Use fit endpoint

tabpfn_client/client.py

@@ -169,7 +169,7 @@ def reset_authorization(self):
     def is_initialized(self):
         return self.access_token is not None and self.access_token != ""
 
-    def upload_train_set(self, X, y) -> str:
+    def fit(self, X, y) -> str:
         """
         Upload a train set to server and return the train set UID if successful.
 
@@ -200,7 +200,7 @@ def upload_train_set(self, X, y) -> str:
             return cached_dataset_uid
 
         response = self.httpx_client.post(
-            url=self.server_endpoints.upload_train_set.path,
+            url=self.server_endpoints.fit.path,
             files=common_utils.to_httpx_post_file_format(
                 [
                     ("x_file", "x_train_filename", X_serialized),
@@ -209,7 +209,7 @@ def upload_train_set(self, X, y) -> str:
             ),
         )
 
-        self._validate_response(response, "upload_train_set")
+        self._validate_response(response, "fit")
 
         train_set_uid = response.json()["train_set_uid"]
         self.dataset_uid_cache_manager.add_dataset_uid(dataset_hash, train_set_uid)
@@ -278,7 +278,7 @@ def predict(
                         raise RuntimeError(
                             "Train set data is required to re-upload but was not provided."
                         )
-                    train_set_uid = self.upload_train_set(X_train, y_train)
+                    train_set_uid = self.fit(X_train, y_train)
                     params["train_set_uid"] = train_set_uid
                     cached_test_set_uid = None
                 else:

tabpfn_client/server_config.yaml

@@ -64,6 +64,11 @@ endpoints:
     methods: [ "POST" ]
     description: "Upload train set"
 
+  fit:
+    path: "/fit/"
+    methods: [ "POST" ]
+    description: "Fit"
+
   predict:
     path: "/predict/"
     methods: [ "POST" ]

tabpfn_client/service_wrapper.py

@@ -186,7 +186,7 @@ def fit(self, X, y) -> str:
                 "Please Note: The email verification token expires in 30 minutes."
             )
 
-        return self.service_client.upload_train_set(X, y)
+        return self.service_client.fit(X, y)
 
     def predict(
         self,