inkplate 10 doesn't show any image

Hello,
got my Inkplate 10 today, seting all up but it doesn't show any picture.
still have the "welcome to ihnkplate" screen.
The Logs said:
21:23:56.558 > [IMAGE] Downloading image: http://<IP_ADDRESS>:5000/1.png
21:23:56.563 > [  9036][E][WiFiClient.cpp:320] setSocketOption(): fail on -1, errno: 9, "Bad file number"
21:23:56.569 > [MQTT] Sending MQTT State: [homeassistant/sensor/homeplate/battery/state] {"voltage":0.28,"battery":0}
21:23:56.766 > [IMAGE] Download done
21:23:56.766 > [STATUS] Rendering image...
21:23:57.801 > [ 10282][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
21:23:58.827 > [ 11307][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
21:24:02.692 > [IMAGE] Image render ready
21:24:02.692 > [IMAGE] displaying....
21:24:03.703 > [ 16184][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
21:24:03.954 > [IMAGE] displaying done.

The MQTT values have arrived in home assistant. Beside the temperature.
(maye thats the i2cRead error?)
Edit:
i think i have v1.3.1 , maye thats the Problem?

Are you compiling with inkplate10v2? The i2cRead errors point to a problem talking to the IO Expander or the PSU for the ePaper.

Tried it with v2 too. but there i didn't get any connection to the inkplate after flashing:
Wrote 1292496 bytes (759139 compressed) at 0x00010000 in 18.9 seconds (effective 548.0 kbit/s)...
Hash of data verified.

Leaving...
Hard resetting via RTS pin...

Please build project in debug configuration to get more details about an exception.
See https://docs.platformio.org/page/projectconf/build_configurations.html

--- Terminal on COM4 | 115200 8-N-1
--- Available filters and text transformations: colorize, debug, default, direct, esp32_exception_decoder, hexlify, log2file, nocontrol, printable, send_on_enter, time
--- More details at https://bit.ly/pio-monitor-filters
--- Quit: Ctrl+C | Menu: Ctrl+T | Help: Ctrl+T followed by Ctrl+H
Disconnected (ClearCommError failed (PermissionError(13, 'Zugriff verweigert', None, 5)))
Reconnecting to COM4
Please build project in debug configuration to get more details about an exception.
See https://docs.platformio.org/page/projectconf/build_configurations.html

got the "permission denied" entry every couple seconds until i cancel with strg+c
maybe its an issue beceause of an other chip they use since vers.1.3?
i found this issue  with esphome.

The PCAL6414 on the Inkplate10v2 is in the library that is used by homeplate
https://github.com/SolderedElectronics/Inkplate-Arduino-library/blob/7.0.0/src/include/Pcal.cpp
Could you post your config.h?
And for the COM4/Zugriff verweigert: Is the Inkplate visible after flashing in the device manager on the same COM port?

the Serial Port is another wierd thing if i'm flashing with V2 settings (doesn't have this behavior with "normal" flash) i attached a little GIF to visualize this.
My config.h looks like:
#pragma once

// WiFi SSID
#define WIFI_SSID "Schneckenhaus"
// WiFi password
#define WIFI_PASSWORD "mypassword"

// hostname
// NOTE: if using multiple homeplate devices, you MUST make the hostname unique
#define HOSTNAME "homeplate"

// How long to sleep between image refreshes
#define TIME_TO_SLEEP_MIN 1

// Static IP information
// If unset uses DHCP, but updates may be slower, set to use a Static IP
// #define STATIC_IP "<IP_ADDRESS>"
// #define STATIC_SUBNET "<IP_ADDRESS>"
// #define STATIC_GATEWAY "<IP_ADDRESS>"
// #define STATIC_DNS "<IP_ADDRESS>"

// NTP Time server to set RTC
#define NTP_SERVER "<IP_ADDRESS>"

// How often to re-sync the clock to NTP
#define NTP_SYNC_INTERVAL (24*60)/TIME_TO_SLEEP_MIN // ~ once a day when updating every TIME_TO_SLEEP_MIN minutes

// URL of PNG image to display
#define IMAGE_URL "http://<IP_ADDRESS>:5000/1.png"

// WiFi QR Code
#define QR_WIFI_NAME "Schneckenhaus"
#define QR_WIFI_PASSWORD "Guest WiFi Password"

// MQTT Broker
#define MQTT_HOST "<IP_ADDRESS>"
#define MQTT_PORT 1883
// Set MQTT_USER & MQTT_PASSWORD if needed
#define MQTT_USER "Huggenknubbel"
#define MQTT_PASSWORD "mypassword"
// Customize node id and device name if needed
//#define MQTT_NODE_ID "homeplate"	// defaults to HOSTNAME
//#define MQTT_DEVICE_NAME "HomePlate"	// defaults to "HomePlate"

// Disables touchpads if they are overly sensitive and result in phantom touch events
// Touchpads are not supported on the Inkplate10v2. This must be false if ARDUINO_INKPLATE10V2 is set
#define TOUCHPAD_ENABLE false

// Timezone
// see timezone_config.h for options
#define TIMEZONE_UTC

// If your Inkplate doesn't have external (or second) MCP I/O expander, you should uncomment next line,
// otherwise your code could hang out when you send code to your Inkplate.
// You can easily check if your Inkplate has second MCP by turning it over and
// if there is missing chip near place where "MCP23017-2" is written, but if there is
// chip soldered, you don't have to uncomment line and use external MCP I/O expander
#define ONE_MCP_MODE

// Displays the time from the RTC whenever a new image is loaded
#define DISPLAY_LAST_UPDATE_TIME false

// keep this to signal the program has a valid config file
#define CONFIG_H

https://github.com/lanrat/homeplate/assets/15655280/fdffc0bc-e9f4-4f9f-9bfe-0c7c1f5d3301

Could you try to build debug, once with -DARDUINO_INKPLATE10 and once with -DARDUINO_INKPLATE10V2
(https://github.com/lanrat/homeplate/blob/4d50ca996f47e8e20787bbf60eeef125b4ea2d14/platformio.ini#L64C3-L64C23)

Debug Output -DARDUINO_INKPLATE10
Wrote 1361056 bytes (798098 compressed) at 0x00010000 in 19.8 seconds (effective 551.2 kbit/s)...
Hash of data verified.

Leaving...
Hard resetting via RTS pin...
--- Terminal on COM4 | 115200 8-N-1
--- Available filters and text transformations: colorize, debug, default, direct, esp32_exception_decoder, hexlify, log2file, nocontrol, printable, send_on_enter, time
--- More details at https://bit.ly/pio-monitor-filters
--- Quit: Ctrl+C | Menu: Ctrl+T | Help: Ctrl+T followed by Ctrl+H
13:15:07.590 > ets Jul 29 2019 12:21:46
13:15:07.590 >
13:15:07.590 > rst:0x1 (POWERON_RESET),boot:0x12 (SPI_FAST_FLASH_BOOT)
13:15:07.590 > configsip: 0, SPIWP:0xee
13:15:07.590 > clk_drv:0x00,q_drv:0x00,d_drv:0x00,cs0_drv:0x00,hd_drv:0x00,wp_drv:0x00
13:15:07.590 > mode:DIO, clock div:2
13:15:07.590 > load:0x3fff0030,len:1184
13:15:07.590 > load:0x40078000,len:13232
13:15:07.590 > load:0x40080400,len:3028
13:15:07.590 > entry 0x400805e4
13:15:07.590 > [     8][D][esp32-hal-cpu.c:244] setCpuFrequencyMhz(): PLL: 480 / 2 = 240 Mhz, APB: 80000000 Hz
13:15:07.590 > [   457][I][esp32-hal-psram.c:96] psramInit(): PSRAM enabled
13:15:07.590 >
13:15:07.590 >
13:15:07.590 > [SETUP] starting, version(Apr 30 2024, 13:14:21) boot: 0
13:15:07.590 > ESP Chip information:
13:15:07.590 > This is esp32 chip with 2 CPU core(s), WiFi/BT/BLE, silicon revision 3, 4MB external flash
13:15:07.590 > Minimum free heap size: 4414999 bytes
13:15:07.590 > Heap summary for capabilities 0x00000C06:
13:15:07.590 >   Totals:
13:15:07.590 >     free 0 allocated 0 min_free 0 largest_free_block 0
13:15:07.590 > [ACTIVITY] startActivity(1) put into queue
13:15:07.590 > [   505][I][esp32-hal-i2c.c:75] i2cInit(): Initialising I2C Master: sda=21 scl=22 freq=100000
13:15:07.590 > [STATUS] Boot 1 normal
13:15:07.909 > [  1642][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:08.935 > [  2668][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:08.944 > [  2676][D][esp32-hal-adc.c:194] __analogReadMilliVolts(): eFuse Vref: Supported
13:15:08.949 > [  2676][I][esp32-hal-adc.c:235] __analogReadMilliVolts(): ADC1: Characterized using eFuse Vref: 1093
13:15:08.957 >
13:15:08.957 > [SETUP] Battery: 0% (0.28v)
13:15:10.005 > [  3738][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:10.011 > [SETUP] starting button task
13:15:10.014 > [SETUP] starting time task
13:15:10.014 > [TIME] local time (3) 00:00:03 Thursday, January 01 1970
13:15:10.019 > [SETUP] starting WiFi task
13:15:10.022 > [WIFI] Connecting...
13:15:10.025 > [SETUP] starting OTA task
13:15:10.028 > [SETUP] starting MQTT task
13:15:10.031 > [MQTT] starting MQTT
13:15:10.031 > [SETUP] starting sleep task
13:15:10.034 > [SETUP] starting activities task
13:15:10.037 > [ACTIVITY] starting activity: 1
13:15:10.040 > [  3767][D][WiFiGeneric.cpp:1039] _eventCallback(): Arduino Event: 0 - WIFI_READY
13:15:10.103 > [  3834][D][WiFiGeneric.cpp:1039] _eventCallback(): Arduino Event: 2 - STA_START
13:15:10.170 > [  3902][D][WiFiGeneric.cpp:1039] _eventCallback(): Arduino Event: 4 - STA_CONNECTED
13:15:10.175 > [WIFI] Connected to AP successfully!
13:15:10.200 > [  3932][D][WiFiGeneric.cpp:1039] _eventCallback(): Arduino Event: 7 - STA_GOT_IP
13:15:10.205 > [  3932][D][WiFiGeneric.cpp:1102] _eventCallback(): STA IP: <IP_ADDRESS>, MASK: <IP_ADDRESS>, GW: <IP_ADDRESS>
13:15:10.217 > [WIFI] IP address: <IP_ADDRESS>
13:15:10.220 > [STATUS] WiFi connected
13:15:10.235 > [WIFI] Connected: <IP_ADDRESS>
13:15:10.269 > [TIME] Syncing RTC to NTP
13:15:10.282 > [MQTT] Connecting to MQTT...
13:15:10.282 > [  4014][I][AsyncMqttClient.cpp:686] connect(): CONNECTING
13:15:10.287 > [SLEEP] delaying sleep for 20 seconds
13:15:10.293 > [STATUS] Downloading image...
13:15:10.293 > [  4020][I][ArduinoOTA.cpp:141] begin(): OTA server at: homeplate.local:3232
13:15:10.301 > [OTA] OTA ready
13:15:10.301 > [  4035][I][AsyncMqttClient.cpp:188] _onConnect(): TCP conn, MQTT CONNECT
13:15:10.310 > [  4036][I][AsyncMqttClient.cpp:369] _addFront(): new front #1
13:15:10.316 > [  4038][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #1: 51/51
13:15:10.321 > [  4043][I][AsyncMqttClient.cpp:424] _handleQueue(): p #1 rel
13:15:10.327 > [  4048][I][AsyncMqttClient.cpp:244] _onAck(): ack 51
13:15:10.338 > [  4069][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:10.341 > [  4069][I][AsyncMqttClient.cpp:262] _onData(): rcv CONNACK
13:15:10.346 > [  4070][I][AsyncMqttClient.cpp:503] _onConnAck(): CONNACK
13:15:10.351 > [MQTT] Connected to MQTT.
13:15:10.353 > [MQTT] Session present: 0
13:15:10.356 > [  4084][I][AsyncMqttClient.cpp:722] subscribe(): SUBSCRIBE
13:15:10.362 > [  4085][I][AsyncMqttClient.cpp:382] _addBack(): new back #8
13:15:10.367 > [  4091][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #8: 39/39
13:15:10.373 > [MQTT] Subscribing at QoS 2, packetId: 1
13:15:10.376 > [MQTT] Sending MQTT Config
13:15:10.379 > [  4107][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:10.384 > [  4108][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:10.391 > [  4112][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:10.396 > [  4117][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:10.401 > [MQTT] Connected
13:15:10.402 > [  4123][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:10.407 > [  4132][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:10.412 > [  4134][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:10.417 > [  4139][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:10.423 > [  4145][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:10.426 > [  4149][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:10.431 > [  4155][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:10.437 > [  4160][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:10.442 > [  4165][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:10.448 > [  4170][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:10.453 > [  4176][I][AsyncMqttClient.cpp:244] _onAck(): ack 39
13:15:10.456 > [  4180][I][AsyncMqttClient.cpp:249] _onData(): data rcv (5)
13:15:10.462 > [  4185][I][AsyncMqttClient.cpp:271] _onData(): rcv SUBACK
13:15:10.467 > [  4191][I][AsyncMqttClient.cpp:524] _onSubAck(): SUBACK
13:15:10.473 > [  4196][I][AsyncMqttClient.cpp:529] _onSubAck(): SUB released
13:15:10.479 > [MQTT] Subscribe acknowledged: packetId: 1 qos: 2
13:15:10.484 > [  4211][I][AsyncMqttClient.cpp:424] _handleQueue(): p #8 rel
13:15:10.490 > [  4214][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 528/528
13:15:10.499 > [  4231][I][AsyncMqttClient.cpp:244] _onAck(): ack 528
13:15:10.502 > [  4231][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:10.507 > [  4231][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:10.513 > [  4236][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:10.534 > [  4266][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:10.537 > [  4268][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 498/498
13:15:10.547 > [  4279][I][AsyncMqttClient.cpp:244] _onAck(): ack 498
13:15:10.550 > [  4280][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:10.555 > [  4280][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:10.561 > [  4284][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:11.036 > [  4767][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:11.038 > [  4769][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 472/472
13:15:11.046 > [  4778][I][AsyncMqttClient.cpp:244] _onAck(): ack 472
13:15:11.049 > [  4778][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:11.054 > [  4779][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:11.060 > [  4783][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:11.246 > [  4978][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:11.294 > [TIME] NTP Sync failed
13:15:11.537 > [  5269][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:11.540 > [  5271][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 472/472
13:15:11.550 > [  5282][I][AsyncMqttClient.cpp:244] _onAck(): ack 472
13:15:11.553 > [  5283][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:11.559 > [  5283][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:11.564 > [  5287][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:12.032 > [MQTT] sending status update
13:15:12.032 > [MQTT] Sending MQTT State: [homeassistant/sensor/homeplate/boot/state] {"boot":1,"activity_count":1,"boot_reason":"normal"}
13:15:12.044 > [  5771][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:12.049 > [  5776][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:12.055 > [  5779][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 536/536
13:15:12.061 > [  5784][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:12.067 > [  5788][I][AsyncMqttClient.cpp:244] _onAck(): ack 536
13:15:12.072 > [  5794][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:12.078 > [  5799][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:12.083 > [  5805][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:12.258 > [MQTT] Sending MQTT State: [homeassistant/sensor/homeplate/wifi_signal/state] {"signal":-54}
13:15:12.263 > [  5989][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:12.269 > [  5991][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:12.274 > [  5996][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:12.280 > [  6004][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:12.284 > [  6005][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 464/464
13:15:12.290 > [  6014][I][AsyncMqttClient.cpp:244] _onAck(): ack 464
13:15:12.295 > [  6019][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:12.301 > [  6027][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:12.307 > [  6030][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:12.546 > [  6277][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:12.549 > [  6280][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 576/576
13:15:12.568 > [  6300][I][AsyncMqttClient.cpp:244] _onAck(): ack 576
13:15:12.570 > [  6300][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:12.576 > [  6300][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:12.581 > [  6305][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:13.006 > [INPUT] Starting input monitoring...
13:15:13.045 > [  6778][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:13.048 > [  6780][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 99/99
13:15:13.060 > [  6793][I][AsyncMqttClient.cpp:244] _onAck(): ack 99
13:15:13.063 > [  6793][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:13.069 > [  6793][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:13.074 > [  6797][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:13.312 > [  7043][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:13.547 > [  7279][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:13.550 > [  7282][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 68/68
13:15:13.562 > [  7294][I][AsyncMqttClient.cpp:244] _onAck(): ack 68
13:15:13.565 > [  7294][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:13.571 > [  7295][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:13.577 > [  7299][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:14.048 > [  7780][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:14.336 > [  8068][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:14.342 > [IMAGE] Downloading image: http://<IP_ADDRESS>:5000/1.png
13:15:14.347 > [  8071][W][HTTPClient.cpp:873] getStream(): getStream: not connected
13:15:14.353 > [  8075][E][WiFiClient.cpp:320] setSocketOption(): fail on -1, errno: 9, "Bad file number"
13:15:14.361 > [  8083][W][HTTPClient.cpp:873] getStream(): getStream: not connected
13:15:14.366 > [  8089][D][HTTPClient.cpp:303] beginInternal(): protocol: http, host: <IP_ADDRESS> port: 5000 url: /1.png
13:15:14.375 > [  8098][D][HTTPClient.cpp:598] sendRequest(): request type: 'GET' redirCount: 0
13:15:14.383 >
13:15:14.392 > [  8125][D][HTTPClient.cpp:1170] connect():  connected to <IP_ADDRESS>:5000
13:15:14.421 > [  8152][D][HTTPClient.cpp:1321] handleHeaderResponse(): code: 200
13:15:14.426 > [  8152][D][HTTPClient.cpp:1324] handleHeaderResponse(): size: 56845
13:15:14.432 > [  8153][D][HTTPClient.cpp:642] sendRequest(): sendRequest code=200
13:15:14.437 >
13:15:14.682 > [  8415][D][HTTPClient.cpp:393] disconnect(): tcp keep open for reuse
13:15:14.688 > [IMAGE] Download done
13:15:14.688 > [STATUS] Rendering image...
13:15:15.348 > [  9080][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:15.353 > [MQTT] Got invalid temperature (-1), not sending status to mqtt
13:15:16.382 > [ 10114][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:17.407 > [ 11139][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:17.418 > [MQTT] Sending MQTT State: [homeassistant/sensor/homeplate/battery/state] {"voltage":0.28,"battery":0}
13:15:17.426 > [ 11150][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:17.432 > [ 11153][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:17.435 > [ 11166][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 78/78
13:15:17.450 > [ 11180][I][AsyncMqttClient.cpp:244] _onAck(): ack 78
13:15:17.453 > [ 11182][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:17.459 > [ 11184][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:17.464 > [ 11187][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:17.552 > [ 11283][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:21.463 > [IMAGE] Image render ready
13:15:21.463 > [IMAGE] displaying....
13:15:22.474 > [ 16206][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:22.725 > [IMAGE] displaying done.
13:15:23.800 > [ 17532][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:24.432 > [MQTT] sending status update
13:15:24.432 > [MQTT] Sending MQTT State: [homeassistant/sensor/homeplate/boot/state] {"boot":1,"activity_count":1,"boot_reason":"normal"}
13:15:24.444 > [ 18176][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:24.449 > [ 18176][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:24.455 > [ 18180][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 99/99
13:15:24.460 > [ 18191][I][AsyncMqttClient.cpp:244] _onAck(): ack 99
13:15:24.466 > [ 18191][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:24.472 > [ 18193][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:24.476 > [ 18199][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:24.552 > [ 18285][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:24.651 > [MQTT] Sending MQTT State: [homeassistant/sensor/homeplate/wifi_signal/state] {"signal":-54}
13:15:24.658 > [ 18384][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:24.663 > [ 18386][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:24.668 > [ 18394][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 68/68
13:15:24.674 > [ 18403][I][AsyncMqttClient.cpp:244] _onAck(): ack 68
13:15:24.679 > [ 18403][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:24.684 > [ 18407][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:24.691 > [ 18413][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:25.055 > [ 18787][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:25.676 > [ 19408][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:25.681 > [MQTT] Got invalid temperature (-1), not sending status to mqtt
13:15:25.690 > [MQTT] Sending MQTT State: [homeassistant/sensor/homeplate/battery/state] {"voltage":0.28,"battery":0}
13:15:25.698 > [ 19422][I][AsyncMqttClient.cpp:740] publish(): PUBLISH
13:15:25.704 > [ 19425][I][AsyncMqttClient.cpp:382] _addBack(): new back #3
13:15:25.706 > [ 19433][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #3: 78/78
13:15:25.715 > [ 19439][I][AsyncMqttClient.cpp:244] _onAck(): ack 78
13:15:25.718 > [ 19441][I][AsyncMqttClient.cpp:249] _onData(): data rcv (4)
13:15:25.722 > [ 19446][I][AsyncMqttClient.cpp:287] _onData(): rcv PUBACK
13:15:25.728 > [ 19451][I][AsyncMqttClient.cpp:635] _onPubAck(): PUB released
13:15:26.056 > [ 19789][I][AsyncMqttClient.cpp:424] _handleQueue(): p #3 rel
13:15:31.031 > [ACTIVITY] startActivity(0) put into queue
13:15:31.035 > [ACTIVITY] starting activity: 0
13:15:31.038 > [SLEEP] prepping for sleep
13:15:32.067 > [ 25799][E][Wire.cpp:513] requestFrom(): i2cRead returned Error 263
13:15:32.073 > [MQTT] Stopping and disconnecting...
13:15:32.075 > [ 25801][I][AsyncMqttClient.cpp:709] disconnect(): DISCONNECT (f:0)
13:15:32.081 > [ 25804][I][AsyncMqttClient.cpp:382] _addBack(): new back #14
13:15:32.087 > [ 25812][I][AsyncMqttClient.cpp:414] _handleQueue(): snd #14: 2/2
13:15:32.092 > [ 25815][I][AsyncMqttClient.cpp:424] _handleQueue(): p #14 rel
13:15:32.097 > [ 25821][I][AsyncMqttClient.cpp:438] _handleQueue(): snd DISCONN, disconnecting
13:15:32.105 > [WIFI] Stopping and disconnecting...[ 25817][I][AsyncMqttClient.cpp:244] _onAck(): ack 2
13:15:32.111 > [ 25838][I][AsyncMqttClient.cpp:224] _onDisconnect(): TCP disconn
13:15:32.119 >
13:15:32.119 > [MQTT] Disconnected from MQTT.
13:15:32.122 > [ 25854][D][WiFiGeneric.cpp:1039] _eventCallback(): Arduino Event: 5 - STA_DISCONNECTED
13:15:32.128 > [ 25855][W][WiFiGeneric.cpp:1061] _eventCallback(): Reason: 8 - ASSOC_LEAVE
13:15:32.137 > [WIFI] Disconnected from WiFi access point
13:15:32.139 > [WIFI] WiFi lost connection. Reason: 8
13:15:32.142 > [ 25869][D][WiFiGeneric.cpp:1039] _eventCallback(): Arduino Event: 3 - STA_STOP
13:15:32.155 > [SLEEP] entering sleep for 1200 seconds (20 min)

Debug Output with -DARDUINO_INKPLATE10V2 doesn't change very much:
Wrote 1360432 bytes (798163 compressed) at 0x00010000 in 19.8 seconds (effective 548.8 kbit/s)...
Hash of data verified.

Leaving...
Hard resetting via RTS pin...
--- Terminal on COM4 | 115200 8-N-1
--- Available filters and text transformations: colorize, debug, default, direct, esp32_exception_decoder, hexlify, log2file, nocontrol, printable, send_on_enter, time
--- More details at https://bit.ly/pio-monitor-filters
--- Quit: Ctrl+C | Menu: Ctrl+T | Help: Ctrl+T followed by Ctrl+H
Disconnected (ClearCommError failed (PermissionError(13, 'Zugriff verweigert', None, 5)))
Reconnecting to COM4     Connected!

Could you try a demo project from the Inkplate Library? As the thermistor is in the PMIC (TPS65186) of the eInk and this is written via I2C during the bootup I suspect there's something wrong talking to that one in particular.
I still have no clue why V2 behaves that different :-(

i tried some examples and they didn't run either.
write an email to the support. Maybe my device is broken?

Yes, sounds like sth. is wrong with the device unfortunately.

I'm found the issue. and i'm speachless about it.
Today the Battery which i ordered arrived and i connected  it. What i should say? It works as expected.
Wow.
Thanks @elratt0r for you help anyway!


running the analysis on a file error

can you please help? i couldnt find the reason of the error

Which pyright version you are using? I'd assume there are some syntax errors or new syntax that pyright cannot parse.


Add missing value iterator in dynamic private_dns_zone_group block

It's done correctly here, so bringing this module in line with that

thanks for your contribution @bobalong79!


feat(tutorial): complete step 1

Closes #
{{short description}}
Changelog
New

{{new thing}}

Changed

{{change thing}}

Removed

{{removed thing}}

I'm not seeing any styles, possibly because npm install was used instead of yarn


Automatically recover from late upgrade for a fork

Description
When a user fails to upgrade prior to a fork, they may import blocks after the fork epoch that are from the old milestone. After Teku is updated, it then fails to start because of those blocks.  Teku should delete the invalid blocks and resume syncing from either the latest finalized state or the last valid block.

Worth noting that in prune mode we won't be able to recover if the invalid blocks go past the finalized checkpoint because we don't have a state for that. We probably shouldn't allow reverting a finalized block even in archive mode so I think this will boil down to detecting that blocks in the database are invalid and effectively deleting the hot database so that we start fresh from the finalized block. It won't be that simple but that's the effect we'll want.

We now have a delete-hot-blocks debug db subcommand which will recover.    It maybe worth only "fixing" the db in this case if an extra option is specified (e.g --data-storage-recovery-enabled) for extra safety.

Experience with Bellatrix suggests that tool is enough - more and more clients are moving to a model that doesn't automatically recover anyway. Closing this.


Endless buffering after temporary network outage

Have you read the FAQ and checked for duplicate open issues?
Yes
What version of Shaka Player are you using?
v4.3.5-uncompiled
Can you reproduce the issue with our latest release version?
Yes
Can you reproduce the issue with the latest code from main?
Yes
Are you using the demo app or your own custom app?
Demo app
If custom app, can you reproduce the issue using our demo app?
Yes
What browser and OS are you using?
Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/<IP_ADDRESS> Safari/537.36
For embedded devices (smart TVs, etc.), what model and firmware version are you using?
n/a
What are the manifest and license server URIs?

https://shaka-player-demo.appspot.com/demo/#audiolang=en-GB;textlang=en-GB;uilang=en-GB;asset=https://dash.akamaized.net/akamai/bbb_30fps/bbb_30fps.mpd;panel=CUSTOM CONTENT;build=uncompiled
What configuration are you using?  What is the output of player.getConfiguration()?

'{"drm":{"retryParameters":{"maxAttempts":2,"baseDelay":1000,"backoffFactor":2,"fuzzFactor":0.5,"timeout":30000,"stallTimeout":5000,"connectionTimeout":10000},"servers":{},"clearKeys":{},"advanced":{"com.widevine.alpha":{"distinctiveIdentifierRequired":false,"persistentStateRequired":false,"videoRobustness":"","audioRobustness":"","sessionType":"","serverCertificate":{},"serverCertificateUri":"","individualizationServer":""},"com.microsoft.playready":{"distinctiveIdentifierRequired":false,"persistentStateRequired":false,"videoRobustness":"","audioRobustness":"","sessionType":"","serverCertificate":{},"serverCertificateUri":"","individualizationServer":""},"com.apple.fps":{"distinctiveIdentifierRequired":false,"persistentStateRequired":false,"videoRobustness":"","audioRobustness":"","sessionType":"","serverCertificate":{},"serverCertificateUri":"","individualizationServer":""},"com.adobe.primetime":{"distinctiveIdentifierRequired":false,"persistentStateRequired":false,"videoRobustness":"","audioRobustness":"","sessionType":"","serverCertificate":{},"serverCertificateUri":"","individualizationServer":""},"org.w3.clearkey":{"distinctiveIdentifierRequired":false,"persistentStateRequired":false,"videoRobustness":"","audioRobustness":"","sessionType":"","serverCertificate":{},"serverCertificateUri":"","individualizationServer":""}},"delayLicenseRequestUntilPlayed":false,"logLicenseExchange":false,"updateExpirationTime":1,"preferredKeySystems":[],"keySystemsMapping":{},"parseInbandPsshEnabled":false},"manifest":{"retryParameters":{"maxAttempts":2,"baseDelay":1000,"backoffFactor":2,"fuzzFactor":0.5,"timeout":30000,"stallTimeout":5000,"connectionTimeout":10000},"availabilityWindowOverride":null,"disableAudio":false,"disableVideo":false,"disableText":false,"disableThumbnails":false,"defaultPresentationDelay":0,"segmentRelativeVttTiming":false,"dash":{"clockSyncUri":"https://shaka-player-demo.appspot.com/time.txt","ignoreDrmInfo":false,"disableXlinkProcessing":false,"xlinkFailGracefully":false,"ignoreMinBufferTime":false,"autoCorrectDrift":true,"initialSegmentLimit":1000,"ignoreSuggestedPresentationDelay":false,"ignoreEmptyAdaptationSet":false,"ignoreMaxSegmentDuration":false,"keySystemsByURI":{"urn:uuid:1077efec-c0b2-4d02-ace3-3c1e52e2fb4b":"org.w3.clearkey","urn:uuid:e2719d58-a985-b3c9-781a-b030af78d30e":"org.w3.clearkey","urn:uuid:edef8ba9-79d6-4ace-a3c8-27dcd51d21ed":"com.widevine.alpha","urn:uuid:9a04f079-9840-4286-ab92-e65be0885f95":"com.microsoft.playready","urn:uuid:79f0049a-4098-8642-ab92-e65be0885f95":"com.microsoft.playready","urn:uuid:f239e769-efa3-4850-9c16-a903c6932efb":"com.adobe.primetime"},"sequenceMode":false},"hls":{"ignoreTextStreamFailures":false,"ignoreImageStreamFailures":false,"defaultAudioCodec":"mp4a.40.2","defaultVideoCodec":"avc1.42E01E","ignoreManifestProgramDateTime":false,"mediaPlaylistFullMimeType":"video/mp2t; codecs=\\"avc1.42E01E, mp4a.40.2\\"","useSafariBehaviorForLive":true,"liveSegmentsDelay":3}},"streaming":{"retryParameters":{"maxAttempts":2,"baseDelay":1000,"backoffFactor":2,"fuzzFactor":0.5,"timeout":30000,"stallTimeout":5000,"connectionTimeout":10000},"rebufferingGoal":2,"bufferingGoal":10,"bufferBehind":30,"ignoreTextStreamFailures":false,"alwaysStreamText":false,"startAtSegmentBoundary":false,"gapDetectionThreshold":0.5,"durationBackoff":1,"forceTransmux":false,"safeSeekOffset":5,"stallEnabled":true,"stallThreshold":1,"stallSkip":0.1,"useNativeHlsOnSafari":true,"inaccurateManifestTolerance":2,"lowLatencyMode":false,"autoLowLatencyMode":false,"forceHTTPS":false,"preferNativeHls":false,"updateIntervalSeconds":1,"dispatchAllEmsgBoxes":false,"observeQualityChanges":false,"maxDisabledTime":30,"parsePrftBox":false},"mediaSource":{"sourceBufferExtraFeatures":""},"offline":{"usePersistentLicense":true,"numberOfParallelDownloads":5},"abr":{"enabled":true,"useNetworkInformation":true,"defaultBandwidthEstimate":1000000,"switchInterval":8,"bandwidthUpgradeTarget":0.85,"bandwidthDowngradeTarget":0.95,"restrictions":{"minWidth":0,"maxWidth":null,"minHeight":0,"maxHeight":null,"minPixels":0,"maxPixels":null,"minFrameRate":0,"maxFrameRate":null,"minBandwidth":0,"maxBandwidth":null},"advanced":{"minTotalBytes":128000,"minBytes":16000,"fastHalfLife":2,"slowHalfLife":5},"restrictToElementSize":false,"restrictToScreenSize":false,"ignoreDevicePixelRatio":false},"autoShowText":3,"preferredAudioLanguage":"en-GB","preferredTextLanguage":"en-GB","preferredVariantRole":"","preferredTextRole":"","preferredAudioChannelCount":2,"preferredVideoCodecs":[],"preferredAudioCodecs":[],"preferForcedSubs":false,"preferredDecodingAttributes":[],"restrictions":{"minWidth":0,"maxWidth":null,"minHeight":0,"maxHeight":null,"minPixels":0,"maxPixels":null,"minFrameRate":0,"maxFrameRate":null,"minBandwidth":0,"maxBandwidth":null},"playRangeStart":0,"playRangeEnd":null,"cmcd":{"enabled":false,"sessionId":"","contentId":"","useHeaders":false},"lcevc":{"enabled":false,"dynamicPerformanceScaling":true,"logLevel":0,"drawLogo":false}}'
What did you do?

Follow link above
Open developer tools and switch to network tab
Start playback
From the network tab select a request and block associated domain
Wait
Unblock request domain as soon as the video start buffering (buffering animation visible)
You should see segment download resuming (HTTP 200) in the network tab.

What did you expect to happen?
Playback resumes after domain is unblocked
What actually happened?

Video remains buffering forever although new segments have been downloaded

The PR is going to introduce some changes and I’d like to discuss them here first as per the contribution guidelines.
When trying to upgrade from Shaka 3 to Shaka 4 we noticed couple of issues related the newly added feature in https://github.com/shaka-project/shaka-player/pull/4189. The two main issues we have are documented here and in #5055.
Furthermore, we could identify following issues with the current implementation:

When a stream fails due to network error we currently only disable the active variant. However, a stream may be present in multiple variants. Thus, to properly disableba stream we should disable all variants containing it.
Also, we think it makes sense to disable a stream only if we have an alternative stream we can switch to. This point is not considered in the current implementation.
When a network error is successfully recovered no error is forwarded to the application. We think the a recoverable network error should be sent in case we recovered (This is of interest for player analytics purposes)
The (active) check-variants-timer is reseted each time a variant is disabled (https://github.com/shaka-project/shaka-player/blob/v4.3.5/lib/player.js#L6105). This may cause a previous stream to be disabled much longer than configured time.

Finally, should a temporarily disabled variant be selectable (via selectVariantTrack()) or should it be removed from the variant list? I’m not sure about the best strategy but imo temporarily disabled variant should remain selectable.
I’d be happy to get some feedback.


[Usage]: Struggling to get fp8 inference working correctly on 8xL40s

Your current environment
Collecting environment information...
PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.29.5
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-187-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA L40S
GPU 1: NVIDIA L40S
GPU 2: NVIDIA L40S
GPU 3: NVIDIA L40S
GPU 4: NVIDIA L40S
GPU 5: NVIDIA L40S
GPU 6: NVIDIA L40S
GPU 7: NVIDIA L40S

Nvidia driver version: 550.90.07
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      52 bits physical, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             96
On-line CPU(s) list:                0-95
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 9474F 48-Core Processor
CPU family:                         25
Model:                              17
Thread(s) per core:                 1
Core(s) per socket:                 48
Socket(s):                          2
Stepping:                           1
Frequency boost:                    enabled
CPU max MHz:                        3600.0000
CPU min MHz:                        1500.0000
BogoMIPS:                           7199.71
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid overflow_recov succor smca flush_l1d
Virtualization:                     AMD-V
L1d cache:                          3 MiB (96 instances)
L1i cache:                          3 MiB (96 instances)
L2 cache:                           96 MiB (96 instances)
L3 cache:                           512 MiB (16 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-47
NUMA node1 CPU(s):                  48-95
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] transformers==4.41.2
[pip3] triton==2.3.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.0.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0	GPU1	GPU2	GPU3	GPU4	GPU5	GPU6	GPU7	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	PIX	PXB	PXB	SYS	SYS	SYS	SYS	0-47	0		N/A
GPU1	PIX	 X 	PXB	PXB	SYS	SYS	SYS	SYS	0-47	0		N/A
GPU2	PXB	PXB	 X 	PIX	SYS	SYS	SYS	SYS	0-47	0		N/A
GPU3	PXB	PXB	PIX	 X 	SYS	SYS	SYS	SYS	0-47	0		N/A
GPU4	SYS	SYS	SYS	SYS	 X 	PIX	PXB	PXB	48-95	1		N/A
GPU5	SYS	SYS	SYS	SYS	PIX	 X 	PXB	PXB	48-95	1		N/A
GPU6	SYS	SYS	SYS	SYS	PXB	PXB	 X 	PIX	48-95	1		N/A
GPU7	SYS	SYS	SYS	SYS	PXB	PXB	PIX	 X 	48-95	1		N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

How would you like to use vllm
I want to run inference of a fine tuned version of llama 3 70B Instruct that I trained, but I used the same quantization code as neuralmagic/Meta-Llama-3-70B-Instruct-FP8. My exact code was:
import json
import random
import os
from transformers import AutoTokenizer
from huggingface_hub import HfApi
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig

# File paths and configuration
jsonl_file = "/vllm-workspace/openAIChatMessagesFormat.jsonl"
pretrained_model_dir = "me/myModelPath"
quantized_model_dir = "me/myModelPath_fp8"

# Initialize Hugging Face API
api = HfApi()

# Create private repository
api.create_repo(
    repo_id=quantized_model_dir,
    private=True,
    exist_ok=True
)

# Load tokenizer and prepare data
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token

with open(jsonl_file, 'r') as file:
    data = [json.loads(line) for line in file]

selected_data = random.sample(data, min(200, len(data)))
examples = [tokenizer.apply_chat_template(item, tokenize=False) for item in selected_data]
examples = tokenizer(examples, padding=True, truncation=False, return_tensors="pt").to("cuda")

quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="static")

model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
model.quantize(examples)

# Save quantized model locally
local_save_dir = "./quantized_model"
model.save_quantized(local_save_dir)

I was going to fp8 quantize the kv cache as well (and I did) but I was getting:
Cannot use FlashAttention-2 backend for FP8 KV cache and it was falling back to Xformers for inf which I thought was the issue so I re-quantized using the above code.
I launch inference with:
python3 -m vllm.entrypoints.openai.api_server \
  --model ./quantized_model \
  --served-model-name me/myModel_fp8 \
  --api-key token-abc123 \
  --max-model-len 8192 \
  --tensor-parallel-size 8 \
  --distributed-executor-backend ray \
  --worker-use-ray \
  --gpu-memory-utilization 0.95 \
  2>&1 | tee vllm_openai_fp8quantization_log.txt

The logs look like this up through the uvicorn server being up:
INFO 07-06 19:39:18 api_server.py:177] vLLM API server version 0.5.0.post1
INFO 07-06 19:39:18 api_server.py:178] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allo>2024-07-06 19:39:19,842 WARNING utils.py:580 -- Detecting docker specified CPUs. In previous versions of Ray, CPU detection in containers was incorrect. Please ensure that Ray ha>2024-07-06 19:39:19,843 WARNING utils.py:592 -- Ray currently does not support initializing Ray with fractional cpus. Your num_cpus will be truncated from 92.16 to 92.
2024-07-06 19:39:20,999 INFO worker.py:1753 -- Started a local Ray instance.
INFO 07-06 19:39:21 llm_engine.py:161] Initializing an LLM engine (v0.5.0.post1) with config: model='./quantized_model', speculative_config=None, tokenizer='./quantized_model>Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
INFO 07-06 19:39:36 utils.py:637] Found nccl from library libnccl.so.2
INFO 07-06 19:39:36 pynccl.py:63] vLLM is using nccl==2.20.5
^[[36m(RayWorkerWrapper pid=64052)^[[0m INFO 07-06 19:39:36 utils.py:637] Found nccl from library libnccl.so.2
^[[36m(RayWorkerWrapper pid=64052)^[[0m INFO 07-06 19:39:36 pynccl.py:63] vLLM is using nccl==2.20.5
WARNING 07-06 19:39:37 custom_all_reduce.py:166] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. To silence this warning, specify disable>WARNING 07-06 19:39:37 fp8.py:48] Detected fp8 checkpoint. Please note that the format is experimental and subject to change.
^[[36m(RayWorkerWrapper pid=64052)^[[0m WARNING 07-06 19:39:37 custom_all_reduce.py:166] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. >^[[36m(RayWorkerWrapper pid=64052)^[[0m WARNING 07-06 19:39:37 fp8.py:48] Detected fp8 checkpoint. Please note that the format is experimental and subject to change.
^[[36m(RayWorkerWrapper pid=64136)^[[0m INFO 07-06 19:39:40 model_runner.py:160] Loading model weights took 8.4627 GB
INFO 07-06 19:39:40 model_runner.py:160] Loading model weights took 8.4627 GB
INFO 07-06 19:39:43 distributed_gpu_executor.py:56] # GPU blocks: 51829, # CPU blocks: 6553
^[[36m(RayWorkerWrapper pid=64634)^[[0m INFO 07-06 19:39:44 model_runner.py:889] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not>^[[36m(RayWorkerWrapper pid=64634)^[[0m INFO 07-06 19:39:44 model_runner.py:893] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, conside>^[[36m(RayWorkerWrapper pid=64634)^[[0m INFO 07-06 19:39:36 utils.py:637] Found nccl from library libnccl.so.2^[[32m [repeated 6x across cluster] (Ray deduplicates logs by defaul>^[[36m(RayWorkerWrapper pid=64634)^[[0m INFO 07-06 19:39:36 pynccl.py:63] vLLM is using nccl==2.20.5^[[32m [repeated 6x across cluster]^[[0m
^[[36m(RayWorkerWrapper pid=64634)^[[0m WARNING 07-06 19:39:37 custom_all_reduce.py:166] Custom allreduce is disabled because it's not supported on more than two PCIe-only GPUs. >^[[36m(RayWorkerWrapper pid=64634)^[[0m WARNING 07-06 19:39:37 fp8.py:48] Detected fp8 checkpoint. Please note that the format is experimental and subject to change.^[[32m [repea>INFO 07-06 19:39:45 model_runner.py:889] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode,>INFO 07-06 19:39:45 model_runner.py:893] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or>^[[36m(RayWorkerWrapper pid=64052)^[[0m INFO 07-06 19:40:02 model_runner.py:965] Graph capturing finished in 17 secs.
^[[36m(RayWorkerWrapper pid=64634)^[[0m INFO 07-06 19:39:40 model_runner.py:160] Loading model weights took 8.4627 GB^[[32m [repeated 6x across cluster]^[[0m
^[[36m(RayWorkerWrapper pid=64472)^[[0m INFO 07-06 19:39:45 model_runner.py:889] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not>^[[36m(RayWorkerWrapper pid=64472)^[[0m INFO 07-06 19:39:45 model_runner.py:893] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, conside>INFO 07-06 19:40:02 model_runner.py:965] Graph capturing finished in 17 secs.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
INFO 07-06 19:40:03 serving_chat.py:92] Using default chat template:
INFO 07-06 19:40:03 serving_chat.py:92] {% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_head>INFO 07-06 19:40:03 serving_chat.py:92]
INFO 07-06 19:40:03 serving_chat.py:92] '+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% e>INFO 07-06 19:40:03 serving_chat.py:92]
INFO 07-06 19:40:03 serving_chat.py:92] ' }}{% endif %}
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
WARNING 07-06 19:40:03 serving_embedding.py:141] embedding_mode is False. Embedding API will not work.
INFO:     Started server process [58592]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://<IP_ADDRESS>:8000 (Press CTRL+C to quit)

The last very important detail/clue is my outputs are all "!!!!!!!!!!!" so not coherent. But the model I quantized works perfectly well. So there's likely an issue with the quantization and the way I'm passing the examples even though I did it exactly like the neural magic repo here
I get ~400 tok/sec with 20 samples to test and with the quantized fp8 model generating nonsense and I get ~1k tok/sec using the full precision model or whatever the defaults are in vllm when I just run:
python3 -m vllm.entrypoints.openai.api_server --model me/myL3_70B_Instruct_ft_model --api-key token-abc123 --max-model-len 8192 --tensor-parallel-size 8 --distributed-executor-backend ray --worker-use-ray --gpu-memory-utilization 0.95 2>&1 | tee vllm_openai_log.txt

If you see anything obvious I'm doing wrong pls let me know.

Do the generations look okay when running in AutoFp8?
I don’t think this is the cause of the issue, but it looks like your model does not have a chat template in the config file and is falling back to default, which the model is not trained with. So if you’re using /chat/completions this will not be ideal

Also - are you able to share the model checkpoint?

It's likely due to the checkpoint. Since "!" is usually ID 0 in tokenizer, the weights may not be loaded correctly.

Ok, that confirms for me that the next test should be running neuralmagic/Meta-Llama-3-8B-Instruct-FP8 - so I've started that and am just waiting on the model to download (it'll take a while as I'm sure you're aware) and I'll report back on whether or not that works/if the throughput goes up as expected.
Re:

your model does not have a chat template in the config file and is falling back to default, which the model is not trained with

It gives the same:
INFO 07-06 19:40:03 serving_chat.py:92] Using default chat template:
INFO 07-06 19:40:03 serving_chat.py:92] {% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_head>INFO 07-06 19:40:03 serving_chat.py:92]
INFO 07-06 19:40:03 serving_chat.py:92] '+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% e>INFO 07-06 19:40:03 serving_chat.py:92]
INFO 07-06 19:40:03 serving_chat.py:92] ' }}{% endif %}

When I use the 70B that's just my LORA merged onto L3 70B instruct (which produces the output I'm expecting), I get the exact same notice, but when I pass the exact same inputs through the exact same script, I can tell that the L3 70B instruct chat template is being applied correctly. But I do think there is some kind of chat template kind of issue with my Auto FP8 script. I can try quantizing to fp8 without the examples but ultimately I'd really like to have the added precision of using the examples so I would like to figure out the issue there. Unfortunately I can't publicly share the model I trained here which makes it a little trickier to get help but I really appreciate the input so far.

Thanks. Quick note - are you applying autofp8 to the model with the merged Lora adapters or before merge?
I’ll be back to my computer in a bit and can look more closely once I return

Slightly offtopic question, does this work in non-distributed setting for you?

Okay, I just ran on L40S with neuralmagic/Meta-Llama-3-8B-Instruct-FP8 on TP=1 and TP=2 and the results look fine. Trying again with 70B model as well, but I don't think we have an issue on the vLLM side, but rather in checkpoint creation
Let me run through an example flow E2E + Ill get back to you

Ok, got my experiment result. Using just neuralmagic/Meta-Llama-3-70B-Instruct-FP8 is indeed faster. Gave me 2127 tok/sec. Not a perfect apples/apples comparison but different enough to confirm that the other parts of the launch command etc. are correct. Let me know if you can tell what's wrong with my autofp8 quantization code - it's almost definitely an issue with the chat template.

Thanks @williambarberjr - a very good debugging strategy is to detokenize an example as you pass it to the model and make sure it looks right.
selected_data = random.sample(data, min(200, len(data)))
examples = [tokenizer.apply_chat_template(item, tokenize=False) for item in selected_data]
examples = tokenizer(examples, padding=True, truncation=False, return_tensors="pt").to("cuda")

print(tokenizer.batch_decode(examples[0])[0])
# ^ result of this will be very illuminating

Can you post what you find here?

Yep, I have learned to do that and did that before running the quantization code so I have that arleady:
<|begin_of_text|><|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are an expert at reviewing website text in markdown format, and responding with a long paragraph that includes 100% of the information the website provides about the product(s) and/or service(s) the business is offering while dropping any marketing speak. You write in a dispassionate, factual tone and again, focus on the product(s) and/or service(s) the business offers.<|eot_id|><|start_header_id|>user<|end_header_id|>

<content source_url="http://custom-chrome.co.uk/">
CUSTOM CHROME RACING [/]
- Home [/]
- ABOUT US [/about-us.html]
- Services [/services.html]
- BUSINESS HOURS [/business-hours.html]
- Contact [/contact.html]
- SHOP [http://www.cherrybomb.co.uk/]
- GALLERY [/gallery.html]
- THE BEND SHOP [http://www.thebendshop.co.uk/]
Home of the Cherry Bomb®
## Exhaust manufacturers
&
fitting centre
Cost effective exhaust repairs for any make of vehicle
PLEASE CALL US FOR A QUOTE
TEL: (024) 76 387 808

CLICK THE LINKS BELOW TO GO TO OUR SHOPS
[www.cherrybomb.co.uk](https://www.cherrybomb.co.uk/) [http://www.cherrybomb.co.uk/]

[www.thebendshop.co.uk](https://www.thebendshop.co.uk/) [http://www.thebendshop.co.uk/]

TEL: (024) 76 387 808
EMAIL<EMAIL_ADDRESS>

© COPYRIGHT 2023,CUSTOM CHROME LTD
ALL RIGHTS RESERVED
Site powered by Weebly. Managed by netnerd.com [https://netnerd.com/]
- Home [/]
- ABOUT US [/about-us.html]
- Services [/services.html]
- BUSINESS HOURS [/business-hours.html]
- Contact [/contact.html]
- SHOP [http://www.cherrybomb.co.uk/]
- GALLERY [/gallery.html]
- THE BEND SHOP [http://www.thebendshop.co.uk/]

</content><|eot_id|><|start_header_id|>assistant<|end_header_id|>

Custom Chrome Racing is an exhaust manufacturer and fitting center located in Coventry, West Midlands, United Kingdom. The company offers exhaust repairs for any make of vehicle. They operate two additional shops: Cherry Bomb, which sells exhaust products, and The Bend Shop. Custom Chrome Racing provides quotes for their services upon request.<|eot_id|>

The <|eot_id|> repeats a lot bc it's the padding token I believe. Though actually maybe that's the issue? I fine tuned the model on openpipe so I can't see the axolotl config but it's possible the padding token used during training was <|end_of_text|>? At any rate, the L3 prompt template is here: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/
I'm using a system message, and everything down to the spaces etc looks correct to me but let me know if you see something, hard to get an LLM to help spot check these bc they often can't see some of the special tokens due to the way the UIs strip them out or something I think.

Ok, revised the code to manually create the prompt template in light of the fact that the tokenizer in this line:
examples = tokenizer(examples, padding=True, truncation=False, return_tensors="pt").to("cuda")
adds <|begin_of_text|> to the beginning and <|eot_id|> to the end for whatever reason.
This is now my code:
# Load tokenizer and prepare data
tokenizer_model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_id, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token

with open(jsonl_file, 'r') as file:
    data = [json.loads(line) for line in file]

selected_data = random.sample(data, min(200, len(data)))
system_prompt = "You are an expert at reviewing website text in markdown format, and responding with a long paragraph that includes 100% of the information the website provides about the product(s) and/or service(s) the business is offering while dropping any marketing speak. You write in a dispassionate, factual tone and again, focus on the product(s) and/or service(s) the business offers."
examples = [f"""<|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{example[1]['content']}\n\nConvert this website's content (provided to you in markdown format) into one long paragraph that includes all of the information the website provides about the products and/or services the business is offering. Replace any marketing tone or language with a dispassionate factual tone and again, focus on the product(s) and/or service(s) the business offers.&^%$<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n{example[2]['content']}""" for example in selected_data]
examples = tokenizer(examples, padding=True, truncation=False, return_tensors="pt").to("cuda")

quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="static")

model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
model.quantize(examples)

# Save quantized model locally
local_save_dir = "./quantized_model_2"
model.save_quantized(local_save_dir)

And the output of peaking at the first example:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are an expert at reviewing website text in markdown format, and responding with a long paragraph that includes 100% of the information the website provides about the product(s) and/or service(s) the business is offering while dropping any marketing speak. You write in a dispassionate, factual tone and again, focus on the product(s) and/or service(s) the business offers.<|eot_id|><|start_header_id|>user<|end_header_id|>

<content source_url="https://www.weilovehealth.com/">
Skip to content
## Just added to your cart
###
Qty:
View cart () [/cart]
Continue shopping

Submit
Close search
#![Weilovehealth]![Weilovehealth] [/]
- Home [/]
- Men's Sexy Underwear
- Vindkan underwear [/collections/vidnkan-underwear]
- DIETARY SUPPLEMENT
- FuXion [/collections/fuxion]
- Prunex 1 [/products/fuxion-prunex-1-weight-loss-detox-tea-instant-w-fiber-blend-for-colon-cleanse-relieve-symptoms-of-constipation-liberate-the-transit-in-digestive-system-5-grams-per-serving-7-sticks-for-1-week-supply]
- Thermo T3 [/products/fuxion-thermo-t3-contains-raspberry-ketones-ketogenic-supplement-exogenous-keto-drink-mix-for-natural-ketosis-transform-fat-into-energy-increase-stamina-for-workout-28-sachets]
- NOCARB-T [/products/fuxion-nocarb-t-instant-drink-mix-w-soluble-fiber-support-stable-blood-sugar-after-rich-dinner-anti-absorbe-glucose-cholesterol-lowering-level-accelerate-metabolism-1-pouch-of-28-sachets]
- VITA XTRA T+ [/products/fast-acting-energizing-tea-by-fuxion-vita-xtra-t-mix-all-natural-herbs-fruits-for-natural-energy-purple-corn-28-sachets]
- GANO+ CAPPUCCINO [/products/fuxion-gano-cappuccino-sugar-free-instant-coffee-improve-your-health-5g-stick-28-sachets]
- FLORA LIV [/products/fuxion-flora-liv-probiotics-10-billion-cfu-essential-multivitamin-and-minerals-28-sachets]
- ON [/products/fuxion-on-delicious-functional-drink-to-active-your-mind-to-be-more-alert-both-work-synergistically-w-vitamin-c-dha-rna-minerals-essential-oils-and-amino-acids-on-28-sticks]
- PASSION [/products/fuxion-passion-increase-your-energy-and-libido-levels-thanks-to-l-arginine-a-powerful-amino-acid-pleasant-invigorating-guarana-flavored-drink-w-natural-anti-oxidantspassion-28-sticks]
- Beauty In [/products/fuxion-beauty-in-improve-the-dermis-structure-w-more-collagen-and-elastin-fibers-bioactive-coq10-antioxidant-combination-for-anti-agingbeauty-in-28-sticks]
- VISALUS [/products/visalus-vi-shape-nutritional-shake-mix-sweet-cream-flavor-best-protein-powder]
- Disposable Face Mask [/collections/mask]
- Contact us [/pages/contact-us]
Search Log in [/account/login] Cart
0 items
[/cart]
- Home [/]
- Men's Sexy Underwear

- Men's Sexy Underwear Menu
-

Men's Sexy Underwear
- Vindkan underwear [/collections/vidnkan-underwear]
- DIETARY SUPPLEMENT

- DIETARY SUPPLEMENT Menu
-

DIETARY SUPPLEMENT
- FuXion

- FuXion Menu
-

FuXion [/collections/fuxion]
- Prunex 1 [/products/fuxion-prunex-1-weight-loss-detox-tea-instant-w-fiber-blend-for-colon-cleanse-relieve-symptoms-of-constipation-liberate-the-transit-in-digestive-system-5-grams-per-serving-7-sticks-for-1-week-supply]
- Thermo T3 [/products/fuxion-thermo-t3-contains-raspberry-ketones-ketogenic-supplement-exogenous-keto-drink-mix-for-natural-ketosis-transform-fat-into-energy-increase-stamina-for-workout-28-sachets]
- NOCARB-T [/products/fuxion-nocarb-t-instant-drink-mix-w-soluble-fiber-support-stable-blood-sugar-after-rich-dinner-anti-absorbe-glucose-cholesterol-lowering-level-accelerate-metabolism-1-pouch-of-28-sachets]
- VITA XTRA T+ [/products/fast-acting-energizing-tea-by-fuxion-vita-xtra-t-mix-all-natural-herbs-fruits-for-natural-energy-purple-corn-28-sachets]
- GANO+ CAPPUCCINO [/products/fuxion-gano-cappuccino-sugar-free-instant-coffee-improve-your-health-5g-stick-28-sachets]
- FLORA LIV [/products/fuxion-flora-liv-probiotics-10-billion-cfu-essential-multivitamin-and-minerals-28-sachets]
- ON [/products/fuxion-on-delicious-functional-drink-to-active-your-mind-to-be-more-alert-both-work-synergistically-w-vitamin-c-dha-rna-minerals-essential-oils-and-amino-acids-on-28-sticks]
- PASSION [/products/fuxion-passion-increase-your-energy-and-libido-levels-thanks-to-l-arginine-a-powerful-amino-acid-pleasant-invigorating-guarana-flavored-drink-w-natural-anti-oxidantspassion-28-sticks]
- Beauty In [/products/fuxion-beauty-in-improve-the-dermis-structure-w-more-collagen-and-elastin-fibers-bioactive-coq10-antioxidant-combination-for-anti-agingbeauty-in-28-sticks]
- VISALUS [/products/visalus-vi-shape-nutritional-shake-mix-sweet-cream-flavor-best-protein-powder]
- Disposable Face Mask [/collections/mask]
- Contact us [/pages/contact-us]

![Image]

![Image]
![Image]
### FuXion Prunex 1 Weight Loss Detox Tea Instant w. Fiber Blend For Colon Cleanse
FuXion Prunex 1 [/products/fuxion-prunex-1-fruit-herbal-tea-for-28-day-colon-detox-cleanse-effectively-improve-bowel-movements-reliable-overnight-relief-from-constipation-stay-comfortable-at-bathroom1-pouch-of-28-sachets]
![Image]
![Image]
### FuXion Nocarb-T Instant Drink Mix w. Soluble Fiber, Support Stable Blood Sugar After Rich Dinner, Anti-Absorbe Glucose,Cholesterol Lowering Level, Accelerate Metabolism-1 Pouch of 28 Sachets
FuXion Nocarb-T [/products/fuxion-nocarb-t-instant-drink-mix-w-soluble-fiber-support-stable-blood-sugar-after-rich-dinner-anti-absorbe-glucose-cholesterol-lowering-level-accelerate-metabolism-1-pouch-of-28-sachets]
![Image]
![Image]
### FuXion Thermo T3 Contains Raspberry Ketones - Ketogenic Supplement, Exogenous Keto Drink Mix for Natural Ketosis - Transform Fat into Energy & Increase Stamina for Workout (28 Sachets)
The Thermo T3 [/products/fuxion-thermo-t3-contains-raspberry-ketones-ketogenic-supplement-exogenous-keto-drink-mix-for-natural-ketosis-transform-fat-into-energy-increase-stamina-for-workout-28-sachets]
![Image]
![Image]
### Fast Acting Energizing Tea by Fuxion Vita Xtra T-Mix All Natural Herbs&Fruits for Natural Energy (Purple Corn, 28 Sachets)

Fuxion Vita Xtra T [/products/fast-acting-energizing-tea-by-fuxion-vita-xtra-t-mix-all-natural-herbs-fruits-for-natural-energy-purple-corn-28-sachets]
## Featured collection
-
2020 VINDKAN Men's pennis Enlargement Underwears Magnetic Micromodal Trunks Therapy Boxer Briefs [/collections/vidnkan-underwear/products/2020-vindkan-mens-pennis-enlargement-underwears-magnetic-micromodal-trunks-therapy-boxer-briefs]
![Image]

![Image]
2020 VINDKAN Men's pennis Enlargement Underwears Magnetic Micromodal Trunks Therapy Boxer Briefs
Regular price $18.99
Sale price $18.99
Regular price $29.99
Unit price /per
Sale Sold out
-
Vi n d K an 2020 VK Men's pennis Enlargement Underwears Magnetic Micromodal Trunks Therapy Boxer Briefs [/collections/vidnkan-underwear/products/vi-n-d-k-an-2020-vk-mens-pennis-enlargement-underwears-magnetic-micromodal-trunks-therapy-boxer-briefs]
![Image]

![Image]
Vi n d K an 2020 VK Men's pennis Enlargement Underwears Magnetic Micromodal Trunks Therapy Boxer Briefs
Regular price $19.99
Sale price $19.99
Regular price
Unit price /per
Sale Sold out
-
2017 VKWEIKU Men's pennis Enlargement Underwears Magnetic Micromodal Trunks Therapy Golden Side Sexy Briefs [/collections/vidnkan-underwear/products/2017-vkweiku-mens-pennis-enlargement-underwears-magnetic-micromodal-trunks-therapy-golden-side-sexy-briefs]
![Image]

![Image]
2017 VKWEIKU Men's pennis Enlargement Underwears Magnetic Micromodal Trunks Therapy Golden Side Sexy Briefs
Regular price $19.99
Sale price $19.99
Regular price $29.99
Unit price /per
Sale Sold out

/products/fuxion-thermo-t3-contains-raspberry-ketones-ketogenic-supplement-exogenous-keto-drink-mix-for-natural-ketosis-transform-fat-into-energy-increase-stamina-for-workout-28-sachets

/products/fuxion-nocarb-t-instant-drink-mix-w-soluble-fiber-support-stable-blood-sugar-after-rich-dinner-anti-absorbe-glucose-cholesterol-lowering-level-accelerate-metabolism-1-pouch-of-28-sachets

/products/fuxion-prunex-1-fruit-herbal-tea-for-28-day-colon-detox-cleanse-effectively-improve-bowel-movements-reliable-overnight-relief-from-constipation-stay-comfortable-at-bathroom1-pouch-of-28-sachets

Quick links
- Search [/search]
- NICE UNDERWEAR IN EBAY [https://www.ebay.com/itm/313144746195]
- Contact us [/pages/contact-us]
- Terms of Service [/policies/terms-of-service]
- Refund policy [/policies/refund-policy]
Newsletter
Subscribe

----------------------------------------

Payment methods
- Amazon
- American Express
- Apple Pay
- Diners Club
- Discover
- Meta Pay
- Google Pay
- Mastercard
- PayPal
- Shop Pay
- Venmo
- Visa
© 2024, Weilovehealth [/] [https://www.shopify.com?utm_campaign=poweredby&utm_medium=shopify&utm_source=onlinestore](https://www.shopify.com/?utm_campaign=poweredby&utm_medium=shopify&utm_source=onlinestore)
Payment methods
- Amazon
- American Express
- Apple Pay
- Diners Club
- Discover
- Meta Pay
- Google Pay
- Mastercard
- PayPal
- Shop Pay
- Venmo
- Visa
© 2024, Weilovehealth [/] [https://www.shopify.com?utm_campaign=poweredby&utm_medium=shopify&utm_source=onlinestore](https://www.shopify.com/?utm_campaign=poweredby&utm_medium=shopify&utm_source=onlinestore)
Use left/right arrows to navigate the slideshow or swipe left/right if using a mobile device
- Choosing a selection results in a full page refresh.
- Press the space key then arrow keys to make a selection.

- Opens in a new window.
- Opens external website.
- Opens external website in a new window.

</content>

Convert this website's content (provided to you in markdown format) into one long paragraph that includes all of the information the website provides about the products and/or services the business is offering. Replace any marketing tone or language with a dispassionate factual tone and again, focus on the product(s) and/or service(s) the business offers.&^%$<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Weilovehealth is an online retailer offering a range of products including men's sexy underwear, dietary supplements, and disposable face masks.  Specific products in this collection include the 2020 VINDKAN Men's Penis Enlargement Underwears Magnetic Micromodal Trunks Therapy Boxer Briefs, priced at $18.99, the Vi n d K an 2020 VK Men's Penis Enlargement Underwears Magnetic Micromodal Trunks Therapy Boxer Briefs, priced at $19.99, and the 2017 VKWEIKU Men's Penis Enlargement Underwears Magnetic Micromodal Trunks Therapy Golden Side Sexy Briefs, also priced at $19.99. The dietary supplement range is branded as FuXion and includes several products. FuXion Prunex 1 is a weight loss detox tea containing a fiber blend for colon cleanse, available in 7 sticks for a 1-week supply, with each serving containing 5 grams. FuXion Thermo T3 is a ketogenic supplement containing raspberry ketones, designed to induce natural ketosis and increase energy, available in 28 sachets. FuXion Nocarb-T is an instant drink mix with soluble fiber, intended to support stable blood sugar levels after rich dinners, anti-absorb glucose, and cholesterol, and accelerate metabolism, available in 1 pouch of 28 sachets. FuXion Vita Xtra T+ is a fast-acting energizing tea made from natural herbs and fruits, including purple corn, available in 28 sachets. FuXion GANO+ CAPPUCCINO is a sugar-free instant coffee, available in 28 sachets. FuXion FLORA LIV is a probiotic supplement containing 10 billion CFU, essential multivitamins, and minerals, available in 28 sachets.  FuXion PASSION is an energy and libido booster containing L-arginine and natural antioxidants, available in 28 sticks. FuXion Beauty In is a supplement intended to improve dermis structure with collagen, elastin fibers, bioactive CoQ10, and antioxidants for anti-aging, available in 28 sticks. FuXion VISALUS is a nutritional shake mix available in sweet cream flavor. The company also offers disposable face masks. Weilovehealth accepts various payment methods including Amazon, American Express, Apple Pay, Diners Club, Discover, Meta Pay, Google Pay, Mastercard, PayPal, Shop Pay, Venmo, and Visa. The website features a search function and a newsletter subscription option.<|eot_id|><|eot_id|><|eot_id|>

Again, is repeated many times. Does that look correct to you?
Also, I ran a test on neuralmagic/Meta-Llama-3-70B-Instruct-FP8-KV and got 2189.75 tokens/second so a tiny gain over 2127 from before, again a very imperfect test but does it make sense that the throughput gain from the kv quantization addition would be small?

Ok, I tried running exactly the code you have here: https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py
Copied here for ref:
from datasets import load_dataset
from transformers import AutoTokenizer

from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig

pretrained_model_dir = "meta-llama/Meta-Llama-3-8B-Instruct"
quantized_model_dir = "Meta-Llama-3-8B-Instruct-FP8"

tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token

ds = load_dataset("mgoin/ultrachat_2k", split="train_sft").select(range(512))
examples = [tokenizer.apply_chat_template(batch["messages"], tokenize=False) for batch in ds]
examples = tokenizer(examples, padding=True, truncation=True, return_tensors="pt").to("cuda")

quantize_config = BaseQuantizeConfig(quant_method="fp8", activation_scheme="static")

model = AutoFP8ForCausalLM.from_pretrained(
    pretrained_model_dir, quantize_config=quantize_config
)
model.quantize(examples)
model.save_quantized(quantized_model_dir)

Changing only the pretrained_model_dir in:
model = AutoFP8ForCausalLM.from_pretrained(
    pretrained_model_dir, quantize_config=quantize_config
)

And it also produces the same chat template issues and a version of L3 70B Instruct that only generates "!!!!!!!!!!!!!!!!!" with my prompt. Whatever the issue is with this code it didn't seem to be resolved when I made what I thought were the correct adjustments to the chat template. Here's the official chat template again: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/

Ok, I can officially stop blowing up your inboxes now. I got it fixed, this is a lot more code than is probably needed but I pulled it from the official Llama 3 repo and made a few small changes until the resulting chat template looked correct. One of the bigger gotchas was EOS needing to be manually set to '<|end_of_text|>' as the official Llama 3 repo https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/ doesn't discuss this but it's standard practice when fine tuning L3 with axolotl so I guessed that's how openpipe setup their config and that did the trick. I also had to make a modification to prevent getting two <|begin_of_text|> tokens at the start. At any rate, the below returns the correct output and runs significantly faster (>1400 tok/sec) on my setup. Thanks again for your help.
import json
import random
import os
from transformers import AutoTokenizer
from huggingface_hub import HfApi
from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig
from typing import List, Dict

# File paths and configuration
jsonl_file = "/vllm-workspace/openAIChatMessagesFormat.jsonl"
pretrained_model_dir = "me/MyFTModel"
quantized_model_dir = "me/MyFTModel_fp8_kv"

# Initialize Hugging Face API
api = HfApi()

# Load tokenizer and prepare data
tokenizer_model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_id, use_fast=True)
tokenizer.pad_token = tokenizer.eos_token

# Load and select data
with open(jsonl_file, 'r') as file:
    data = [json.loads(line) for line in file]
selected_data = random.sample(data, min(300, len(data)))

# Define system prompt
system_prompt = "You are an expert at reviewing website text in markdown format, and responding with a long paragraph that includes 100% of the information the website provides about the product(s) and/or service(s) the business is offering while dropping any marketing speak. You write in a dispassionate, factual tone and again, focus on the product(s) and/or service(s) the business offers."

# Custom ChatFormat class based on the official library
class ChatFormat:
    def __init__(self, tokenizer):
        self.tokenizer = tokenizer

    def encode_header(self, role: str) -> List[int]:
        tokens = []
        tokens.append(self.tokenizer.convert_tokens_to_ids("<|start_header_id|>"))
        tokens.extend(self.tokenizer.encode(role, add_special_tokens=False))
        tokens.append(self.tokenizer.convert_tokens_to_ids("<|end_header_id|>"))
        tokens.extend(self.tokenizer.encode("\n\n", add_special_tokens=False))
        return tokens

    def encode_message(self, message: Dict[str, str]) -> List[int]:
        tokens = self.encode_header(message["role"])
        tokens.extend(self.tokenizer.encode(message["content"].strip(), add_special_tokens=False))
        tokens.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
        return tokens

    def encode_dialog_prompt(self, dialog: List[Dict[str, str]]) -> List[int]:
        tokens = []
        # tokens.append(self.tokenizer.convert_tokens_to_ids("<|begin_of_text|>"))
        for message in dialog:
            tokens.extend(self.encode_message(message))
        # Add the start of an assistant message for the model to complete
        tokens.extend(self.encode_header("assistant"))
        return tokens

# Initialize ChatFormat
chat_format = ChatFormat(tokenizer)

examples = []
for example in selected_data:
    dialog = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": f"{example[1]['content']}\n\nConvert this website's content (provided to you in markdown format) into one long paragraph that includes all of the information the website provides about the products and/or services the business is offering. Replace any marketing tone or language with a dispassionate factual tone and again, focus on the product(s) and/or service(s) the business offers.&^%$"},
        {"role": "assistant", "content": example[2]['content']}
    ]

    # Instead of tokenizing, we'll just format the dialog with special tokens as strings
    formatted_dialog = ""
    for message in dialog:
        formatted_dialog += f"<|start_header_id|>{message['role']}<|end_header_id|>\n\n{message['content']}<|eot_id|>"

    # Add the start of an assistant message for the model to complete
    formatted_dialog += "<|start_header_id|>assistant<|end_header_id|>\n\n"

    examples.append(formatted_dialog)

# Now tokenize the formatted examples
tokenizer_model_dir = "meta-llama/Meta-Llama-3-70B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_model_dir, use_fast=True)
tokenizer.pad_token = '<|end_of_text|>'

# Tokenize the examples
tokenized_examples = tokenizer(examples[:100], padding=True, truncation=False, return_tensors="pt").to("cuda")

quantize_config = BaseQuantizeConfig(
    quant_method="fp8",
    activation_scheme="static",
    ignore_patterns=["re:.*lm_head"],
    kv_cache_quant_targets=("k_proj", "v_proj"),
)

model = AutoFP8ForCausalLM.from_pretrained(pretrained_model_dir, quantize_config)
model.quantize(tokenized_examples)

# Save quantized model locally
local_save_dir = "./quantized_model_2"
model.save_quantized(local_save_dir)

Thank you!

No problem. In general it seems that quantizing is sensitive to the pad token choice. We are about to release vllm-project/llm-compressor, which handles this by masking out the pad token. Thanks!

+1


Docs request: linking settings with rules

I made https://github.com/charliermarsh/ruff/issues/4106 because I didn't know how to configure ruff's internal isort.  I had just been looking here: https://beta.ruff.rs/docs/rules/#isort-i.
However, there's a whole isort config section here: https://beta.ruff.rs/docs/settings/#isort
The request: can we link the settings page in the rules page, for all tools where both pages exist?

It would be nice to include a reference + link to the settings at the top of each rule section.


Add scores of xDAN-L1-Think model on the test field of MT-Bench / CoT

Add scores of xDAN-L1-Think model on the test field of MT-Bench / CoT.
Thanks for your eval service.

I have finished the DS-1000 evaluation so a total score is now also computed. You can also view the update leaderboard here: https://fasteval.github.io/FastEval/#?branch=e688d63ff77548b83f252f31dfdbce514a009b52
Before merging this PR, could you please answer the following questions about this model:

What's the number of parameters? As you can see, for the other open source models on the leaderboard, there is a size specified and I would also like to add this information here. Normally it's taken from the model name, but the information isn't part of that name in this case.
Should the model name be xDAN-L1-Think-0930-e35 or something else? It's possible to make it a bit shorter or change it in some way as you can also see for the other models on the leaderboard.
Will the model be public and have an huggingface URL? All of the other models on the leaderboard (except the OpenAI models) are public, and I would like to keep it that way and also add an URL for the model.

I have finished the DS-1000 evaluation so a total score is now also computed. You can also view the update leaderboard here: https://fasteval.github.io/FastEval/#?branch=e688d63ff77548b83f252f31dfdbce514a009b52
Before merging this PR, could you please answer the following questions about this model:

What's the number of parameters? As you can see, for the other open source models on the leaderboard, there is a size specified and I would also like to add this information here. Normally it's taken from the model name, but the information isn't part of that name in this case.
Should the model name be xDAN-L1-Think-0930-e35 or something else? It's possible to make it a bit shorter or change it in some way as you can also see for the other models on the leaderboard.
Will the model be public and have an huggingface URL? All of the other models on the leaderboard (except the OpenAI models) are public, and I would like to keep it that way and also add an URL for the model.

13B model and trained based on llama2
The name of "xDAN-L1-Think-13B-v0.5" is better
The model public is needed or not?

Could I set a grant check for public?

I've added the parameter count & added the alternative name. The updated leaderboard can be viewed here: https://fasteval.github.io/FastEval/#?branch=c35612d37be7bdb82c0b47c2341a4b93ef3d2913

Could I set a grant check for public?

I'm not sure what you mean by this "grant check". Could you explain?

The model public is needed or not?

FastEval is primarily for advancing open source models so I would make it a requirement for the model to be public to appear on the main leaderboard. If it's not public, you can still view your model with the URL above.

Could I set a grant check for public?

I just realized you probably meant https://huggingface.co/docs/hub/models-gated. That would be also ok if the access is granted automatically rather than manually.


Ingress Stats Tracking: How to opt out of flow stats, track non-field items like frame size and filter ingress field stats

Adding @ankur-sheth @winstonliu1111 for inputs if any.
Context
Flow in athena is analogous to FlowGroup in IxNetwork, because Rate, Size and Duration can be independently controlled per FlowGroup (which internally means, they're mapped to one or more hardware streams). Packet flows belonging to different PGIDs then constitute what IxNetwork calls Flow.
PGIDs are usually internally used to track statistics for:

Header / Custom fields
Non-field items like frame size and FlowGroup

IxNetwork, by default, only tracks port statistics.
Problem

Currently we do not have a way to specify tracking options for non-field items. Apart from not being able to track statistics per frame-size, this also means we cannot opt out of per Flow statistics (even if one doesn't need it).
Even if we decide to mandate per Flow stats, noting that ingress field stats are not part of per flow stats, it's not clear from doc how to filter out some rows in returned results.

e.g. From below, at first glance, the filter option should help reduce number of rows (and it does when viewing flow stats). But upon closer examination, it will just affect aggregation value when viewing ingress field stats.
Hence, FlowStatRequest should at least allow filtering based on number of rows (irrespective of whether it's per flow or per field value)
# Two flows are configured, each with 4 incr UDP Source Port values, which repeats across 100 packets.

# Stats per Flow (flow_names=['f1', 'f2'])
flow    frames_rx
f1          100
f2          100

# Stats per Flow (flow_names=['f1'])
flow    frames_rx
f1          100

# Flow stats with "UDP sport" as ingress name (flow_names=['f1', 'f2'])
flow    UDP sport    frames_rx
f1        5000          25
f1        5001          25
f1        5002          25
f1        5003          25
f2        5000          25
f2        5001          25
f2        5002          25
f2        5003          25

# Flow stats with "UDP sport" as ingress name (flow_names=['f1'])
flow    UDP sport    frames_rx
f1        5000          25
f1        5001          25
f1        5002          25
f1        5003          25

# How to get this table ? Stats per UDP Source Port (flow_names=['f1', 'f2'])
UDP sport    frames_rx
5000          50
5001          50
5002          50
5003          50

# How to get this table ? Stats per UDP Source Port 5001 and 5003 (flow_names=['f1', 'f2'])
UDP sport    frames_rx
5001          50
5003          50

These are agnostic models, please frame your issue in terms of want a user wants to and/or see.
The current design of flow results allows a user to retrieve results for all flows or to select flow results for a subset of flow.
In addition the user can tag individual fields of a packet which can then be used to get a more detailed set of results.
Are there users that need to get a more detailed set of results not just by packet fields but by framesize?

In other words i'm not familiar with pgids, port streams, ixnetwork.

These are agnostic models, please frame your issue in terms of want a user wants to and/or see.
The current design of flow results allows a user to retrieve results for all flows or to select flow results for a subset of flow.
In addition the user can tag individual fields of a packet which can then be used to get a more detailed set of results.
Are there users that need to get a more detailed set of results not just by packet fields but by framesize?

I understand, and that's why Problem section only talks about issues with tracking flow results in athena API (code snippet shows the examples as well).
As a summary,

It is not clear whether per-ingress-field stats can exist independent of per flow stats (IMHO, it should)
e.g. I only need to see stat rows distributed among UDP source ports 5000, 5001, 5002 and 5003 (4 rows). But if I configure two flows (destined for same rx) with same set of UDP source ports, per current API, it would show me 8 rows (4 UDP rows x 2 flows).
NOTE: I'm not saying this is incorrect behaviour, I'm just saying that there needs to be a way to see stats independent of per-flow stats.

We should be able to track statistics using non-field items like frame size. I cannot confirm whether there are customers that really need to see stats distributed based on frame size, since I have only seen this used in in-house scripts (hence, the realisation that cases like this exist too).

With version 0.4.0 per flow ingress tracking is available using the flow.packet...metric_group property.
By default ingress tracking is disabled unless specifically enabled on a per flow basis using teh flow.metrics.enable property.
When a customer requires features such as frame size tracking the model can be extended using the per flow metrics container.


Why is spawn scrap limited to existing scrap?

I am more looking at using this to help with development, and i have been trying to spawn scrap while in company to sell.
The in game dev menu accessed with https://thunderstore.io/c/lethal-company/p/megumin/LethalDevMode/ spawns any item except the scrap information such as value isn't set. I have tried setting the value of the scrap myself but been getting some mixed results.
The chat commands doesn't let me spawn any scrap because im in company.
Basically im trying to ask if you would be able to look into making all scrap spawnable with a scrap value (maybe using the same list the debug menu uses, StartOfRound allItemsList) regardless of what scrap has / can be spawned in any given level.

yes i will

i am not sure if i can add that, it really seems to be from unity itself

I figured it out and will add the functionality in the next update

This was implemented with Update 2.0.0 in the /spawnitem or /spwitm command. The DebugMenu is now also enabled by default.


QueueFragment updateShuffle();

Hi so i dont like the QueueFragment in the NowPlayingActivity
Ive decided to remove it add it to its own fragment also changed the QueueFragment to extend DialogFragment and added it to the toolbar menu...
Now the problem is when i click the shuffle button the apk force closes and restarts ok so when i go back into the now playing the repeat is on and the queue list is correctly updated... but click the shuffle again to turn it off it force closes again...
The queue works if i click a song or the menu ect only the update shuffle giving me problems
Is there anything i can do ?

You should try to take issues like this to a forum like StackOverflow (and include a stack trace) instead of posting them to the issue tracker since this involves modifying Jockey. If I had to guess, though, I think that you're getting a NullPointerException because of a particular line in NowPlayingActivity:
public class NowPlayingActivity extends BaseActivity implements ... {

    private QueueFragment queueFragment;

    ...

    private void toggleShuffle() {

        ...

        // If a QueueFragment isn't attached to the activity, this will throw a NPE:
        queueFragment.updateShuffle();
    }
}

Please direct any follow-up questions to the appropriate development forum.


Handle does not work with Filter?

See this example, the filter is working and the item is draggable (because no explicit handle has been defined):
http://jsbin.com/qivikakago/edit?html,js,output
However, once a handle has been defined the text is no longer clickable:
http://jsbin.com/pepaxuceqa/1/edit?html,js,output
Best regards
Rawa

Try master.

I know the issue. Apparently the filter must be wrapped in handle if there is handle defined. Haven't got time to fix it. @RubaXa if you have time... :)

First problem is that the part you are filtering does not include the handle element, so Sortable is allowing the mousedown event through on the handle.
Second problem with the selection is that you need to set preventOnFilter: false to allow selecting of filtered elements.


Only run Smoke Tests for releasing layer

Description:
The goal for this PR is to only run Soak Tests for the Lambda Layer which is being released.
NOTE: However, this PR only allows Soak Tests to be run for 1 Sample App, not multiple sample apps (as seen is available for the java-wrapper).
Link to tracking Issue: #143
Testing:
Will be tested when we run this workflow
Documentation:
N/A

Test the changes in this PR by using a separate test branch to successfully run release workflow for the 5 layers.
Marking as ready for review.

Merging since latest changes worked for Python and for nodejs


⚠ Bofrost.de has degraded performance

In b1a4c74, Bofrost.de (https://www.bofrost.de) experienced degraded performance:

HTTP code: 200
Response time: 865 ms

Resolved: Bofrost.de performance has improved in 15e231d after 9 minutes.


added alias for xclip command

Description
Added shortcut for xclip commands
Motivation and Context
The complete command is lengthy
How Has This Been Tested?
Check screenshots
Screenshots (if appropriate):

Types of changes

[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

[x] My code follows the code style of this project.
[x] If my change requires a change to the documentation, I have updated the documentation accordingly.
[x] I have read the CONTRIBUTING document.
[ ] If I have added a new file, I also added it to clean_files.txt and formatted it using lint_clean_files.sh.
[ ] I have added tests to cover my changes, and all the new and existing tests pass.

Hi @tbhaxor, thanks for contributing!
I think xclip is managed the the clipboard alias file. If you want, you can clean up the file and add new aliases of your choice, also, linting the existing alias file would be great!

Hi @tbhaxor, thanks for contributing!
I think xclip is managed the the clipboard alias file. If you want, you can clean up the file and add new aliases of your choice, also, linting the existing alias file would be great!

Hi @tbhaxor, thanks for contributing!
I think xclip is managed the the clipboard alias file. If you want, you can clean up the file and add new aliases of your choice, also, linting the existing alias file would be great!

And you want separate PR for that :P

Hi @tbhaxor, thanks for contributing!
I think xclip is managed the the clipboard alias file. If you want, you can clean up the file and add new aliases of your choice, also, linting the existing alias file would be great!

And you want separate PR for that :P

Hi @tbhaxor, thanks for contributing!
I think xclip is managed the the clipboard alias file. If you want, you can clean up the file and add new aliases of your choice, also, linting the existing alias file would be great!

And you want separate PR for that :P

I just don't think we need a new file, you can modify the existing one, and if you want, create a follow up PR with cleanup :smile:

Hi @tbhaxor, thanks for contributing!
I think xclip is managed the the clipboard alias file. If you want, you can clean up the file and add new aliases of your choice, also, linting the existing alias file would be great!

And you want separate PR for that :P

I just don't think we need a new file, you can modify the existing one, and if you want, create a follow up PR with cleanup :smile:


[Bug] Can not cleanup heartbeat data if scaling down broker

Search before asking

[X] I searched in the issues and found nothing similar.

Version
pulsar version: 2.10
Minimal reproduce step
scaling down broker, by close and remove broker
What did you expect to see?
scaling down broker can cleanup all resource on broker
What did you see instead?
heartbeat topic ledger of the scaling-down-broker still exist
Anything else?
How can we cleanup resource when scaling down broker ?
I search in code but can not find such method.
Are you willing to submit a PR?

[ ] I'm willing to submit a PR!

It's a known issue. Unloaded topic could not be deleted

@Technoboy- Is this issue being fixed or has been fixed ?
I think heartbeat namespace and topic is somewhat special. Each broker has its own heartbeat resource. Maybe we can delete heartbeat resource when we close the broker.  What do you think about it ?

@Technoboy- Is this issue being fixed or has been fixed ?
I think heartbeat namespace and topic is somewhat special. Each broker has its own heartbeat resource. Maybe we can delete heartbeat resource when we close the broker. What do you think about it ?

yes, good idea

issue has fixed. close it


Fix missing CORS headers in case of exception in server

This should work now when Flask is in "production" mode. Being in debug mode enables the flask internal debugger which bypasses all middleware when unhandled exceptions occur.
We could consider adding a catchall error-handler to prevent unhandled exceptions from ever occurring in the future.

This should work now when Flask is in "production" mode. Being in debug mode enables the flask internal debugger which bypasses all middleware when unhandled exceptions occur.
We could consider adding a catchall error-handler to prevent unhandled exceptions from ever occurring in the future.


Update.bat Not working and Cant find VPNWrap.INI to make file changes

After downloading and running the Update.bat as suggested, I keep getting an error that the .INI cant be read. I tried to update the .INI manually but cant find the .INI file to update or rewrite. Please i need help on how to locate the .INI file. It keep showing Not Supported. What else can be done for me to be able to updat it automatically. Please help

See this: https://github.com/stascorp/rdpwrap/issues/215#issuecomment-285806630

Also try to reinstall it cleanly (uninstall first, then install latest version).

Thanks for your @binarymaster, I forgot to add that I'm using Windows 10. i uninstall and reinstall as you suggested but keep getting the same error. here is the screenshot maybe this would help. Please note that I"m not a newbie to computers and software so I'm quite sure of what I'm doing. I want to know if there's anything I'm missing or didnt get right. I need your help so badly. Thanks

You did not uninstalled it.

really, I have checked my control panel and coulnd't find it, where else can i find it to uninstall please

You did not run uninstall.bat

Yes i uninstalled it and reinstall the new version as you suggested but each time i try to config it shows me the same messages..

Each time i uninstall.bat it log me off automatically and now i can't login from my office right now.. here is the screenshot maybe you could understand wnat I'm talking about.

Please provide uninstall.bat output log for the details. And use something other except RDP for maintenance, like Telnet or PSExec.


feat: add attachment flags

Summary

Implements discord/discord-api-docs#6272

This is a Code Change

[ ] I have tested my changes.
[x] I have updated the documentation to reflect the changes.
[x] I have run task pyright and fixed the relevant issues.

While the flags from https://github.com/discord/discord-api-docs/pull/6272#issuecomment-1622620128 need to be added too, I'd like for them to be in the actual documentation.

Draft as this is waiting on a docs PR.

Docs PR has been merged.


Investigate errors on missing $CONDA

Seen in https://github.com/conda/conda-standalone/actions/runs/8799309039/job/24148102977?pr=67 and https://github.com/conda/conda-build/actions/runs/8799101015/job/24147451008

Yoohoo, reproduces now! We were not considering that GH-hosted x64 runners on macOS could happen. But that's the problem with macos-13. It's x64 and doesn't bundle any $CONDA.
I'll submit a potential fix now.


Many ip banned...

Hello
I got many IP banned (i use proxies) whereas the IP i use are working fine with RocketMap...
Any idea ?

Timeouts? What does the scan.py log say exactly

No many things actually...
[2017-02-22 14:24:58][    INFO][root] Starting up!
[2017-02-22 14:24:59][ WARNING][spawns] Empty altitude response for (45.754, 4.823), falling back to random.
[2017-02-22 14:27:06][ WARNING][spawns] Empty altitude response for (45.754, 4.814), falling back to random.
[2017-02-22 14:29:14][ WARNING][spawns] Empty altitude response for (45.754, 4.805), falling back to random.
[2017-02-22 14:31:21][ WARNING][spawns] Empty altitude response for (45.754, 4.796), falling back to random.
[2017-02-22 14:31:21][ WARNING][spawns] Empty altitude response for (45.739, 4.823), falling back to random.
[2017-02-22 14:33:28][ WARNING][spawns] Empty altitude response for (45.739, 4.814), falling back to random.
[2017-02-22 14:33:28][ WARNING][spawns] Empty altitude response for (45.739, 4.805), falling back to random.
[2017-02-22 14:35:36][ WARNING][spawns] Empty altitude response for (45.739, 4.796), falling back to random.
[2017-02-22 14:37:43][ WARNING][spawns] Empty altitude response for (45.723, 4.823), falling back to random.
[2017-02-22 14:37:43][ WARNING][spawns] Empty altitude response for (45.723, 4.814), falling back to random.
[2017-02-22 14:37:43][ WARNING][spawns] Empty altitude response for (45.723, 4.805), falling back to random.
[2017-02-22 14:37:43][ WARNING][spawns] Empty altitude response for (45.723, 4.796), falling back to random.
[2017-02-22 14:37:43][ WARNING][spawns] Empty altitude response for (45.708, 4.823), falling back to random.
[2017-02-22 14:37:43][ WARNING][spawns] Empty altitude response for (45.708, 4.814), falling back to random.
[2017-02-22 14:39:51][ WARNING][spawns] Empty altitude response for (45.708, 4.805), falling back to random.
[2017-02-22 14:41:58][ WARNING][spawns] Empty altitude response for (45.708, 4.796), falling back to random.
[2017-02-22 14:42:00][ WARNING][worker-7] Swapping out http://xxxxxxxxxxxxxxxxxx due to IP ban.
[2017-02-22 14:42:00][ WARNING][worker-5] Swapping out http://xxxxxxxxxxxxxxxxxx  due to IP ban.
[2017-02-22 14:42:01][ WARNING][worker-11] Swapping out http://xxxxxxxxxxxxxxxxxx  due to IP ban.
[...]
[2017-02-22 14:42:05][ WARNING][worker-2] Swapping out http://xxxxxxxxxxxxxxxxxx due to IP ban.
[2017-02-22 14:42:06][ WARNING][worker-4] Swapping out http://xxxxxxxxxxxxxxxxxx  due to IP ban.

Well, for the altitude error you have to either supply your own in the config. you are probably erroring out on the dummy one, or comment it out and just use random.
Haven't had IP ban error before but maybe someone can help you out on discord.
Just for information, how many workers are you using per proxy?

Bt any chance do you have the discord link to join ?
I have 20 proxies for 1080 workers on rocketmap, it works great

Yeah, a discord link would be appreciated.

Hmm, that only occurs when you get a 403 during an RPC. The way that is handled in pgoapi (what RocketMap uses) and pogo_async (what Monocle uses) is essentially the same, they both raise NianticIPBannedException. I don't know how RocketMap is handling that exception.
And here's a Discord invite.