feat: telemetry funnel to understand if users have trouble with new flow

.gitignore

@@ -159,6 +159,9 @@ cython_debug/
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 .idea/
 
+# WandB
+wandb
+
 .DS_Store
 ./src/.DS_Store
 

changelog/815.added.md

@@ -0,0 +1 @@
+Add modular experiment logging for finetuning with `experiment_logger` parameter, including `WandbLogger` for W&B tracking and a `FinetuningLogger` protocol for custom integrations.

changelog/862.added.md

@@ -0,0 +1 @@
+Add three-tier authentication flow: browser-based login for graphical environments, headless interactive login with clipboard copy for SSH/cluster sessions, and clear step-by-step instructions for fully non-interactive environments.

changelog/864.added.md

@@ -0,0 +1 @@
+Add telemetry funnel for the license acceptance flow to track user success rates and churn across graphical, headless, and non-interactive environments.

examples/finetune_classifier.py

@@ -115,6 +115,9 @@ def main() -> None:
         print("--- 2. Initializing and Fitting Model ---\n")
 
     # Instantiate the wrapper with your desired hyperparameters
+    # To enable WandB logging, pass an experiment_logger:
+    # .  from tabpfn.finetuning.logging import WandbLogger
+    #   experiment_logger=WandbLogger(project="my-project", run_name="my-run", entity="my-entity")
     finetuned_clf = FinetunedTabPFNClassifier(
         device="cuda",
         epochs=NUM_EPOCHS,

examples/finetune_regressor.py

@@ -101,6 +101,9 @@ def main() -> None:
         print("--- 2. Initializing and Fitting Model ---\n")
 
     # Instantiate the wrapper with your desired hyperparameters
+    # To enable WandB logging, pass an experiment_logger:
+    # .  from tabpfn.finetuning.logging import WandbLogger
+    #   experiment_logger=WandbLogger(project="my-project", run_name="my-run", entity="my-entity")
     finetuned_reg = FinetunedTabPFNRegressor(
         device="cuda",
         epochs=NUM_EPOCHS,

pyproject.toml

@@ -70,6 +70,9 @@ classifiers = [
 ]
 license = { file = "LICENSE" }
 
+[project.optional-dependencies]
+wandb = ["wandb>=0.25.1"]
+
 [project.urls]
 documentation = "https://priorlabs.ai/docs"
 source = "https://github.com/priorlabs/tabpfn"

src/tabpfn/auth_token.py

@@ -0,0 +1,54 @@
+"""Token cache I/O for TabPFN authentication.
+
+Pure I/O helpers with no dependencies on other TabPFN modules, so they
+can be imported from both ``browser_auth`` and ``telemetry`` without
+creating a circular import.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+_CACHE_DIR = Path.home() / ".cache" / "tabpfn"
+_TOKEN_FILE = _CACHE_DIR / "auth_token"
+
+# tabpfn-client stores its token here — we read it as a fallback.
+_CLIENT_TOKEN_FILE = Path.home() / ".tabpfn" / "token"
+
+
+def get_cached_token() -> str | None:
+    """Return a cached token.
+
+    Checks (in priority order):
+
+    1. ``TABPFN_TOKEN`` environment variable
+    2. ``~/.cache/tabpfn/auth_token``
+    3. ``~/.tabpfn/token`` (tabpfn-client's cache)
+    """
+    env_token = os.environ.get("TABPFN_TOKEN")
+    if env_token:
+        return env_token.strip() or None
+
+    for path in (_TOKEN_FILE, _CLIENT_TOKEN_FILE):
+        if path.is_file():
+            token = path.read_text().strip()
+            if len(token) > 0:
+                return token
+
+    return None
+
+
+def save_token(token: str) -> None:
+    """Persist *token* to ``~/.cache/tabpfn/auth_token``."""
+    _CACHE_DIR.mkdir(parents=True, exist_ok=True)
+    _TOKEN_FILE.write_text(token)
+    logger.debug("Token saved to %s", _TOKEN_FILE)
+
+
+def delete_cached_token() -> None:
+    """Remove the cached token file (if it exists)."""
+    _TOKEN_FILE.unlink(missing_ok=True)