rm scripts

scripts/run_dht.py

@@ -1,23 +0,0 @@
-import argparse
-import tesseract
-from tesseract.utils import find_open_port
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--port', type=int, default=None, required=False)
-    parser.add_argument('--initial_peers', type=str, default="[]", required=False)
-    parser.add_argument('--lifetime_seconds', type=int, default=None, required=False)
-
-    args = parser.parse_args()
-    initial_peers = eval(args.initial_peers)
-    print("Parsed initial peers:", initial_peers)
-
-    network = tesseract.TesseractNetwork(*initial_peers, port=args.port or find_open_port())
-
-    try:
-        network.start()
-        print(f"Running network node on port {network.port}")
-        network.join(timeout=args.lifetime_seconds)
-    finally:
-        network.shutdown()

scripts/run_server.py

@@ -1,76 +0,0 @@
-import argparse
-import resource
-import os
-import sys
-
-import torch
-import tesseract
-
-sys.path.append(os.path.dirname(__file__) + '/../tests')
-from test_utils import layers
-from tesseract import find_open_port
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--expert_cls', type=str, default='ffn', required=False)
-    parser.add_argument('--num_experts', type=int, default=1, required=False)
-    parser.add_argument('--num_handlers', type=int, default=None, required=False)
-    parser.add_argument('--hidden_dim', type=int, default=1024, required=False)
-    parser.add_argument('--max_batch_size', type=int, default=16384, required=False)
-    parser.add_argument('--expert_prefix', type=str, default='expert', required=False)
-    parser.add_argument('--expert_offset', type=int, default=0, required=False)
-    parser.add_argument('--device', type=str, default=None, required=False)
-    parser.add_argument('--port', type=int, default=None, required=False)
-    parser.add_argument('--host', type=str, default='0.0.0.0', required=False)
-    parser.add_argument('--no_network', action='store_true')
-    parser.add_argument('--no_optimizer', action='store_true')
-    parser.add_argument('--initial_peers', type=str, default="[]", required=False)
-    parser.add_argument('--network_port', type=int, default=None, required=False)
-    parser.add_argument('--lifetime_seconds', type=int, default=None, required=False)
-    parser.add_argument('--increase_file_limit', action='store_true')
-
-    args = parser.parse_args()
-    if args.increase_file_limit:
-        soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
-        try:
-            print("Setting open file limit to soft={}, hard={}".format(max(soft, 2 ** 15), max(hard, 2 ** 15)))
-            resource.setrlimit(resource.RLIMIT_NOFILE, (max(soft, 2 ** 15), max(hard, 2 ** 15)))
-        except:
-            print("Could not increase open file limit, currently at soft={}, hard={}".format(soft, hard))
-
-    assert args.expert_cls in layers.name_to_block
-    args.num_handlers = args.num_handlers or args.num_experts * 8
-
-    device = args.device or ('cuda' if torch.cuda.is_available() else 'cpu')
-
-    # initialize network
-    network = None
-    if not args.no_network:
-        initial_peers = eval(args.initial_peers)
-        print("Parsed initial peers:", initial_peers)
-
-        network = tesseract.TesseractNetwork(*initial_peers, port=args.network_port or find_open_port(), start=True)
-        print(f"Running network node on port {network.port}")
-
-    # initialize experts
-    experts = {}
-    for i in range(args.num_experts):
-        expert = torch.jit.script(layers.name_to_block[args.expert_cls](args.hidden_dim))
-        opt = torch.optim.SGD(expert.parameters(), 0.0) if args.no_optimizer else torch.optim.Adam(expert.parameters())
-        expert_uid = f'{args.expert_prefix}.{i + args.expert_offset}'
-        experts[expert_uid] = tesseract.ExpertBackend(name=expert_uid, expert=expert, opt=opt,
-                                                      args_schema=(tesseract.BatchTensorProto(args.hidden_dim),),
-                                                      outputs_schema=tesseract.BatchTensorProto(args.hidden_dim),
-                                                      max_batch_size=args.max_batch_size,
-                                                      )
-    # start server
-    server = tesseract.TesseractServer(
-        network, experts, addr=args.host, port=args.port or find_open_port(),
-        conn_handler_processes=args.num_handlers, device=device)
-    try:
-        server.run_in_background(await_ready=True)
-        print(f"Running server at {server.addr}:{server.port}")
-        print(f"Active experts of type {args.expert_cls}: {list(experts.keys())}")
-        server.join(timeout=args.lifetime_seconds)
-    finally:
-        server.shutdown()