[3.0.1-rc1] Stuck at Still creating...

My config is below, I can't seem to get past "Still creating..." with agent set to both 1 or 0. I tried it with the root user and token user described in the install guide as well.  I don't see anything happen in the UI or any requests being made. I do see requests going out and getting 200 OK with:
provider "proxmox" {
  pm_api_url = "${var.proxmoxURL}"
  pm_user = "root@pam"
  pm_password = "<password>"
  pm_log_enable = true
  pm_log_file   = "terraform-plugin-proxmox.log"
  pm_debug      = true
  pm_log_levels = {
    _default    = "debug"
    _capturelog = ""
  }
}

I'm using the following:
Proxmox 8.1.4
Terraform 1.7.1
Telmate/proxmox 3.0.1-rc1

  resource "proxmox_vm_qemu" "anima-cluster" {
  count       = var.clusterSize
  name        = "${var.prefix}${count.index + 1}"
  vmid        = var.vmidStartRange + count.index
  ipconfig0  = "ip=${var.networkID}.${count.index + var.networkIPStartRange}${var.networkCIDR},gw=${var.networkID}.1"
  nameserver = "${var.networkID}.1"
  searchdomain = "home.arpa"

  desc        = var.mdClusterDescription
  target_node = var.targetNode

  clone = var.cloudTemplate
  full_clone = true
  onboot = true

  cpu = "host"
  cores   = var.cores
  sockets = var.sockets
  memory  = var.memory
  agent = var.qemuAgent

  disks {
    scsi {
      scsi0 {
        disk {
          storage = var.storageDevice
          size = var.diskSize
          emulatessd = var.ssd
          discard = true
        }
      }
    }
  }

  network {
    bridge    = "vmbr0"
    model     = "virtio"
    firewall  = false
    link_down = false
  }

  os_type   = "cloud-init"
  ciuser = var.username
  cipassword = var.password
  sshkeys = var.sshKeys
}

proxmoxURL = "https://<domain>:8006/api2/json"
pmUser = "terraform-prov@pve!anima-cluster"
pmTokenSecret = "<token>"
cloudTemplate = "eterna:mantic"
prefix = "anima-"
clusterSize = 6
cores = 4
sockets = 1
memory = 8192
qemuAgent = 0
targetNode = "pve"
networkID = "10.0.0"
networkCIDR = "/24"
networkIPStartRange = 9
vmidStartRange = 200
storageDevice = "eterna"
diskSize = 100
ssd = true
username = "<username>"
password = "<password>"
sshKeys = <<-EOT
ssh-ed25519 ...
EOT
mdClusterDescription = <<-EOF
# Home Lab Cluster
EOF

After reviewing the detailed logs, I've observed that the VMID specified in my configuration (starting from 200) isn't being respected during the VM creation process. The logs indicate that Terraform queries for next available VMIDs much lower than 200, suggesting a potential issue with how VMIDs are determined or an oversight in my configuration.
Additionally, despite the logs showing successful API interactions (200 OK responses) and the Proxmox provider seemingly proceeding with the setup, there's a notable lack of evidence that VM creation attempts are actually being made. This is confusing because I don't observe any errors or failed attempts in the logs; it's as if the creation process stalls or never initiates beyond the preliminary API calls.

Hello,
I met the same problem.
The follow configuration works for me:
terraform {
  required_providers {
    # Proxmox
    proxmox = {
      source  = "Telmate/proxmox"
      version = "3.0.1-rc1"
    }
  }

  required_version = ">= 1.7.1"
}

provider "proxmox" {
  pm_api_url = "https://<IP_ADDRESS>:8006/api2/json"

  pm_user     = "<user>"
  pm_password = "<password>"

  pm_debug = true
  pm_log_levels = {
    _default    = "debug"
    _capturelog = ""
  }
}

resource "proxmox_vm_qemu" "postgre" {
  # count = 1
  vmid = 100
  name = "postgre"
  desc = "PostgreSQL host node"

  # Valid nodes: proxmox-1, proxmox-2
  target_node = "proxmox-1"
  clone = "ubuntu-2204-cloudinit"

  os_type = "ubuntu"

  # Default to bios is seabios
  bios    = "seabios"

  cores   = 32
  sockets = 1
  cpu     = "EPYC-v3"
  memory  = 131072

  scsihw = "virtio-scsi-pci"
  bootdisk = "scsi0"

  # Disk settings
  disks {
    scsi {
      scsi0 {
        disk {
          size = 2048
          storage = "local-lvm"
        }
      }
    }
  }

  # Network settings
  network {
    model = "virtio"
    bridge = "vmbr0"
  }
  ipconfig0 = "gw=<IP_ADDRESS>,ip=<IP_ADDRESS>/24"

  # Specify the cloud-init cdrom storage
  cloudinit_cdrom_storage = "local-lvm"

  # User settings
  ciuser = "testuser"
  cipassword = "123456"
  sshkeys = file("/root/.ssh/id_rsa.pub")
}

The configuration works if the agent is not set, but there will be a warning and I cannot destroy it with terraform destroy:
proxmox_vm_qemu.postgre: Creation complete after 31s [id=proxmox-1/qemu/100]
2024-02-01T15:12:20.078+0800 [DEBUG] State storage *statemgr.Filesystem declined to persist a state snapshot
2024-02-01T15:12:20.079+0800 [DEBUG] provider.stdio: received EOF, stopping recv loop: err="rpc error: code = Unavailable desc = error reading from server: EOF"
2024-02-01T15:12:20.086+0800 [DEBUG] provider: plugin process exited: path=.terraform/providers/registry.terraform.io/telmate/proxmox/3.0.1-rc1/linux_amd64/terraform-provider-proxmox_v3.0.1-rc1 pid=419832
2024-02-01T15:12:20.086+0800 [DEBUG] provider: plugin exited
╷
│ Warning: Qemu Guest Agent support is disabled from proxmox config.
│
│   with proxmox_vm_qemu.postgre,
│   on main.tf line 29, in resource "proxmox_vm_qemu" "postgre":
│    29: resource "proxmox_vm_qemu" "postgre" {
│
│ Qemu Guest Agent support is required to make communications with the VM
╵

Apply complete! Resources: 1 added, 0 changed, 0 destroyed.

terraform destroy will stuck at:
2024-02-01T15:21:57.190+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:21:57 [DEBUG][initConnInfo] check ip result error 500 QEMU guest agent is not running: timestamp="2024-02-01T15:21:57.190+0800"
2024-02-01T15:22:02.195+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:22:02 >>>>>>>>>> REQUEST:
GET /api2/json/nodes/proxmox-1/qemu/100/agent/network-get-interfaces HTTP/1.1
Host: <IP_ADDRESS>:8006
User-Agent: Go-http-client/1.1
Accept: application/json
Authorization: PVEAuthCookie=PVE:terraform-prov@pve:65BB4679::J8jzwPehBoiyX8JU+T2xJW7wmr6JK8vdDCNvD64lFxwd6xX0b6wV4ERAtjOK1jDKrk4jop31IbDLy1lzfmtnRPqKLAKnUqG/D9SyqrLdr8hzS2EVG/MWrcIDC10UrmADAiKC5hJ9iUUPSy3YK4ELM55Ombs9FL+6iEPv+QS8OjuQzQsishDrWhagPVSmLovrgJmHoceUtC6rp7VY/nf376ygUnyqaaWua3yiaH0xCRzzkbsPMxw4om66Fbu3pxdUuHlndPcEaSw1zaOXOyKH9TQO8Q4m8rHF9xCNeObYEzzYnnMy44p7HFCcJJ2sXiaoJ2/gxDdnrZHfR7p9IIoJCg==
CSRFPreventionToken: 65BB4679:LTTji15BKgB4jGql8am3z5ktRAh3cRFWCTE9D/NehUw
Accept-Encoding: gzip
: timestamp="2024-02-01T15:22:02.195+0800"
2024-02-01T15:22:05.215+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:22:05 <<<<<<<<<< RESULT:
HTTP/1.1 500 QEMU guest agent is not running
Connection: close
Content-Length: 13
Cache-Control: max-age=0
Content-Type: application/json;charset=UTF-8
Date: Thu, 01 Feb 2024 07:22:05 GMT
Expires: Thu, 01 Feb 2024 07:22:05 GMT
Pragma: no-cache
Server: pve-api-daemon/3.0

{"data":null}: timestamp="2024-02-01T15:22:05.215+0800"
2024-02-01T15:22:05.215+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:22:05 [DEBUG][initConnInfo] check ip result error 500 QEMU guest agent is not running: timestamp="2024-02-01T15:22:05.215+0800"
2024-02-01T15:22:10.220+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:22:10 >>>>>>>>>> REQUEST:

If the agent is set to 1 the terraform apply will stuck at:
2024-02-01T15:24:31.062+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:24:31 [DEBUG][initConnInfo] check ip result error 500 QEMU guest agent is not running: timestamp="2024-02-01T15:24:31.061+0800"
2024-02-01T15:24:36.066+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:24:36 >>>>>>>>>> REQUEST:
GET /api2/json/nodes/proxmox-1/qemu/100/agent/network-get-interfaces HTTP/1.1
Host: <IP_ADDRESS>:8006
User-Agent: Go-http-client/1.1
Accept: application/json
Authorization: PVEAuthCookie=PVE:terraform-prov@pve:65BB470C::Bpd6DQNC/ato3DSqjuhe8GYTUedlc2MctfE2oW/1Ldze8eeKZo4QeqKnFPF3T9alBhlqZebpCnmJyU2HIb+62d2T6WOUPynx1NAd1SOn00c65vJcWj9FgX5W7juaM+2p/lMAMomw7lwHbiKm5vszY5t9bZ94jB5ezaXYo0ZA8ssq7wmkt73Ov5tHlBeQVoTbb7kuQz3CPWYqfKvzjqmc5Ci7lpPOWAgvk45ZD71PQEllkVW/Z3o5+rg2G+e8X2GHRG7YrsYPmgHRIclK145pqWnY270M+5UiAiC3V0Z2k4jU8YPGEkBBhJ9/t5VXnk8VSYD1Jbi4UehxA60ZswmVWg==
CSRFPreventionToken: 65BB470C:MSnspjD2luV3BgKtjrt0FsCuleoLDpoG8GF8EEWIJSk
Accept-Encoding: gzip
: timestamp="2024-02-01T15:24:36.066+0800"
proxmox_vm_qemu.postgre: Still creating... [40s elapsed]
2024-02-01T15:24:39.083+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:24:39 <<<<<<<<<< RESULT:
HTTP/1.1 500 QEMU guest agent is not running
Connection: close
Content-Length: 13
Cache-Control: max-age=0
Content-Type: application/json;charset=UTF-8
Date: Thu, 01 Feb 2024 07:24:39 GMT
Expires: Thu, 01 Feb 2024 07:24:39 GMT
Pragma: no-cache
Server: pve-api-daemon/3.0

{"data":null}: timestamp="2024-02-01T15:24:39.083+0800"
2024-02-01T15:24:39.083+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:24:39 [DEBUG][initConnInfo] check ip result error 500 QEMU guest agent is not running: timestamp="2024-02-01T15:24:39.083+0800"
2024-02-01T15:24:44.088+0800 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/01 15:24:44 >>>>>>>>>> REQUEST:

Sorry, the ubuntu cloudimg does not integrate the qemu-guest-agent.
I ran sudo apt install -y qemu-guest-agent && sudo systemctl start qemu-guest-agent in the virtual machine created with terraform through the vnc console in the Proxmox Web UI. And then, the terrafrom destroy works.
It seems like that I need to execute these two commands in the cloud init stage.

This looks like a bug in the handling of the mutex that limits limit the amount of parallel requests to the Proxmox API. This is controlled by pm_parallel in the provider config. If you set pm_parallel to a value larger than the number of resources you're trying to create, this effectively disables the artificial limit and should let terraform continue the creation process.

Mine config looked almost the same before version 3.0.1 when I was using 2.9.3 with Proxmox 7.1. Upgrade to 8.1.3 and got some issues when running terraform with the provider version 2.9.3.. so the next plan was change provider to this one 3.0.1. That gave me resolved issues with the code. I don't remember but there were some issues with string in network rather int and so on. Can't remember. Irrelevant because 3.0.1 fixed that. But.. I stuck on your problem which you are facing "Still creating.." but with small detail related that cloud-init drive is removed during deployment and can't figure it why. And I'm tired of it. Every time a deploy it cloudinit disappear during the process.
Repo is here, I don't wanna put all content of those files here
The most important is the null_resource for creating config files
null_resource.cloud_init_config_files[0]: Creating... 2024-02-05T18:43:33.699+0100 [INFO]  Starting apply for null_resource.cloud_init_config_files[0] null_resource.cloud_init_network-config_files[0]: Creating... 2024-02-05T18:43:33.699+0100 [INFO]  Starting apply for null_resource.cloud_init_network-config_files[0] 2024-02-05T18:43:33.699+0100 [DEBUG] null_resource.cloud_init_config_files[0]: applying the planned Create change 2024-02-05T18:43:33.699+0100 [DEBUG] null_resource.cloud_init_network-config_files[0]: applying the planned Create change null_resource.cloud_init_network-config_files[0]: Provisioning with 'file'... null_resource.cloud_init_config_files[0]: Provisioning with 'file'... 2024-02-05T18:43:33.701+0100 [DEBUG] Connecting to <IP_ADDRESS>:22 for SSH 2024-02-05T18:43:33.701+0100 [DEBUG] Connecting to <IP_ADDRESS>:22 for SSH 2024-02-05T18:43:33.705+0100 [DEBUG] Connection established. Handshaking for user root 2024-02-05T18:43:33.705+0100 [DEBUG] Connection established. Handshaking for user root 2024-02-05T18:43:33.784+0100 [DEBUG] Telling SSH config to forward to agent 2024-02-05T18:43:33.784+0100 [DEBUG] Setting up a session to request agent forwarding 2024-02-05T18:43:33.789+0100 [DEBUG] Telling SSH config to forward to agent 2024-02-05T18:43:33.789+0100 [DEBUG] Setting up a session to request agent forwarding 2024-02-05T18:43:33.833+0100 [INFO]  agent forwarding enabled 2024-02-05T18:43:33.833+0100 [DEBUG] starting ssh KeepAlives 2024-02-05T18:43:33.833+0100 [DEBUG] opening new ssh session 2024-02-05T18:43:33.841+0100 [INFO]  agent forwarding enabled 2024-02-05T18:43:33.841+0100 [DEBUG] starting ssh KeepAlives 2024-02-05T18:43:33.841+0100 [DEBUG] Starting remote scp process:  'scp' -vt /var/lib/vz/snippets 2024-02-05T18:43:33.841+0100 [DEBUG] opening new ssh session 2024-02-05T18:43:33.844+0100 [DEBUG] Started SCP session, beginning transfers... 2024-02-05T18:43:33.844+0100 [DEBUG] Beginning file upload... 2024-02-05T18:43:33.844+0100 [DEBUG] Starting remote scp process:  'scp' -vt /var/lib/vz/snippets 2024-02-05T18:43:33.848+0100 [DEBUG] SCP session complete, closing stdin pipe. 2024-02-05T18:43:33.848+0100 [DEBUG] Waiting for SSH session to complete. 2024-02-05T18:43:33.853+0100 [DEBUG] Started SCP session, beginning transfers... 2024-02-05T18:43:33.853+0100 [DEBUG] Beginning file upload... 2024-02-05T18:43:33.853+0100 [ERROR] scp stderr: "Sink: C0644 1899 user-data_vm-srv-app-1.yaml\nscp: debug1: fd 0 clearing O_NONBLOCK\r\n" null_resource.cloud_init_config_files[0]: Creation complete after 0s [id=8398213386398904967] 2024-02-05T18:43:33.856+0100 [DEBUG] SCP session complete, closing stdin pipe. 2024-02-05T18:43:33.856+0100 [DEBUG] Waiting for SSH session to complete. 2024-02-05T18:43:33.858+0100 [DEBUG] State storage *statemgr.Filesystem declined to persist a state snapshot 2024-02-05T18:43:33.861+0100 [ERROR] scp stderr: "Sink: C0644 306 network-config_vm-srv-app-1.yaml\nscp: debug1: fd 0 clearing O_NONBLOCK\r\n" null_resource.cloud_init_network-config_files[0]: Creation complete after 0s [id=4576244245995302674] 2024-02-05T18:43:33.866+0100 [DEBUG] State storage *statemgr.Filesystem declined to persist a state snapshot 2024-02-05T18:43:33.866+0100 [DEBUG] provider.stdio: received EOF, stopping recv loop: err="rpc error: code = Unavailable desc = error reading from server: EOF" 2024-02-05T18:43:33.867+0100 [DEBUG] provider: plugin process exited: path=.terraform/providers/registry.terraform.io/hashicorp/null/3.2.2/darwin_arm64/terraform-provider-null_v3.2.2_x5 pid=36107 2024-02-05T18:43:33.867+0100 [DEBUG] provider: plugin exited 2024-02-05T18:43:33.934+0100 [WARN]  Provider "registry.terraform.io/telmate/proxmox" produced an invalid plan for proxmox_vm_qemu.cloudinit["srv-app-1"], but we are tolerating it because it is using the legacy plugin SDK.     The following problems may be the cause of any confusing errors from downstream operations:       - .oncreate: planned value cty.False for a non-computed attribute       - .force_create: planned value cty.False for a non-computed attribute       - .define_connection_info: planned value cty.True for a non-computed attribute       - .preprovision: planned value cty.True for a non-computed attribute       - .bios: planned value cty.StringVal("seabios") for a non-computed attribute       - .onboot: planned value cty.False for a non-computed attribute       - .tablet: planned value cty.True for a non-computed attribute       - .cpu: planned value cty.StringVal("host") for a non-computed attribute       - .vlan: planned value cty.NumberIntVal(-1) for a non-computed attribute       - .clone_wait: planned value cty.NumberIntVal(10) for a non-computed attribute       - .additional_wait: planned value cty.NumberIntVal(5) for a non-computed attribute       - .balloon: planned value cty.NumberIntVal(0) for a non-computed attribute       - .hotplug: planned value cty.StringVal("network,disk,usb") for a non-computed attribute       - .kvm: planned value cty.True for a non-computed attribute       - .vcpus: planned value cty.NumberIntVal(0) for a non-computed attribute       - .full_clone: planned value cty.True for a non-computed attribute       - .vm_state: planned value cty.StringVal("running") for a non-computed attribute       - .guest_agent_ready_timeout: planned value cty.NumberIntVal(100) for a non-computed attribute       - .automatic_reboot: planned value cty.True for a non-computed attribute       - .smbios: attribute representing nested block must not be unknown itself; set nested attribute values to unknown instead       - .disks[0].scsi[0].scsi0[0].disk[0].backup: planned value cty.True for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].mbps_r_burst: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].format: planned value cty.StringVal("raw") for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].iops_r_burst: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].mbps_r_concurrent: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].mbps_wr_burst: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].iops_wr_burst: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].iops_r_burst_length: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].mbps_wr_concurrent: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].iops_wr_burst_length: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].iops_wr_concurrent: planned value cty.NumberIntVal(0) for a non-computed attribute       - .disks[0].scsi[0].scsi0[0].disk[0].iops_r_concurrent: planned value cty.NumberIntVal(0) for a non-computed attribute       - .network[0].firewall: planned value cty.False for a non-computed attribute       - .network[0].link_down: planned value cty.False for a non-computed attribute       - .network[0].tag: planned value cty.NumberIntVal(-1) for a non-computed attribute proxmox_vm_qemu.cloudinit["srv-app-1"]: Creating... 2024-02-05T18:43:33.934+0100 [INFO]  Starting apply for proxmox_vm_qemu.cloudinit["srv-app-1"] 2024-02-05T18:43:33.939+0100 [DEBUG] proxmox_vm_qemu.cloudinit["srv-app-1"]: applying the planned Create change 2024-02-05T18:43:33.948+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:43:33 [DEBUG] setting computed for "smbios" from ComputedKeys: timestamp=2024-02-05T18:43:33.948+0100 2024-02-05T18:43:33.948+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:43:33 [DEBUG] setting computed for "unused_disk" from ComputedKeys: timestamp=2024-02-05T18:43:33.948+0100 2024-02-05T18:43:33.948+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:43:33 [DEBUG][QemuVmCreate] checking for duplicate name: srv-app-1: timestamp=2024-02-05T18:43:33.948+0100 2024-02-05T18:43:33.965+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:43:33 [DEBUG][QemuVmCreate] cloning VM: timestamp=2024-02-05T18:43:33.965+0100 proxmox_vm_qemu.cloudinit["srv-app-1"]: Still creating... [10s elapsed] proxmox_vm_qemu.cloudinit["srv-app-1"]: Still creating... [20s elapsed] 2024-02-05T18:44:00.128+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:44:00 [DEBUG][QemuVmCreate] update VM after clone: timestamp=2024-02-05T18:44:00.127+0100 proxmox_vm_qemu.cloudinit["srv-app-1"]: Still creating... [30s elapsed] proxmox_vm_qemu.cloudinit["srv-app-1"]: Still creating... [40s elapsed] 2024-02-05T18:44:19.826+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:44:19 [DEBUG][QemuVmCreate] starting VM: timestamp=2024-02-05T18:44:19.825+0100 2024-02-05T18:44:21.892+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:44:21 [DEBUG][QemuVmCreate] vm creation done!: timestamp=2024-02-05T18:44:21.892+0100 2024-02-05T18:44:21.956+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:44:21 [DEBUG] VM status: running: timestamp=2024-02-05T18:44:21.955+0100 2024-02-05T18:44:21.956+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:44:21 [DEBUG] VM is running, checking the IP: timestamp=2024-02-05T18:44:21.956+0100 2024-02-05T18:44:21.956+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:44:21 [INFO][initConnInfo] trying to get vm ip address for provisioner: timestamp=2024-02-05T18:44:21.956+0100 2024-02-05T18:44:21.956+0100 [INFO]  provider.terraform-provider-proxmox_v3.0.1-rc1: 2024/02/05 18:44:21 [DEBUG][initConnInfo] retrying for at most  20m0s minutes before giving up: timestamp=2024-02-05T18:44:21.956+0100
I don't know how to put that damn cloudinit storage when creating the templated. Some of tutorials like this one https://gist.github.com/chriswayg/b6421dcc69cb3b7e41f2998f1150e1df are importing the disk from local
qm importdisk 9110 debian-10.0.2-20190721-openstack-amd64.qcow2 local -format qcow2 which is likely impossible because storage local does not support vm images
Some of you here doesn't even use cloudinit_cdrom_storage and some of you are using it although you don't supposed do that when not using cicustom according to tf provider docs. I was using official proxmox docs to create template https://pve.proxmox.com/wiki/Cloud-Init_Support and prepared ansible scripts with command module and additional conditional so it can run all over again on already created templates by skipping task (command & shell don't provide idempotency)
So first of all I'm running this script to prepare new templates. And previous code which I shared with you is deploying VMs based on that templates (the one I declare in clone argument). Every time running thes damn script everything goes well until suddenly it maks some weird operations on cloudinit and removes it so I have no cloudinit in the VM.

@electropolis Currently in 3.0.1-rc1 cloudinit_cdrom_storage along with any ci setting like ciuser must be set to add a cloud-init disk. Gonna update the docs.

@electropolis Currently in 3.0.1-rc1 cloudinit_cdrom_storage along with any ci setting like ciuser must be set to add a cloud-init disk. Gonna update the docs.

Awesome. The cloudinit resource docs have also some old probably attrubutes like disk or volume. And regarding the problem with missing cloudinit o should refer to mentioned issue and wait there for results ?

@electropolis if you see any more issues/inconsistencies could you put them here? #932

@electropolis Currently in 3.0.1-rc1 cloudinit_cdrom_storage along with any ci setting like ciuser must be set to add a cloud-init disk. Gonna update the docs.

by the way you know that right now setting cloudinit disk doesn't work at all


Support eslint v4

When installing with<EMAIL_ADDRESS>I get:
npm WARN<EMAIL_ADDRESS>requires a peer of<EMAIL_ADDRESS>but none was installed.

Whoops, looks like this was addressed already!


Can't launch Vulkan App when using VkPhysicalDeviceBufferDeviceAddressFeatures in the VkDeviceCreateInfo next pointer chain

Description
I want to launch a vulkano (https://github.com/vulkano-rs/vulkano/) app using RenderDoc but it fails to create a vulkan device.
Here is the log of trying to run the vulkano triangle example: renderdoc-triangle.log
But the code can be reduced to just the instance and device creation
use std::sync::Arc;
use vulkano::device::Features;
use vulkano::device::{Device, DeviceExtensions};
use vulkano::instance::InstanceExtensions;
use vulkano::instance::{Instance, PhysicalDevice};

fn main() {
    let instance = Instance::new(None, &InstanceExtensions::none(), None).unwrap();
    let physical = PhysicalDevice::enumerate(&instance).next().unwrap();

    let queue_family = physical.queue_families().next().unwrap();

    let (device, mut queues) = Device::new(
        physical,
        &Features::none(),
        &DeviceExtensions::none(),
        [].iter().cloned(),
    )
    .unwrap();
}

The hello_triangle vulkan sample runs without problems so I assumed that the problem lies within Vulkano. But after some investigation it seems to be a problem within RenderDoc.
I already filed an issue to Vulkano (https://github.com/vulkano-rs/vulkano/issues/1416). There is more information about my problem.
Vulkano sets the pNext pointer instead of the pEnabledFeatures pointer and uses physical device feature extensions. This seems to be the only reason why it doesn't work but the VulkanSamples work.
Here is the log when setting pNext (and pEnabledFeatures) to null: renderdoc-fixed.log
The device creation succeeds but this doesn't help me because I (or Vulkano) need the pNext pointer.
I also found two related issues: #1744 and #1157 which seem to describe my problem (or at least something similar) and are closed but they seem to not apply to my situation.
So in a nutshell:
What I want: Use pNext pointer in DeviceCreatInfo to point to VkPhysicalDeviceFeatures2 or similar and launch using RenderDoc.
What happens: App crashes because device creation failed. Happens only with RenderDoc.
What I expected to happen: Just work...
Steps to reproduce

Set pNext of DeviceCreateInfo to a valid value in any vulkan app or use the example Rust code from above
Launch the app with RenderDoc
See that the device creation failed

Environment

RenderDoc version: 1.10
Operating System: Gentoo Linux amd64
Graphics API: Vulkan 1.1.0

I do not believe it is accurate to say that any valid value of vkDeviceCreateInfo::pNext will crash. What is possible is that some particular structs on some particular drivers can fail, but I'd need more information to narrow down what is happening. I will try to run the Rust example but since Rust is painful to set up I might be able to see the problem more quickly if you can tell me exactly what you are passing to the vkCreateDevice call.
Just to sanity check, is your code correctly checking for all features that you enable before you enable them? The sample you posted doesn't specify any of this so I assume it's happening in your vulkan wrapper library.
I'll look into this further but it won't be until later next week as I'm off work just now. However I will need more explicit instructions on how to reproduce the problem using your rust code since I don't know how to compile a file like that into an executable I can run.

Ok, I managed to narrow it down to a simple C++ example.
#include <vulkan/vulkan.h>
#include <iostream>
#include <stdexcept>
#include <vector>
#include <cstring>
#include <cstdlib>
#include <optional>

const std::vector<const char*> validationLayers = {
    "VK_LAYER_KHRONOS_validation",
    "VK_LAYER_RENDERDOC_Capture"
};

int main() {
    VkApplicationInfo appInfo{};
    appInfo.sType = VK_STRUCTURE_TYPE_APPLICATION_INFO;
    appInfo.pApplicationName = "Hello Triangle";
    appInfo.applicationVersion = VK_MAKE_VERSION(1, 0, 0);
    appInfo.pEngineName = "No Engine";
    appInfo.engineVersion = VK_MAKE_VERSION(1, 0, 0);
    appInfo.apiVersion = VK_API_VERSION_1_1;

    VkInstanceCreateInfo createInfo{};
    createInfo.sType = VK_STRUCTURE_TYPE_INSTANCE_CREATE_INFO;
    createInfo.pNext = nullptr;
    createInfo.flags = 0;
    createInfo.pApplicationInfo = &appInfo;
    createInfo.ppEnabledLayerNames = validationLayers.data();
    createInfo.enabledLayerCount = validationLayers.size();

    VkInstance instance = nullptr;

    if (vkCreateInstance(&createInfo, nullptr, &instance) != VK_SUCCESS) {
        throw std::runtime_error("failed to create instance!");
    }

    uint32_t deviceCount = 0;
    vkEnumeratePhysicalDevices(instance, &deviceCount, nullptr);

    std::vector<VkPhysicalDevice> devices(deviceCount);
    vkEnumeratePhysicalDevices(instance, &deviceCount, devices.data());

    VkPhysicalDevice physicalDevice = devices.front();

    VkDeviceQueueCreateInfo queueCreateInfo{};
    queueCreateInfo.sType = VK_STRUCTURE_TYPE_DEVICE_QUEUE_CREATE_INFO;
    queueCreateInfo.queueFamilyIndex = 0;
    queueCreateInfo.queueCount = 1;
    float queuePriority = 1.0f;
    queueCreateInfo.pQueuePriorities = &queuePriority;

    VkPhysicalDeviceFeatures features{};
    features.robustBufferAccess = true;

    // How it is officially supported
    VkPhysicalDeviceBufferDeviceAddressFeatures moreFeatures{};
    moreFeatures.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_BUFFER_DEVICE_ADDRESS_FEATURES;
    moreFeatures.pNext = nullptr;

    // How Vulkano does it
    // VkPhysicalDeviceBufferAddressFeaturesEXT moreFeatures{};
    // moreFeatures.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_BUFFER_ADDRESS_FEATURES_EXT;
    // moreFeatures.pNext = nullptr;

    // They should do the same.
    // Both work without RenderDoc. None with RenderDoc.

    VkPhysicalDeviceFeatures2 features2{};
    features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
    features2.pNext = &moreFeatures;
    features2.features = features;

    VkDeviceCreateInfo deviceCreateInfo{};
    deviceCreateInfo.sType = VK_STRUCTURE_TYPE_DEVICE_CREATE_INFO;
    deviceCreateInfo.pNext = &features2;
    deviceCreateInfo.pQueueCreateInfos = &queueCreateInfo;
    deviceCreateInfo.queueCreateInfoCount = 1;
    deviceCreateInfo.pEnabledFeatures = nullptr;
    deviceCreateInfo.ppEnabledLayerNames = validationLayers.data();
    deviceCreateInfo.enabledLayerCount = validationLayers.size();

    VkDevice device = nullptr;

    if (vkCreateDevice(physicalDevice, &deviceCreateInfo, nullptr, &device) != VK_SUCCESS) {
        throw std::runtime_error("failed to create logical device!");
    }

    vkDestroyDevice(device, nullptr);
    vkDestroyInstance(instance, nullptr);
}

This does nothing more than to create a device with additional device features and validation layers enabled.
Vulkano seems to use VkPhysicalDeviceBufferAddressFeaturesEXT but I can't find it in the official specs so I used VkPhysicalDeviceBufferDeviceAddressFeatures for this example which seems to do the same thing and is officially supported if I didn't misread the specs.
I don't enable any features but just specify VkPhysicalDeviceBufferDeviceAddressFeatures in the next chain. Without it, everything works.

GPU: NVIDIA GeForce GTX 1060 6GB
GPU Drivers: NVIDIA Accelerated Graphics Driver (450.66)

That code looks invalid to me. You must not pass a struct in a pNext chain unless you have enabled the requisite core version or extensions. VkPhysicalDeviceBufferDeviceAddressFeatures or its aliases either come from either KHR/EXT_buffer_device_address or vulkan core 1.2. You have enabled vulkan core 1.1 with no extensions, and so you must not pass that struct. Does it still happen if you check to see that the extension is available and then enable it?
Note also that you should not ever enable RenderDoc's layer manually like that. That is not designed to work and is not supported, you should always allow the RenderDoc UI to enable the layer for you.

Okay I changed the code to this
#include <vulkan/vulkan.h>
#include <iostream>
#include <stdexcept>
#include <vector>
#include <cstring>
#include <cstdlib>
#include <optional>

const std::vector<const char*> validationLayers = {
    "VK_LAYER_KHRONOS_validation"
};

int main() {
    VkApplicationInfo appInfo{};
    appInfo.sType = VK_STRUCTURE_TYPE_APPLICATION_INFO;
    appInfo.pApplicationName = "Hello Triangle";
    appInfo.applicationVersion = VK_MAKE_VERSION(1, 0, 0);
    appInfo.pEngineName = "No Engine";
    appInfo.engineVersion = VK_MAKE_VERSION(1, 0, 0);
    appInfo.apiVersion = VK_API_VERSION_1_1;

    const std::vector<const char*> instanceExtensions = {
        "VK_KHR_get_physical_device_properties2"
    };

    VkInstanceCreateInfo createInfo{};
    createInfo.sType = VK_STRUCTURE_TYPE_INSTANCE_CREATE_INFO;
    createInfo.pNext = nullptr;
    createInfo.flags = 0;
    createInfo.pApplicationInfo = &appInfo;
    createInfo.ppEnabledLayerNames = validationLayers.data();
    createInfo.enabledLayerCount = validationLayers.size();

    createInfo.enabledExtensionCount = instanceExtensions.size();
    createInfo.ppEnabledExtensionNames = instanceExtensions.data();

    uint32_t supportedInstanceExtensionCount = 0;
    vkEnumerateInstanceExtensionProperties(nullptr, &supportedInstanceExtensionCount, nullptr);
    std::vector<VkExtensionProperties> supportedInstanceExtensions(supportedInstanceExtensionCount);
    vkEnumerateInstanceExtensionProperties(nullptr, &supportedInstanceExtensionCount, supportedInstanceExtensions.data());

    std::cout << "available instance extensions:\n";

    for (const auto& extension : supportedInstanceExtensions) {
        std::cout << '\t' << extension.extensionName << '\n';
    }

    VkInstance instance = nullptr;

    if (vkCreateInstance(&createInfo, nullptr, &instance) != VK_SUCCESS) {
        throw std::runtime_error("failed to create instance!");
    }

    uint32_t deviceCount = 0;
    vkEnumeratePhysicalDevices(instance, &deviceCount, nullptr);

    std::vector<VkPhysicalDevice> devices(deviceCount);
    vkEnumeratePhysicalDevices(instance, &deviceCount, devices.data());

    VkPhysicalDevice physicalDevice = devices.front();

    VkDeviceQueueCreateInfo queueCreateInfo{};
    queueCreateInfo.sType = VK_STRUCTURE_TYPE_DEVICE_QUEUE_CREATE_INFO;
    queueCreateInfo.queueFamilyIndex = 0;
    queueCreateInfo.queueCount = 1;
    float queuePriority = 1.0f;
    queueCreateInfo.pQueuePriorities = &queuePriority;

    VkPhysicalDeviceFeatures features{};
    features.robustBufferAccess = true;

    // How it is officially supported
    VkPhysicalDeviceBufferDeviceAddressFeatures moreFeatures{};
    moreFeatures.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_BUFFER_DEVICE_ADDRESS_FEATURES;
    moreFeatures.pNext = nullptr;

    // How Vulkano does it
    // VkPhysicalDeviceBufferAddressFeaturesEXT moreFeatures{};
    // moreFeatures.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_BUFFER_ADDRESS_FEATURES_EXT;
    // moreFeatures.pNext = nullptr;

    // They should do the same.
    // Both work without RenderDoc. None with RenderDoc.

    VkPhysicalDeviceFeatures2 features2{};
    features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
    features2.pNext = &moreFeatures;
    features2.features = features;

    VkDeviceCreateInfo deviceCreateInfo{};
    deviceCreateInfo.sType = VK_STRUCTURE_TYPE_DEVICE_CREATE_INFO;
    deviceCreateInfo.pNext = &features2;
    deviceCreateInfo.pQueueCreateInfos = &queueCreateInfo;
    deviceCreateInfo.queueCreateInfoCount = 1;
    deviceCreateInfo.pEnabledFeatures = nullptr;
    deviceCreateInfo.ppEnabledLayerNames = validationLayers.data();
    deviceCreateInfo.enabledLayerCount = validationLayers.size();

    const std::vector<const char*> deviceExtensions = {
        "VK_KHR_buffer_device_address"
    };

    deviceCreateInfo.enabledExtensionCount = deviceExtensions.size();
    deviceCreateInfo.ppEnabledExtensionNames = deviceExtensions.data();

    uint32_t supportedDeviceExtensionCount = 0;
    vkEnumerateDeviceExtensionProperties(physicalDevice, nullptr, &supportedDeviceExtensionCount, nullptr);
    std::vector<VkExtensionProperties> supportedDeviceExtensions(supportedDeviceExtensionCount);
    vkEnumerateDeviceExtensionProperties(physicalDevice, nullptr, &supportedDeviceExtensionCount, supportedDeviceExtensions.data());

    std::cout << "available device extensions:\n";

    for (const auto& extension : supportedDeviceExtensions) {
        std::cout << '\t' << extension.extensionName << '\n';
    }

    VkDevice device = nullptr;

    if (vkCreateDevice(physicalDevice, &deviceCreateInfo, nullptr, &device) != VK_SUCCESS) {
        throw std::runtime_error("failed to create logical device!");
    }

    vkDestroyDevice(device, nullptr);
    vkDestroyInstance(instance, nullptr);
}

I enabled VK_KHR_buffer_device_address and its dependencies.
I also print a list of supported device extensions which definitely includes VK_KHR_buffer_device_address.
When I launch it with RenderDoc I get a new warning: Core 11512 14:48:44 vk_core.cpp(3729) Warning [Loader Message] loader_validate_device_extensions: Device extension VK_KHR_buffer_device_address not supported by selected physical device or enabled layers.
I only enabled the layer VK_LAYER_KHRONOS_validation and VK_KHR_buffer_device_address is supported by my device. So why this warning?

You don't actually verify that the extension you're enabling is available, you only print the available extensions and then enable VK_KHR_buffer_device_address anyway. RenderDoc modifies the list of extensions available to only those ones it can support for capture and replay, which may mean it removes some extensions that are otherwise available. Your application must correctly check for extensions it enables before running.
Judging by the log your GPU and driver doesn't support the bufferDeviceAddressCaptureReplay feature meaning RenderDoc can't support the buffer device address feature at all. If you have a proper extension and feature check then you will be able to detect that when running under RenderDoc, then it's up to you whether you can support a fallback path to not use the extension or fail to start.
This seems like an application bug then, not a RenderDoc bug.

Yes this seems to be a problem with Vulkano, not RenderDoc. Thank you very much for your help, now I understand what the problem is. Vulkano always uses a feature that depends on an extension (VK_KHR_buffer_device_address) that is only missing with RenderDoc. I should return to my original issue...


NCCL version

Hi
i have installed  environment in the yaml file and installed torch 1.8 follow the setting in readme
my cuda version is 11.4, it seems that it is a version conflict of NCCL, pytorch and cuda
Is my cuda version to high?
ssh://xh@<IP_ADDRESS>:22/home2/xh/.conda/envs/skg/bin/python -u -m torch.distributed.launch --nproc_per_node 4 --master_port 1234 train.py --seed 2 --cfg Salesforce/T5_base_prefix_compwebq.cfg --run_name T5_base_prefix_compwebq --logging_strategy steps --logging_first_step true --logging_steps 4 --evaluation_strategy steps --eval_steps 500 --metric_for_best_model avr --greater_is_better true --save_strategy steps --save_steps 500 --save_total_limit 1 --load_best_model_at_end --gradient_accumulation_steps 2 --num_train_epochs 400 --adafactor true --learning_rate 5e-5 --do_train --do_eval --do_predict --predict_with_generate --output_dir output/T5_base_prefix_compwebq --overwrite_output_dir --per_device_train_batch_size 2 --per_device_eval_batch_size 4 --generation_num_beams 4 --generation_max_length 128 --input_max_length 1024 --ddp_find_unused_parameters true
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
*****************************************
/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/__init__.py:422: UserWarning: torch.set_deterministic is deprecated and will be removed in a future release. Please use torch.use_deterministic_algorithms instead
  "torch.set_deterministic is deprecated and will be removed in a future "
[W Context.cpp:70] Warning: torch.use_deterministic_algorithms is in beta, and its design and functionality may change in the future. (function operator())
INFO:filelock:Lock<PHONE_NUMBER>87800 acquired on .lock
/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/__init__.py:422: UserWarning: torch.set_deterministic is deprecated and will be removed in a future release. Please use torch.use_deterministic_algorithms instead
  "torch.set_deterministic is deprecated and will be removed in a future "
[W Context.cpp:70] Warning: torch.use_deterministic_algorithms is in beta, and its design and functionality may change in the future. (function operator())
/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/__init__.py:422: UserWarning: torch.set_deterministic is deprecated and will be removed in a future release. Please use torch.use_deterministic_algorithms instead
  "torch.set_deterministic is deprecated and will be removed in a future "
[W Context.cpp:70] Warning: torch.use_deterministic_algorithms is in beta, and its design and functionality may change in the future. (function operator())
/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/__init__.py:422: UserWarning: torch.set_deterministic is deprecated and will be removed in a future release. Please use torch.use_deterministic_algorithms instead
  "torch.set_deterministic is deprecated and will be removed in a future "
[W Context.cpp:70] Warning: torch.use_deterministic_algorithms is in beta, and its design and functionality may change in the future. (function operator())
INFO:filelock:Lock<PHONE_NUMBER>87800 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>53768 acquired on .lock
INFO:filelock:Lock<PHONE_NUMBER>53768 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>87640 acquired on .lock
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    return self._setup_devices
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    cached = self.fget(obj)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
INFO:filelock:Lock<PHONE_NUMBER>87640 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>54096 acquired on .lock
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    return self._setup_devices
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    cached = self.fget(obj)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
INFO:filelock:Lock<PHONE_NUMBER>54096 released on .lock
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    return self._setup_devices
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    cached = self.fget(obj)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    return self._setup_devices
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    cached = self.fget(obj)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
Killing subprocess 30316
Killing subprocess 30320
Killing subprocess 30321
Killing subprocess 30322
Traceback (most recent call last):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in <module>
    main()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main
    sigkill_handler(signal.SIGTERM, None)  # not coming back
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
    raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
subprocess.CalledProcessError: Command '['/home2/xh/.conda/envs/skg/bin/python', '-u', 'train.py', '--local_rank=3', '--seed', '2', '--cfg', 'Salesforce/T5_base_prefix_compwebq.cfg', '--run_name', 'T5_base_prefix_compwebq', '--logging_strategy', 'steps', '--logging_first_step', 'true', '--logging_steps', '4', '--evaluation_strategy', 'steps', '--eval_steps', '500', '--metric_for_best_model', 'avr', '--greater_is_better', 'true', '--save_strategy', 'steps', '--save_steps', '500', '--save_total_limit', '1', '--load_best_model_at_end', '--gradient_accumulation_steps', '2', '--num_train_epochs', '400', '--adafactor', 'true', '--learning_rate', '5e-5', '--do_train', '--do_eval', '--do_predict', '--predict_with_generate', '--output_dir', 'output/T5_base_prefix_compwebq', '--overwrite_output_dir', '--per_device_train_batch_size', '2', '--per_device_eval_batch_size', '4', '--generation_num_beams', '4', '--generation_max_length', '128', '--input_max_length', '1024', '--ddp_find_unused_parameters', 'true']' returned non-zero exit status 1.

Process finished with exit code 1

I also tried torch 1.11+ cu113，got another error

(skg) xh@4210GPU:~/PycharmProject/UnifiedSKG$ python -m torch.distributed.launch --nproc_per_node 4 --master_port 1234 train.py --seed 2 --cfg Salesforce/T5_base_finetune_compwebq.cfg --run_name T5_base_finetune_compwebq --logging_strategy steps --logging_first_step true --logging_steps 4 --evaluation_strategy steps --eval_steps 500 --metric_for_best_model avr --greater_is_better true --save_strategy steps --save_steps 500 --save_total_limit 1 --load_best_model_at_end --gradient_accumulation_steps 2 --num_train_epochs 400 --adafactor true --learning_rate 5e-5 --do_train --do_eval --do_predict --predict_with_generate --output_dir output/T5_base_finetune_compwebq --overwrite_output_dir --per_device_train_batch_size 1 --per_device_eval_batch_size 4 --generation_num_beams 4 --generation_max_length 128 --input_max_length 1024 --ddp_find_unused_parameters true
/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py:186: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions

  FutureWarning,
WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
*****************************************
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 29, in main
    torch.set_deterministic(True)
AttributeError: module 'torch' has no attribute 'set_deterministic'
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 29, in main
    torch.set_deterministic(True)
AttributeError: module 'torch' has no attribute 'set_deterministic'
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 29, in main
    torch.set_deterministic(True)
AttributeError: module 'torch' has no attribute 'set_deterministic'
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 29, in main
    torch.set_deterministic(True)
AttributeError: module 'torch' has no attribute 'set_deterministic'
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 17913) of binary: /home2/xh/.conda/envs/skg/bin/python
Traceback (most recent call last):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/run.py", line 718, in run
    )(*cmd_args)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 247, in launch_agent
    failures=result.failures,
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
train.py FAILED
------------------------------------------------------------
Failures:
[1]:
  time      : 2022-03-14_21:36:50
  host      : 4210GPU
  rank      : 1 (local_rank: 1)
  exitcode  : 1 (pid: 17914)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[2]:
  time      : 2022-03-14_21:36:50
  host      : 4210GPU
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 17915)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
[3]:
  time      : 2022-03-14_21:36:50
  host      : 4210GPU
  rank      : 3 (local_rank: 3)
  exitcode  : 1 (pid: 17916)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2022-03-14_21:36:50
  host      : 4210GPU
  rank      : 0 (local_rank: 0)
  exitcode  : 1 (pid: 17913)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

Looking forward to your reply.
Thank you.

Another information, I only use one GPU.

Indeed, we met the similar situation during our experiments on some machines(we used a lot of GPUs on different kinds of HPC). I remember we fixed that issue by installing proper PyTorch for your CUDA version.

I will try it, thanks

sorry for bothering, I still can not  run through it
if i do not choose GPU, it seems work fine(not sure, but it ends of out of memory)
but If i choose one GPU it will got error
INFO:filelock:Lock<PHONE_NUMBER>78032 acquired on .lock
INFO:filelock:Lock<PHONE_NUMBER>78032 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>57376 acquired on .lock
INFO:filelock:Lock<PHONE_NUMBER>57376 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>60216 acquired on .lock
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    return self._setup_devices
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    cached = self.fget(obj)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
INFO:filelock:Lock<PHONE_NUMBER>60216 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>74008 acquired on .lock
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    return self._setup_devices
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    cached = self.fget(obj)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
INFO:filelock:Lock<PHONE_NUMBER>74008 released on .lock
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
Traceback (most recent call last):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    return self._setup_devices
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    cached = self.fget(obj)
if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper

  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    return self._setup_devices
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
    cached = self.fget(obj)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 510, in init_process_group
    timeout=timeout))
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 603, in _new_process_group_helper
    timeout)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
Killing subprocess 4345
Killing subprocess 4346
Killing subprocess 4347
Killing subprocess 4348
Traceback (most recent call last):
  File "/home2/xh/.conda/envs/skg/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 340, in <module>
    main()
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 326, in main
    sigkill_handler(signal.SIGTERM, None)  # not coming back
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 301, in sigkill_handler
    raise subprocess.CalledProcessError(returncode=last_return_code, cmd=cmd)
subprocess.CalledProcessError: Command '['/home2/xh/.conda/envs/skg/bin/python', '-u', 'train.py', '--local_rank=3', '--seed', '2', '--cfg', 'Salesforce/T5_base_finetune_compwebq.cfg', '--run_name', 'T5_base_finetune_compwebq', '--logging_strategy', 'steps', '--logging_first_step', 'true', '--logging_steps', '4', '--evaluation_strategy', 'steps', '--eval_steps', '500', '--metric_for_best_model', 'avr', '--greater_is_better', 'true', '--save_strategy', 'steps', '--save_steps', '500', '--save_total_limit', '1', '--load_best_model_at_end', '--gradient_accumulation_steps', '2', '--num_train_epochs', '400', '--adafactor', 'true', '--learning_rate', '5e-5', '--do_train', '--do_eval', '--do_predict', '--predict_with_generate', '--output_dir', 'output/T5_base_finetune_compwebq', '--overwrite_output_dir', '--per_device_train_batch_size', '2', '--per_device_eval_batch_size', '4', '--generation_num_beams', '4', '--generation_max_length', '128', '--input_max_length', '1024', '--ddp_find_unused_parameters', 'true']' returned non-zero exit status 1.

Process finished with exit code 1

Another question is, does it support torch 1.11? because it got error  AttributeError: module 'torch' has no attribute 'set_deterministic'  when use torch 1.11+cu113
Thanks

I think it is a PyTorch version issue. My personal experience is that removing the following lines works for other PyTorch versions.
https://github.com/HKUNLP/UnifiedSKG/blob/fab45fea3a349c9dbda4ed34482df227920272db/train.py#L27-L29

It may sacrifice reproducibility, if it is not your main concern.

thanks for your reply, but it still not work.
this error occurs only when i chose a single GPU and it the error message seems like it happens because distributed training?

Ohh the above answer is for your second question. After removing the three lines, does torch 1.11+cu113 work?
For your first question, we are still exploring.

Ohh the above answer is for your second question. After removing the three lines, does torch 1.11+cu113 work? For your first question, we are still exploring.
it does not work, this is the error log of single GPU

ssh://xhuang@<IP_ADDRESS>:22/home2/xhuang/.conda/envs/skg/bin/python -u -m torch.distributed.launch --nproc_per_node 4 --master_port 1234 train.py --seed 2 --cfg Salesforce/T5_base_finetune_compwebq.cfg --run_name T5_base_finetune_compwebq --logging_strategy steps --logging_first_step true --logging_steps 4 --evaluation_strategy steps --eval_steps 500 --metric_for_best_model avr --greater_is_better true --save_strategy steps --save_steps 500 --save_total_limit 1 --load_best_model_at_end --gradient_accumulation_steps 2 --num_train_epochs 400 --adafactor true --learning_rate 5e-5 --do_train --do_eval --do_predict --predict_with_generate --output_dir output/T5_base_finetune_compwebq --overwrite_output_dir --per_device_train_batch_size 2 --per_device_eval_batch_size 4 --generation_num_beams 4 --generation_max_length 128 --input_max_length 1024 --ddp_find_unused_parameters true
/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py:186: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions

  FutureWarning,
WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
*****************************************
INFO:filelock:Lock<PHONE_NUMBER>86112 acquired on .lock
INFO:filelock:Lock<PHONE_NUMBER>86112 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>88456 acquired on .lock
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    return self._setup_devices
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    cached = self.fget(obj)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 610, in init_process_group
    timeout=timeout,
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 738, in _new_process_group_helper
    pg = ProcessGroupNCCL(prefix_store, rank, world_size, pg_options)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
INFO:filelock:Lock<PHONE_NUMBER>88456 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>35328 acquired on .lock
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 41, in main
    training_args, = parser.parse_args_into_dataclasses()
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/hf_argparser.py", line 191, in parse_args_into_dataclasses
    obj = dtype(**inputs)
  File "<string>", line 83, in __init__
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 702, in __post_init__
    if is_torch_available() and self.device.type != "cuda" and (self.fp16 or self.fp16_full_eval):
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 873, in device
    return self._setup_devices
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1717, in __get__
    cached = self.fget(obj)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/file_utils.py", line 1727, in wrapper
    return func(*args, **kwargs)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/transformers/training_args.py", line 858, in _setup_devices
    torch.distributed.init_process_group(backend="nccl")
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 610, in init_process_group
    timeout=timeout,
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/distributed_c10d.py", line 738, in _new_process_group_helper
    pg = ProcessGroupNCCL(prefix_store, rank, world_size, pg_options)
RuntimeError: ProcessGroupNCCL is only supported with GPUs, no GPUs found!
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 7061 closing signal SIGTERM
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 7062 closing signal SIGTERM
WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 7067 closing signal SIGTERM
ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 2 (pid: 7066) of binary: /home2/xhuang/.conda/envs/skg/bin/python
Traceback (most recent call last):
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 193, in <module>
    main()
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 189, in main
    launch(args)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py", line 174, in launch
    run(args)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/run.py", line 718, in run
    )(*cmd_args)
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 131, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home2/xhuang/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launcher/api.py", line 247, in launch_agent
    failures=result.failures,
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
train.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2022-03-16_11:27:44
  host      : 4210GPU
  rank      : 2 (local_rank: 2)
  exitcode  : 1 (pid: 7066)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================

Process finished with exit code 1

here is the log when do not choose GPU, it seems work fine
ssh://xh@<IP_ADDRESS>:22/home2/xh/.conda/envs/skg/bin/python -u -m torch.distributed.launch --nproc_per_node 4 --master_port 1234 train.py --seed 2 --cfg Salesforce/T5_base_finetune_compwebq.cfg --run_name T5_base_finetune_compwebq --logging_strategy steps --logging_first_step true --logging_steps 4 --evaluation_strategy steps --eval_steps 500 --metric_for_best_model avr --greater_is_better true --save_strategy steps --save_steps 500 --save_total_limit 1 --load_best_model_at_end --gradient_accumulation_steps 2 --num_train_epochs 400 --adafactor true --learning_rate 5e-5 --do_train --do_eval --do_predict --predict_with_generate --output_dir output/T5_base_finetune_compwebq --overwrite_output_dir --per_device_train_batch_size 2 --per_device_eval_batch_size 4 --generation_num_beams 4 --generation_max_length 128 --input_max_length 1024 --ddp_find_unused_parameters true
/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/distributed/launch.py:186: FutureWarning: The module torch.distributed.launch is deprecated
and will be removed in future. Use torchrun.
Note that --use_env is set by default in torchrun.
If your script expects `--local_rank` argument to be set, please
change it to read from `os.environ['LOCAL_RANK']` instead. See
https://pytorch.org/docs/stable/distributed.html#launch-utility for
further instructions

  FutureWarning,
WARNING:torch.distributed.run:
*****************************************
Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
*****************************************
INFO:filelock:Lock<PHONE_NUMBER>49744 acquired on .lock
INFO:filelock:Lock<PHONE_NUMBER>49744 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>90688 acquired on .lock
INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:1 to store for rank: 3
INFO:filelock:Lock<PHONE_NUMBER>90688 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>64600 acquired on .lock
INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:1 to store for rank: 1
INFO:filelock:Lock<PHONE_NUMBER>64600 released on .lock
INFO:filelock:Lock<PHONE_NUMBER>81760 acquired on .lock
INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:1 to store for rank: 2
INFO:filelock:Lock<PHONE_NUMBER>81760 released on .lock
INFO:torch.distributed.distributed_c10d:Added key: store_based_barrier_key:1 to store for rank: 0
INFO:torch.distributed.distributed_c10d:Rank 0: Completed store-based barrier for key:store_based_barrier_key:1 with 4 nodes.
INFO:torch.distributed.distributed_c10d:Rank 3: Completed store-based barrier for key:store_based_barrier_key:1 with 4 nodes.
INFO:torch.distributed.distributed_c10d:Rank 2: Completed store-based barrier for key:store_based_barrier_key:1 with 4 nodes.
INFO:torch.distributed.distributed_c10d:Rank 1: Completed store-based barrier for key:store_based_barrier_key:1 with 4 nodes.
task_args.bert.location: t5-base
task_args.bert.location: t5-base
task_args.bert.location: t5-base
WARNING:datasets.builder:Reusing dataset complex_web_questions (./data/complex_web_questions/compwebq/1.0.0/99dbaa17d7f00c56fd6810e977c673ffdd1e4f645fb01e302e6f33bd8de8556b)
  0%|                                                     | 0/3 [00:00<?, ?it/s]WARNING:datasets.builder:Reusing dataset complex_web_questions (./data/complex_web_questions/compwebq/1.0.0/99dbaa17d7f00c56fd6810e977c673ffdd1e4f645fb01e302e6f33bd8de8556b)
WARNING:datasets.builder:Reusing dataset complex_web_questions (./data/complex_web_questions/compwebq/1.0.0/99dbaa17d7f00c56fd6810e977c673ffdd1e4f645fb01e302e6f33bd8de8556b)
100%|████████████████████████████████████████████| 3/3 [00:00<00:00, 782.23it/s]
100%|████████████████████████████████████████████| 3/3 [00:00<00:00, 814.48it/s]
100%|████████████████████████████████████████████| 3/3 [00:00<00:00, 596.74it/s]
wandb: Currently logged in as: myproject (use `wandb login --relogin` to force relogin)
wandb: wandb version 0.12.11 is available!  To upgrade, please run:
wandb:  $ pip install wandb --upgrade
wandb: Tracking run with wandb version 0.12.1
wandb: Syncing run T5_base_finetune_compwebq
wandb: ⭐ View project at https://wandb.ai/myproject/skg
wandb: 🚀 View run at https://wandb.ai/myproject/skg/runs/2n7lsqyq
wandb: Run data is saved locally in /home2/xh/PycharmProject/UnifiedSKG/wandb/run-20220316_114350-2n7lsqyq
wandb: Run `wandb offline` to turn off syncing.

task_args.bert.location: t5-base
WARNING:datasets.builder:Reusing dataset complex_web_questions (./data/complex_web_questions/compwebq/1.0.0/99dbaa17d7f00c56fd6810e977c673ffdd1e4f645fb01e302e6f33bd8de8556b)
100%|████████████████████████████████████████████| 3/3 [00:00<00:00, 923.04it/s]
Before upsampling {'META_TUNING/compwebq.cfg': 27639}
Upsampling weights {'META_TUNING/compwebq.cfg': 1.0}
After upsampling {'META_TUNING/compwebq.cfg': 27639}
Before upsampling {'META_TUNING/compwebq.cfg': 27639}
Upsampling weights {'META_TUNING/compwebq.cfg': 1.0}
After upsampling {'META_TUNING/compwebq.cfg': 27639}
Before upsampling {'META_TUNING/compwebq.cfg': 27639}
Upsampling weights {'META_TUNING/compwebq.cfg': 1.0}
After upsampling {'META_TUNING/compwebq.cfg': 27639}
Before upsampling {'META_TUNING/compwebq.cfg': 27639}
Upsampling weights {'META_TUNING/compwebq.cfg': 1.0}
After upsampling {'META_TUNING/compwebq.cfg': 27639}
Trainer build successfully.
Trainer build successfully.
Trainer build successfully.
Traceback (most recent call last):
  File "train.py", line 225, in <module>
    main()
  File "train.py", line 146, in main
    callbacks=[early_stopping_callback],
  File "/home2/xh/PycharmProject/UnifiedSKG/utils/trainer.py", line 50, in __init__
    super().__init__(*args, **kwargs)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/trainer.py", line 367, in __init__
    self._move_model_to_device(model, args.device)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/transformers/trainer.py", line 509, in _move_model_to_device
    model = model.to(device)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/nn/modules/module.py", line 907, in to
    return self._apply(convert)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/nn/modules/module.py", line 578, in _apply
    module._apply(fn)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/nn/modules/module.py", line 578, in _apply
    module._apply(fn)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/nn/modules/module.py", line 601, in _apply
    param_applied = fn(param)
  File "/home2/xh/.conda/envs/skg/lib/python3.7/site-packages/torch/nn/modules/module.py", line 905, in convert
    return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
RuntimeError: CUDA error: out of memory
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.

Here is a minimal example for distributed training:
https://towardsdatascience.com/distributed-neural-network-training-in-pytorch-5e766e2a9e62
Could you verify if it works on your machine?

If i choose 4 GPUs, it works fine. if i do not choose GPU, it also only use 4GPUs
you said in readme that this command use 4 GPU, so simply set CUDA_VISIBLE_DEVICES may not work.


New version

Can we expect new version? (It was broken by #37)
Because current 2.7.1 fails my functionality with not able to parse 1.0.0-rc.0 version.

@werwolfby 2.7.2 should be out there https://pypi.python.org/pypi/semver

^)


Question: is it possible to export all data and import to another container?

I know official Testlamate has export and import feature, but I'm not sure how to run that within HA add-on. Is it possible to export the data the same way as official Teslamate does and import to another Teslamate container?

The addon has read/write access to /share. This location can be used to export the data.

The addon has read/write access to /share. This location can be used to export the data.

I've made an Teslamate official backup in other system and I have the file teslamate.bck.
Can I import this old data in your add-on?
Oficial restore requires docker-compose instaled and a docker-Compose.yml file in the folder, but  I think HAOS don't use docker-compose.
Thanks and help

I've managed to install docker-compose on Home Assistant OS 2021.12.10 with:
sudo curl -L --fail https://github.com/docker/compose/releases/download/1.29.2/run.sh -o /usr/local/bin/docker-compose
But when I try to restore backup by offical way:
docker-compose exec -T database psql -U teslamate -d teslamate < teslamate.bck.
It gets error, because the official Docker-compose.yml file contains information about images and volumes that are different from the ones used on add-on.
Is there a way to import official backup data into the add-on?
Thanks for you help

You have shell access, so you could enter the container using docker exec instead of docker-compose.
You will need to place the database backup file on a volume that can be read by the container.
The container names can be listed by running docker ps
You will need an elevated shell for this, I use the SSH & Web Terminal addon with protection mode disabled, giving me full access to the system.

You have shell access, so you could enter the container using docker exec instead of docker-compose.
You will need to place the database backup file on a volume that can be read by the container.
The container names can be listed by running docker ps
You will need an elevated shell for this, I use the SSH & Web Terminal addon with protection mode disabled, giving me full access to the system.

Thanks for your answer.
I've managed execute docker exec command outside the docker and psql comands inside the docker, but I can't pass the teslamate.bkn file inside the  postgres's volume.
Your Postgres Add-on create these volumes:
Host/volume	Path in container
/dev	/dev
/mnt/data/supervisor/addons/data/29b65938_postgres	/data
When I try to copy teslamate.bkn file to /dev folder Home Assistant OS cancel it saying System file is read only.
Finaly  /data folder, it seem not conectect to Volume and it can acces to backup file.
/mnt/data/supervisor/addons/data/29b65938_postgres doesn't exist.
Thanks

Hi,
If you're running this from the postgres container then that doesn't have access to /share.
I can update it so that it does.

So I didn't find out how to export from cl, what I eventually did was to install a pgAdmin add-on so I can backup and export from there

Can someone provide step by step instructions on how to export the data so that I can import it again in the standalone version?


MySQL Connection - Schema error

I'm trying to use MySQL as the database. I've set the correct attributes, and when I run the bot, I get this error:
sqlalchemy.exc.CompileError: VARCHAR requires a length on dialect mysql

sqlalchemy.exc.CompileError: (in table 'tag', column 'name'): VARCHAR requires a length on dialect mysql

Server version: 5.7.21-0ubuntu<IP_ADDRESS> (Ubuntu)
SQLAlchemy (1.2.5)
ChatterBot (0.8.4)
chatterbot-corpus (1.1.1)
discord.py (0.16.12)

I believe this was fixed by #1200. @itsmehemant123 I haven't released a package update containing the fix yet. I'm planning on releasing version 0.8.5 within the next few days wich will include the fix.

The new version was released. Apologies about the the delay.

Super! Thanks for the great work @gunthercox !


PR: Expose some widgets and utility functions through the api.

Fixes #4397
I just imported functions and classes into the api, should I create dummy functions to expose stuff https://github.com/spyder-ide/spyder/issues/4397#issuecomment-297181157 ?

Closing for now. We should revisit this once the split-plugins branch is merged.


Verification Application

What is your username and discriminator?
Queen Vulpix#7301
Why did you join Discord Linux?
to learn more about linux and to get some help
Where did you hear about Discord Linux?
Google
What is the passphrase that was assigned to you by the bot?
sudo -i partridge0doubt-rip-announce

mis typed it
sudo -i partridge-doubt-rip-announcement

From simonizor#0420 :
Looks good.  I've given you a temporary role so that you can enter the passphrase in the welcome channel.


Confused and not clear docs about serialize

Issue Summary
I need to execute few SQL queries and wrap it in transaction, to ensure data consistency.
I see DB have method serialize and i've read the docs https://github.com/tryghost/node-sqlite3/wiki/Control-Flow
It is absolutely not clear how this method works technically. Some questions i have after read:

How works this wrapper? Why SQL queries inside callback will executes one by one?
Can i wrap callbacks to promises? Is it will still executes one by one?
How to wrap to promises to make my code with complex request inside transaction are flatten?

Steps to Reproduce

Go to https://github.com/tryghost/node-sqlite3/wiki/Control-Flow
Read the docs and try to understand how serialize works under the hood, and how to create flat async code with few SQL queries inside transaction

Version
5.1.6
Node.js Version
v18.16.0
How did you install the library?
npm i sqlite3

For promises, I created prun using p/deferred from the promesa library in clojurescript:
(defn- prun [db sql & [args]]
  (let [p (p/deferred)]
    #_(js/console.debug (format "running sql: %s with args: %s" sql args))
    (.run db sql (if args (clj->js args) #js [])
          (fn [err]
            (if err
              (p/reject! p (js->clj err))
              (p/resolve! p (this-as t {:last-id (.-lastID t)
                                        :changes (.-changes t)})))))))

You could do similarly in plain JavaScript.


Configuration system

Create a configuration system for the following:

where to store the leveldb
whether or not to output in verbose mode
where to store the output file

etc.

Working on a YAML configuration system that is similar to Confire and uses gopkg.in/yaml.v2 as the YAML parsing dependency.

Couple of choices:

All configuration/data will be stored in a centralized location
On windows the config is stored in %USERPROFILE%\AppData\Roaming\Crate
On Unix/*nix systems the config is stored in $HOME/.crate
The database is stored in data
Logs are stored in logs
We could also easily store pid files in this location etc
The config file will be in config.yaml


Add ContextMenu component

Context menu should be accessible with these commands:

$refs.ctx.open($event, {data}) – open inside the same component, so all methods are visible. data becomes ctx's data
$iui.ctx.open($event, {data}) – open one registered global context menu. Needs more thoughts of what'll be inside of it, and how will it be registered inside iui
 – simple and logical version

Will have the following properties:

initialRender – display ctx with v-if or v-show (might be the different name)
closeOnResize, closeOnScroll, closeOnClickAway – closable props (think of the use of the last one)
offset, width, height, margin??? – properties for calculating the position
position – auto |left|top|right|bottom| relative
relative – DOM element's borders for which the ctx should be relative to
value

Needed the examples of simple context-menu, nested ctx, relative positioned ctx, auto positioned ctx

Implemented in #25


Fix a typo in ContextualizerFactory#createContextualizer, Javadoc, cleanup

Just an addition to the whole chain; last being https://github.com/wildfly/wildfly/pull/15355.

  There will be no conflicts to resolve later in the chain.

@pferraro Perfect.


FancyZones editor enhancement

Would love to see the size of the zone by pixel count during editing. Possibly even percentage of total resolution. Another idea might be to put guides or a grid on the screen underneath the zone editor.

Duplicate of https://github.com/microsoft/PowerToys/issues/463


[Question]  How to Enable Formio-grid Column Filtering & Sorting

Hi Everyone,
We are trying to use the formio-grid with the enableFiltering: true option set in the component but it has not effect.
Looking at the @angular/formio grid package I do not see the gridOptions property on the interface.
What is the proper way to enable filtering across all the columns? Here is the element configuration we are using. with Angular 11.
<formio-grid
src="{{ config.appUrl }}/dataupdate/"
createText="New Update"
(rowSelect)="onSelect($event)"
(createItem)="onCreateItem()"
gridOptions="{'enableFiltering': true}"

we are trying to get column filtering and sorting enabled as well to no avail. Has anyone been able to pass the gridOptions to the ui-grid within the component class? The formio-grid docs are for angularjs and it appears to be a different implementation as the @formio/angular library. Any help would be great!

We're currently addressing a backlog of GitHub issues, and as part of this effort, some inactive issues may be marked as closed. This isn't a dismissal, but a step toward more efficient tracking.
If you feel the issue is still relevant, please re-open and we'll ensure it gets the attention it deserves. Your understanding is appreciated as we work to enhance our open-source responsiveness.


item_assets slow search

Version Number
1.18.3
What branch are you on?
master
Describe the Bug
See thread on discord:
https://discord.com/channels/822460010649878528/1079511593064349747
While creating a collection in pmm, pmm tries to update details of the items in the collection. I have 2 asset folders on in my config (depth 2), but I have no assets for the actual movies in the collection. Finding the poster for the collection, which I have, is no problem. But Asset Warning: unable to find asset folder is taking at least 10 seconds for each movie.
Relevant Collection/Overlay/Playlist Definition
######################################################
#              COLLECTIONS                           #
######################################################
templates:
  Chart:
    sort_title: 0<<num>><<collection_name>>
    sync_mode: sync
    smart_label: random
    collection_mode: hide               # hides in library
    visible_home: true                  # shows on home screen

  Collection:
    optional:
      - collection
      - movie
    tmdb_collection_details: <<collection>>
    tmdb_movie: <<movie>>
    sync_mode: sync
    # item_assets: true
    collection_order: release
    delete_below_minimum: true

collections:
  IMDb Top 250 Movies:
    imdb_chart: top_movies
    collection_order: custom
    sync_mode: sync
    sort_title: 01-Top Movies
    visible_library: false
    collection_mode: hide               # hides in library

  Recently Released Movies:
    sort_title: 00-Recently Released
    smart_filter:
      all:
        release: 90
    summary: Released Movies in the last 90 Days
    collection_mode: hide
    visible_home: true                  # shows on home screen

  # ATMOS+DV:
   # plex_all: true
    # filters:
      # - has_dolby_vision: true
        # audio_track_title.regex: (?i)\batmos(\b|\d)
      # - has_dolby_vision: true
        # filepath.regex: (?i)\batmos(\b|\d)
  # Atmos:
    # sort_title: 00-Atmos
    # smart_filter:
      # all:
        # audio_codec:
        # - dolby_atmos
        # - truehd_atmos
        # - plus_atmos
    # summary: Dolby Atmos
    # collection_mode: hide
    # visible_home: true                  # shows on home screen

  Trending Movies:
    template: { name: Chart, num: 2 }
    tmdb_trending_daily: 30
    tmdb_trending_weekly: 30
    sync_mode: sync
    summary: Trending Movies

  Popular Movies:
    template: { name: Chart, num: 3 }
    tmdb_popular: 40
    imdb_list:
      url: https://www.imdb.com/search/title/?title_type=feature,documentary,short
      limit: 40
    summary: Popular Movies Across The Internet
    radarr_add_missing: true
    radarr_tag: pmm - Popular

  DCU:
    template:
      name: Collection
    imdb_list: https://www.imdb.com/list/ls047446610/
  MCU:
    template:
      name: Collection
    imdb_list: https://www.imdb.com/list/ls066946827/
  MCU - Chronological:
    template:
      name: Collection
    imdb_list: https://www.imdb.com/list/ls021001839/

  Top 10 Pirated Movies:
    template: { name: Chart, num: 4 }
    # sort_title: 01-Top Movies
    trakt_list: https://trakt.tv/users/linaspurinis/lists/top-10-pirated-movies-of-the-week?sort=rank,asc
    sync_mode: sync

  Top 10 Watched Movies Of The Week:
    template: { name: Chart, num: 5 }
    trakt_list: https://trakt.tv/users/linaspurinis/lists/top-watched-movies-of-the-week?sort=rank,asc
    sync_mode: sync
    limit: 30

# dynamic_collections:
  # TMDb Collections:
    # type: tmdb_collection
    # remove_suffix: "Collection"

Logs
https://gist.github.com/ikwtif/a06da7bd2d92fc1e0e59203931985617

An update to resolve this has been pushed with 1.19, @ikwtif this should be resolved.


fix Avoid return ColumnConst for vliteral

Proposed changes
Issue Number: close #xxx
Problem summary
ColumnConst is hard to use and very error-prone.
eg,
the remove_nullable() method will not remove the nullable of inner column data of ColumnConst.
And is_nullable() method is comment out in ColumnConst with unknown reason.
So this PR, I change the default behavior of vliteral, to not return ColumnConst.
Checklist(Required)

Does it affect the original behavior:

[ ] Yes
[ ] No
[ ] I don't know

Has unit tests been added:

[ ] Yes
[ ] No
[ ] No Need

Has document been added or modified:

[ ] Yes
[ ] No
[ ] No Need

Does it need to update dependencies:

[ ] Yes
[ ] No

Are there any changes that cannot be rolled back:

[ ] Yes (If Yes, please explain WHY)
[ ] No

Further comments
If this is a relatively large or complex change, kick off the discussion at<EMAIL_ADDRESS>by explaining why you chose the solution you did and what alternatives you considered, etc...

Wrong fix


Add a more specific message for duplicated items in Ftrack

Changelog Description
Add precision in duplicate warning pop up telling the user to delete or rename the duplicated item.
Testing notes:

on Ftrack, create an asset already existing in a different folder
after the synchro, a pop up should appear with the modified message

This will be added in release/3.16.9-quad-1.3.0.


Insufficient option usage help

It's possible to write this:
		cli.StringFlag{
			Name:  "config, c",
			Usage: "Load configuration from this file",
		},

Now the help says:
GLOBAL OPTIONS:
   --config, -c 	Load configuration from this file
  [...]

But this doesn't tell the end user that the flag takes a value. The usual way is to show that it is:
   --config FILE, -c FILE	Load configuration from FILE

As far as I can see, you can't do this. The flag package, of course, has its own way:
flag.String("c", "", "Load configuration from `file`")

Here, the backtick-enclosed part of the help will be used to infer what the name of the value is.

Agreed, support for placeholders would be nice. Thanks for the idea!


shuffle seed brokers before fetching metadata

Yesterday, a component of ours had vastly increased latency during an
outage, to the point where callers were timing out and seeing issues.
Upon tracking this down, we found that the cause of the latency was that
broker 0 was replaced. This component uses the sync producer, and as a
result of broker 0 being replaced, any produce request that fetched
metadata stalled for the default connect timeout (10s).
Instead, fetch metadata from seed brokers in a random order. This means
that a single broker going down will slow down at most 1/NBROKERS
metadata fetches, rather than every single one.

@dasch I think the circleci failure is unrelated, is it?

It seems so, but restarting the build doesn't seem to fix it...

Merging this, will fix CI tomorrow.


[proposition] memory issue, use file.copy instead of converting to byte list and back

Hi there!
I'd like to make a proposition for the saveFile when providing a file to save, maybe I didn't understand the use case but here is an example issue:
When dealing with large files in cache in the case of a video editing app for example, some media files may find themselves in the temporary directory or not a desired location, so they need to be moved to a more definitive place through this package.
However, and it's what I encountered, saving very large files will cause the app to crash since the file is converted to a byte list, so into the device memory, then to the desired location.
My proposition is to use file.save provided natively by Dart, instead of doing the conversion to byte list.
I've had to debug an app that suffered from this issue and the behavior monitored through memory device tools was identical for all files used.
I can make a PR if this is desirable.

Hey,
My hands are full right now, please create a PR, I'll merge it and release.
Thanks a lot for a suggestion I was already thinking about this because there's already an issue open for app crashing while saving the large file.
Thank You so much

My pleasure, and sounds good, will create one in the next few days then:)

Yeah thanks alot man @Silfalion

Merged your PR