trackio guide updates

trl/references/trackio_guide.md

@@ -1,11 +1,12 @@
 # Trackio Integration for TRL Training
 
-**Trackio** is a local-first experiment tracking library that provides real-time metrics visualization via a Gradio dashboard.
+**Trackio** is an experiment tracking library that provides real-time metrics visualization for remote training on Hugging Face Jobs infrastructure.
 
-⚠️ **IMPORTANT**: Trackio is local-first, which means:
-- It runs a dashboard on the machine where training happens
-- For Jobs training, sync to a Hugging Face Space to view metrics
-- Without a Space, metrics are only accessible during the job (then lost)
+⚠️ **IMPORTANT**: For Jobs training (remote cloud GPUs):
+- Training happens on ephemeral cloud runners (not your local machine)
+- Trackio syncs metrics to a Hugging Face Space for real-time monitoring
+- Without a Space, metrics are lost when the job completes
+- The Space dashboard persists your training metrics permanently
 
 ## Setting Up Trackio for Jobs
 
@@ -35,7 +36,7 @@ import trackio
 
 trackio.init(
     project="my-training",
-    space_id="username/my-trackio-dashboard",  # CRITICAL for Jobs!
+    space_id="username/trackio",  # CRITICAL for Jobs! Replace 'username' with your HF username
     config={
         "model": "Qwen/Qwen2.5-0.5B",
         "dataset": "trl-lib/Capybara",
@@ -78,23 +79,11 @@ Trackio automatically logs:
 ## Viewing the Dashboard
 
 After starting training:
-1. Navigate to the Space: `https://huggingface.co/spaces/username/my-trackio-dashboard`
+1. Navigate to the Space: `https://huggingface.co/spaces/username/trackio`
 2. The Gradio dashboard shows all tracked experiments
 3. Filter by project, compare runs, view charts with smoothing
 
-## Alternative: TensorBoard (Simpler for Jobs)
-
-For simpler setup without needing a Space:
-```python
-SFTConfig(
-    report_to="tensorboard",  # Logs saved with model to Hub
-)
-```
-
-TensorBoard logs are automatically saved with the model and viewable via TensorBoard locally after downloading.
-
 ## Recommendation
 
 - **Trackio**: Best for real-time monitoring during long training runs
-- **TensorBoard**: Best for post-training analysis, simpler setup
 - **Weights & Biases**: Best for team collaboration, requires account

trl/references/training_patterns.md

@@ -39,7 +39,7 @@ from datasets import load_dataset
 from trl import DPOTrainer, DPOConfig
 import trackio
 
-trackio.init(project="dpo-training", space_id="username/my-dashboard")
+trackio.init(project="dpo-training", space_id="username/trackio")
 
 dataset = load_dataset("trl-lib/ultrafeedback_binarized", split="train")
 

trl/scripts/train_dpo_example.py

@@ -32,7 +32,7 @@ from trl import DPOTrainer, DPOConfig
 # Initialize Trackio for real-time monitoring
 trackio.init(
     project="qwen-dpo-alignment",
-    space_id="username/my-trackio-dashboard",
+    space_id="username/trackio",
     config={
         "model": "Qwen/Qwen2.5-0.5B-Instruct",
         "dataset": "trl-lib/ultrafeedback_binarized",
@@ -110,4 +110,4 @@ trainer.push_to_hub()
 trackio.finish()
 
 print("✅ Complete! Model at: https://huggingface.co/username/qwen-dpo-aligned")
-print("📊 View metrics at: https://huggingface.co/spaces/username/my-trackio-dashboard")
+print("📊 View metrics at: https://huggingface.co/spaces/username/trackio")

trl/scripts/train_grpo_example.py

@@ -36,7 +36,7 @@ from trl import GRPOTrainer, GRPOConfig
 # Initialize Trackio for real-time monitoring
 trackio.init(
     project="qwen-grpo-math",
-    space_id="username/my-trackio-dashboard",
+    space_id="username/trackio",
     config={
         "model": "Qwen/Qwen2.5-0.5B-Instruct",
         "dataset": "trl-lib/math_shepherd",
@@ -94,4 +94,4 @@ trainer.push_to_hub()
 trackio.finish()
 
 print("✅ Complete! Model at: https://huggingface.co/username/qwen-grpo-math")
-print("📊 View metrics at: https://huggingface.co/spaces/username/my-trackio-dashboard")
+print("📊 View metrics at: https://huggingface.co/spaces/username/trackio")

trl/scripts/train_sft_example.py

@@ -39,7 +39,7 @@ from trl import SFTTrainer, SFTConfig
 # Initialize Trackio for real-time monitoring
 trackio.init(
     project="qwen-capybara-sft",
-    space_id="username/my-trackio-dashboard",  # Creates Space if it doesn't exist
+    space_id="username/trackio",  # Creates Space if it doesn't exist
     config={
         "model": "Qwen/Qwen2.5-0.5B",
         "dataset": "trl-lib/Capybara",
@@ -124,4 +124,4 @@ trainer.push_to_hub()
 trackio.finish()
 
 print("✅ Complete! Model at: https://huggingface.co/username/qwen-capybara-sft")
-print("📊 View metrics at: https://huggingface.co/spaces/username/my-trackio-dashboard")
+print("📊 View metrics at: https://huggingface.co/spaces/username/trackio")