updated docs for Studio checkpoints being enabled (#1427)

Author: ilongin

docs/guide/checkpoints.md

@@ -2,10 +2,12 @@
 
 Checkpoints allow DataChain to automatically skip re-creating datasets that were successfully saved in previous script runs. When a script fails or is interrupted, you can re-run it and DataChain will resume from where it left off, reusing datasets that were already created.
 
-**Note:** Checkpoints are currently available only for local script runs. Support for Studio is planned for future releases.
+Checkpoints are available for both local script runs and Studio executions.
 
 ## How Checkpoints Work
 
+### Local Script Runs
+
 When you run a Python script locally (e.g., `python my_script.py`), DataChain automatically:
 
 1. **Creates a job** for the script execution, using the script's absolute path as the job name
@@ -16,6 +18,18 @@ When you run a Python script locally (e.g., `python my_script.py`), DataChain au
 
 This means that if your script creates multiple datasets and fails partway through, the next run will skip recreating the datasets that were already successfully saved.
 
+### Studio Runs
+
+When running jobs on Studio, the checkpoint workflow is managed through the UI:
+
+1. **Job execution** is triggered using the Run button in the Studio interface
+2. **Checkpoint control** is explicit - you choose between:
+   - **Run from scratch**: Ignores any existing checkpoints and recreates all datasets
+   - **Continue from last checkpoint**: Resumes from the last successful checkpoint, skipping already-completed stages
+3. **Parent-child job linking** is handled automatically by the system - no need for script path matching or job name conventions
+4. **Checkpoint behavior** during execution is the same as local runs: datasets are saved at each `.save()` call and can be reused on retry
+
+
 ## Example
 
 Consider this script that processes data in multiple stages:
@@ -66,7 +80,6 @@ Checkpoints are **not** used when:
 - Running code interactively (Python REPL, Jupyter notebooks)
 - Running code as a module (e.g., `python -m mymodule`)
 - The `DATACHAIN_CHECKPOINTS_RESET` environment variable is set (see below)
-- Running on Studio (checkpoints support planned for future releases)
 
 ## Resetting Checkpoints
 
@@ -176,17 +189,12 @@ for ds in dc.datasets():
 
 ## Limitations
 
-- **Local only:** Checkpoints currently work only for local script runs. Studio support is planned.
 - **Script-based:** Code must be run as a script (not interactively or as a module).
 - **Hash-based matching:** Any change to the chain will create a different hash, preventing checkpoint reuse.
 - **Same script path:** The script must be run from the same absolute path for parent job linking to work.
 
 ## Future Plans
 
-### Studio Support
-
-Support for checkpoints on Studio is planned for future releases, which will enable checkpoint functionality for collaborative workflows and cloud-based data processing.
-
 ### UDF-Level Checkpoints
 
 Currently, checkpoints are created only when datasets are saved using `.save()`. This means that if a script fails during a long-running UDF operation (like `.map()`, `.gen()`, or `.agg()`), the entire UDF computation must be rerun on the next execution.