File size: 15,645 Bytes
8a37e0a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 | # The InvokeAI Download Queue
The DownloadQueueService provides a multithreaded parallel download
queue for arbitrary URLs, with queue prioritization, event handling,
and restart capabilities.
## Simple Example
```
from invokeai.app.services.download import DownloadQueueService, TqdmProgress
download_queue = DownloadQueueService()
for url in ['https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/a-painting-of-a-fire.png?raw=true',
'https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/birdhouse.png?raw=true',
'https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/missing.png',
'https://civitai.com/api/download/models/152309?type=Model&format=SafeTensor',
]:
# urls start downloading as soon as download() is called
download_queue.download(source=url,
dest='/tmp/downloads',
on_progress=TqdmProgress().update
)
download_queue.join() # wait for all downloads to finish
for job in download_queue.list_jobs():
print(job.model_dump_json(exclude_none=True, indent=4),"\n")
```
Output:
```
{
"source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/a-painting-of-a-fire.png?raw=true",
"dest": "/tmp/downloads",
"id": 0,
"priority": 10,
"status": "completed",
"download_path": "/tmp/downloads/a-painting-of-a-fire.png",
"job_started": "2023-12-04T05:34:41.742174",
"job_ended": "2023-12-04T05:34:42.592035",
"bytes": 666734,
"total_bytes": 666734
}
{
"source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/birdhouse.png?raw=true",
"dest": "/tmp/downloads",
"id": 1,
"priority": 10,
"status": "completed",
"download_path": "/tmp/downloads/birdhouse.png",
"job_started": "2023-12-04T05:34:41.741975",
"job_ended": "2023-12-04T05:34:42.652841",
"bytes": 774949,
"total_bytes": 774949
}
{
"source": "https://github.com/invoke-ai/InvokeAI/blob/main/invokeai/assets/missing.png",
"dest": "/tmp/downloads",
"id": 2,
"priority": 10,
"status": "error",
"job_started": "2023-12-04T05:34:41.742079",
"job_ended": "2023-12-04T05:34:42.147625",
"bytes": 0,
"total_bytes": 0,
"error_type": "HTTPError(Not Found)",
"error": "Traceback (most recent call last):\n File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 182, in _download_next_item\n self._do_download(job)\n File \"/home/lstein/Projects/InvokeAI/invokeai/app/services/download/download_default.py\", line 206, in _do_download\n raise HTTPError(resp.reason)\nrequests.exceptions.HTTPError: Not Found\n"
}
{
"source": "https://civitai.com/api/download/models/152309?type=Model&format=SafeTensor",
"dest": "/tmp/downloads",
"id": 3,
"priority": 10,
"status": "completed",
"download_path": "/tmp/downloads/xl_more_art-full_v1.safetensors",
"job_started": "2023-12-04T05:34:42.147645",
"job_ended": "2023-12-04T05:34:43.735990",
"bytes": 719020768,
"total_bytes": 719020768
}
```
## The API
The default download queue is `DownloadQueueService`, an
implementation of ABC `DownloadQueueServiceBase`. It juggles multiple
background download requests and provides facilities for interrogating
and cancelling the requests. Access to a current or past download task
is mediated via `DownloadJob` objects which report the current status
of a job request
### The Queue Object
A default download queue is located in
`ApiDependencies.invoker.services.download_queue`. However, you can
create additional instances if you need to isolate your queue from the
main one.
```
queue = DownloadQueueService(event_bus=events)
```
`DownloadQueueService()` takes three optional arguments:
| **Argument** | **Type** | **Default** | **Description** |
|----------------|-----------------|---------------|-----------------|
| `max_parallel_dl` | int | 5 | Maximum number of simultaneous downloads allowed |
| `event_bus` | EventServiceBase | None | System-wide FastAPI event bus for reporting download events |
| `requests_session` | requests.sessions.Session | None | An alternative requests Session object to use for the download |
`max_parallel_dl` specifies how many download jobs are allowed to run
simultaneously. Each will run in a different thread of execution.
`event_bus` is an EventServiceBase, typically the one created at
InvokeAI startup. If present, download events are periodically emitted
on this bus to allow clients to follow download progress.
`requests_session` is a url library requests Session object. It is
used for testing.
### The Job object
The queue operates on a series of download job objects. These objects
specify the source and destination of the download, and keep track of
the progress of the download.
Two job types are defined. `DownloadJob` and
`MultiFileDownloadJob`. The former is a pydantic object with the
following fields:
| **Field** | **Type** | **Default** | **Description** |
|----------------|-----------------|---------------|-----------------|
| _Fields passed in at job creation time_ |
| `source` | AnyHttpUrl | | Where to download from |
| `dest` | Path | | Where to download to |
| `access_token` | str | | [optional] string containing authentication token for access |
| `on_start` | Callable | | [optional] callback when the download starts |
| `on_progress` | Callable | | [optional] callback called at intervals during download progress |
| `on_complete` | Callable | | [optional] callback called after successful download completion |
| `on_error` | Callable | | [optional] callback called after an error occurs |
| `id` | int | auto assigned | Job ID, an integer >= 0 |
| `priority` | int | 10 | Job priority. Lower priorities run before higher priorities |
| |
| _Fields updated over the course of the download task_
| `status` | DownloadJobStatus| | Status code |
| `download_path` | Path | | Path to the location of the downloaded file |
| `job_started` | float | | Timestamp for when the job started running |
| `job_ended` | float | | Timestamp for when the job completed or errored out |
| `job_sequence` | int | | A counter that is incremented each time a model is dequeued |
| `bytes` | int | 0 | Bytes downloaded so far |
| `total_bytes` | int | 0 | Total size of the file at the remote site |
| `error_type` | str | | String version of the exception that caused an error during download |
| `error` | str | | String version of the traceback associated with an error |
| `cancelled` | bool | False | Set to true if the job was cancelled by the caller|
When you create a job, you can assign it a `priority`. If multiple
jobs are queued, the job with the lowest priority runs first.
Every job has a `source` and a `dest`. `source` is a pydantic.networks AnyHttpUrl object.
The `dest` is a path on the local filesystem that specifies the
destination for the downloaded object. Its semantics are
described below.
When the job is submitted, it is assigned a numeric `id`. The id can
then be used to fetch the job object from the queue.
The `status` field is updated by the queue to indicate where the job
is in its lifecycle. Values are defined in the string enum
`DownloadJobStatus`, a symbol available from
`invokeai.app.services.download_manager`. Possible values are:
| **Value** | **String Value** | ** Description ** |
|--------------|---------------------|-------------------|
| `WAITING` | waiting | Job is on the queue but not yet running|
| `RUNNING` | running | The download is started |
| `COMPLETED` | completed | Job has finished its work without an error |
| `ERROR` | error | Job encountered an error and will not run again|
`job_started` and `job_ended` indicate when the job
was started (using a python timestamp) and when it completed.
In case of an error, the job's status will be set to `DownloadJobStatus.ERROR`, the text of the
Exception that caused the error will be placed in the `error_type`
field and the traceback that led to the error will be in `error`.
A cancelled job will have status `DownloadJobStatus.ERROR` and an
`error_type` field of "DownloadJobCancelledException". In addition,
the job's `cancelled` property will be set to True.
The `MultiFileDownloadJob` is used for diffusers model downloads,
which contain multiple files and directories under a common root:
| **Field** | **Type** | **Default** | **Description** |
|----------------|-----------------|---------------|-----------------|
| _Fields passed in at job creation time_ |
| `download_parts` | Set[DownloadJob]| | Component download jobs |
| `dest` | Path | | Where to download to |
| `on_start` | Callable | | [optional] callback when the download starts |
| `on_progress` | Callable | | [optional] callback called at intervals during download progress |
| `on_complete` | Callable | | [optional] callback called after successful download completion |
| `on_error` | Callable | | [optional] callback called after an error occurs |
| `id` | int | auto assigned | Job ID, an integer >= 0 |
| _Fields updated over the course of the download task_
| `status` | DownloadJobStatus| | Status code |
| `download_path` | Path | | Path to the root of the downloaded files |
| `bytes` | int | 0 | Bytes downloaded so far |
| `total_bytes` | int | 0 | Total size of the file at the remote site |
| `error_type` | str | | String version of the exception that caused an error during download |
| `error` | str | | String version of the traceback associated with an error |
| `cancelled` | bool | False | Set to true if the job was cancelled by the caller|
Note that the MultiFileDownloadJob does not support the `priority`,
`job_started`, `job_ended` or `content_type` attributes. You can get
these from the individual download jobs in `download_parts`.
### Callbacks
Download jobs can be associated with a series of callbacks, each with
the signature `Callable[["DownloadJob"], None]`. The callbacks are assigned
using optional arguments `on_start`, `on_progress`, `on_complete` and
`on_error`. When the corresponding event occurs, the callback wil be
invoked and passed the job. The callback will be run in a `try:`
context in the same thread as the download job. Any exceptions that
occur during execution of the callback will be caught and converted
into a log error message, thereby allowing the download to continue.
#### `TqdmProgress`
The `invokeai.app.services.download.download_default` module defines a
class named `TqdmProgress` which can be used as an `on_progress`
handler to display a completion bar in the console. Use as follows:
```
from invokeai.app.services.download import TqdmProgress
download_queue.download(source='http://some.server.somewhere/some_file',
dest='/tmp/downloads',
on_progress=TqdmProgress().update
)
```
### Events
If the queue was initialized with the InvokeAI event bus (the case
when using `ApiDependencies.invoker.services.download_queue`), then
download events will also be issued on the bus. The events are:
* `download_started` -- This is issued when a job is taken off the
queue and a request is made to the remote server for the URL headers, but before any data
has been downloaded. The event payload will contain the keys `source`
and `download_path`. The latter contains the path that the URL will be
downloaded to.
* `download_progress -- This is issued periodically as the download
runs. The payload contains the keys `source`, `download_path`,
`current_bytes` and `total_bytes`. The latter two fields can be
used to display the percent complete.
* `download_complete` -- This is issued when the download completes
successfully. The payload contains the keys `source`, `download_path`
and `total_bytes`.
* `download_error` -- This is issued when the download stops because
of an error condition. The payload contains the fields `error_type`
and `error`. The former is the text representation of the exception,
and the latter is a traceback showing where the error occurred.
### Job control
To create a job call the queue's `download()` method. You can list all
jobs using `list_jobs()`, fetch a single job by its with
`id_to_job()`, cancel a running job with `cancel_job()`, cancel all
running jobs with `cancel_all_jobs()`, and wait for all jobs to finish
with `join()`.
#### job = queue.download(source, dest, priority, access_token, on_start, on_progress, on_complete, on_cancelled, on_error)
Create a new download job and put it on the queue, returning the
DownloadJob object.
#### multifile_job = queue.multifile_download(parts, dest, access_token, on_start, on_progress, on_complete, on_cancelled, on_error)
This is similar to download(), but instead of taking a single source,
it accepts a `parts` argument consisting of a list of
`RemoteModelFile` objects. Each part corresponds to a URL/Path pair,
where the URL is the location of the remote file, and the Path is the
destination.
`RemoteModelFile` can be imported from `invokeai.backend.model_manager.metadata`, and
consists of a url/path pair. Note that the path *must* be relative.
The method returns a `MultiFileDownloadJob`.
```
from invokeai.backend.model_manager.metadata import RemoteModelFile
remote_file_1 = RemoteModelFile(url='http://www.foo.bar/my/pytorch_model.safetensors'',
path='my_model/textencoder/pytorch_model.safetensors'
)
remote_file_2 = RemoteModelFile(url='http://www.bar.baz/vae.ckpt',
path='my_model/vae/diffusers_model.safetensors'
)
job = queue.multifile_download(parts=[remote_file_1, remote_file_2],
dest='/tmp/downloads',
on_progress=TqdmProgress().update)
queue.wait_for_job(job)
print(f"The files were downloaded to {job.download_path}")
```
#### jobs = queue.list_jobs()
Return a list of all active and inactive `DownloadJob`s.
#### job = queue.id_to_job(id)
Return the job corresponding to given ID.
Return a list of all active and inactive `DownloadJob`s.
#### queue.prune_jobs()
Remove inactive (complete or errored) jobs from the listing returned
by `list_jobs()`.
#### queue.join()
Block until all pending jobs have run to completion or errored out.
|