ProcessPoolExecutor] is another alternative for long running tasks that do not release the GIL, such as CPU-bound pure Python code. Note, however, that ProcessPoolExecutor [https://docs.python.org/3/library/concurrent AbstractEventLoop.set_default_executor() is used to change it to something else. Due to the GIL, CPU-bound jobs will block request processing for the current process unless the default Executor [https://docs Warning This helper is only to be used for functions that do not perform any blocking I/O or lengthy CPU-bound operations, since the entire async loop will be blocked while the wrapped function is executed.

AbstractEventLoop.set_default_executor() is used to change it to something else. Due to the GIL, CPU-bound jobs will block request processing for the current process unless the default Executor [https://docs Warning This helper is only to be used for functions that do not perform any blocking I/O or lengthy CPU-bound operations, since the entire async loop will be blocked while the wrapped function is executed. AbstractEventLoop.set_default_executor` is used to change it to something else. Due to the GIL, CPU-bound jobs will block request processing for the current process unless the default

AbstractEventLoop.set_default_executor() is used to change it to something else. Due to the GIL, CPU-bound jobs will block request processing for the current process unless the default Executor [https://docs Warning This helper is only to be used for functions that do not perform any blocking I/O or lengthy CPU-bound operations, since the entire async loop will be blocked while the wrapped function is executed. AbstractEventLoop.set_default_executor` is used to change it to something else. Due to the GIL, CPU-bound jobs will block request processing for the current process unless the default

AbstractEventLoop.set_default_executor() is used to change it to something else. Due to the GIL, CPU-bound jobs will block request processing for the current process unless the default Executor [https://docs Warning This helper is only to be used for functions that do not perform any blocking I/O or lengthy CPU-bound operations, since the entire async loop will be blocked while the wrapped function is executed. AbstractEventLoop.set_default_executor` is used to change it to something else. Due to the GIL, CPU-bound jobs will block request processing for the current process unless the default

prefork. However, you need to ensure one task doesn’t block the event loop too long. Generally, CPU-bound operations don’t go well with Evenetlet. Also note that some libraries, usually with C extensions MapRoute instance. Examples: task_routes = { 'celery.ping': 'default', 'mytasks.add': 'cpu-bound', 'feed.tasks.*': 'feeds', # <-- glob pattern re.compile(r'(image|video)\ threads executing tasks. If you’re doing mostly I/O you can have more processes, but if mostly CPU-bound, try to keep it close to the number of CPUs on your machine. If not set, the number of CPUs/cores

prefork. However, you need to ensure one task doesn’t block the event loop too long. Generally, CPU-bound operations don’t go well with Evenetlet. Also note that some libraries, usually with C extensions MapRoute instance. Examples: task_routes = { 'celery.ping': 'default', 'mytasks.add': 'cpu-bound', 'feed.tasks.*': 'feeds', # <-- glob pattern re.compile(r'(image|video)\ threads executing tasks. If you’re doing mostly I/O you can have more processes, but if mostly CPU-bound, try to keep it close to the number of CPUs on your machine. If not set, the number of CPUs/cores

prefork. However, you need to ensure one task doesn’t block the event loop too long. Generally, CPU-bound operations don’t go well with Evenetlet. Also note that some libraries, usually with C extensions MapRoute instance. Examples: task_routes = { 'celery.ping': 'default', 'mytasks.add': 'cpu-bound', 'feed.tasks.*': 'feeds', # <-- glob pattern re.compile(r'(image|video)\ threads executing tasks. If you’re doing mostly I/O you can have more processes, but if mostly CPU-bound, try to keep it close to the number of CPUs on your machine. If not set, the number of CPUs/cores

threads executing tasks. If you’re doing mostly I/O you can have more processes, but if mostly CPU-bound, try to keep it close to the number of CPUs on your machine. If not set, the number of CPUs/cores instance. Examples: CELERY_ROUTES = {"celery.ping": "default", "mytasks.add": "cpu-bound", "video.encode": { "queue": "video", "exchange": Examples: >>> CELERY_ROUTES = {"celery.ping": "default", "mytasks.add": "cpu-bound", "video.encode": { "queue": "video",

prefork. However, you need to ensure one task doesn’t block the event loop too long. Generally, CPU-bound operations don’t go well with Eventlet. Also note that some libraries, usually with C extensions MapRoute instance. Examples: task_routes = { 'celery.ping': 'default', 'mytasks.add': 'cpu-bound', 'feed.tasks.*': 'feeds', # <-- glob pattern re.compile(r'(image|video)\ threads executing tasks. If you’re doing mostly I/O you can have more processes, but if mostly CPU-bound, try to keep it close to the number of CPUs on your machine. If not set, the number of CPUs/cores

prefork. However, you need to ensure one task doesn’t block the event loop too long. Generally, CPU-bound operations don’t go well with Eventlet. Also note that some libraries, usually with C extensions MapRoute instance. Examples: task_routes = { 'celery.ping': 'default', 'mytasks.add': 'cpu-bound', 'feed.tasks.*': 'feeds', # <-- glob pattern re.compile(r'(image|video)\ threads executing tasks. If you’re doing mostly I/O you can have more processes, but if mostly CPU-bound, try to keep it close to the number of CPUs on your machine. If not set, the number of CPUs/cores