To control what ClearML automatically logs, see this [FAQ](../../faq.md#controlling_logging).
At the beginning of your code, import the `clearml` package:
@@ -113,7 +113,7 @@ ClearML results page: https://app.clear.ml/projects/4043a1657f374e9298649c6ba72a
**That’s it!** You are done integrating ClearML with your code :)
Now, [command-line arguments](../../fundamentals/hyperparameters.md#command-line-parsing), [console output](../../fundamentals/logger.md#types-of-logged-results) as well as Tensorboard and Matplotlib will automatically be logged in the UI under the created Task.
Now, [command-line arguments](../../fundamentals/hyperparameters.md#tracking-hyperparameters), [console output](../../fundamentals/logger.md#types-of-logged-results) as well as Tensorboard and Matplotlib will automatically be logged in the UI under the created Task.
<br/>
Sit back, relax, and watch your models converge :) or continue to see what else can be done with ClearML [here](ds_second_steps.md).

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docs/getting_started/ds/ds_second_steps.md
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@@ -68,7 +68,7 @@ If the object type is unknown ClearML pickles it and uploads the pickle file.
task.upload_artifacts(my_numpy_matrix, name='features')
```
Check out all [artifact logging](../../fundamentals/artifacts.md) options.
Check out all [artifact logging](../../clearml_sdk/task_sdk.md#artifacts) options.
### Using Artifacts

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docs/getting_started/mlops/mlops_first_steps.md
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@@ -131,7 +131,7 @@ cloned_task.set_parameter(name='internal/magic', value=42)
```
#### Report Artifacts
Artifacts are files created by your task. Users can upload [multiple types of data](../../fundamentals/artifacts.md#logging-artifacts),
Artifacts are files created by your task. Users can upload [multiple types of data](../../clearml_sdk/task_sdk.md#logging-artifacts),
objects and files to a task anywhere from code.
```python
@@ -141,7 +141,7 @@ Task.current_task().upload_artifact(name='a_file', artifact_object='local_file.b
Task.current_task().upload_artifact(name='numpy', artifact_object=np.ones(4,4))
```
Artifacts serve as a great way to pass and reuse data between tasks. Artifacts can be [retrieved](../../fundamentals/artifacts.md#using-artifacts)
Artifacts serve as a great way to pass and reuse data between tasks. Artifacts can be [retrieved](../../clearml_sdk/task_sdk.md#using-artifacts)
by accessing the Task that created them. These artifacts can be modified and uploaded to other tasks.
```python