diff --git a/docs/requirements.txt b/docs/requirements.txt
index ce2b7952f..d8b5a55ca 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -18,6 +18,7 @@ parameterized==0.7.4
 tabulate
 pycocotools>=2.0.1
 fvcore
+iopath==0.1.9
 fairscale
 git+git://github.com/facebookresearch/ClassyVision.git
 https://download.pytorch.org/whl/cpu/torch-1.5.0%2Bcpu-cp37-cp37m-linux_x86_64.whl
diff --git a/extra_scripts/convert_caffe2_to_torchvision_resnet.py b/extra_scripts/convert_caffe2_to_torchvision_resnet.py
index 82fe3cb08..30c98df6b 100644
--- a/extra_scripts/convert_caffe2_to_torchvision_resnet.py
+++ b/extra_scripts/convert_caffe2_to_torchvision_resnet.py
@@ -19,7 +19,7 @@
 from collections import OrderedDict
 
 import torch
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 
 
 # create the logger
@@ -124,7 +124,7 @@ def _rename_weights_for_resnet(weights, stage_names):
 
 
 def _load_c2_pickled_weights(file_path):
-    with PathManager.open(file_path, "rb") as f:
+    with g_pathmgr.open(file_path, "rb") as f:
         data = pickle.load(f, encoding="latin1")
     if "blobs" in data:
         weights = data["blobs"]
diff --git a/extra_scripts/convert_caffe2_to_vissl_alexnet.py b/extra_scripts/convert_caffe2_to_vissl_alexnet.py
index aa4bb1680..cbbcd3499 100644
--- a/extra_scripts/convert_caffe2_to_vissl_alexnet.py
+++ b/extra_scripts/convert_caffe2_to_vissl_alexnet.py
@@ -23,7 +23,7 @@
 
 import numpy as np
 import torch
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 
 
 # create the logger
@@ -58,7 +58,7 @@ def remove_jigsaw_names(data):
 
 
 def _load_c2_pickled_weights(file_path):
-    with PathManager.open(file_path, "rb") as f:
+    with g_pathmgr.open(file_path, "rb") as f:
         data = pickle.load(f, encoding="latin1")
     if "blobs" in data:
         weights = data["blobs"]
@@ -71,7 +71,7 @@ def _load_c2_weights(file_path):
     if file_path.endswith("pkl"):
         weights = _load_c2_pickled_weights(file_path)
     elif file_path.endswith("npy"):
-        with PathManager.open(file_path, "rb") as fopen:
+        with g_pathmgr.open(file_path, "rb") as fopen:
             weights = np.load(fopen, allow_pickle=True, encoding="latin1")[()]
     return weights
 
diff --git a/extra_scripts/convert_folder_to_filelist.py b/extra_scripts/convert_folder_to_filelist.py
index 4d547a7e6..35c420712 100644
--- a/extra_scripts/convert_folder_to_filelist.py
+++ b/extra_scripts/convert_folder_to_filelist.py
@@ -21,7 +21,7 @@
 import argparse
 import os
 
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.env import setup_path_manager
 from vissl.utils.io import save_file
 
@@ -58,13 +58,13 @@ def get_argument_parser():
 
     setup_path_manager()
 
-    splits = PathManager.ls(args.input)
+    splits = g_pathmgr.ls(args.input)
     print(f"The following splits are found: { ','.join(splits) }")
 
     dataset_summary = {}
 
     for split in ["train", "trainval", "val", "test"]:
-        if not PathManager.exists(os.path.join(args.input, split)):
+        if not g_pathmgr.exists(os.path.join(args.input, split)):
             continue
 
         dataset_summary[split] = {}
@@ -72,7 +72,7 @@ def get_argument_parser():
         img_labels = []
 
         split_path = os.path.join(args.input, split)
-        label_paths = PathManager.ls(split_path)
+        label_paths = g_pathmgr.ls(split_path)
         dataset_summary[split]["labels"] = label_paths
         dataset_summary[split]["num_labels"] = len(label_paths)
         print(f"{len(label_paths)} classes found for { split } split.")
@@ -81,7 +81,7 @@ def get_argument_parser():
         # Populate the img_paths and img_labels based on torchvision image folder file structure.
         for label in label_paths:
             label_path = os.path.join(split_path, label)
-            images = PathManager.ls(os.path.join(split_path, label))
+            images = g_pathmgr.ls(os.path.join(split_path, label))
             print(f"{len(images)} examples found for { label }, { split }.")
             total_split_examples += len(images)
             for image in images:
@@ -94,17 +94,17 @@ def get_argument_parser():
         # Remove the split .npy filelist if they exist and resave them..
         image_path = os.path.join(args.output, f"{split}_images.npy")
 
-        PathManager.rm(image_path)
+        g_pathmgr.rm(image_path)
         save_file(img_paths, image_path)
         print(f"Saved { image_path }")
 
         label_path = os.path.join(args.output, f"{split}_labels.npy")
 
-        PathManager.rm(label_path)
+        g_pathmgr.rm(label_path)
         save_file(img_labels, label_path)
         print(f"Saved { label_path }")
 
     # Save dataset summary.
     dataset_summary_path = os.path.join(args.output, "dataset_summary.json")
-    PathManager.rm(dataset_summary_path)
+    g_pathmgr.rm(dataset_summary_path)
     save_file(dataset_summary, dataset_summary_path)
diff --git a/extra_scripts/convert_sharded_checkpoint.py b/extra_scripts/convert_sharded_checkpoint.py
index a629470ec..d7747feae 100644
--- a/extra_scripts/convert_sharded_checkpoint.py
+++ b/extra_scripts/convert_sharded_checkpoint.py
@@ -20,7 +20,7 @@
 import enum
 import os
 
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.checkpoint import CheckpointFormatConverter
 from vissl.utils.env import setup_path_manager
 from vissl.utils.io import makedir
@@ -41,7 +41,7 @@ class CheckpointType(enum.Enum):
 
 
 def convert_checkpoint(input_path: str, output_path: str, output_type: str):
-    assert PathManager.exists(
+    assert g_pathmgr.exists(
         input_path
     ), f"Checkpoint input path: {input_path} not found."
 
diff --git a/extra_scripts/convert_vissl_to_torchvision.py b/extra_scripts/convert_vissl_to_torchvision.py
index 4ae29b697..d73a71360 100644
--- a/extra_scripts/convert_vissl_to_torchvision.py
+++ b/extra_scripts/convert_vissl_to_torchvision.py
@@ -12,7 +12,7 @@
 import sys
 
 import torch
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.checkpoint import replace_module_prefix
 from vissl.utils.io import is_url
 
@@ -30,7 +30,7 @@
 
 
 def convert_and_save_model(args, replace_prefix):
-    assert PathManager.exists(args.output_dir), "Output directory does NOT exist"
+    assert g_pathmgr.exists(args.output_dir), "Output directory does NOT exist"
 
     # load the model
     model_path = args.model_url_or_file
diff --git a/extra_scripts/create_low_shot_samples.py b/extra_scripts/create_low_shot_samples.py
index b03d1dac7..61a48aaec 100644
--- a/extra_scripts/create_low_shot_samples.py
+++ b/extra_scripts/create_low_shot_samples.py
@@ -11,7 +11,7 @@
 import random
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.io import load_file, save_file
 
 
@@ -171,7 +171,7 @@ def main():
     )
     opts = parser.parse_args()
 
-    assert PathManager.exists(opts.targets_data_file), "Target file not found. Abort"
+    assert g_pathmgr.exists(opts.targets_data_file), "Target file not found. Abort"
     targets = load_file(opts.targets_data_file)
     sample_ids = list(range(1, 1 + opts.num_samples))
 
diff --git a/extra_scripts/datasets/create_coco_data_files.py b/extra_scripts/datasets/create_coco_data_files.py
index 726bdb7ad..50df9a821 100644
--- a/extra_scripts/datasets/create_coco_data_files.py
+++ b/extra_scripts/datasets/create_coco_data_files.py
@@ -15,7 +15,7 @@
 import sys
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from pycocotools.coco import COCO
 
 
@@ -53,8 +53,8 @@ def get_valid_objs(entry, objs):
 
 
 def get_imgs_labels_info(split, json_file, args):
-    assert PathManager.exists(json_file), "Data source does not exist. Abort"
-    json_data = json.load(PathManager.open(json_file, "r"))
+    assert g_pathmgr.exists(json_file), "Data source does not exist. Abort"
+    json_data = json.load(g_pathmgr.open(json_file, "r"))
     image_index = [x["id"] for x in json_data["images"]]
     coco = COCO(json_file)
 
diff --git a/extra_scripts/datasets/create_imagenet_a_data_files.py b/extra_scripts/datasets/create_imagenet_a_data_files.py
index 6a6205ae5..52f0a160f 100644
--- a/extra_scripts/datasets/create_imagenet_a_data_files.py
+++ b/extra_scripts/datasets/create_imagenet_a_data_files.py
@@ -8,7 +8,7 @@
 
 import numpy as np
 import torchvision.datasets as datasets
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from tqdm import tqdm
 from vissl.utils.download import download_and_extract_archive
 from vissl.utils.io import cleanup_dir
@@ -44,7 +44,7 @@ def get_argument_parser():
 
 def remove_file_name_whitespace(input_path: str):
     """
-    Remove the whitespace in the file names for better compatibility with PathManager.
+    Remove the whitespace in the file names for better compatibility with g_pathmgr.
     """
     for class_folder_path in os.listdir(input_path):
         # All necessary folders start with n.
@@ -82,7 +82,7 @@ class ImagenetTargetMapper:
     )
 
     def __init__(self):
-        with PathManager.open(self.IMAGENET_TARGETS_URL) as f:
+        with g_pathmgr.open(self.IMAGENET_TARGETS_URL) as f:
             imagenet_classes = [line.strip() for line in f.readlines()]
             imagenet_classes.sort()
         self.label_to_id = {label: i for i, label in enumerate(imagenet_classes)}
@@ -143,7 +143,7 @@ def cleanup_unused_files(output_path: str):
         download_datasets(args.input)
 
     input_path = os.path.join(args.input, "imagenet-a")
-    assert PathManager.exists(input_path), "Input data path does not exist"
+    assert g_pathmgr.exists(input_path), "Input data path does not exist"
     remove_file_name_whitespace(input_path)
     create_imagenet_test_files(input_path, args.output)
 
diff --git a/extra_scripts/datasets/create_imagenet_data_files.py b/extra_scripts/datasets/create_imagenet_data_files.py
index 19bee9d51..e6e993a57 100644
--- a/extra_scripts/datasets/create_imagenet_data_files.py
+++ b/extra_scripts/datasets/create_imagenet_data_files.py
@@ -16,7 +16,7 @@
 import sys
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 
 
 # initiate the logger
@@ -36,7 +36,7 @@ def get_all_classes(data_dir):
 
 
 def get_images_labels_info(split, args):
-    assert PathManager.exists(args.data_source_dir), "Data source NOT found. Abort!"
+    assert g_pathmgr.exists(args.data_source_dir), "Data source NOT found. Abort!"
     data_dir = f"{args.data_source_dir}/{split}"
     class_idx = get_all_classes(data_dir)
     logger.info("Number of classes in {} data: {}".format(split, len(class_idx)))
@@ -103,7 +103,7 @@ def main():
             json_out_path = f"{args.output_dir}/{partition}_targets.json"
             import json
 
-            with PathManager.open(json_out_path, "w") as fp:
+            with g_pathmgr.open(json_out_path, "w") as fp:
                 json.dump(output_dict, fp)
             logger.info("Saved Json to: {}".format(json_out_path))
     logger.info("DONE!")
diff --git a/extra_scripts/datasets/create_imagenet_r_data_files.py b/extra_scripts/datasets/create_imagenet_r_data_files.py
index 55fd5a482..4d20e52f4 100644
--- a/extra_scripts/datasets/create_imagenet_r_data_files.py
+++ b/extra_scripts/datasets/create_imagenet_r_data_files.py
@@ -10,7 +10,7 @@
     create_imagenet_test_files,
     remove_file_name_whitespace,
 )
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.download import download_and_extract_archive
 from vissl.utils.io import cleanup_dir
 
@@ -77,7 +77,7 @@ def cleanup_unused_files(output_path: str):
 
     dataset_name = "imagenet-r"
     input_path = os.path.join(args.input, dataset_name)
-    assert PathManager.exists(input_path), "Input data path does not exist"
+    assert g_pathmgr.exists(input_path), "Input data path does not exist"
     remove_file_name_whitespace(input_path)
     create_imagenet_test_files(input_path, args.output)
 
diff --git a/extra_scripts/datasets/create_imagenet_sketch_data_files.py b/extra_scripts/datasets/create_imagenet_sketch_data_files.py
index 94c773940..0c11c3b9b 100644
--- a/extra_scripts/datasets/create_imagenet_sketch_data_files.py
+++ b/extra_scripts/datasets/create_imagenet_sketch_data_files.py
@@ -10,7 +10,7 @@
     create_imagenet_test_files,
     remove_file_name_whitespace,
 )
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from torchvision.datasets.utils import extract_archive
 from vissl.utils.download import download_google_drive_url
 from vissl.utils.io import cleanup_dir
@@ -82,7 +82,7 @@ def cleanup_unused_files(output_path: str):
         download_datasets(args.input)
 
     input_path = os.path.join(args.input, "imagenet_sketch")
-    assert PathManager.exists(input_path), "Input data path does not exist"
+    assert g_pathmgr.exists(input_path), "Input data path does not exist"
     remove_file_name_whitespace(input_path)
     create_imagenet_test_files(input_path, args.output)
 
diff --git a/extra_scripts/datasets/create_inaturalist2018_data_files.py b/extra_scripts/datasets/create_inaturalist2018_data_files.py
index e97680335..593282ed4 100644
--- a/extra_scripts/datasets/create_inaturalist2018_data_files.py
+++ b/extra_scripts/datasets/create_inaturalist2018_data_files.py
@@ -20,7 +20,7 @@
 import sys
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.download import download_and_extract_archive
 from vissl.utils.io import save_file
 
@@ -111,10 +111,10 @@ def main():
     args = parser.parse_args()
 
     # Make sure that the input and output directories exist.
-    assert PathManager.exists(
+    assert g_pathmgr.exists(
         args.input_dir_path
     ), "Data input directory not found! Please create the directory"
-    assert PathManager.exists(
+    assert g_pathmgr.exists(
         args.output_dir_path
     ), "Data output directory not found! Please create the directory"
 
diff --git a/extra_scripts/datasets/create_sun397_data_files.py b/extra_scripts/datasets/create_sun397_data_files.py
index 220d20548..16a381d01 100644
--- a/extra_scripts/datasets/create_sun397_data_files.py
+++ b/extra_scripts/datasets/create_sun397_data_files.py
@@ -10,7 +10,7 @@
 from typing import Any, List
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from tqdm import tqdm
 from vissl.utils.download import download_and_extract_archive
 
@@ -95,7 +95,7 @@ def create_sun397_disk_filelist_dataset(input_path: str, output_path: str, seed:
     by allocating 70% of labels to "train", 10% to "val" and 20% to "test".
     """
     random.seed(seed)
-    PathManager.mkdirs(output_path)
+    g_pathmgr.mkdirs(output_path)
 
     # List all the available classes in SUN397 and their path
     image_folder = os.path.join(input_path, "SUN397")
@@ -129,10 +129,10 @@ def create_sun397_disk_filelist_dataset(input_path: str, output_path: str, seed:
     # Save each split
     for split, samples in splits_data.items():
         image_output_path = os.path.join(output_path, f"{split}_images.npy")
-        with PathManager.open(image_output_path, mode="wb") as f:
+        with g_pathmgr.open(image_output_path, mode="wb") as f:
             np.save(f, np.array(samples.image_paths))
         label_output_path = os.path.join(output_path, f"{split}_labels.npy")
-        with PathManager.open(label_output_path, mode="wb") as f:
+        with g_pathmgr.open(label_output_path, mode="wb") as f:
             np.save(f, np.array(samples.image_labels))
 
 
diff --git a/extra_scripts/datasets/create_voc_data_files.py b/extra_scripts/datasets/create_voc_data_files.py
index 04db9d4fb..9dae1abc8 100644
--- a/extra_scripts/datasets/create_voc_data_files.py
+++ b/extra_scripts/datasets/create_voc_data_files.py
@@ -16,7 +16,7 @@
 from glob import glob
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 
 
 # initiate the logger
@@ -39,7 +39,7 @@ def validate_files(input_files):
 
 def get_data_files(split, args):
     data_dir = f"{args.data_source_dir}/ImageSets/Main"
-    assert PathManager.exists(data_dir), "Data: {} doesn't exist".format(data_dir)
+    assert g_pathmgr.exists(data_dir), "Data: {} doesn't exist".format(data_dir)
     test_data_files = glob(os.path.join(data_dir, "*_test.txt"))
     test_data_files = validate_files(test_data_files)
     if args.separate_partitions > 0:
@@ -68,7 +68,7 @@ def get_data_files(split, args):
 
 
 def get_images_labels_info(split, args):
-    assert PathManager.exists(args.data_source_dir), "Data source NOT found. Abort"
+    assert g_pathmgr.exists(args.data_source_dir), "Data source NOT found. Abort"
 
     data_files = get_data_files(split, args)
     # we will construct a map for image name to the vector of -1, 0, 1
@@ -77,7 +77,7 @@ def get_images_labels_info(split, args):
     for cls_num, data_path in enumerate(sorted(data_files)):
         # for this class, we have images and each image will have label
         # 1, -1, 0 -> present, not present, ignore respectively as in VOC data.
-        with PathManager.open(data_path, "r") as fopen:
+        with g_pathmgr.open(data_path, "r") as fopen:
             for line in fopen:
                 try:
                     img_name, orig_label = line.strip().split()
@@ -174,7 +174,7 @@ def main():
             json_out_path = f"{args.output_dir}/{partition}_targets.json"
             import json
 
-            with PathManager.open(json_out_path, "w") as fp:
+            with g_pathmgr.open(json_out_path, "w") as fp:
                 json.dump(output_dict, fp)
             logger.info("Saved Json to: {}".format(json_out_path))
     logger.info("DONE!")
diff --git a/requirements.txt b/requirements.txt
index dfac70660..95a5cfc58 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,6 +3,7 @@
 cython==0.29.22
 fairscale@https://github.com/facebookresearch/fairscale/tarball/df7db85cef7f9c30a5b821007754b96eb1f977b6
 fvcore==0.1.3.post20210317
+iopath==0.1.9
 hydra-core==1.0.7
 numpy==1.19.5
 parameterized==0.7.4
diff --git a/tools/cluster_features_and_label.py b/tools/cluster_features_and_label.py
index d5b074b05..941250169 100644
--- a/tools/cluster_features_and_label.py
+++ b/tools/cluster_features_and_label.py
@@ -16,7 +16,7 @@
 
 import numpy as np
 import torch
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.config import AttrDict
 from vissl.data import build_dataset
 from vissl.hooks import default_hook_generator
@@ -167,7 +167,7 @@ def _create_dataset_split(
         "images": image_paths,
     }
     output_dir = cfg.CLUSTERFIT.OUTPUT_DIR
-    PathManager.mkdirs(output_dir)
+    g_pathmgr.mkdirs(output_dir)
     output_prefix = (
         f"{data_name}_{data_split}_{layer_name}_N{num_clusters}_D{features_dim}"
     )
diff --git a/tools/instance_retrieval_test.py b/tools/instance_retrieval_test.py
index b1696361d..9ca41a485 100644
--- a/tools/instance_retrieval_test.py
+++ b/tools/instance_retrieval_test.py
@@ -13,7 +13,7 @@
 import torch
 import torchvision
 from classy_vision.generic.util import copy_model_to_gpu, load_checkpoint
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.config import AttrDict
 from vissl.models import build_model
 from vissl.utils.checkpoint import (
@@ -63,7 +63,7 @@ def build_retrieval_model(cfg):
     """
     logging.info("Building model....")
     model = build_model(cfg.MODEL, cfg.OPTIMIZER)
-    if PathManager.exists(cfg.MODEL.WEIGHTS_INIT.PARAMS_FILE):
+    if g_pathmgr.exists(cfg.MODEL.WEIGHTS_INIT.PARAMS_FILE):
         init_weights_path = cfg.MODEL.WEIGHTS_INIT.PARAMS_FILE
         logging.info(f"Initializing model from: {init_weights_path}")
         weights = load_checkpoint(init_weights_path, device=torch.device("cuda"))
@@ -129,7 +129,7 @@ def process_train_image(i, out_dir, verbose=False):
         if out_dir:
             fname_out = f"{out_dir}/{i}.npy"
 
-        if fname_out and PathManager.exists(fname_out):
+        if fname_out and g_pathmgr.exists(fname_out):
             feat = load_file(fname_out)
             train_features.append(feat)
         else:
@@ -297,7 +297,7 @@ def get_dataset_features(
         if db_fname_out_dir:
             db_fname_out = f"{db_fname_out_dir}/{idx}.npy"
 
-        if db_fname_out and PathManager.exists(db_fname_out):
+        if db_fname_out and g_pathmgr.exists(db_fname_out):
             db_feature = load_file(db_fname_out)
         else:
             db_feature = process_eval_image(
@@ -360,7 +360,7 @@ def get_queries_features(
         if q_fname_out_dir:
             q_fname_out = f"{q_fname_out_dir}/{idx}.npy"
 
-        if q_fname_out and PathManager.exists(q_fname_out):
+        if q_fname_out and g_pathmgr.exists(q_fname_out):
             query_feature = load_file(q_fname_out)
         else:
             query_feature = process_eval_image(
@@ -413,7 +413,7 @@ def get_train_dataset(cfg, root_dataset_path, train_dataset_name, eval_binary_pa
     if cfg.IMG_RETRIEVAL.TRAIN_PCA_WHITENING:
         train_data_path = f"{root_dataset_path}/{train_dataset_name}"
 
-        assert PathManager.exists(train_data_path), f"Unknown path: {train_data_path}"
+        assert g_pathmgr.exists(train_data_path), f"Unknown path: {train_data_path}"
 
         num_samples = (
             None
@@ -462,7 +462,7 @@ def compute_l2_distance_matrix(features_queries, features_dataset):
 
 def get_eval_dataset(cfg, root_dataset_path, eval_dataset_name, eval_binary_path):
     eval_data_path = f"{root_dataset_path}/{eval_dataset_name}"
-    assert PathManager.exists(eval_data_path), f"Unknown path: {eval_data_path}"
+    assert g_pathmgr.exists(eval_data_path), f"Unknown path: {eval_data_path}"
 
     num_samples = (
         None
@@ -556,7 +556,7 @@ def instance_retrieval_test(args, cfg):
         if temp_dir:
 
             pca_out_fname = f"{temp_dir}/{train_dataset_name}_S{resize_img}_PCA.pickle"
-        if pca_out_fname and PathManager.exists(pca_out_fname):
+        if pca_out_fname and g_pathmgr.exists(pca_out_fname):
             logging.info("Loading PCA...")
             pca = load_pca(pca_out_fname)
         else:
diff --git a/tools/launch_benchmark_suite_scheduler_slurm.py b/tools/launch_benchmark_suite_scheduler_slurm.py
index d06affe4e..5ed70362b 100644
--- a/tools/launch_benchmark_suite_scheduler_slurm.py
+++ b/tools/launch_benchmark_suite_scheduler_slurm.py
@@ -7,7 +7,7 @@
 
 import pkg_resources
 import submitit
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.config.attr_dict import AttrDict
 from vissl.utils.benchmark_suite_scheduler import BenchmarkSuiteScheduler
 from vissl.utils.hydra_config import assert_hydra_dependency
@@ -56,7 +56,7 @@ def checkpoint(self):
 
 
 def launch_benchmark_suite_scheduler(config_file):
-    assert PathManager.exists(config_file), "Slurm evaluator config file must exist"
+    assert g_pathmgr.exists(config_file), "Slurm evaluator config file must exist"
 
     user_config = load_file(config_file)
     config = _DEFAULT_CONFIG.copy()
diff --git a/vissl/data/dataset_catalog.py b/vissl/data/dataset_catalog.py
index 9bcf5b6c3..4ab6327cd 100644
--- a/vissl/data/dataset_catalog.py
+++ b/vissl/data/dataset_catalog.py
@@ -13,7 +13,7 @@
 from typing import List
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.data.datasets import get_coco_imgs_labels_info, get_voc_images_labels_info
 from vissl.utils.misc import get_json_data_catalog_file
 from vissl.utils.slurm import get_slurm_dir
@@ -39,7 +39,7 @@ def register_json(json_catalog_path):
         Args:
             filepath: a .json filepath that contains the data to be registered
         """
-        with PathManager.open(json_catalog_path) as fopen:
+        with g_pathmgr.open(json_catalog_path) as fopen:
             data_catalog = json.load(fopen)
         for key, value in data_catalog.items():
             VisslDatasetCatalog.register_data(key, value)
@@ -135,17 +135,17 @@ def get_local_path(input_file, dest_dir):
     - If both above fail, we return the input_file as is.
     """
     out = ""
-    if PathManager.isfile(input_file):
+    if g_pathmgr.isfile(input_file):
         out = os.path.join(dest_dir, os.path.basename(input_file))
-    elif PathManager.isdir(input_file):
+    elif g_pathmgr.isdir(input_file):
         data_name = input_file.strip("/").split("/")[-1]
         if "SLURM_JOBID" in os.environ:
             dest_dir = get_slurm_dir(dest_dir)
         dest_dir = os.path.join(dest_dir, data_name)
         complete_flag = os.path.join(dest_dir, "copy_complete")
-        if PathManager.isfile(complete_flag):
+        if g_pathmgr.isfile(complete_flag):
             out = dest_dir
-    if PathManager.exists(out):
+    if g_pathmgr.exists(out):
         return out
     else:
         return input_file
@@ -172,9 +172,9 @@ def check_data_exists(data_files):
     we iteratively check for each file in the list.
     """
     if isinstance(data_files, list):
-        return np.all([PathManager.exists(item) for item in data_files])
+        return np.all([g_pathmgr.exists(item) for item in data_files])
     else:
-        return PathManager.exists(data_files)
+        return g_pathmgr.exists(data_files)
 
 
 def register_pascal_voc():
@@ -188,7 +188,7 @@ def register_pascal_voc():
     for voc_data in voc_datasets:
         data_info = VisslDatasetCatalog.get(voc_data)
         data_folder = data_info["train"][0]
-        if PathManager.exists(data_folder):
+        if g_pathmgr.exists(data_folder):
             train_data_info = get_voc_images_labels_info("train", data_folder)
             test_data_info = get_voc_images_labels_info("val", data_folder)
             data_info["train"] = train_data_info
@@ -208,7 +208,7 @@ def register_coco():
     """
     data_info = VisslDatasetCatalog.get("coco2014_folder")
     data_folder = data_info["train"][0]
-    if PathManager.exists(data_folder):
+    if g_pathmgr.exists(data_folder):
         train_data_info = get_coco_imgs_labels_info("train", data_folder)
         test_data_info = get_coco_imgs_labels_info("val", data_folder)
         data_info["train"] = train_data_info
@@ -228,7 +228,7 @@ def register_datasets(json_catalog_path):
     Args:
         json_catalog_path (str): the path to the json dataset catalog
     """
-    if PathManager.exists(json_catalog_path):
+    if g_pathmgr.exists(json_catalog_path):
         logging.info(f"Registering datasets: {json_catalog_path}")
         VisslDatasetCatalog.clear()
         VisslDatasetCatalog.register_json(json_catalog_path)
diff --git a/vissl/data/datasets/coco.py b/vissl/data/datasets/coco.py
index ce8eb6566..e36ce669a 100644
--- a/vissl/data/datasets/coco.py
+++ b/vissl/data/datasets/coco.py
@@ -14,7 +14,7 @@
 import os
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 
 # COCO API
 from vissl.utils.io import makedir, save_file
@@ -60,8 +60,8 @@ def get_coco_imgs_labels_info(split, data_source_dir, args):
     from pycocotools.coco import COCO
 
     json_file = f"{data_source_dir}/annotations/instances_{split}2014.json"
-    assert PathManager.exists(json_file), "Annotations file does not exist. Abort"
-    json_data = json.load(PathManager.open(json_file, "r"))
+    assert g_pathmgr.exists(json_file), "Annotations file does not exist. Abort"
+    json_data = json.load(g_pathmgr.open(json_file, "r"))
     image_index = [x["id"] for x in json_data["images"]]
     coco = COCO(json_file)
 
diff --git a/vissl/data/datasets/pascal_voc.py b/vissl/data/datasets/pascal_voc.py
index f37548f18..198cedff2 100644
--- a/vissl/data/datasets/pascal_voc.py
+++ b/vissl/data/datasets/pascal_voc.py
@@ -14,7 +14,7 @@
 from glob import glob
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.io import makedir, save_file
 
 
@@ -40,7 +40,7 @@ def validate_files(input_files):
 
 def get_data_files(split, data_source_dir):
     data_dir = f"{data_source_dir}/ImageSets/Main"
-    assert PathManager.exists(data_dir), "Data: {} doesn't exist".format(data_dir)
+    assert g_pathmgr.exists(data_dir), "Data: {} doesn't exist".format(data_dir)
     test_data_files = glob(os.path.join(data_dir, "*_test.txt"))
     test_data_files = validate_files(test_data_files)
     train_data_files = glob(os.path.join(data_dir, "*_trainval.txt"))
@@ -56,7 +56,7 @@ def get_data_files(split, data_source_dir):
 
 
 def get_voc_images_labels_info(split, data_source_dir):
-    assert PathManager.exists(data_source_dir), "Data source NOT found. Abort"
+    assert g_pathmgr.exists(data_source_dir), "Data source NOT found. Abort"
     data_files = get_data_files(split, data_source_dir)
     # we will construct a map for image name to the vector of -1, 0, 1
     # we sort the data_files which gives sorted class names as well
@@ -64,7 +64,7 @@ def get_voc_images_labels_info(split, data_source_dir):
     for cls_num, data_path in enumerate(sorted(data_files)):
         # for this class, we have images and each image will have label
         # 1, -1, 0 -> present, not present, ignore respectively as in VOC data.
-        with PathManager.open(data_path, "r") as fopen:
+        with g_pathmgr.open(data_path, "r") as fopen:
             for line in fopen:
                 try:
                     img_name, orig_label = line.strip().split()
diff --git a/vissl/data/disk_dataset.py b/vissl/data/disk_dataset.py
index c8c0231e3..19b3c2dbd 100644
--- a/vissl/data/disk_dataset.py
+++ b/vissl/data/disk_dataset.py
@@ -5,7 +5,7 @@
 
 import logging
 
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from PIL import Image
 from torchvision.datasets import ImageFolder
 from vissl.data.data_helper import QueueDataset, get_mean_image
@@ -54,9 +54,9 @@ def __init__(self, cfg, data_source, path, split, dataset_name):
             "disk_folder",
         ], "data_source must be either disk_filelist or disk_folder"
         if data_source == "disk_filelist":
-            assert PathManager.isfile(path), f"File {path} does not exist"
+            assert g_pathmgr.isfile(path), f"File {path} does not exist"
         elif data_source == "disk_folder":
-            assert PathManager.isdir(path), f"Directory {path} does not exist"
+            assert g_pathmgr.isdir(path), f"Directory {path} does not exist"
         self.cfg = cfg
         self.split = split
         self.dataset_name = dataset_name
@@ -144,7 +144,7 @@ def __getitem__(self, idx):
                     replace_prefix=self._remove_prefix,
                     new_prefix=self._new_prefix,
                 )
-                with PathManager.open(image_path, "rb") as fopen:
+                with g_pathmgr.open(image_path, "rb") as fopen:
                     img = Image.open(fopen).convert("RGB")
             elif self.data_source == "disk_folder":
                 img = self.image_dataset[idx][0]
diff --git a/vissl/data/ssl_dataset.py b/vissl/data/ssl_dataset.py
index a787ef8cd..d35b4026a 100644
--- a/vissl/data/ssl_dataset.py
+++ b/vissl/data/ssl_dataset.py
@@ -8,7 +8,7 @@
 
 import numpy as np
 from classy_vision.generic.distributed_util import get_world_size
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.config import AttrDict
 from vissl.data import dataset_catalog
 from vissl.data.data_helper import balanced_sub_sampling, unbalanced_sub_sampling
@@ -168,22 +168,22 @@ def load_single_label_file(self, path: str):
         To save memory, if the mmap_mode is set to True for loading, we try to load
         the images in mmap_mode. If it fails, we simply load the labels without mmap
         """
-        assert PathManager.isfile(path), f"Path to labels {path} is not a file"
+        assert g_pathmgr.isfile(path), f"Path to labels {path} is not a file"
         assert path.endswith("npy"), "Please specify a numpy file for labels"
         if self.cfg["DATA"][self.split].MMAP_MODE:
             try:
-                with PathManager.open(path, "rb") as fopen:
+                with g_pathmgr.open(path, "rb") as fopen:
                     labels = np.load(fopen, allow_pickle=True, mmap_mode="r")
             except ValueError as e:
-                logging.info(f"Could not mmap {path}: {e}. Trying without PathManager")
+                logging.info(f"Could not mmap {path}: {e}. Trying without g_pathmgr")
                 labels = np.load(path, allow_pickle=True, mmap_mode="r")
-                logging.info("Successfully loaded without PathManager")
+                logging.info("Successfully loaded without g_pathmgr")
             except Exception:
-                logging.info("Could not mmap without PathManager. Trying without mmap")
-                with PathManager.open(path, "rb") as fopen:
+                logging.info("Could not mmap without g_pathmgr. Trying without mmap")
+                with g_pathmgr.open(path, "rb") as fopen:
                     labels = np.load(fopen, allow_pickle=True)
         else:
-            with PathManager.open(path, "rb") as fopen:
+            with g_pathmgr.open(path, "rb") as fopen:
                 labels = np.load(fopen, allow_pickle=True)
         return labels
 
diff --git a/vissl/data/ssl_transforms/shuffle_img_patches.py b/vissl/data/ssl_transforms/shuffle_img_patches.py
index 92617790c..d601d3588 100644
--- a/vissl/data/ssl_transforms/shuffle_img_patches.py
+++ b/vissl/data/ssl_transforms/shuffle_img_patches.py
@@ -12,7 +12,7 @@
 import torch
 from classy_vision.dataset.transforms import register_transform
 from classy_vision.dataset.transforms.classy_transform import ClassyTransform
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.io import cache_url, is_url, load_file
 
 
@@ -42,7 +42,7 @@ def _load_perms(self):
             cache_dir = os.path.join(temp_cache_dir, "perm_file_cache")
             cached_url_path = cache_url(url=self.perm_file, cache_dir=cache_dir)
             self.perm_file = cached_url_path
-        assert PathManager.exists(
+        assert g_pathmgr.exists(
             self.perm_file
         ), f"Permutation file NOT found: {self.perm_file}"
         logging.info(f"Loading permutation: {self.perm_file}")
diff --git a/vissl/data/torchvision_dataset.py b/vissl/data/torchvision_dataset.py
index 61c16bb9c..95bf911dc 100644
--- a/vissl/data/torchvision_dataset.py
+++ b/vissl/data/torchvision_dataset.py
@@ -5,7 +5,7 @@
 
 from typing import List, Tuple
 
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from PIL import Image
 from torch.utils.data import Dataset
 from torchvision.datasets import CIFAR10, CIFAR100, MNIST, STL10, SVHN
@@ -42,7 +42,7 @@ def __init__(
         self, cfg: AttrDict, data_source: str, path: str, split: str, dataset_name: str
     ):
         super().__init__()
-        assert PathManager.isdir(path), f"Directory {path} does not exist"
+        assert g_pathmgr.isdir(path), f"Directory {path} does not exist"
         self.dataset_name = dataset_name
         self.path = path
         self.split = split.lower()
diff --git a/vissl/hooks/log_hooks.py b/vissl/hooks/log_hooks.py
index dcbf88e06..9194dde3b 100644
--- a/vissl/hooks/log_hooks.py
+++ b/vissl/hooks/log_hooks.py
@@ -19,7 +19,7 @@
 from classy_vision.generic.distributed_util import get_rank, is_primary
 from classy_vision.hooks.classy_hook import ClassyHook
 from fairscale.nn.data_parallel import FullyShardedDataParallel as FSDP
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.checkpoint import CheckpointWriter, is_checkpoint_phase
 from vissl.utils.env import get_machine_local_and_dist_rank
 from vissl.utils.io import save_file
@@ -184,7 +184,7 @@ def __init__(
         self.btime_freq: Optional[int] = btime_freq
         self.json_stdout_logger = None
         if is_primary():
-            self.json_stdout_logger = PathManager.open(
+            self.json_stdout_logger = g_pathmgr.open(
                 f"{checkpoint_folder}/stdout.json",
                 mode="a",
                 buffering=10 * 1024,  # 10KB
@@ -331,9 +331,9 @@ def on_forward(self, task: "tasks.ClassyTask") -> None:
             input_sample_file = (
                 f"{task.checkpoint_folder}/rank{dist_rank}_input_sample.pth"
             )
-            with PathManager.open(model_output_file, "wb") as fwrite:
+            with g_pathmgr.open(model_output_file, "wb") as fwrite:
                 torch.save(model_output, fwrite)
-            with PathManager.open(input_sample_file, "wb") as fwrite:
+            with g_pathmgr.open(input_sample_file, "wb") as fwrite:
                 torch.save(task.last_batch.sample, fwrite)
             logging.info(f"Saved model output: {model_output_file}")
             logging.info(f"Saved model input: {input_sample_file}")
diff --git a/vissl/losses/swav_loss.py b/vissl/losses/swav_loss.py
index c56c1fd09..8b4464cba 100644
--- a/vissl/losses/swav_loss.py
+++ b/vissl/losses/swav_loss.py
@@ -18,7 +18,7 @@
     get_world_size,
 )
 from classy_vision.losses import ClassyLoss, register_loss
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from torch import nn
 from vissl.config import AttrDict
 from vissl.losses.distibuted_sinkhornknopp import distributed_sinkhornknopp
@@ -279,9 +279,9 @@ def forward(self, scores: torch.Tensor, head_id: int):
                     self.output_dir,
                     "rank" + str(self.dist_rank) + "_assignments" + str(i) + ".pth",
                 )
-                with PathManager.open(scores_output_file, "wb") as fwrite:
+                with g_pathmgr.open(scores_output_file, "wb") as fwrite:
                     torch.save(scores, fwrite)
-                with PathManager.open(assignments_out_file, "wb") as fwrite:
+                with g_pathmgr.open(assignments_out_file, "wb") as fwrite:
                     torch.save(assignments, fwrite)
                 logging.info(f"Saved the scores matrix to: {scores_output_file}")
                 logging.info(f"Saved the assignment matrix to: {assignments_out_file}")
diff --git a/vissl/trainer/train_task.py b/vissl/trainer/train_task.py
index 9fa805cbb..c81edcf40 100644
--- a/vissl/trainer/train_task.py
+++ b/vissl/trainer/train_task.py
@@ -14,7 +14,7 @@
 from classy_vision.optim import build_optimizer, build_optimizer_schedulers
 from classy_vision.tasks import ClassificationTask, register_task
 from classy_vision.tasks.classification_task import AmpType, BroadcastBuffersMode
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from torch.cuda.amp import GradScaler as TorchGradScaler
 from vissl.config import AttrDict
 from vissl.data import (
@@ -422,7 +422,7 @@ def _restore_model_weights(self, model):
         assert init_weights_path, "Shouldn't call this when init_weight_path is empty"
         logging.info(f"Initializing model from: {init_weights_path}")
 
-        if PathManager.exists(init_weights_path):
+        if g_pathmgr.exists(init_weights_path):
             checkpoint = CheckpointLoader.load_and_broadcast_init_weights(
                 checkpoint_path=init_weights_path, device=torch.device("cpu")
             )
@@ -480,7 +480,7 @@ def _build_model(self):
             self.checkpoint_path is None
             and self.config["MODEL"]["WEIGHTS_INIT"]["PARAMS_FILE"]
         ):
-            assert PathManager.exists(
+            assert g_pathmgr.exists(
                 self.config["MODEL"]["WEIGHTS_INIT"]["PARAMS_FILE"]
             ), "Specified PARAMS_FILE does NOT exist"
         # If we want to initialize the model in case of finetuning or evaluation,
@@ -489,7 +489,7 @@ def _build_model(self):
         if (
             self.checkpoint_path is None
             and self.config["MODEL"]["WEIGHTS_INIT"]["PARAMS_FILE"]
-            and PathManager.exists(self.config["MODEL"]["WEIGHTS_INIT"]["PARAMS_FILE"])
+            and g_pathmgr.exists(self.config["MODEL"]["WEIGHTS_INIT"]["PARAMS_FILE"])
         ):
             model = self._restore_model_weights(model)
 
diff --git a/vissl/utils/benchmark_suite_scheduler.py b/vissl/utils/benchmark_suite_scheduler.py
index 1d1e5fb1e..0be0c8f93 100644
--- a/vissl/utils/benchmark_suite_scheduler.py
+++ b/vissl/utils/benchmark_suite_scheduler.py
@@ -13,7 +13,7 @@
 from typing import List
 
 import submitit
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.config.attr_dict import AttrDict
 from vissl.utils.distributed_launcher import launch_distributed_on_slurm
 from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
@@ -163,7 +163,7 @@ def evaluate(self):
                 )
 
             if (
-                PathManager.exists(self.training_checkpoint_file)
+                g_pathmgr.exists(self.training_checkpoint_file)
                 and self._max_training_iterations()
             ):
                 # Load training yaml config.
@@ -206,10 +206,10 @@ def _max_training_iterations(self):
         )
 
         # If the stdout.json path doesn't exist, return None.
-        if not PathManager.exists(training_stdout_json_file):
+        if not g_pathmgr.exists(training_stdout_json_file):
             return None
 
-        with PathManager.open(training_stdout_json_file, "rb") as f:
+        with g_pathmgr.open(training_stdout_json_file, "rb") as f:
             # First line of stdout.json must have max_iterations in the first line
             try:
                 first_json_line = json.loads(next(f))
@@ -294,10 +294,10 @@ def _validate_evaluation_setup(self):
                 % self.training_config.CHECKPOINT.CHECKPOINT_FREQUENCY
             ) == 0, "Evaluation phase frequency must evenly divide the checkpoint phase frequency"  # NOQA
 
-        assert PathManager.exists(
+        assert g_pathmgr.exists(
             self.training_config.SLURM.LOG_FOLDER
         ), "Training slurm log folder must exist"
-        assert PathManager.exists(
+        assert g_pathmgr.exists(
             self.training_config.CHECKPOINT.DIR
         ), "Training slurm checkpoint folder must exist"
 
@@ -315,7 +315,7 @@ def _evaluate_checkpoints(self):
             checkpoint_str = os.path.join(
                 self.training_config.CHECKPOINT.DIR, f"{ checkpoint_str }.torch"
             )
-            if PathManager.exists(checkpoint_str):
+            if g_pathmgr.exists(checkpoint_str):
                 self._evaluate_checkpoint(checkpoint_str, benchmarks)
 
     def _evaluate_checkpoint(self, checkpoint_str, benchmarks):
@@ -382,7 +382,7 @@ def _launch_slurm_job(self, args, config):
         return launch_distributed_on_slurm(engine_name=args.engine_name, cfg=config)
 
     def _write_json_file(self, data, file_name):
-        with PathManager.open(file_name, "w") as fopen:
+        with g_pathmgr.open(file_name, "w") as fopen:
             fopen.write(json.dumps(data, sort_keys=True))
             fopen.flush()
 
@@ -435,10 +435,10 @@ def _update_benchmark_state(self, benchmark, job):
     def _get_benchmark_metrics(self, benchmark):
         metrics_file = os.path.join(benchmark["slurm_checkpoint_dir"], "metrics.json")
 
-        if PathManager.exists(metrics_file):
+        if g_pathmgr.exists(metrics_file):
             # Open metrics file from finished evaluation job.
             metrics = []
-            with PathManager.open(metrics_file, "rb") as f:
+            with g_pathmgr.open(metrics_file, "rb") as f:
                 for line in f:
                     metrics.append(json.loads(line))
 
@@ -476,7 +476,7 @@ def _generate_initial_benchmark_results(self):
         )
         autoload_slurm_evaluator_checkpoint = (
             self.autoload_slurm_evaluator_checkpoint
-            and PathManager.exists(default_checkpoint)
+            and g_pathmgr.exists(default_checkpoint)
         )
 
         if autoload_slurm_evaluator_checkpoint or self.slurm_evaluator_checkpoint:
diff --git a/vissl/utils/checkpoint.py b/vissl/utils/checkpoint.py
index 8b74f53ce..545653a66 100644
--- a/vissl/utils/checkpoint.py
+++ b/vissl/utils/checkpoint.py
@@ -17,7 +17,7 @@
     save_checkpoint,
 )
 from fairscale.nn import FullyShardedDataParallel
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.config import AttrDict
 from vissl.utils.env import get_machine_local_and_dist_rank
 from vissl.utils.io import abspath, create_file_symlink, makedir
@@ -241,7 +241,7 @@ def sharded_to_consolidated_checkpoint(
             },
         }
         logging.info(f"Saving consolidated checkpoint at: {output_checkpoint_path}")
-        with PathManager.open(output_checkpoint_path, "wb") as f:
+        with g_pathmgr.open(output_checkpoint_path, "wb") as f:
             torch.save(consolidated_checkpoint, f)
         logging.info(f"Done! Checkpoint available at: {output_checkpoint_path}")
 
@@ -271,14 +271,14 @@ def sharded_to_sliced_checkpoint(
             "type": CheckpointItemType.slice_list.name,
             "layers": saved_parameters,
         }
-        with PathManager.open(output_checkpoint_path, "wb") as f:
+        with g_pathmgr.open(output_checkpoint_path, "wb") as f:
             torch.save(checkpoint_list, f)
         logging.info(f"Done! Checkpoint available at: {output_checkpoint_path}")
 
     @classmethod
     def _read_shards(cls, input_checkpoint_path: str, device="cpu"):
         logging.info(f"Reading sharded checkpoint from: {input_checkpoint_path}")
-        with PathManager.open(input_checkpoint_path, "rb") as f:
+        with g_pathmgr.open(input_checkpoint_path, "rb") as f:
             checkpoint = torch.load(f, map_location=device)
 
         assert checkpoint["type"] == CheckpointItemType.shard_list.name
@@ -288,7 +288,7 @@ def _read_shards(cls, input_checkpoint_path: str, device="cpu"):
                 checkpoint_folder = os.path.split(input_checkpoint_path)[0]
                 shard_path = os.path.join(checkpoint_folder, shard_path)
 
-            with PathManager.open(shard_path, "rb") as f:
+            with g_pathmgr.open(shard_path, "rb") as f:
                 shard_content = torch.load(f, map_location=device)
 
             trunk_data = shard_content["classy_state_dict"]["base_model"]["model"][
@@ -341,7 +341,7 @@ def save_model_weights(cls, model: FullyShardedDataParallel, checkpoint_path: st
                 "type": CheckpointItemType.slice_list.name,
                 "layers": saved_parameters,
             }
-            with PathManager.open(checkpoint_path, "wb") as f:
+            with g_pathmgr.open(checkpoint_path, "wb") as f:
                 torch.save(checkpoint_list, f)
 
     @classmethod
@@ -358,7 +358,7 @@ def save_slice(cls, checkpoint_path: str, param_path: str, param) -> str:
         file_path = os.path.join(checkpoint_sub_folder, f"{hash_name}.torch")
         file_path = abspath(file_path)
         checkpoint_slice = {"type": CheckpointItemType.slice.name, "weight": param}
-        with PathManager.open(file_path, "wb") as f:
+        with g_pathmgr.open(file_path, "wb") as f:
             torch.save(checkpoint_slice, f)
         return file_path
 
@@ -386,7 +386,7 @@ def _init_weight_from_slice(
         weight_path = cls._clean_path(weight_path)
         file_name = checkpoint["layers"].get(weight_path, None)
         assert file_name is not None, f"Could not find buffer: {weight_path}"
-        with PathManager.open(file_name, "rb") as f:
+        with g_pathmgr.open(file_name, "rb") as f:
             layer_checkpoint = torch.load(f)
         assert layer_checkpoint["type"] == CheckpointItemType.slice.name
         weight.copy_(layer_checkpoint["weight"])
@@ -461,7 +461,7 @@ def get_checkpoint_folder(config: AttrDict):
         odir = f"{odir}/{config.DISTRIBUTED.RUN_ID}"
 
     makedir(odir)
-    assert PathManager.exists(
+    assert g_pathmgr.exists(
         config.CHECKPOINT.DIR
     ), f"Please specify config.CHECKPOINT.DIR parameter. Invalid: {config.CHECKPOINT.DIR}"
     return odir
@@ -512,7 +512,7 @@ def has_checkpoint(checkpoint_folder: str, skip_final: bool = False):
     Returns:
         checkpoint_exists (bool): whether checkpoint exists or not
     """
-    checkpointed_files = PathManager.ls(checkpoint_folder)
+    checkpointed_files = g_pathmgr.ls(checkpoint_folder)
     checkpoint_exists = False
     for f in checkpointed_files:
         if f.endswith(".torch") and ("model_final" not in f or not skip_final):
@@ -535,7 +535,7 @@ def has_final_checkpoint(
     Returns:
         has_final_checkpoint: whether the final checkpoint exists or not
     """
-    checkpointed_files = PathManager.ls(checkpoint_folder)
+    checkpointed_files = g_pathmgr.ls(checkpoint_folder)
     torch_files = filter(lambda x: x.endswith(".torch"), checkpointed_files)
     final_files = filter(lambda x: final_checkpoint_pattern in x, torch_files)
     return len(list(final_files)) > 0
@@ -561,7 +561,7 @@ def get_checkpoint_resume_files(
                    Sometimes the latest checkpoints could be corrupt so this option
                    helps to resume from instead a few checkpoints before the last checkpoint.
     """
-    all_files = PathManager.ls(checkpoint_folder)
+    all_files = g_pathmgr.ls(checkpoint_folder)
     all_iters = []
     replace_prefix = "model_phase"
     # if we checkpoint at iterations too, we start from an iteration checkpoint
diff --git a/vissl/utils/cluster_utils.py b/vissl/utils/cluster_utils.py
index 90734bf81..12c9dce29 100644
--- a/vissl/utils/cluster_utils.py
+++ b/vissl/utils/cluster_utils.py
@@ -9,7 +9,7 @@
 
 import numpy as np
 import torch
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.config import AttrDict
 from vissl.data import build_dataset
 
@@ -32,7 +32,7 @@ class ClusterAssignmentLoader:
     @classmethod
     def save_cluster_assignment(cls, output_dir: str, assignments: ClusterAssignment):
         output_file = os.path.join(output_dir, "cluster_assignments.torch")
-        with PathManager.open(output_file, "wb") as f:
+        with g_pathmgr.open(output_file, "wb") as f:
             content = {
                 cls._CONFIG_KEY: assignments.config,
                 cls._ASSIGN_KEY: assignments.cluster_assignments,
@@ -41,7 +41,7 @@ def save_cluster_assignment(cls, output_dir: str, assignments: ClusterAssignment
 
     @classmethod
     def load_cluster_assigment(cls, file_path: str) -> ClusterAssignment:
-        with PathManager.open(file_path, "rb") as f:
+        with g_pathmgr.open(file_path, "rb") as f:
             content = torch.load(f)
             return ClusterAssignment(
                 config=content[cls._CONFIG_KEY],
diff --git a/vissl/utils/distributed_launcher.py b/vissl/utils/distributed_launcher.py
index dadc2eb77..8e1d0af73 100644
--- a/vissl/utils/distributed_launcher.py
+++ b/vissl/utils/distributed_launcher.py
@@ -13,7 +13,7 @@
 from typing import Any, Callable, List
 
 import torch
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.config import AttrDict
 from vissl.data.dataset_catalog import get_data_files
 from vissl.engines import run_engine
@@ -109,7 +109,7 @@ def launch_distributed(
     # Get the checkpoint where to resume from. The get_resume_checkpoint function will
     # automatically take care of detecting whether it's a resume or not.
     symlink_checkpoint_path = f"{checkpoint_folder}/checkpoint.torch"
-    if cfg.CHECKPOINT.USE_SYMLINK_CHECKPOINT_FOR_RESUME and PathManager.exists(
+    if cfg.CHECKPOINT.USE_SYMLINK_CHECKPOINT_FOR_RESUME and g_pathmgr.exists(
         symlink_checkpoint_path
     ):
         checkpoint_path = f"{checkpoint_folder}/checkpoint.torch"
@@ -125,7 +125,7 @@ def launch_distributed(
     if checkpoint_path is None and cfg["MODEL"]["WEIGHTS_INIT"]["PARAMS_FILE"]:
         params_file = cfg["MODEL"]["WEIGHTS_INIT"]["PARAMS_FILE"]
         error_message = f"Specified PARAMS_FILE does NOT exist: {params_file}"
-        assert PathManager.exists(params_file), error_message
+        assert g_pathmgr.exists(params_file), error_message
 
     # copy the data to local if user wants. This can speed up dataloading.
     _copy_to_local(cfg)
@@ -245,7 +245,7 @@ def launch_distributed_on_slurm(cfg: AttrDict, engine_name: str):
     # setup the log folder
     log_folder = cfg.SLURM.LOG_FOLDER
     makedir(log_folder)
-    assert PathManager.exists(
+    assert g_pathmgr.exists(
         log_folder
     ), f"Specified config.SLURM.LOG_FOLDER={log_folder} doesn't exist"
     assert cfg.SLURM.PARTITION, "SLURM.PARTITION must be set when using SLURM"
diff --git a/vissl/utils/download.py b/vissl/utils/download.py
index de2315c11..4a2fb1cf1 100644
--- a/vissl/utils/download.py
+++ b/vissl/utils/download.py
@@ -87,7 +87,7 @@ def download_google_drive_url(url: str, output_path: str, output_file_name: str)
                         progress_bar.update(len(block))
 
 
-# The following methods are copied from torchvision, but we use PathManager
+# The following methods are copied from torchvision, but we use g_pathmgr
 # instead of `os` lib to support multiple distributed file systems.
 def _get_google_drive_file_id(url: str) -> Optional[str]:
     parts = urlparse(url)
diff --git a/vissl/utils/env.py b/vissl/utils/env.py
index 487f35e90..381993953 100644
--- a/vissl/utils/env.py
+++ b/vissl/utils/env.py
@@ -33,7 +33,7 @@ def set_env_vars(local_rank: int, node_id: int, cfg: AttrDict):
 
 def setup_path_manager():
     """
-    Registering the right options for the PathManager:
+    Registering the right options for the g_pathmgr:
     Override this function in your build system to
     support different distributed file system
     """
diff --git a/vissl/utils/extract_features_utils.py b/vissl/utils/extract_features_utils.py
index 5fe04e238..0679ce7e4 100644
--- a/vissl/utils/extract_features_utils.py
+++ b/vissl/utils/extract_features_utils.py
@@ -10,7 +10,7 @@
 from typing import List, NamedTuple
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.io import load_file
 
 
@@ -59,7 +59,7 @@ def get_shard_file_names(
         # dataset split and a given layer
         feature_regex = re.compile(rf"(.*)_{split}_{layer}_features.npy")
         prefixes = []
-        for file_path in PathManager.ls(input_dir):
+        for file_path in g_pathmgr.ls(input_dir):
             match = feature_regex.match(file_path)
             if match is not None:
                 prefixes.append(match.group(1))
diff --git a/vissl/utils/instance_retrieval_utils/data_util.py b/vissl/utils/instance_retrieval_utils/data_util.py
index 871b86144..66eee0dcd 100644
--- a/vissl/utils/instance_retrieval_utils/data_util.py
+++ b/vissl/utils/instance_retrieval_utils/data_util.py
@@ -14,7 +14,7 @@
 import scipy.io
 import torch
 import torchvision.transforms.functional as TF
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from PIL import Image, ImageFile
 from torch.nn import functional as F
 from torchvision import transforms
@@ -218,7 +218,7 @@ class WhiteningTrainingImageDataset:
     """
 
     def __init__(self, base_dir: str, image_list_file: str, num_samples: int = 0):
-        with PathManager.open(image_list_file) as fopen:
+        with g_pathmgr.open(image_list_file) as fopen:
             self.image_list = fopen.readlines()
         if num_samples > 0:
             self.image_list = self.image_list[:num_samples]
@@ -504,7 +504,7 @@ def load_and_prepare_whitening_image(self, fname):
         from the filename, load the whitening image and prepare it to be used by
         applying data transforms
         """
-        with PathManager.open(fname, "rb") as f:
+        with g_pathmgr.open(fname, "rb") as f:
             im = Image.open(f)
         if im.mode != "RGB":
             im = im.convert(mode="RGB")
@@ -517,7 +517,7 @@ def load_and_prepare_instre_image(self, fname):
         from the filename, load the db or query image and prepare it to be used by
         applying data transforms
         """
-        with PathManager.open(fname, "rb") as f:
+        with g_pathmgr.open(fname, "rb") as f:
             im = Image.open(f)
         if self.transforms is not None:
             im = self.transforms(im)
@@ -530,7 +530,7 @@ def load_and_prepare_image(self, fname, roi=None):
         the image once again. ROI format is (xmin,ymin,xmax,ymax)
         """
         # Read image, get aspect ratio, and resize such as the largest side equals S
-        with PathManager.open(fname, "rb") as f:
+        with g_pathmgr.open(fname, "rb") as f:
             img = Image.open(f).convert(mode="RGB")
         im_resized, ratio = self.apply_img_transform(img)
         # If there is a roi, adapt the roi to the new size and crop. Do not rescale
@@ -552,7 +552,7 @@ def load_and_prepare_revisited_image(self, img_path, roi=None):
         # open path as file to avoid ResourceWarning
         # (https://github.com/python-pillow/Pillow/issues/835)
 
-        with PathManager.open(img_path, "rb") as f:
+        with g_pathmgr.open(img_path, "rb") as f:
             img = Image.open(f).convert("RGB")
 
         im_resized, ratio = self.apply_img_transform(img)
@@ -600,7 +600,7 @@ def __init__(
     def _get_filenames(self, data_path: str):
         fnames = []
 
-        for fname in sorted(PathManager.ls(data_path)):
+        for fname in sorted(g_pathmgr.ls(data_path)):
             # Only put images in fnames.
             if not fname.endswith(".jpg"):
                 continue
@@ -722,11 +722,11 @@ def load(self, num_samples=None):
         self.lab_root = f"{self.path}/lab/"
         self.img_root = f"{self.path}/jpg/"
         logging.info(f"Loading data: {self.path}")
-        lab_filenames = np.sort(PathManager.ls(self.lab_root))
+        lab_filenames = np.sort(g_pathmgr.ls(self.lab_root))
         # Get the filenames without the extension
         self.img_filenames = [
             e[:-4]
-            for e in np.sort(PathManager.ls(self.img_root))
+            for e in np.sort(g_pathmgr.ls(self.img_root))
             if e[:-4] not in self.blacklisted
         ]
 
@@ -746,7 +746,7 @@ def load(self, num_samples=None):
         for e in lab_filenames:
             if e.endswith("_query.txt"):
                 q_name = e[: -len("_query.txt")]
-                with PathManager.open(f"{self.lab_root}/{e}") as fopen:
+                with g_pathmgr.open(f"{self.lab_root}/{e}") as fopen:
                     q_data = fopen.readline().split(" ")
                 if q_data[0].startswith("oxc1_"):
                     q_filename = q_data[0][5:]
@@ -754,11 +754,11 @@ def load(self, num_samples=None):
                     q_filename = q_data[0]
                 self.filename_to_name[q_filename] = q_name
                 self.name_to_filename[q_name] = q_filename
-                with PathManager.open(f"{self.lab_root}/{q_name}_ok.txt") as fopen:
+                with g_pathmgr.open(f"{self.lab_root}/{q_name}_ok.txt") as fopen:
                     good = {e.strip() for e in fopen}
-                with PathManager.open(f"{self.lab_root}/{q_name}_good.txt") as fopen:
+                with g_pathmgr.open(f"{self.lab_root}/{q_name}_good.txt") as fopen:
                     good = good.union({e.strip() for e in fopen})
-                with PathManager.open(f"{self.lab_root}/{q_name}_junk.txt") as fopen:
+                with g_pathmgr.open(f"{self.lab_root}/{q_name}_junk.txt") as fopen:
                     junk = {e.strip() for e in fopen}
                 good_plus_junk = good.union(junk)
                 self.relevants[q_name] = [
@@ -813,7 +813,7 @@ def score_rnk_partial(self, i, idx, temp_dir):
         """
         rnk = np.array(self.img_filenames[: self.N_images])[idx]
 
-        with PathManager.open(f"{temp_dir}/{self.q_names[i]}.rnk", "w") as f:
+        with g_pathmgr.open(f"{temp_dir}/{self.q_names[i]}.rnk", "w") as f:
             f.write("\n".join(rnk) + "\n")
 
         cmd = (
@@ -894,7 +894,7 @@ def __init__(
     def _get_filenames(self, data_path: str):
         fnames = []
 
-        for fname in sorted(PathManager.ls(data_path)):
+        for fname in sorted(g_pathmgr.ls(data_path)):
             # Only put images in fnames.
             if not fname.endswith(".jpg"):
                 continue
diff --git a/vissl/utils/io.py b/vissl/utils/io.py
index ee26300f3..8d38f77cd 100644
--- a/vissl/utils/io.py
+++ b/vissl/utils/io.py
@@ -13,8 +13,8 @@
 
 import numpy as np
 import yaml
-from fvcore.common.download import download
-from fvcore.common.file_io import PathManager, file_lock
+from iopath.common.download import download
+from iopath.common.file_io import g_pathmgr, file_lock
 from vissl.utils.slurm import get_slurm_dir
 
 
@@ -44,9 +44,9 @@ def create_file_symlink(file1, file2):
     latest successful checkpoint.
     """
     try:
-        if PathManager.exists(file2):
-            PathManager.rm(file2)
-        PathManager.symlink(file1, file2)
+        if g_pathmgr.exists(file2):
+            g_pathmgr.rm(file2)
+        g_pathmgr.symlink(file1, file2)
     except Exception as e:
         logging.info(f"Could NOT create symlink. Error: {e}")
 
@@ -63,22 +63,22 @@ def save_file(data, filename, append_to_json=True, verbose=True):
         logging.info(f"Saving data to file: {filename}")
     file_ext = os.path.splitext(filename)[1]
     if file_ext in [".pkl", ".pickle"]:
-        with PathManager.open(filename, "wb") as fopen:
+        with g_pathmgr.open(filename, "wb") as fopen:
             pickle.dump(data, fopen, pickle.HIGHEST_PROTOCOL)
     elif file_ext == ".npy":
-        with PathManager.open(filename, "wb") as fopen:
+        with g_pathmgr.open(filename, "wb") as fopen:
             np.save(fopen, data)
     elif file_ext == ".json":
         if append_to_json:
-            with PathManager.open(filename, "a") as fopen:
+            with g_pathmgr.open(filename, "a") as fopen:
                 fopen.write(json.dumps(data, sort_keys=True) + "\n")
                 fopen.flush()
         else:
-            with PathManager.open(filename, "w") as fopen:
+            with g_pathmgr.open(filename, "w") as fopen:
                 fopen.write(json.dumps(data, sort_keys=True) + "\n")
                 fopen.flush()
     elif file_ext == ".yaml":
-        with PathManager.open(filename, "w") as fopen:
+        with g_pathmgr.open(filename, "w") as fopen:
             dump = yaml.dump(data)
             fopen.write(dump)
             fopen.flush()
@@ -101,31 +101,31 @@ def load_file(filename, mmap_mode=None):
     logging.info(f"Loading data from file: {filename}")
     file_ext = os.path.splitext(filename)[1]
     if file_ext in [".pkl", ".pickle"]:
-        with PathManager.open(filename, "rb") as fopen:
+        with g_pathmgr.open(filename, "rb") as fopen:
             data = pickle.load(fopen, encoding="latin1")
     elif file_ext == ".npy":
         if mmap_mode:
             try:
-                with PathManager.open(filename, "rb") as fopen:
+                with g_pathmgr.open(filename, "rb") as fopen:
                     data = np.load(fopen, encoding="latin1", mmap_mode=mmap_mode)
             except ValueError as e:
                 logging.info(
-                    f"Could not mmap {filename}: {e}. Trying without PathManager"
+                    f"Could not mmap {filename}: {e}. Trying without g_pathmgr"
                 )
                 data = np.load(filename, encoding="latin1", mmap_mode=mmap_mode)
-                logging.info("Successfully loaded without PathManager")
+                logging.info("Successfully loaded without g_pathmgr")
             except Exception:
-                logging.info("Could not mmap without PathManager. Trying without mmap")
-                with PathManager.open(filename, "rb") as fopen:
+                logging.info("Could not mmap without g_pathmgr. Trying without mmap")
+                with g_pathmgr.open(filename, "rb") as fopen:
                     data = np.load(fopen, encoding="latin1")
         else:
-            with PathManager.open(filename, "rb") as fopen:
+            with g_pathmgr.open(filename, "rb") as fopen:
                 data = np.load(fopen, encoding="latin1")
     elif file_ext == ".json":
-        with PathManager.open(filename, "r") as fopen:
+        with g_pathmgr.open(filename, "r") as fopen:
             data = json.load(fopen)
     elif file_ext == ".yaml":
-        with PathManager.open(filename, "r") as fopen:
+        with g_pathmgr.open(filename, "r") as fopen:
             data = yaml.load(fopen, Loader=yaml.FullLoader)
     else:
         raise Exception(f"Reading from {file_ext} is not supported yet")
@@ -150,8 +150,8 @@ def makedir(dir_path):
     """
     is_success = False
     try:
-        if not PathManager.exists(dir_path):
-            PathManager.mkdirs(dir_path)
+        if not g_pathmgr.exists(dir_path):
+            g_pathmgr.mkdirs(dir_path)
         is_success = True
     except BaseException:
         logging.info(f"Error creating directory: {dir_path}")
@@ -171,7 +171,7 @@ def cleanup_dir(dir):
     Utility for deleting a directory. Useful for cleaning the storage space
     that contains various training artifacts like checkpoints, data etc.
     """
-    if PathManager.exists(dir):
+    if g_pathmgr.exists(dir):
         logging.info(f"Deleting directory: {dir}")
         os.system(f"rm -rf {dir}")
     logging.info(f"Deleted contents of directory: {dir}")
@@ -190,8 +190,8 @@ def copy_file(input_file, destination_dir, tmp_destination_dir):
     Copy a given input_file from source to the destination directory.
 
     Steps:
-    1. We use PathManager to extract the data to local path.
-    2. we simply move the files from the PathManager cached local directory
+    1. We use g_pathmgr to extract the data to local path.
+    2. we simply move the files from the g_pathmgr cached local directory
        to the user specified destination directory. We use rsync.
        How destination dir is chosen:
             a) If user is using slurm, we set destination_dir = slurm_dir (see get_slurm_dir)
@@ -203,10 +203,10 @@ def copy_file(input_file, destination_dir, tmp_destination_dir):
         output_file (str): the new path of the file
         destination_dir (str): the destination dir that was actually used
     """
-    # we first extract the local path for the files. PathManager
+    # we first extract the local path for the files. g_pathmgr
     # determines the local path itself and copies data there.
     logging.info(f"Copying {input_file} to local path...")
-    out = PathManager.get_local_path(input_file)
+    out = g_pathmgr.get_local_path(input_file)
     output_dir = os.path.dirname(out)
     logging.info(f"File coped to: {out}")
 
@@ -217,7 +217,7 @@ def copy_file(input_file, destination_dir, tmp_destination_dir):
             f"destination directory: {destination_dir}"
         )
     # if the user wants to copy the files to a specific location,
-    # we simply move the files from the PathManager cached directory
+    # we simply move the files from the g_pathmgr cached directory
     # to the user specified directory.
     destination_dir = get_slurm_dir(destination_dir)
     if "SLURM_JOBID" in os.environ:
@@ -225,7 +225,7 @@ def copy_file(input_file, destination_dir, tmp_destination_dir):
     if destination_dir is not None:
         makedir(destination_dir)
         output_file = f"{destination_dir}/{os.path.basename(input_file)}"
-        if PathManager.exists(output_file):
+        if g_pathmgr.exists(output_file):
             logging.info(f"File already copied: {output_file}")
             return output_file, destination_dir
 
@@ -264,7 +264,7 @@ def copy_dir(input_dir, destination_dir, num_threads):
     destination_dir = f"{destination_dir}/{data_name}"
     makedir(destination_dir)
     complete_flag = f"{destination_dir}/copy_complete"
-    if PathManager.isfile(complete_flag):
+    if g_pathmgr.isfile(complete_flag):
         logging.info(f"Found Data already copied: {destination_dir}...")
         return destination_dir
     logging.info(
@@ -276,7 +276,7 @@ def copy_dir(input_dir, destination_dir, num_threads):
         f"rsync -ruW --inplace {{}} {destination_dir}"
     )
     os.system(cmd)
-    PathManager.open(complete_flag, "a").close()
+    g_pathmgr.open(complete_flag, "a").close()
     logging.info("Copied to local directory")
     return destination_dir, destination_dir
 
@@ -297,11 +297,11 @@ def copy_data(input_file, destination_dir, num_threads, tmp_destination_dir):
         makedir(destination_dir)
     else:
         destination_dir = None
-    if PathManager.isfile(input_file):
+    if g_pathmgr.isfile(input_file):
         output_file, output_dir = copy_file(
             input_file, destination_dir, tmp_destination_dir
         )
-    elif PathManager.isdir(input_file):
+    elif g_pathmgr.isdir(input_file):
         output_file, output_dir = copy_dir(input_file, destination_dir, num_threads)
     else:
         raise RuntimeError("The input_file is neither a file nor a directory")
diff --git a/vissl/utils/logger.py b/vissl/utils/logger.py
index c24287e4e..bcf4199a4 100644
--- a/vissl/utils/logger.py
+++ b/vissl/utils/logger.py
@@ -9,7 +9,7 @@
 import subprocess
 import sys
 
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from vissl.utils.io import makedir
 
 
@@ -61,7 +61,7 @@ def _cached_log_stream(filename):
     # we tune the buffering value so that the logs are updated
     # frequently.
     log_buffer_kb = 10 * 1024  # 10KB
-    io = PathManager.open(filename, mode="a", buffering=log_buffer_kb)
+    io = g_pathmgr.open(filename, mode="a", buffering=log_buffer_kb)
     atexit.register(io.close)
     return io
 
diff --git a/vissl/utils/misc.py b/vissl/utils/misc.py
index 5bfc9fb19..6e988b86b 100644
--- a/vissl/utils/misc.py
+++ b/vissl/utils/misc.py
@@ -15,7 +15,7 @@
 import pkg_resources
 import torch
 import torch.multiprocessing as mp
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from scipy.sparse import csr_matrix
 from vissl.utils.extract_features_utils import ExtractedFeaturesLoader
 
@@ -198,7 +198,7 @@ def get_json_catalog_path(default_dataset_catalog_path: str) -> str:
 
     # If catalog path is the default and we cannot find it, we want to continue without failing.
     if os.environ.get("VISSL_DATASET_CATALOG_PATH", False):
-        assert PathManager.exists(
+        assert g_pathmgr.exists(
             dataset_catalog_path
         ), f"Dataset catalog path: { dataset_catalog_path } not found."
 
diff --git a/vissl/utils/svm_utils/svm_low_shot_trainer.py b/vissl/utils/svm_utils/svm_low_shot_trainer.py
index d0591e744..f06430f7e 100644
--- a/vissl/utils/svm_utils/svm_low_shot_trainer.py
+++ b/vissl/utils/svm_utils/svm_low_shot_trainer.py
@@ -7,7 +7,7 @@
 import pickle
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from sklearn.svm import LinearSVC
 from vissl.utils.io import load_file, save_file
 from vissl.utils.svm_utils.evaluate import get_precision_recall
@@ -106,7 +106,7 @@ def train(self, features, targets, sample_num, low_shot_kvalue):
                 cost = self.costs_list[cost_idx]
                 suffix = f"sample{sample_num}_k{low_shot_kvalue}"
                 out_file = self._get_svm_low_shot_model_filename(cls_num, cost, suffix)
-                if PathManager.exists(out_file) and not self.config.force_retrain:
+                if g_pathmgr.exists(out_file) and not self.config.force_retrain:
                     logging.info(f"SVM model exists: {out_file}")
                     continue
                 logging.info(f"Training model with the cost: {cost}")
@@ -134,7 +134,7 @@ def train(self, features, targets, sample_num, low_shot_kvalue):
                 )
                 clf.fit(train_feats, train_cls_labels)
                 logging.info(f"Saving SVM model to: {out_file}")
-                with PathManager.open(out_file, "wb") as fwrite:
+                with g_pathmgr.open(out_file, "wb") as fwrite:
                     pickle.dump(clf, fwrite)
         logging.info(f"Done training: sample: {sample_num} k-value: {low_shot_kvalue}")
 
@@ -213,7 +213,7 @@ def aggregate_stats(self, k_values, sample_inds):
                 sample_idx = sample_inds[inds]
                 file_name = f"test_ap_sample{sample_idx}_k{k_low}.npy"
                 filepath = f"{self.output_dir}/{file_name}"
-                if PathManager.exists(filepath):
+                if g_pathmgr.exists(filepath):
                     k_val_output.append(load_file(filepath))
                 else:
                     logging.info(f"file does not exist: {filepath}")
diff --git a/vissl/utils/svm_utils/svm_trainer.py b/vissl/utils/svm_utils/svm_trainer.py
index d04036150..53883135a 100644
--- a/vissl/utils/svm_utils/svm_trainer.py
+++ b/vissl/utils/svm_utils/svm_trainer.py
@@ -8,7 +8,7 @@
 import threading
 
 import numpy as np
-from fvcore.common.file_io import PathManager
+from iopath.common.file_io import g_pathmgr
 from sklearn.model_selection import cross_val_score
 from sklearn.svm import LinearSVC
 from vissl.utils.io import load_file, save_file
@@ -34,7 +34,7 @@ def __init__(self, config, layer, output_dir):
 
     def _get_output_dir(self, cfg_out_dir):
         odir = f"{cfg_out_dir}/{self.layer}"
-        PathManager.mkdirs(odir)
+        g_pathmgr.mkdirs(odir)
         logging.info(f"Output directory for SVM results: {odir}")
         return odir
 
@@ -43,8 +43,8 @@ def load_input_data(self, data_file, targets_file):
         Given the input data (features) and targets (labels) files, load the
         features of shape N x D and labels of shape (N,)
         """
-        assert PathManager.exists(data_file), "Data file not found. Abort!"
-        assert PathManager.exists(targets_file), "Targets file not found. Abort!"
+        assert g_pathmgr.exists(data_file), "Data file not found. Abort!"
+        assert g_pathmgr.exists(targets_file), "Targets file not found. Abort!"
         # load the features and the targets
         logging.info("loading features and targets...")
         targets = load_file(targets_file)
@@ -105,9 +105,7 @@ def get_best_cost_value(self):
         """
         crossval_ap_file = f"{self.output_dir}/crossval_ap.npy"
         chosen_cost_file = f"{self.output_dir}/chosen_cost.npy"
-        if PathManager.exists(crossval_ap_file) and PathManager.exists(
-            chosen_cost_file
-        ):
+        if g_pathmgr.exists(crossval_ap_file) and g_pathmgr.exists(chosen_cost_file):
             self.chosen_cost = load_file(chosen_cost_file)
             self.train_ap_matrix = load_file(crossval_ap_file)
             return self.chosen_cost
@@ -143,8 +141,8 @@ def train_cls(self, features, targets, cls_num):
             cost = self.costs_list[cost_idx]
             out_file, ap_out_file = self._get_svm_model_filename(cls_num, cost)
             if (
-                PathManager.exists(out_file)
-                and PathManager.exists(ap_out_file)
+                g_pathmgr.exists(out_file)
+                and g_pathmgr.exists(ap_out_file)
                 and not self.config.force_retrain
             ):
                 logging.info(f"SVM model exists: {out_file}")
@@ -191,7 +189,7 @@ def train_cls(self, features, targets, cls_num):
             logging.info(f"Saving cls cost AP to: {ap_out_file}")
             save_file(np.array([ap_scores.mean()]), ap_out_file)
             logging.info(f"Saving SVM model to: {out_file}")
-            with PathManager.open(out_file, "wb") as fwrite:
+            with g_pathmgr.open(out_file, "wb") as fwrite:
                 pickle.dump(clf, fwrite)
 
     def train(self, features, targets):