Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

This commit is contained in:
Kolp
2026-09-24 13:22:23 +07:00
commit 642cc11a9f
18968 changed files with 5683248 additions and 0 deletions
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from __future__ import annotations
from typing import Any, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from types import TracebackType
def is_available() -> bool:
return hasattr(torch._C, "_dist_autograd_init")
if is_available() and not torch._C._dist_autograd_init():
raise RuntimeError("Failed to initialize torch.distributed.autograd")
if is_available():
from torch._C._distributed_autograd import (
_current_context,
_get_debug_info,
_get_max_id,
_init,
_is_valid_context,
_new_context,
_release_context,
_retrieve_context,
backward,
DistAutogradContext,
get_gradients,
)
__all__ = ["context", "is_available"]
class context:
"""
Context object to wrap forward and backward passes when using
distributed autograd. The ``context_id`` generated in the ``with``
statement is required to uniquely identify a distributed backward pass
on all workers. Each worker stores metadata associated with this
``context_id``, which is required to correctly execute a distributed
autograd pass.
Example::
>>> # xdoctest: +SKIP
>>> import torch.distributed.autograd as dist_autograd
>>> with dist_autograd.context() as context_id:
>>> t1 = torch.rand((3, 3), requires_grad=True)
>>> t2 = torch.rand((3, 3), requires_grad=True)
>>> loss = rpc.rpc_sync("worker1", torch.add, args=(t1, t2)).sum()
>>> dist_autograd.backward(context_id, [loss])
"""
def __enter__(self) -> int:
self.autograd_context = _new_context()
return self.autograd_context._context_id()
def __exit__(
self,
exc_type: type[BaseException] | None,
exc_value: BaseException | None,
traceback: TracebackType | None,
) -> None:
_release_context(self.autograd_context._context_id())