the following Feature not present on the CPU: 0:6

最新推荐文章于 2022-11-25 22:40:15 发布
最新推荐文章于 2022-11-25 22:40:15 发布 · 138 阅读 · 0
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#Ubuntu #虚拟机

本文介绍了在VirtualBox中安装Ubuntu 8.0.4.2 Server时遇到的PAE问题及解决方案。通过启用虚拟机设置中的PAE/NX选项，成功解决了因CPU不支持PAE而导致的安装问题。

在VirtualBox上安装Ubuntu 8.0.4.2 Server的时候遇到的问题

网上找了下原因，原来Ubuntu需要CPU支持PAE

 

解决方法：很简单，虚拟机设置，高级里面有个复选框，PAE/NX打上勾，启动，正常

The BR(Brotli) Compressed Data Format
哦，原来你也在这里。
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E:\python\python.exe "E:\Program Files\JetBrains\pycharm_project\PointNetCFD-main\PointNetCFD.py" 2025-05-20 16:17:39.868194: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. 2025-05-20 16:17:41.799963: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`. Number of data is: 75 E:\python\Lib\site-packages\keras\src\layers\convolutional\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs) 2025-05-20 16:17:47.483218: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations. To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d), but are not present in its tracked objects: <tf.Variable 'conv1d/kernel:0' shape=(1, 2, 64) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_1), but are not present in its tracked objects: <tf.Variable 'conv1d_1/kernel:0' shape=(1, 64, 64) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_2), but are not present in its tracked objects: <tf.Variable 'conv1d_2/kernel:0' shape=(1, 64, 64) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_3), but are not present in its tracked objects: <tf.Variable 'conv1d_3/kernel:0' shape=(1, 64, 128) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_4), but are not present in its tracked objects: <tf.Variable 'conv1d_4/kernel:0' shape=(1, 128, 1024) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_5), but are not present in its tracked objects: <tf.Variable 'conv1d_5/kernel:0' shape=(1, 1088, 512) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_6), but are not present in its tracked objects: <tf.Variable 'conv1d_6/kernel:0' shape=(1, 512, 256) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_7), but are not present in its tracked objects: <tf.Variable 'conv1d_7/kernel:0' shape=(1, 256, 128) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_8), but are not present in its tracked objects: <tf.Variable 'conv1d_8/kernel:0' shape=(1, 128, 128) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. WARNING:tensorflow: The following Variables were used a Lambda layer's call (tf.compat.v1.nn.conv1d_9), but are not present in its tracked objects: <tf.Variable 'conv1d_9/kernel:0' shape=(1, 128, 3) dtype=float32> It is possible that this is intended behavior, but it is more likely an omission. This is a strong indication that this layer should be formulated as a subclassed Layer rather than a Lambda layer. Traceback (most recent call last): File "E:\Program Files\JetBrains\pycharm_project\PointNetCFD-main\PointNetCFD.py", line 199, in <module> model.compile(optimizers.Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=0.000001, decay=decaying_rate) ^^^^^^^^^^^^^^^ AttributeError: module 'tensorflow.python.keras.optimizers' has no attribute 'Adam' Process finished with exit code 1
05-22
以上就是针对 `module 'tensorflow.python.keras.optimizers' has no attribute 'Adam'` 这一常见异常状况所提供的全面解析及其对应措施说明[^6]。 ```python import tensorflow as tf # 正确的方式是在 TF 2.x 下...
protected-mode no port 6379 tcp-backlog 511 timeout 0 tcp-keepalive 300 daemonize no pidfile /var/run/redis_6379.pid loglevel notice logfile "" databases 16 always-show-logo no set-proc-title yes proc-title-template "{title} {listen-addr} {server-mode}" stop-writes-on-bgsave-error yes rdbcompression yes rdbchecksum yes dbfilename dump.rdb rdb-del-sync-files no dir ./ replica-serve-stale-data yes replica-read-only yes repl-diskless-sync no repl-diskless-sync-delay 5 repl-diskless-load disabled repl-disable-tcp-nodelay no replica-priority 100 acllog-max-len 128 requirepass Guyuan@2021 # New users are initialized with restrictive permissions by default, via the # equivalent of this ACL rule 'off resetkeys -@all'. Starting with Redis 6.2, it # is possible to manage access to Pub/Sub channels with ACL rules as well. The # default Pub/Sub channels permission if new users is controlled by the # acl-pubsub-default configuration directive, which accepts one of these values: # # allchannels: grants access to all Pub/Sub channels # resetchannels: revokes access to all Pub/Sub channels # # To ensure backward compatibility while upgrading Redis 6.0, acl-pubsub-default # defaults to the 'allchannels' permission. # # Future compatibility note: it is very likely that in a future version of Redis # the directive's default of 'allchannels' will be changed to 'resetchannels' in # order to provide better out-of-the-box Pub/Sub security. Therefore, it is # recommended that you explicitly define Pub/Sub permissions for all users # rather then rely on implicit default values. Once you've set explicit # Pub/Sub for all existing users, you should uncomment the following line. # # acl-pubsub-default resetchannels # Command renaming (DEPRECATED). # # ------------------------------------------------------------------------ # WARNING: avoid using this option if possible. Instead use ACLs to remove # commands from the default user, and put them only in some admin user you # create for administrative purposes. # ------------------------------------------------------------------------ # # It is possible to change the name of dangerous commands in a shared # environment. For instance the CONFIG command may be renamed into something # hard to guess so that it will still be available for internal-use tools # but not available for general clients. # # Example: # # rename-command CONFIG b840fc02d524045429941cc15f59e41cb7be6c52 # # It is also possible to completely kill a command by renaming it into # an empty string: # # rename-command CONFIG "" # # Please note that changing the name of commands that are logged into the # AOF file or transmitted to replicas may cause problems. ################################### CLIENTS #################################### # Set the max number of connected clients at the same time. By default # this limit is set to 10000 clients, however if the Redis server is not # able to configure the process file limit to allow for the specified limit # the max number of allowed clients is set to the current file limit # minus 32 (as Redis reserves a few file descriptors for internal uses). # # Once the limit is reached Redis will close all the new connections sending # an error 'max number of clients reached'. # # IMPORTANT: When Redis Cluster is used, the max number of connections is also # shared with the cluster bus: every node in the cluster will use two # connections, one incoming and another outgoing. It is important to size the # limit accordingly in case of very large clusters. # # maxclients 10000 ############################## MEMORY MANAGEMENT ################################ # Set a memory usage limit to the specified amount of bytes. # When the memory limit is reached Redis will try to remove keys # according to the eviction policy selected (see maxmemory-policy). # # If Redis can't remove keys according to the policy, or if the policy is # set to 'noeviction', Redis will start to reply with errors to commands # that would use more memory, like SET, LPUSH, and so on, and will continue # to reply to read-only commands like GET. # # This option is usually useful when using Redis as an LRU or LFU cache, or to # set a hard memory limit for an instance (using the 'noeviction' policy). # # WARNING: If you have replicas attached to an instance with maxmemory on, # the size of the output buffers needed to feed the replicas are subtracted # from the used memory count, so that network problems / resyncs will # not trigger a loop where keys are evicted, and in turn the output # buffer of replicas is full with DELs of keys evicted triggering the deletion # of more keys, and so forth until the database is completely emptied. # # In short... if you have replicas attached it is suggested that you set a lower # limit for maxmemory so that there is some free RAM on the system for replica # output buffers (but this is not needed if the policy is 'noeviction'). # # maxmemory <bytes> # MAXMEMORY POLICY: how Redis will select what to remove when maxmemory # is reached. You can select one from the following behaviors: # # volatile-lru -> Evict using approximated LRU, only keys with an expire set. # allkeys-lru -> Evict any key using approximated LRU. # volatile-lfu -> Evict using approximated LFU, only keys with an expire set. # allkeys-lfu -> Evict any key using approximated LFU. # volatile-random -> Remove a random key having an expire set. # allkeys-random -> Remove a random key, any key. # volatile-ttl -> Remove the key with the nearest expire time (minor TTL) # noeviction -> Don't evict anything, just return an error on write operations. # # LRU means Least Recently Used # LFU means Least Frequently Used # # Both LRU, LFU and volatile-ttl are implemented using approximated # randomized algorithms. # # Note: with any of the above policies, when there are no suitable keys for # eviction, Redis will return an error on write operations that require # more memory. These are usually commands that create new keys, add data or # modify existing keys. A few examples are: SET, INCR, HSET, LPUSH, SUNIONSTORE, # SORT (due to the STORE argument), and EXEC (if the transaction includes any # command that requires memory). # # The default is: # # maxmemory-policy noeviction # LRU, LFU and minimal TTL algorithms are not precise algorithms but approximated # algorithms (in order to save memory), so you can tune it for speed or # accuracy. By default Redis will check five keys and pick the one that was # used least recently, you can change the sample size using the following # configuration directive. # # The default of 5 produces good enough results. 10 Approximates very closely # true LRU but costs more CPU. 3 is faster but not very accurate. # # maxmemory-samples 5 # Eviction processing is designed to function well with the default setting. # If there is an unusually large amount of write traffic, this value may need to # be increased. Decreasing this value may reduce latency at the risk of # eviction processing effectiveness # 0 = minimum latency, 10 = default, 100 = process without regard to latency # # maxmemory-eviction-tenacity 10 # Starting from Redis 5, by default a replica will ignore its maxmemory setting # (unless it is promoted to master after a failover or manually). It means # that the eviction of keys will be just handled by the master, sending the # DEL commands to the replica as keys evict in the master side. # # This behavior ensures that masters and replicas stay consistent, and is usually # what you want, however if your replica is writable, or you want the replica # to have a different memory setting, and you are sure all the writes performed # to the replica are idempotent, then you may change this default (but be sure # to understand what you are doing). # # Note that since the replica by default does not evict, it may end using more # memory than the one set via maxmemory (there are certain buffers that may # be larger on the replica, or data structures may sometimes take more memory # and so forth). So make sure you monitor your replicas and make sure they # have enough memory to never hit a real out-of-memory condition before the # master hits the configured maxmemory setting. # # replica-ignore-maxmemory yes # Redis reclaims expired keys in two ways: upon access when those keys are # found to be expired, and also in background, in what is called the # "active expire key". The key space is slowly and interactively scanned # looking for expired keys to reclaim, so that it is possible to free memory # of keys that are expired and will never be accessed again in a short time. # # The default effort of the expire cycle will try to avoid having more than # ten percent of expired keys still in memory, and will try to avoid consuming # more than 25% of total memory and to add latency to the system. However # it is possible to increase the expire "effort" that is normally set to # "1", to a greater value, up to the value "10". At its maximum value the # system will use more CPU, longer cycles (and technically may introduce # more latency), and will tolerate less already expired keys still present # in the system. It's a tradeoff between memory, CPU and latency. # # active-expire-effort 1 ############################# LAZY FREEING #################################### # Redis has two primitives to delete keys. One is called DEL and is a blocking # deletion of the object. It means that the server stops processing new commands # in order to reclaim all the memory associated with an object in a synchronous # way. If the key deleted is associated with a small object, the time needed # in order to execute the DEL command is very small and comparable to most other # O(1) or O(log_N) commands in Redis. However if the key is associated with an # aggregated value containing millions of elements, the server can block for # a long time (even seconds) in order to complete the operation. # # For the above reasons Redis also offers non blocking deletion primitives # such as UNLINK (non blocking DEL) and the ASYNC option of FLUSHALL and # FLUSHDB commands, in order to reclaim memory in background. Those commands # are executed in constant time. Another thread will incrementally free the # object in the background as fast as possible. # # DEL, UNLINK and ASYNC option of FLUSHALL and FLUSHDB are user-controlled. # It's up to the design of the application to understand when it is a good # idea to use one or the other. However the Redis server sometimes has to # delete keys or flush the whole database as a side effect of other operations. # Specifically Redis deletes objects independently of a user call in the # following scenarios: # # 1) On eviction, because of the maxmemory and maxmemory policy configurations, # in order to make room for new data, without going over the specified # memory limit. # 2) Because of expire: when a key with an associated time to live (see the # EXPIRE command) must be deleted from memory. # 3) Because of a side effect of a command that stores data on a key that may # already exist. For example the RENAME command may delete the old key # content when it is replaced with another one. Similarly SUNIONSTORE # or SORT with STORE option may delete existing keys. The SET command # itself removes any old content of the specified key in order to replace # it with the specified string. # 4) During replication, when a replica performs a full resynchronization with # its master, the content of the whole database is removed in order to # load the RDB file just transferred. # # In all the above cases the default is to delete objects in a blocking way, # like if DEL was called. However you can configure each case specifically # in order to instead release memory in a non-blocking way like if UNLINK # was called, using the following configuration directives. lazyfree-lazy-eviction no lazyfree-lazy-expire no lazyfree-lazy-server-del no replica-lazy-flush no # It is also possible, for the case when to replace the user code DEL calls # with UNLINK calls is not easy, to modify the default behavior of the DEL # command to act exactly like UNLINK, using the following configuration # directive: lazyfree-lazy-user-del no # FLUSHDB, FLUSHALL, and SCRIPT FLUSH support both asynchronous and synchronous # deletion, which can be controlled by passing the [SYNC|ASYNC] flags into the # commands. When neither flag is passed, this directive will be used to determine # if the data should be deleted asynchronously. lazyfree-lazy-user-flush no ################################ THREADED I/O ################################# # Redis is mostly single threaded, however there are certain threaded # operations such as UNLINK, slow I/O accesses and other things that are # performed on side threads. # # Now it is also possible to handle Redis clients socket reads and writes # in different I/O threads. Since especially writing is so slow, normally # Redis users use pipelining in order to speed up the Redis performances per # core, and spawn multiple instances in order to scale more. Using I/O # threads it is possible to easily speedup two times Redis without resorting # to pipelining nor sharding of the instance. # # By default threading is disabled, we suggest enabling it only in machines # that have at least 4 or more cores, leaving at least one spare core. # Using more than 8 threads is unlikely to help much. We also recommend using # threaded I/O only if you actually have performance problems, with Redis # instances being able to use a quite big percentage of CPU time, otherwise # there is no point in using this feature. # # So for instance if you have a four cores boxes, try to use 2 or 3 I/O # threads, if you have a 8 cores, try to use 6 threads. In order to # enable I/O threads use the following configuration directive: # # io-threads 4 # # Setting io-threads to 1 will just use the main thread as usual. # When I/O threads are enabled, we only use threads for writes, that is # to thread the write(2) syscall and transfer the client buffers to the # socket. However it is also possible to enable threading of reads and # protocol parsing using the following configuration directive, by setting # it to yes: # # io-threads-do-reads no # # Usually threading reads doesn't help much. # # NOTE 1: This configuration directive cannot be changed at runtime via # CONFIG SET. Aso this feature currently does not work when SSL is # enabled. # # NOTE 2: If you want to test the Redis speedup using redis-benchmark, make # sure you also run the benchmark itself in threaded mode, using the # --threads option to match the number of Redis threads, otherwise you'll not # be able to notice the improvements. ############################ KERNEL OOM CONTROL ############################## # On Linux, it is possible to hint the kernel OOM killer on what processes # should be killed first when out of memory. # # Enabling this feature makes Redis actively control the oom_score_adj value # for all its processes, depending on their role. The default scores will # attempt to have background child processes killed before all others, and # replicas killed before masters. # # Redis supports three options: # # no: Don't make changes to oom-score-adj (default). # yes: Alias to "relative" see below. # absolute: Values in oom-score-adj-values are written as is to the kernel. # relative: Values are used relative to the initial value of oom_score_adj when # the server starts and are then clamped to a range of -1000 to 1000. # Because typically the initial value is 0, they will often match the # absolute values. oom-score-adj no # When oom-score-adj is used, this directive controls the specific values used # for master, replica and background child processes. Values range -2000 to # 2000 (higher means more likely to be killed). # # Unprivileged processes (not root, and without CAP_SYS_RESOURCE capabilities) # can freely increase their value, but not decrease it below its initial # settings. This means that setting oom-score-adj to "relative" and setting the # oom-score-adj-values to positive values will always succeed. oom-score-adj-values 0 200 800 #################### KERNEL transparent hugepage CONTROL ###################### # Usually the kernel Transparent Huge Pages control is set to "madvise" or # or "never" by default (/sys/kernel/mm/transparent_hugepage/enabled), in which # case this config has no effect. On systems in which it is set to "always", # redis will attempt to disable it specifically for the redis process in order # to avoid latency problems specifically with fork(2) and CoW. # If for some reason you prefer to keep it enabled, you can set this config to # "no" and the kernel global to "always". disable-thp yes ############################## APPEND ONLY MODE ############################### # By default Redis asynchronously dumps the dataset on disk. This mode is # good enough in many applications, but an issue with the Redis process or # a power outage may result into a few minutes of writes lost (depending on # the configured save points). # # The Append Only File is an alternative persistence mode that provides # much better durability. For instance using the default data fsync policy # (see later in the config file) Redis can lose just one second of writes in a # dramatic event like a server power outage, or a single write if something # wrong with the Redis process itself happens, but the operating system is # still running correctly. # # AOF and RDB persistence can be enabled at the same time without problems. # If the AOF is enabled on startup Redis will load the AOF, that is the file # with the better durability guarantees. # # Please check https://redis.io/topics/persistence for more information. appendonly yes # The name of the append only file (default: "appendonly.aof") appendfilename "appendonly.aof" # The fsync() call tells the Operating System to actually write data on disk # instead of waiting for more data in the output buffer. Some OS will really flush # data on disk, some other OS will just try to do it ASAP. # # Redis supports three different modes: # # no: don't fsync, just let the OS flush the data when it wants. Faster. # always: fsync after every write to the append only log. Slow, Safest. # everysec: fsync only one time every second. Compromise. # # The default is "everysec", as that's usually the right compromise between # speed and data safety. It's up to you to understand if you can relax this to # "no" that will let the operating system flush the output buffer when # it wants, for better performances (but if you can live with the idea of # some data loss consider the default persistence mode that's snapshotting), # or on the contrary, use "always" that's very slow but a bit safer than # everysec. # # More details please check the following article: # http://antirez.com/post/redis-persistence-demystified.html # # If unsure, use "everysec". # appendfsync always appendfsync everysec # appendfsync no # When the AOF fsync policy is set to always or everysec, and a background # saving process (a background save or AOF log background rewriting) is # performing a lot of I/O against the disk, in some Linux configurations # Redis may block too long on the fsync() call. Note that there is no fix for # this currently, as even performing fsync in a different thread will block # our synchronous write(2) call. # # In order to mitigate this problem it's possible to use the following option # that will prevent fsync() from being called in the main process while a # BGSAVE or BGREWRITEAOF is in progress. # # This means that while another child is saving, the durability of Redis is # the same as "appendfsync none". In practical terms, this means that it is # possible to lose up to 30 seconds of log in the worst scenario (with the # default Linux settings). # # If you have latency problems turn this to "yes". Otherwise leave it as # "no" that is the safest pick from the point of view of durability. no-appendfsync-on-rewrite no # Automatic rewrite of the append only file. # Redis is able to automatically rewrite the log file implicitly calling # BGREWRITEAOF when the AOF log size grows by the specified percentage. # # This is how it works: Redis remembers the size of the AOF file after the # latest rewrite (if no rewrite has happened since the restart, the size of # the AOF at startup is used). # # This base size is compared to the current size. If the current size is # bigger than the specified percentage, the rewrite is triggered. Also # you need to specify a minimal size for the AOF file to be rewritten, this # is useful to avoid rewriting the AOF file even if the percentage increase # is reached but it is still pretty small. # # Specify a percentage of zero in order to disable the automatic AOF # rewrite feature. auto-aof-rewrite-percentage 100 auto-aof-rewrite-min-size 64mb # An AOF file may be found to be truncated at the end during the Redis # startup process, when the AOF data gets loaded back into memory. # This may happen when the system where Redis is running # crashes, especially when an ext4 filesystem is mounted without the # data=ordered option (however this can't happen when Redis itself # crashes or aborts but the operating system still works correctly). # # Redis can either exit with an error when this happens, or load as much # data as possible (the default now) and start if the AOF file is found # to be truncated at the end. The following option controls this behavior. # # If aof-load-truncated is set to yes, a truncated AOF file is loaded and # the Redis server starts emitting a log to inform the user of the event. # Otherwise if the option is set to no, the server aborts with an error # and refuses to start. When the option is set to no, the user requires # to fix the AOF file using the "redis-check-aof" utility before to restart # the server. # # Note that if the AOF file will be found to be corrupted in the middle # the server will still exit with an error. This option only applies when # Redis will try to read more data from the AOF file but not enough bytes # will be found. aof-load-truncated yes # When rewriting the AOF file, Redis is able to use an RDB preamble in the # AOF file for faster rewrites and recoveries. When this option is turned # on the rewritten AOF file is composed of two different stanzas: # # [RDB file][AOF tail] # # When loading, Redis recognizes that the AOF file starts with the "REDIS" # string and loads the prefixed RDB file, then continues loading the AOF # tail. aof-use-rdb-preamble yes ################################ LUA SCRIPTING ############################### # Max execution time of a Lua script in milliseconds. # # If the maximum execution time is reached Redis will log that a script is # still in execution after the maximum allowed time and will start to # reply to queries with an error. # # When a long running script exceeds the maximum execution time only the # SCRIPT KILL and SHUTDOWN NOSAVE commands are available. The first can be # used to stop a script that did not yet call any write commands. The second # is the only way to shut down the server in the case a write command was # already issued by the script but the user doesn't want to wait for the natural # termination of the script. # # Set it to 0 or a negative value for unlimited execution without warnings. lua-time-limit 5000 ################################ REDIS CLUSTER ############################### # Normal Redis instances can't be part of a Redis Cluster; only nodes that are # started as cluster nodes can. In order to start a Redis instance as a # cluster node enable the cluster support uncommenting the following: # # cluster-enabled yes # Every cluster node has a cluster configuration file. This file is not # intended to be edited by hand. It is created and updated by Redis nodes. # Every Redis Cluster node requires a different cluster configuration file. # Make sure that instances running in the same system do not have # overlapping cluster configuration file names. # # cluster-config-file nodes-6379.conf # Cluster node timeout is the amount of milliseconds a node must be unreachable # for it to be considered in failure state. # Most other internal time limits are a multiple of the node timeout. # # cluster-node-timeout 15000 # A replica of a failing master will avoid to start a failover if its data # looks too old. # # There is no simple way for a replica to actually have an exact measure of # its "data age", so the following two checks are performed: # # 1) If there are multiple replicas able to failover, they exchange messages # in order to try to give an advantage to the replica with the best # replication offset (more data from the master processed). # Replicas will try to get their rank by offset, and apply to the start # of the failover a delay proportional to their rank. # # 2) Every single replica computes the time of the last interaction with # its master. This can be the last ping or command received (if the master # is still in the "connected" state), or the time that elapsed since the # disconnection with the master (if the replication link is currently down). # If the last interaction is too old, the replica will not try to failover # at all. # # The point "2" can be tuned by user. Specifically a replica will not perform # the failover if, since the last interaction with the master, the time # elapsed is greater than: # # (node-timeout * cluster-replica-validity-factor) + repl-ping-replica-period # # So for example if node-timeout is 30 seconds, and the cluster-replica-validity-factor # is 10, and assuming a default repl-ping-replica-period of 10 seconds, the # replica will not try to failover if it was not able to talk with the master # for longer than 310 seconds. # # A large cluster-replica-validity-factor may allow replicas with too old data to failover # a master, while a too small value may prevent the cluster from being able to # elect a replica at all. # # For maximum availability, it is possible to set the cluster-replica-validity-factor # to a value of 0, which means, that replicas will always try to failover the # master regardless of the last time they interacted with the master. # (However they'll always try to apply a delay proportional to their # offset rank). # # Zero is the only value able to guarantee that when all the partitions heal # the cluster will always be able to continue. # # cluster-replica-validity-factor 10 # Cluster replicas are able to migrate to orphaned masters, that are masters # that are left without working replicas. This improves the cluster ability # to resist to failures as otherwise an orphaned master can't be failed over # in case of failure if it has no working replicas. # # Replicas migrate to orphaned masters only if there are still at least a # given number of other working replicas for their old master. This number # is the "migration barrier". A migration barrier of 1 means that a replica # will migrate only if there is at least 1 other working replica for its master # and so forth. It usually reflects the number of replicas you want for every # master in your cluster. # # Default is 1 (replicas migrate only if their masters remain with at least # one replica). To disable migration just set it to a very large value or # set cluster-allow-replica-migration to 'no'. # A value of 0 can be set but is useful only for debugging and dangerous # in production. # # cluster-migration-barrier 1 # Turning off this option allows to use less automatic cluster configuration. # It both disables migration to orphaned masters and migration from masters # that became empty. # # Default is 'yes' (allow automatic migrations). # # cluster-allow-replica-migration yes # By default Redis Cluster nodes stop accepting queries if they detect there # is at least a hash slot uncovered (no available node is serving it). # This way if the cluster is partially down (for example a range of hash slots # are no longer covered) all the cluster becomes, eventually, unavailable. # It automatically returns available as soon as all the slots are covered again. # # However sometimes you want the subset of the cluster which is working, # to continue to accept queries for the part of the key space that is still # covered. In order to do so, just set the cluster-require-full-coverage # option to no. # # cluster-require-full-coverage yes # This option, when set to yes, prevents replicas from trying to failover its # master during master failures. However the replica can still perform a # manual failover, if forced to do so. # # This is useful in different scenarios, especially in the case of multiple # data center operations, where we want one side to never be promoted if not # in the case of a total DC failure. # # cluster-replica-no-failover no # This option, when set to yes, allows nodes to serve read traffic while the # the cluster is in a down state, as long as it believes it owns the slots. # # This is useful for two cases. The first case is for when an application # doesn't require consistency of data during node failures or network partitions. # One example of this is a cache, where as long as the node has the data it # should be able to serve it. # # The second use case is for configurations that don't meet the recommended # three shards but want to enable cluster mode and scale later. A # master outage in a 1 or 2 shard configuration causes a read/write outage to the # entire cluster without this option set, with it set there is only a write outage. # Without a quorum of masters, slot ownership will not change automatically. # # cluster-allow-reads-when-down no # In order to setup your cluster make sure to read the documentation # available at https://redis.io web site. ########################## CLUSTER DOCKER/NAT support ######################## # In certain deployments, Redis Cluster nodes address discovery fails, because # addresses are NAT-ted or because ports are forwarded (the typical case is # Docker and other containers). # # In order to make Redis Cluster working in such environments, a static # configuration where each node knows its public address is needed. The # following four options are used for this scope, and are: # # * cluster-announce-ip # * cluster-announce-port # * cluster-announce-tls-port # * cluster-announce-bus-port # # Each instructs the node about its address, client ports (for connections # without and with TLS) and cluster message bus port. The information is then # published in the header of the bus packets so that other nodes will be able to # correctly map the address of the node publishing the information. # # If cluster-tls is set to yes and cluster-announce-tls-port is omitted or set # to zero, then cluster-announce-port refers to the TLS port. Note also that # cluster-announce-tls-port has no effect if cluster-tls is set to no. # # If the above options are not used, the normal Redis Cluster auto-detection # will be used instead. # # Note that when remapped, the bus port may not be at the fixed offset of # clients port + 10000, so you can specify any port and bus-port depending # on how they get remapped. If the bus-port is not set, a fixed offset of # 10000 will be used as usual. # # Example: # # cluster-announce-ip 10.1.1.5 # cluster-announce-tls-port 6379 # cluster-announce-port 0 # cluster-announce-bus-port 6380 ################################## SLOW LOG ################################### # The Redis Slow Log is a system to log queries that exceeded a specified # execution time. The execution time does not include the I/O operations # like talking with the client, sending the reply and so forth, # but just the time needed to actually execute the command (this is the only # stage of command execution where the thread is blocked and can not serve # other requests in the meantime). # # You can configure the slow log with two parameters: one tells Redis # what is the execution time, in microseconds, to exceed in order for the # command to get logged, and the other parameter is the length of the # slow log. When a new command is logged the oldest one is removed from the # queue of logged commands. # The following time is expressed in microseconds, so 1000000 is equivalent # to one second. Note that a negative number disables the slow log, while # a value of zero forces the logging of every command. slowlog-log-slower-than 10000 # There is no limit to this length. Just be aware that it will consume memory. # You can reclaim memory used by the slow log with SLOWLOG RESET. slowlog-max-len 128 ################################ LATENCY MONITOR ############################## # The Redis latency monitoring subsystem samples different operations # at runtime in order to collect data related to possible sources of # latency of a Redis instance. # # Via the LATENCY command this information is available to the user that can # print graphs and obtain reports. # # The system only logs operations that were performed in a time equal or # greater than the amount of milliseconds specified via the # latency-monitor-threshold configuration directive. When its value is set # to zero, the latency monitor is turned off. # # By default latency monitoring is disabled since it is mostly not needed # if you don't have latency issues, and collecting data has a performance # impact, that while very small, can be measured under big load. Latency # monitoring can easily be enabled at runtime using the command # "CONFIG SET latency-monitor-threshold <milliseconds>" if needed. latency-monitor-threshold 0 ############################# EVENT NOTIFICATION ############################## # Redis can notify Pub/Sub clients about events happening in the key space. # This feature is documented at https://redis.io/topics/notifications # # For instance if keyspace events notification is enabled, and a client # performs a DEL operation on key "foo" stored in the Database 0, two # messages will be published via Pub/Sub: # # PUBLISH __keyspace@0__:foo del # PUBLISH __keyevent@0__:del foo # # It is possible to select the events that Redis will notify among a set # of classes. Every class is identified by a single character: # # K Keyspace events, published with __keyspace@<db>__ prefix. # E Keyevent events, published with __keyevent@<db>__ prefix. # g Generic commands (non-type specific) like DEL, EXPIRE, RENAME, ... # $ String commands # l List commands # s Set commands # h Hash commands # z Sorted set commands # x Expired events (events generated every time a key expires) # e Evicted events (events generated when a key is evicted for maxmemory) # t Stream commands # d Module key type events # m Key-miss events (Note: It is not included in the 'A' class) # A Alias for g$lshzxetd, so that the "AKE" string means all the events # (Except key-miss events which are excluded from 'A' due to their # unique nature). # # The "notify-keyspace-events" takes as argument a string that is composed # of zero or multiple characters. The empty string means that notifications # are disabled. # # Example: to enable list and generic events, from the point of view of the # event name, use: # # notify-keyspace-events Elg # # Example 2: to get the stream of the expired keys subscribing to channel # name __keyevent@0__:expired use: # # notify-keyspace-events Ex # # By default all notifications are disabled because most users don't need # this feature and the feature has some overhead. Note that if you don't # specify at least one of K or E, no events will be delivered. notify-keyspace-events "" ############################### GOPHER SERVER ################################# # Redis contains an implementation of the Gopher protocol, as specified in # the RFC 1436 (https://www.ietf.org/rfc/rfc1436.txt). # # The Gopher protocol was very popular in the late '90s. It is an alternative # to the web, and the implementation both server and client side is so simple # that the Redis server has just 100 lines of code in order to implement this # support. # # What do you do with Gopher nowadays? Well Gopher never *really* died, and # lately there is a movement in order for the Gopher more hierarchical content # composed of just plain text documents to be resurrected. Some want a simpler # internet, others believe that the mainstream internet became too much # controlled, and it's cool to create an alternative space for people that # want a bit of fresh air. # # Anyway for the 10nth birthday of the Redis, we gave it the Gopher protocol # as a gift. # # --- HOW IT WORKS? --- # # The Redis Gopher support uses the inline protocol of Redis, and specifically # two kind of inline requests that were anyway illegal: an empty request # or any request that starts with "/" (there are no Redis commands starting # with such a slash). Normal RESP2/RESP3 requests are completely out of the # path of the Gopher protocol implementation and are served as usual as well. # # If you open a connection to Redis when Gopher is enabled and send it # a string like "/foo", if there is a key named "/foo" it is served via the # Gopher protocol. # # In order to create a real Gopher "hole" (the name of a Gopher site in Gopher # talking), you likely need a script like the following: # # https://github.com/antirez/gopher2redis # # --- SECURITY WARNING --- # # If you plan to put Redis on the internet in a publicly accessible address # to server Gopher pages MAKE SURE TO SET A PASSWORD to the instance. # Once a password is set: # # 1. The Gopher server (when enabled, not by default) will still serve # content via Gopher. # 2. However other commands cannot be called before the client will # authenticate. # # So use the 'requirepass' option to protect your instance. # # Note that Gopher is not currently supported when 'io-threads-do-reads' # is enabled. # # To enable Gopher support, uncomment the following line and set the option # from no (the default) to yes. # # gopher-enabled no ############################### ADVANCED CONFIG ############################### # Hashes are encoded using a memory efficient data structure when they have a # small number of entries, and the biggest entry does not exceed a given # threshold. These thresholds can be configured using the following directives. hash-max-ziplist-entries 512 hash-max-ziplist-value 64 # Lists are also encoded in a special way to save a lot of space. # The number of entries allowed per internal list node can be specified # as a fixed maximum size or a maximum number of elements. # For a fixed maximum size, use -5 through -1, meaning: # -5: max size: 64 Kb <-- not recommended for normal workloads # -4: max size: 32 Kb <-- not recommended # -3: max size: 16 Kb <-- probably not recommended # -2: max size: 8 Kb <-- good # -1: max size: 4 Kb <-- good # Positive numbers mean store up to _exactly_ that number of elements # per list node. # The highest performing option is usually -2 (8 Kb size) or -1 (4 Kb size), # but if your use case is unique, adjust the settings as necessary. list-max-ziplist-size -2 # Lists may also be compressed. # Compress depth is the number of quicklist ziplist nodes from *each* side of # the list to *exclude* from compression. The head and tail of the list # are always uncompressed for fast push/pop operations. Settings are: # 0: disable all list compression # 1: depth 1 means "don't start compressing until after 1 node into the list, # going from either the head or tail" # So: [head]->node->node->...->node->[tail] # [head], [tail] will always be uncompressed; inner nodes will compress. # 2: [head]->[next]->node->node->...->node->[prev]->[tail] # 2 here means: don't compress head or head->next or tail->prev or tail, # but compress all nodes between them. # 3: [head]->[next]->[next]->node->node->...->node->[prev]->[prev]->[tail] # etc. list-compress-depth 0 # Sets have a special encoding in just one case: when a set is composed # of just strings that happen to be integers in radix 10 in the range # of 64 bit signed integers. # The following configuration setting sets the limit in the size of the # set in order to use this special memory saving encoding. set-max-intset-entries 512 # Similarly to hashes and lists, sorted sets are also specially encoded in # order to save a lot of space. This encoding is only used when the length and # elements of a sorted set are below the following limits: zset-max-ziplist-entries 128 zset-max-ziplist-value 64 # HyperLogLog sparse representation bytes limit. The limit includes the # 16 bytes header. When an HyperLogLog using the sparse representation crosses # this limit, it is converted into the dense representation. # # A value greater than 16000 is totally useless, since at that point the # dense representation is more memory efficient. # # The suggested value is ~ 3000 in order to have the benefits of # the space efficient encoding without slowing down too much PFADD, # which is O(N) with the sparse encoding. The value can be raised to # ~ 10000 when CPU is not a concern, but space is, and the data set is # composed of many HyperLogLogs with cardinality in the 0 - 15000 range. hll-sparse-max-bytes 3000 # Streams macro node max size / items. The stream data structure is a radix # tree of big nodes that encode multiple items inside. Using this configuration # it is possible to configure how big a single node can be in bytes, and the # maximum number of items it may contain before switching to a new node when # appending new stream entries. If any of the following settings are set to # zero, the limit is ignored, so for instance it is possible to set just a # max entries limit by setting max-bytes to 0 and max-entries to the desired # value. stream-node-max-bytes 4096 stream-node-max-entries 100 # Active rehashing uses 1 millisecond every 100 milliseconds of CPU time in # order to help rehashing the main Redis hash table (the one mapping top-level # keys to values). The hash table implementation Redis uses (see dict.c) # performs a lazy rehashing: the more operation you run into a hash table # that is rehashing, the more rehashing "steps" are performed, so if the # server is idle the rehashing is never complete and some more memory is used # by the hash table. # # The default is to use this millisecond 10 times every second in order to # actively rehash the main dictionaries, freeing memory when possible. # # If unsure: # use "activerehashing no" if you have hard latency requirements and it is # not a good thing in your environment that Redis can reply from time to time # to queries with 2 milliseconds delay. # # use "activerehashing yes" if you don't have such hard requirements but # want to free memory asap when possible. activerehashing yes # The client output buffer limits can be used to force disconnection of clients # that are not reading data from the server fast enough for some reason (a # common reason is that a Pub/Sub client can't consume messages as fast as the # publisher can produce them). # # The limit can be set differently for the three different classes of clients: # # normal -> normal clients including MONITOR clients # replica -> replica clients # pubsub -> clients subscribed to at least one pubsub channel or pattern # # The syntax of every client-output-buffer-limit directive is the following: # # client-output-buffer-limit <class> <hard limit> <soft limit> <soft seconds> # # A client is immediately disconnected once the hard limit is reached, or if # the soft limit is reached and remains reached for the specified number of # seconds (continuously). # So for instance if the hard limit is 32 megabytes and the soft limit is # 16 megabytes / 10 seconds, the client will get disconnected immediately # if the size of the output buffers reach 32 megabytes, but will also get # disconnected if the client reaches 16 megabytes and continuously overcomes # the limit for 10 seconds. # # By default normal clients are not limited because they don't receive data # without asking (in a push way), but just after a request, so only # asynchronous clients may create a scenario where data is requested faster # than it can read. # # Instead there is a default limit for pubsub and replica clients, since # subscribers and replicas receive data in a push fashion. # # Both the hard or the soft limit can be disabled by setting them to zero. client-output-buffer-limit normal 0 0 0 client-output-buffer-limit replica 256mb 64mb 60 client-output-buffer-limit pubsub 32mb 8mb 60 # Client query buffers accumulate new commands. They are limited to a fixed # amount by default in order to avoid that a protocol desynchronization (for # instance due to a bug in the client) will lead to unbound memory usage in # the query buffer. However you can configure it here if you have very special # needs, such us huge multi/exec requests or alike. # # client-query-buffer-limit 1gb # In the Redis protocol, bulk requests, that are, elements representing single # strings, are normally limited to 512 mb. However you can change this limit # here, but must be 1mb or greater # # proto-max-bulk-len 512mb # Redis calls an internal function to perform many background tasks, like # closing connections of clients in timeout, purging expired keys that are # never requested, and so forth. # # Not all tasks are performed with the same frequency, but Redis checks for # tasks to perform according to the specified "hz" value. # # By default "hz" is set to 10. Raising the value will use more CPU when # Redis is idle, but at the same time will make Redis more responsive when # there are many keys expiring at the same time, and timeouts may be # handled with more precision. # # The range is between 1 and 500, however a value over 100 is usually not # a good idea. Most users should use the default of 10 and raise this up to # 100 only in environments where very low latency is required. hz 10 # Normally it is useful to have an HZ value which is proportional to the # number of clients connected. This is useful in order, for instance, to # avoid too many clients are processed for each background task invocation # in order to avoid latency spikes. # # Since the default HZ value by default is conservatively set to 10, Redis # offers, and enables by default, the ability to use an adaptive HZ value # which will temporarily raise when there are many connected clients. # # When dynamic HZ is enabled, the actual configured HZ will be used # as a baseline, but multiples of the configured HZ value will be actually # used as needed once more clients are connected. In this way an idle # instance will use very little CPU time while a busy instance will be # more responsive. dynamic-hz yes # When a child rewrites the AOF file, if the following option is enabled # the file will be fsync-ed every 32 MB of data generated. This is useful # in order to commit the file to the disk more incrementally and avoid # big latency spikes. aof-rewrite-incremental-fsync yes # When redis saves RDB file, if the following option is enabled # the file will be fsync-ed every 32 MB of data generated. This is useful # in order to commit the file to the disk more incrementally and avoid # big latency spikes. rdb-save-incremental-fsync yes # Redis LFU eviction (see maxmemory setting) can be tuned. However it is a good # idea to start with the default settings and only change them after investigating # how to improve the performances and how the keys LFU change over time, which # is possible to inspect via the OBJECT FREQ command. # # There are two tunable parameters in the Redis LFU implementation: the # counter logarithm factor and the counter decay time. It is important to # understand what the two parameters mean before changing them. # # The LFU counter is just 8 bits per key, it's maximum value is 255, so Redis # uses a probabilistic increment with logarithmic behavior. Given the value # of the old counter, when a key is accessed, the counter is incremented in # this way: # # 1. A random number R between 0 and 1 is extracted. # 2. A probability P is calculated as 1/(old_value*lfu_log_factor+1). # 3. The counter is incremented only if R < P. # # The default lfu-log-factor is 10. This is a table of how the frequency # counter changes with a different number of accesses with different # logarithmic factors: # # +--------+------------+------------+------------+------------+------------+ # | factor | 100 hits | 1000 hits | 100K hits | 1M hits | 10M hits | # +--------+------------+------------+------------+------------+------------+ # | 0 | 104 | 255 | 255 | 255 | 255 | # +--------+------------+------------+------------+------------+------------+ # | 1 | 18 | 49 | 255 | 255 | 255 | # +--------+------------+------------+------------+------------+------------+ # | 10 | 10 | 18 | 142 | 255 | 255 | # +--------+------------+------------+------------+------------+------------+ # | 100 | 8 | 11 | 49 | 143 | 255 | # +--------+------------+------------+------------+------------+------------+ # # NOTE: The above table was obtained by running the following commands: # # redis-benchmark -n 1000000 incr foo # redis-cli object freq foo # # NOTE 2: The counter initial value is 5 in order to give new objects a chance # to accumulate hits. # # The counter decay time is the time, in minutes, that must elapse in order # for the key counter to be divided by two (or decremented if it has a value # less <= 10). # # The default value for the lfu-decay-time is 1. A special value of 0 means to # decay the counter every time it happens to be scanned. # # lfu-log-factor 10 # lfu-decay-time 1 ########################### ACTIVE DEFRAGMENTATION ####################### # # What is active defragmentation? # ------------------------------- # # Active (online) defragmentation allows a Redis server to compact the # spaces left between small allocations and deallocations of data in memory, # thus allowing to reclaim back memory. # # Fragmentation is a natural process that happens with every allocator (but # less so with Jemalloc, fortunately) and certain workloads. Normally a server # restart is needed in order to lower the fragmentation, or at least to flush # away all the data and create it again. However thanks to this feature # implemented by Oran Agra for Redis 4.0 this process can happen at runtime # in a "hot" way, while the server is running. # # Basically when the fragmentation is over a certain level (see the # configuration options below) Redis will start to create new copies of the # values in contiguous memory regions by exploiting certain specific Jemalloc # features (in order to understand if an allocation is causing fragmentation # and to allocate it in a better place), and at the same time, will release the # old copies of the data. This process, repeated incrementally for all the keys # will cause the fragmentation to drop back to normal values. # # Important things to understand: # # 1. This feature is disabled by default, and only works if you compiled Redis # to use the copy of Jemalloc we ship with the source code of Redis. # This is the default with Linux builds. # # 2. You never need to enable this feature if you don't have fragmentation # issues. # # 3. Once you experience fragmentation, you can enable this feature when # needed with the command "CONFIG SET activedefrag yes". # # The configuration parameters are able to fine tune the behavior of the # defragmentation process. If you are not sure about what they mean it is # a good idea to leave the defaults untouched. # Enabled active defragmentation # activedefrag no # Minimum amount of fragmentation waste to start active defrag # active-defrag-ignore-bytes 100mb # Minimum percentage of fragmentation to start active defrag # active-defrag-threshold-lower 10 # Maximum percentage of fragmentation at which we use maximum effort # active-defrag-threshold-upper 100 # Minimal effort for defrag in CPU percentage, to be used when the lower # threshold is reached # active-defrag-cycle-min 1 # Maximal effort for defrag in CPU percentage, to be used when the upper # threshold is reached # active-defrag-cycle-max 25 # Maximum number of set/hash/zset/list fields that will be processed from # the main dictionary scan # active-defrag-max-scan-fields 1000 # Jemalloc background thread for purging will be enabled by default jemalloc-bg-thread yes # It is possible to pin different threads and processes of Redis to specific # CPUs in your system, in order to maximize the performances of the server. # This is useful both in order to pin different Redis threads in different # CPUs, but also in order to make sure that multiple Redis instances running # in the same host will be pinned to different CPUs. # # Normally you can do this using the "taskset" command, however it is also # possible to this via Redis configuration directly, both in Linux and FreeBSD. # # You can pin the server/IO threads, bio threads, aof rewrite child process, and # the bgsave child process. The syntax to specify the cpu list is the same as # the taskset command: # # Set redis server/io threads to cpu affinity 0,2,4,6: # server_cpulist 0-7:2 # # Set bio threads to cpu affinity 1,3: # bio_cpulist 1,3 # # Set aof rewrite child process to cpu affinity 8,9,10,11: # aof_rewrite_cpulist 8-11 # # Set bgsave child process to cpu affinity 1,10,11 # bgsave_cpulist 1,10-11 # In some cases redis will emit warnings and even refuse to start if it detects # that the system is in bad state, it is possible to suppress these warnings # by setting the following config which takes a space delimited list of warnings # to suppress # # ignore-warnings ARM64-COW-BUG 在里面那边加上bind 0.0.0.0
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tcp 0 0 0.0.0.0:6379 0.0.0.0:* LISTEN - ``` #### 解决常见问题 如果遇到`(error) NOAUTH Authentication required`错误提示，表明当前Redis实例已启用了身份验证机制而未提供有效凭证。此时需按照前述步骤确认...
(venv) D:\Audio2Face\Audio2Face-3D-SDK>trtexec --version &&&& RUNNING TensorRT.trtexec [TensorRT v101401] [b48] # trtexec --version [11/27/2025-14:33:27] [I] TF32 is enabled by default. Add --noTF32 flag to further improve accuracy with some performance cost. === Model Options === --onnx=<file> ONNX model === Build Options === --minShapes=spec Build with dynamic shapes using a profile with the min shapes provided --optShapes=spec Build with dynamic shapes using a profile with the opt shapes provided --maxShapes=spec Build with dynamic shapes using a profile with the max shapes provided --minShapesCalib=spec Calibrate with dynamic shapes using a profile with the min shapes provided --optShapesCalib=spec Calibrate with dynamic shapes using a profile with the opt shapes provided --maxShapesCalib=spec Calibrate with dynamic shapes using a profile with the max shapes provided Note: All three of min, opt and max shapes must be supplied. However, if only opt shapes is supplied then it will be expanded so that min shapes and max shapes are set to the same values as opt shapes. Input names can be wrapped with escaped single quotes (ex: 'Input:0'). Example input shapes spec: input0:1x3x256x256,input1:1x3x128x128 For scalars (0-D shapes), use input0:scalar or simply input0: with nothing after the colon. Each input shape is supplied as a key-value pair where key is the input name and value is the dimensions (including the batch dimension) to be used for that input. Each key-value pair has the key and value separated using a colon (:). Multiple input shapes can be provided via comma-separated key-value pairs, and each input name can contain at most one wildcard ('*') character. --inputIOFormats=spec Type and format of each of the input tensors (default = all inputs in fp32:chw) See --outputIOFormats help for the grammar of type and format list. Note: If this option is specified, please set comma-separated types and formats for all inputs following the same order as network inputs ID (even if only one input needs specifying IO format) or set the type and format once for broadcasting. --outputIOFormats=spec Type and format of each of the output tensors (default = all outputs in fp32:chw) Note: If this option is specified, please set comma-separated types and formats for all outputs following the same order as network outputs ID (even if only one output needs specifying IO format) or set the type and format once for broadcasting. IO Formats: spec ::= IOfmt[","spec] IOfmt ::= type:fmt type ::= "fp32"|"fp16"|"bf16"|"int32"|"int64"|"int8"|"uint8"|"bool" fmt ::= ("chw"|"chw2"|"hwc8"|"chw4"|"chw16"|"chw32"|"dhwc8"| "cdhw32"|"hwc"|"dla_linear"|"dla_hwc4"|"hwc16"|"dhwc")["+"fmt] --memPoolSize=poolspec Specify the size constraints of the designated memory pool(s) Supports the following base-2 suffixes: B (Bytes), G (Gibibytes), K (Kibibytes), M (Mebibytes). If none of suffixes is appended, the defualt unit is in MiB. Note: Also accepts decimal sizes, e.g. 0.25M. Will be rounded down to the nearest integer bytes. In particular, for dlaSRAM the bytes will be rounded down to the nearest power of 2. Pool constraint: poolspec ::= poolfmt[","poolspec] poolfmt ::= pool:size pool ::= "workspace"|"dlaSRAM"|"dlaLocalDRAM"|"dlaGlobalDRAM"|"tacticSharedMem" --profilingVerbosity=mode Specify profiling verbosity. mode ::= layer_names_only|detailed|none (default = layer_names_only). Please only assign once. --avgTiming=M Set the number of times averaged in each iteration for kernel selection (default = 8) --refit Mark the engine as refittable. This will allow the inspection of refittable layers and weights within the engine. --stripWeights Strip weights from plan. This flag works with either refit or refit with identical weights. Default to latter, but you can switch to the former by enabling both --stripWeights and --refit at the same time. --stripAllWeights Alias for combining the --refit and --stripWeights options. It marks all weights as refittable, disregarding any performance impact. Additionally, it strips all refittable weights after the engine is built. --weightless [Deprecated] this knob has been deprecated. Please use --stripWeights --versionCompatible, --vc Mark the engine as version compatible. This allows the engine to be used with newer versions of TensorRT on the same host OS, as well as TensorRT's dispatch and lean runtimes. --pluginInstanceNorm, --pi Set `kNATIVE_INSTANCENORM` to false in the ONNX parser. This will cause the ONNX parser to use a plugin InstanceNorm implementation over the native implementation when parsing. --uint8AsymmetricQuantizationDLA Set `kENABLE_UINT8_AND_ASYMMETRIC_QUANTIZATION_DLA` to true in the ONNX parser. This directs the onnx parser to allow UINT8 as a quantization data type and import zero point values directly without converting to float type or all-zero values. Should only be set with DLA software version >= 3.16. --useRuntime=runtime TensorRT runtime to execute engine. "lean" and "dispatch" require loading VC engine and do not support building an engine. runtime::= "full"|"lean"|"dispatch" --leanDLLPath=<file> External lean runtime DLL to use in version compatible mode. --excludeLeanRuntime When --versionCompatible is enabled, this flag indicates that the generated engine should not include an embedded lean runtime. If this is set, the user must explicitly specify a valid lean runtime to use when loading the engine. --monitorMemory Enable memory monitor report for debugging usage. (default = disabled) --sparsity=spec Control sparsity (default = disabled). Sparsity: spec ::= "disable", "enable", "force" Note: Description about each of these options is as below disable = do not enable sparse tactics in the builder (this is the default) enable = enable sparse tactics in the builder (but these tactics will only be considered if the weights have the right sparsity pattern) force = enable sparse tactics in the builder and force-overwrite the weights to have a sparsity pattern (even if you loaded a model yourself) [Deprecated] this knob has been deprecated. Please use <polygraphy surgeon prune> to rewrite the weights. --noTF32 Disable tf32 precision (default is to enable tf32, in addition to fp32) --fp16 Enable fp16 precision, in addition to fp32 (default = disabled) --bf16 Enable bf16 precision, in addition to fp32 (default = disabled) --int8 Enable int8 precision, in addition to fp32 (default = disabled) --fp8 Enable fp8 precision, in addition to fp32 (default = disabled) --int4 Enable int4 precision, in addition to fp32 (default = disabled) --best Enable all precisions to achieve the best performance (default = disabled) Note: --fp16, --bf16, --int8, --fp8, --int4, --best are deprecated and superseded by strong typing. The AutoCast tool (https://nvidia.github.io/TensorRT-Model-Optimizer/guides/8_autocast.html) can be used to convert the network to be strongly typed. --stronglyTyped Create a strongly typed network. (default = disabled) --directIO [Deprecated] Avoid reformatting at network boundaries. (default = disabled) --precisionConstraints=spec Control precision constraint setting. (default = none) Precision Constraints: spec ::= "none" | "obey" | "prefer" none = no constraints prefer = meet precision constraints set by --layerPrecisions/--layerOutputTypes if possible obey = meet precision constraints set by --layerPrecisions/--layerOutputTypes or fail otherwise --layerPrecisions=spec Control per-layer precision constraints. Effective only when precisionConstraints is set to "obey" or "prefer". (default = none) The specs are read left-to-right, and later ones override earlier ones. Each layer name can contain at most one wildcard ('*') character. Per-layer precision spec ::= layerPrecision[","spec] layerPrecision ::= layerName":"precision precision ::= "fp32"|"fp16"|"bf16"|"int32"|"int8" --layerOutputTypes=spec Control per-layer output type constraints. Effective only when precisionConstraints is set to "obey" or "prefer". (default = none The specs are read left-to-right, and later ones override earlier ones. Each layer name can contain at most one wildcard ('*') character. If a layer has more than one output, then multiple types separated by "+" can be provided for this layer. Per-layer output type spec ::= layerOutputTypes[","spec] layerOutputTypes ::= layerName":"type type ::= "fp32"|"fp16"|"bf16"|"int32"|"int8"["+"type] --layerDeviceTypes=spec Specify layer-specific device type. The specs are read left-to-right, and later ones override earlier ones. If a layer does not have a device type specified, the layer will opt for the default device type. Per-layer device type spec ::= layerDeviceTypePair[","spec] layerDeviceTypePair ::= layerName":"deviceType deviceType ::= "GPU"|"DLA" --decomposableAttentions=spec Specify decomposable attentions by comma-separated names. The specs are read left-to-right, and later ones override earlier ones. Each layer name can contain at most one wildcard ('*') character. --calib=<file> Read INT8 calibration cache file --safe Enable build safety certified engine, --stronglyTyped will be enabled by default with this option. If DLA is enabled, --buildDLAStandalone will be specified --dumpKernelText Dump the kernel text to a file, only available when --safe is enabled --buildDLAStandalone Enable build DLA standalone loadable which can be loaded by cuDLA, when this option is enabled, --allowGPUFallback is disallowed and --skipInference is enabled by default. Additionally, specifying --inputIOFormats and --outputIOFormats restricts I/O data type and memory layout (default = disabled) --allowGPUFallback When DLA is enabled, allow GPU fallback for unsupported layers (default = disabled) --consistency Perform consistency checking on safety certified engine --restricted Enable safety scope checking with kSAFETY_SCOPE build flag --saveEngine=<file> Save the serialized engine --loadEngine=<file> Load a serialized engine --asyncFileReader Load a serialized engine using async stream reader. Should be combined with --loadEngine. --getPlanVersionOnly Print TensorRT version when loaded plan was created. Works without deserialization of the plan. Use together with --loadEngine. Supported only for engines created with 8.6 and forward. --tacticSources=tactics Specify the tactics to be used by adding (+) or removing (-) tactics from the default tactic sources (default = all available tactics). Note: Currently only cuDNN, cuBLAS, cuBLAS-LT, and edge mask convolutions are listed as optional tactics. Tactic Sources: tactics ::= tactic[","tactics] tactic ::= (+|-)lib lib ::= "CUBLAS"|"CUBLAS_LT"|"CUDNN"|"EDGE_MASK_CONVOLUTIONS" |"JIT_CONVOLUTIONS" For example, to disable cudnn and enable cublas: --tacticSources=-CUDNN,+CUBLAS --noBuilderCache Disable timing cache in builder (default is to enable timing cache) --noCompilationCache Disable Compilation cache in builder, and the cache is part of timing cache (default is to enable compilation cache) --errorOnTimingCacheMiss Emit error when a tactic being timed is not present in the timing cache (default = false) --timingCacheFile=<file> Save/load the serialized global timing cache --preview=features Specify preview feature to be used by adding (+) or removing (-) preview features from the default Preview Features: features ::= feature[","features] feature ::= (+|-)flag flag ::= "aliasedPluginIO1003" |"runtimeActivationResize" |"profileSharing0806" --builderOptimizationLevel Set the builder optimization level. (default is 3) A Higher level allows TensorRT to spend more time searching for better optimization strategy. Valid values include integers from 0 to the maximum optimization level, which is currently 5. --maxTactics Set the maximum number of tactics to time when there is a choice of tactics. (default is -1) Larger number of tactics allow TensorRT to spend more building time on evaluating tactics. Default value -1 means TensorRT can decide the number of tactics based on its own heuristic. --hardwareCompatibilityLevel=mode Make the engine file compatible with other GPU architectures. (default = none) Hardware Compatibility Level: mode ::= "none" | "ampere+" | "sameComputeCapability" none = no compatibility ampere+ = compatible with Ampere and newer GPUs sameComputeCapability = compatible with GPUs that have the same Compute Capability version --runtimePlatform=platform Set the target platform for runtime execution. (default = SameAsBuild) When this option is enabled, --skipInference is enabled by default. RuntimePlatfrom: platform ::= "SameAsBuild" | "WindowsAMD64" SameAsBuild = no requirement for cross-platform compatibility. WindowsAMD64 = set the target platform for engine execution as Windows AMD64 system --tempdir=<dir> Overrides the default temporary directory TensorRT will use when creating temporary files. See IRuntime::setTemporaryDirectory API documentation for more information. --tempfileControls=controls Controls what TensorRT is allowed to use when creating temporary executable files. Should be a comma-separated list with entries in the format (in_memory|temporary):(allow|deny). in_memory: Controls whether TensorRT is allowed to create temporary in-memory executable files. temporary: Controls whether TensorRT is allowed to create temporary executable files in the filesystem (in the directory given by --tempdir). For example, to allow in-memory files and disallow temporary files: --tempfileControls=in_memory:allow,temporary:deny If a flag is unspecified, the default behavior is "allow". --maxAuxStreams=N Set maximum number of auxiliary streams per inference stream that TRT is allowed to use to run kernels in parallel if the network contains ops that can run in parallel, with the cost of more memory usage. Set this to 0 for optimal memory usage. (default = using heuristics) --profile Build with dynamic shapes using a profile with the min/max/opt shapes provided. Can be specified multiple times to create multiple profiles with contiguous index. (ex: --profile=0 --minShapes=<spec> --optShapes=<spec> --maxShapes=<spec> --profile=1 ...) --calibProfile Select the optimization profile to calibrate by index. (default = 0) --allowWeightStreaming Enable a weight streaming engine. Must be specified with --stronglyTyped. TensorRT will disable weight streaming at runtime unless --weightStreamingBudget is specified. --markDebug Specify list of names of tensors to be marked as debug tensors. Separate names with a comma --markUnfusedTensorsAsDebugTensors Mark unfused tensors as debug tensors --tilingOptimizationLevel Set the tiling optimization level. (default is 0) A Higher level allows TensorRT to spend more time searching for better optimization strategy. Valid values include integers from 0 to the maximum tiling optimization level(3). --l2LimitForTiling Set the L2 cache usage limit for tiling optimization(default is -1) --remoteAutoTuningConfig Set the remote auto tuning config. Must be specified with --safe. Format: protocol://username[:password]@hostname[:port]?param1=value1&param2=value2 Example: ssh://user:pass@192.0.2.100:22?remote_exec_path=/opt/tensorrt/bin&remote_lib_path=/opt/tensorrt/lib --refitFromOnnx Refit the loaded engine with the weights from the provided ONNX model. The model should be identical to the one used to generate the engine. === Inference Options === --shapes=spec Set input shapes for dynamic shapes inference inputs. Note: Input names can be wrapped with escaped single quotes (ex: 'Input:0'). Example input shapes spec: input0:1x3x256x256, input1:1x3x128x128 For scalars (0-D shapes), use input0:scalar or simply input0: with nothing after the colon. Each input shape is supplied as a key-value pair where key is the input name and value is the dimensions (including the batch dimension) to be used for that input. Each key-value pair has the key and value separated using a colon (:). Multiple input shapes can be provided via comma-separated key-value pairs, and each input name can contain at most one wildcard ('*') character. --loadInputs=spec Load input values from files (default = generate random inputs). Input names can be wrapped with single quotes (ex: 'Input:0') Input values spec ::= Ival[","spec] Ival ::= name":"file Consult the README for more information on generating files for custom inputs. --iterations=N Run at least N inference iterations (default = 10) --warmUp=N Run for N milliseconds to warmup before measuring performance (default = 200) --duration=N Run performance measurements for at least N seconds wallclock time (default = 3) If -1 is specified, inference will keep running unless stopped manually --sleepTime=N Delay inference start with a gap of N milliseconds between launch and compute (default = 0) --idleTime=N Sleep N milliseconds between two continuous iterations(default = 0) --infStreams=N Instantiate N execution contexts to run inference concurrently (default = 1) --exposeDMA Serialize DMA transfers to and from device (default = disabled). --noDataTransfers Disable DMA transfers to and from device (default = enabled). Note some device-to-host data transfers will remain if output dumping is enabled via the --dumpOutput or --exportOutput flags. --useManagedMemory Use managed memory instead of separate host and device allocations (default = disabled). --useSpinWait Actively synchronize on GPU events. This option may decrease synchronization time but increase CPU usage and power (default = disabled) --threads Enable multithreading to drive engines with independent threads or speed up refitting (default = disabled) --useCudaGraph Use CUDA graph to capture engine execution and then launch inference (default = disabled). This flag may be ignored if the graph capture fails. --timeDeserialize Time the amount of time it takes to deserialize the network and exit. --timeRefit Time the amount of time it takes to refit the engine before inference. --separateProfileRun Do not attach the profiler in the benchmark run; if profiling is enabled, a second profile run will be executed (default = disabled) --skipInference Exit after the engine has been built and skip inference perf measurement (default = disabled) --persistentCacheRatio Set the persistentCacheLimit in ratio, 0.5 represent half of max persistent L2 size (default = 0) --useProfile Set the optimization profile for the inference context (default = 0 ). --allocationStrategy=spec Specify how the internal device memory for inference is allocated. Strategy: spec ::= "static"|"profile"|"runtime" static = Allocate device memory based on max size across all profiles. profile = Allocate device memory based on max size of the current profile. runtime = Allocate device memory based on the actual input shapes. --saveDebugTensors Specify list of names of tensors to turn on the debug state and filename to save raw outputs to. These tensors must be specified as debug tensors during build time. Input values spec ::= Ival[","spec] Ival ::= name":"file --saveAllDebugTensors Save all debug tensors to files. Including debug tensors marked by --markDebug and --markUnfusedTensorsAsDebugTensors Multiple file formats can be saved simultaneously. Input values spec ::= format[","format] format ::= "summary"|"numpy"|"string"|"raw" --weightStreamingBudget Set the maximum amount of GPU memory TensorRT is allowed to use for weights. It can take on the following values: -2: (default) Disable weight streaming at runtime. -1: TensorRT will automatically decide the budget. 0-100%: Percentage of streamable weights that reside on the GPU. 0% saves the most memory but will have the worst performance. Requires the '%' character. >=0B: The exact amount of streamable weights that reside on the GPU. Supports the following base-2 suffixes: B (Bytes), G (Gibibytes), K (Kibibytes), M (Mebibytes). === Reporting Options === --verbose Use verbose logging (default = false) --avgRuns=N Report performance measurements averaged over N consecutive iterations (default = 10) --percentile=P1,P2,P3,... Report performance for the P1,P2,P3,... percentages (0<=P_i<=100, 0 representing max perf, and 100 representing min perf; (default = 90,95,99%) --dumpRefit Print the refittable layers and weights from a refittable engine --dumpOutput Print the output tensor(s) of the last inference iteration (default = disabled) --dumpRawBindingsToFile Print the input/output tensor(s) of the last inference iteration to file(default = disabled) --dumpProfile Print profile information per layer (default = disabled) --dumpLayerInfo Print layer information of the engine to console (default = disabled) --dumpOptimizationProfile Print the optimization profile(s) information (default = disabled) --exportTimes=<file> Write the timing results in a json file (default = disabled) --exportOutput=<file> Write the output tensors to a json file (default = disabled) --exportProfile=<file> Write the profile information per layer in a json file (default = disabled) --exportLayerInfo=<file> Write the layer information of the engine in a json file (default = disabled) === System Options === --device=N Select cuda device N (default = 0) --useDLACore=N Select DLA core N for layers that support DLA (default = none) --staticPlugins Plugin library (.so) to load statically (can be specified multiple times) --dynamicPlugins Plugin library (.so) to load dynamically and may be serialized with the engine if they are included in --setPluginsToSerialize (can be specified multiple times) --setPluginsToSerialize Plugin library (.so) to be serialized with the engine (can be specified multiple times) --ignoreParsedPluginLibs By default, when building a version-compatible engine, plugin libraries specified by the ONNX parser are implicitly serialized with the engine (unless --excludeLeanRuntime is specified) and loaded dynamically. Enable this flag to ignore these plugin libraries instead. --safetyPlugins Plugin library (.so) for TensorRT auto safety to manually load safety plugins specified by the command line arguments. Example: --safetyPlugins=/path/to/plugin_lib.so[pluginNamespace1::plugin1,pluginNamespace2::plugin2]. The option can be specified multiple times with different plugin libraries. === Help === --help, -h Print this message [11/27/2025-14:33:27] [E] Model missing or format not recognized &&&& FAILED TensorRT.trtexec [TensorRT v101401] [b48] # trtexec --version
11-28
具体来说，当执行trtexec --version时，出现错误“Model missing or format not recognized”。用户想知道这个错误的原因、解决办法，以及trtexec工具的使用方法和参数含义。 从系统级指令中，我需要： - 使用$...$...
【医学图像处理】基于openslide的SVS切片多尺度分析：病理AI研究中的高分辨率图像处理与批量裁剪技术应用
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内容概要：本文是一份关于使用openslide处理显微镜病理SVS切片的全流程技术教程，涵盖从环境搭建、基础读取与可视化，到进阶裁剪、批量处理以及科研级应用拓展。文章详细介绍了SVS切片的特点和openslide库的功能，提供了Python代码示例，实现切片多层级图像提取、感兴趣区域裁剪、大批量SVS文件自动化处理，并进一步引导读者结合深度学习模型进行病变分割、细胞计数、多尺度分析及交互式可视化报告开发。同时附带常见问题避坑指南，帮助用户应对内存不足、坐标错乱和格式兼容等问题。; 适合人群：计算机或生物医学工程相关专业，正在进行病理图像分析方向毕业设计的学生，具备一定Python编程和图像处理基础的研发人员。; 使用场景及目标：① 掌握openslide对超大SVS切片的高效读取与多层级可视化方法；② 实现病理图像的精准裁剪与批量预处理，为AI模型训练准备数据；③ 构建从全切片到局部细节的多尺度分析流程，提升毕设的技术深度与科研价值。; 阅读建议：建议边实践边学习，配合提供的代码链接动手操作，优先在小样本上验证流程，并结合ImageScope软件进行坐标定位与结果验证，注意处理大文件时的内存优化策略。
update9-20260103.5.211.slice.img.7z.003
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小雉系统分卷源码
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下载方式：https://pan.quark.cn/s/f4ef8119fda2 通过QT学习实例，可以展示QLineEdit的多样化应用方式，并运用QLCDNumber来以类似液晶显示屏的数字形式呈现数值
半监督和无监督极限学习机（SS-US-ELM）（Matlab代码实现）
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半监督和无监督极限学习机（SS-US-ELM）（Matlab代码实现）内容概要：本文档主要介绍了一种半监督和无监督极限学习机（SS-US-ELM）的Matlab代码实现方法，属于机器学习与深度学习领域的前沿探索内容。文中强调该技术在时序预测、分类识别、回归分析等方面的应用，并指出其适用于缺乏充足标签数据的实际科研场景。资源还涵盖了极限学习机（ELM）系列算法与其他智能优化算法、神经网络模型的结合应用，展示了其在电力系统、负荷预测、电池健康状态评估等多个工程领域的实践价值。此外，文档附带了多个Matlab仿真实例及相关网盘资源链接，便于读者复现和扩展研究。; 适合人群：具备一定Matlab编程基础，从事科研工作或研究生阶段的学习者，尤其适合研究机器学习、电力系统优化、智能算法应用等相关方向的人员。; 使用场景及目标：①用于解决标签数据稀缺情况下的分类与预测问题；②结合智能优化算法提升极限学习机性能；③应用于电力负荷预测、新能源系统调度、电池状态估计等实际工程问题的研究与模型构建。; 阅读建议：建议读者结合提供的Matlab代码实例进行实践操作，优先掌握ELM基本原理后再深入SS-US-ELM的实现逻辑，同时利用网盘资源完成代码复现与参数调优，以达到理论与实践相结合的学习效果。
电机控制基于Simulink的永磁同步电机模型预测控制仿真：FCS-MPCC算法实现与动态性能分析
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内容概要：本文详细介绍了一种基于Simulink的表贴式永磁同步电机（SPMSM）有限控制集模型预测电流控制（FCS-MPCC）仿真系统。通过构建PMSM数学模型、坐标变换、MPC控制器、SVPWM调制等模块，实现了对电机定子电流的高精度跟踪控制，具备快速动态响应和低稳态误差的特点。文中提供了完整的仿真建模步骤、关键参数设置、核心MATLAB函数代码及仿真结果分析，涵盖转速、电流、转矩和三相电流波形，验证了MPC控制策略在动态性能、稳态精度和抗负载扰动方面的优越性，并提出了参数自整定、加权代价函数、模型预测转矩控制和弱磁扩速等优化方向。; 适合人群：自动化、电气工程及其相关专业本科生、研究生，以及从事电机控制算法研究与仿真的工程技术人员；具备一定的电机原理、自动控制理论和Simulink仿真基础者更佳； 使用场景及目标：①用于永磁同步电机模型预测控制的教学演示、课程设计或毕业设计项目；②作为电机先进控制算法（如MPC、MPTC）的仿真验证平台；③支撑科研中对控制性能优化（如动态响应、抗干扰能力）的研究需求； 阅读建议：建议读者结合Simulink环境动手搭建模型，深入理解各模块间的信号流向与控制逻辑，重点掌握预测模型构建、代价函数设计与开关状态选择机制，并可通过修改电机参数或控制策略进行拓展实验，以增强实践与创新能力。
2021年湖北省黄石市中考数学试卷及解析
01-03
根据原作 https://pan.quark.cn/s/23d6270309e5 的源码改编 湖北省黄石市2021年中考数学试卷所包含的知识点广泛涉及了中学数学的基础领域，涵盖了实数、科学记数法、分式方程、几何体的三视图、立体几何、概率统计以及代数方程等多个方面。 接下来将对每道试题所关联的知识点进行深入剖析：1. 实数与倒数的定义：该题目旨在检验学生对倒数概念的掌握程度，即一个数a的倒数表达为1/a，因此-7的倒数可表示为-1/7。 2. 科学记数法的运用：科学记数法是一种表示极大或极小数字的方法，其形式为a×10^n，其中1≤|a|<10，n为整数。 此题要求学生运用科学记数法表示一个天文单位的距离，将1.4960亿千米转换为1.4960×10^8千米。 3. 分式方程的求解方法：考察学生解决包含分母的方程的能力，题目要求找出满足方程3/(2x-1)=1的x值，需通过消除分母的方式转化为整式方程进行解答。 4. 三视图的辨认：该题目测试学生对于几何体三视图（主视图、左视图、俯视图）的认识，需要识别出具有两个相同视图而另一个不同的几何体。 5. 立体几何与表面积的计算：题目要求学生计算由直角三角形旋转形成的圆锥的表面积，要求学生对圆锥的底面积和侧面积公式有所了解并加以运用。 6. 统计学的基础概念：题目涉及众数、平均数、极差和中位数的定义，要求学生根据提供的数据信息选择恰当的统计量。 7. 方程的整数解求解：考察学生在实际问题中进行数学建模的能力，通过建立方程来计算在特定条件下帐篷的搭建方案数量。 8. 三角学的实际应用：题目通过在直角三角形中运用三角函数来求解特定线段的长度。 利用正弦定理求解AD的长度是解答该问题的关键。 9. 几何变换的应用：题目要求学生运用三角板的旋转来求解特定点的...
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