diff --git a/environment.yml b/environment.yml
index dd8fec7f..b15dc206 100644
--- a/environment.yml
+++ b/environment.yml
@@ -1,18 +1,19 @@
-name: happypose
+name: happypose2
 channels:
    - conda-forge
    - pytorch
+   - nvidia
    - anaconda
    - defaults
 dependencies:
+  - nvidia::cudatoolkit==11.3.1
   - python=3.9
   - pip
   - wget
   - python-wget
   - joblib
-  - pytorch==1.11.0
+  - pytorch::pytorch==1.11.0
   - torchvision==0.12.0
-  - cudatoolkit==11.3.1
   - ipython
   - ipykernel
   - jupyterlab
@@ -90,4 +91,4 @@ dependencies:
       - webdataset
       - opencv-contrib-python 
       - roma 
-      - torchgeometry
\ No newline at end of file
+      - torchgeometry
diff --git a/happypose/pose_estimators/megapose/src/megapose/evaluation/prediction_runner.py b/happypose/pose_estimators/megapose/src/megapose/evaluation/prediction_runner.py
index 2fc853ef..a0afe405 100644
--- a/happypose/pose_estimators/megapose/src/megapose/evaluation/prediction_runner.py
+++ b/happypose/pose_estimators/megapose/src/megapose/evaluation/prediction_runner.py
@@ -106,6 +106,9 @@
 
 CNOS_SUBMISSION_PATHS = {ds_name: CNOS_SUBMISSION_DIR / fname for ds_name, fname in CNOS_SUBMISSION_FILES.items()}
 # Check if all paths exist
+print("cnos values =", CNOS_SUBMISSION_PATHS.values())
+print("len cnos =", len(CNOS_SUBMISSION_FILES))
+print("sum=", sum(p.exists() for p in CNOS_SUBMISSION_PATHS.values()))
 assert( sum(p.exists() for p in CNOS_SUBMISSION_PATHS.values()) == len(CNOS_SUBMISSION_FILES))
 ##################################
 ##################################
@@ -292,7 +295,7 @@ def get_predictions(self, pose_estimator: PoseEstimator) -> Dict[str, PoseEstima
 
             # ############ RUN ONLY BEGINNING OF DATASET
             # # if n > 0:
-            # if n < 298:
+            #if n < 220:
             # # if n != 582:
             #     print('################')
             #     print('Prediction runner SKIP')
diff --git a/happypose/pose_estimators/megapose/src/megapose/scripts/distributed.py b/happypose/pose_estimators/megapose/src/megapose/scripts/distributed.py
index e7a4aa4d..995f1f89 100644
--- a/happypose/pose_estimators/megapose/src/megapose/scripts/distributed.py
+++ b/happypose/pose_estimators/megapose/src/megapose/scripts/distributed.py
@@ -120,7 +120,7 @@ def init_distributed_mode():
         backend="nccl",
         rank=rank,
         world_size=world_size,
-        timeout=datetime.timedelta(seconds=1800 * 4),
+        timeout=datetime.timedelta(seconds=1800 * 16),
     )
     torch.distributed.barrier()
 
diff --git a/happypose/toolbox/utils/distributed.py b/happypose/toolbox/utils/distributed.py
index 72750675..90089555 100644
--- a/happypose/toolbox/utils/distributed.py
+++ b/happypose/toolbox/utils/distributed.py
@@ -39,7 +39,7 @@ def get_tmp_dir() -> Path:
     if "JOB_DIR" in os.environ:
         tmp_dir = Path(os.environ["JOB_DIR"]) / "tmp"
     else:
-        tmp_dir = Path("/tmp/megapose_job")
+        tmp_dir = Path("/gpfsscratch/rech/zja/udg82mu/happypose_datasets/results/tmp/megapose_job")
     tmp_dir.parent.mkdir(exist_ok=True)
     tmp_dir.mkdir(exist_ok=True)
     return tmp_dir
@@ -149,6 +149,6 @@ def init_distributed_mode() -> None:
         backend="nccl",
         rank=rank,
         world_size=world_size,
-        timeout=datetime.timedelta(seconds=4 * 1800),  # 2 hours
+        timeout=datetime.timedelta(seconds=16 * 1800),  # 2 hours
     )
     torch.distributed.barrier()
diff --git a/happypose/toolbox/utils/tensor_collection.py b/happypose/toolbox/utils/tensor_collection.py
index e019a3c1..caf3b76d 100644
--- a/happypose/toolbox/utils/tensor_collection.py
+++ b/happypose/toolbox/utils/tensor_collection.py
@@ -26,6 +26,9 @@
 # MegaPose
 from happypose.toolbox.utils.distributed import get_rank, get_world_size
 
+from happypose.pose_estimators.megapose.src.megapose.config import (
+    RESULTS_DIR,
+)
 
 def concatenate(datas):
     datas = [data for data in datas if len(data) > 0]
@@ -168,6 +171,7 @@ def gather_distributed(self, tmp_dir=None):
 
         if rank > 0:
             tmp_file = tmp_file_template.format(rank=rank)
+            print("tmp_file =", tmp_file)
             torch.save(self, tmp_file)
 
         if world_size > 1: