ue" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-7B-Chat") # Instead of using Qwen model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", torch_dtype="auto", device_map="auto", attn_implementation="flash_attention_2", ) 为了解决下载问题，我们建议您尝试从 ModelScope 进行下载，只需将上述代码的第一行更改为以下内容： ue" model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen1.5-7B-Chat", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-7B-Chat") # Instead of using

/bin/grep etcd | /bin/grep -v grep Expected result: 'true' is equal to 'true' 2.3 Ensure that the --auto-tls argument is not set to true (Scored) Result: PASS Remediation: Edit the etcd pod specification remove the -- auto-tls parameter or set it to false. --auto-tls=false Audit: /bin/ps -ef | /bin/grep etcd | /bin/grep -v grep Expected result: '--auto-tls' is not present OR '--auto-tls' is not present etcd | /bin/grep -v grep Expected result: 'true' is equal to 'true' 2.6 Ensure that the --peer-auto-tls argument is not set to true (Scored) Result: PASS Remediation: Edit the etcd pod specification

/bin/grep etcd | /bin/grep -v grep Expected result: 'true' is equal to 'true' 2.3 Ensure that the --auto-tls argument is not set to true (Scored) Result: PASS Remediation: Edit the etcd pod specification remove the -- auto-tls parameter or set it to false. --auto-tls=false Audit: /bin/ps -ef | /bin/grep etcd | /bin/grep -v grep Expected result: '--auto-tls' is not present OR '--auto-tls' is not present etcd | /bin/grep -v grep Expected result: 'true' is equal to 'true' 2.6 Ensure that the --peer-auto-tls argument is not set to true (Scored) Result: PASS Remediation: Edit the etcd pod specification

file object opened binary mode. In most cases, it is not necessary to specify mode as Pandas will auto-detect whether the file object is opened in text or binary mode. In [127]: import io In [128]: data ptrepack. In addition, ptrepack can change compression levels after the fact. ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5 Furthermore ptrepack in.h5 out.h5 will repack can be one of pyarrow, or fastparquet, or auto. If the engine is NOT specified, then the pd.options.io.parquet.engine option is checked; if this is also auto, then pyarrow is tried, and falling back to

file object opened binary mode. In most cases, it is not necessary to specify mode as Pandas will auto-detect whether the file object is opened in text or binary mode. In [133]: import io In [134]: data ptrepack. In addition, ptrepack can change compression levels after the fact. ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5 Furthermore ptrepack in.h5 out.h5 will repack can be one of pyarrow, or fastparquet, or auto. If the engine is NOT specified, then the pd.options.io.parquet.engine option is checked; if this is also auto, then pyarrow is tried, and falling back to

file object opened binary mode. In most cases, it is not necessary to specify mode as Pandas will auto-detect whether the file object is opened in text or binary mode. In [132]: import io In [133]: data ptrepack. In addition, ptrepack can change compression levels after the fact. ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5 Furthermore ptrepack in.h5 out.h5 will repack can be one of pyarrow, or fastparquet, or auto. If the engine is NOT specified, then the pd.options.io.parquet.engine option is checked; if this is also auto, then pyarrow is tried, and falling back to

• Changed the default configuration value for options.matplotlib.register_converters from True to "auto" (GH18720). Now, pandas custom formatters will only be applied to plots created by pandas, through ptrepack. In addition, ptrepack can change compression levels after the fact. ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5 Furthermore ptrepack in.h5 out.h5 will repack can be one of pyarrow, or fastparquet, or auto. If the engine is NOT specified, then the pd.options.io.parquet.engine option is checked; if this is also auto, then pyarrow is tried, and falling back to

file object opened binary mode. In most cases, it is not necessary to specify mode as Pandas will auto-detect whether the file object is opened in text or binary mode. In [123]: import io In [124]: data ptrepack. In addition, ptrepack can change compression levels after the fact. ptrepack --chunkshape=auto --propindexes --complevel=9 --complib=blosc in.h5 out.h5 Furthermore ptrepack in.h5 out.h5 will repack can be one of pyarrow, or fastparquet, or auto. If the engine is NOT specified, then the pd.options.io.parquet.engine option is checked; if this is also auto, then pyarrow is tried, and falling back to

supports tab completion so if your start typing a command it will show possible completions and also auto complete if there is only one completion. DEPLOY A SAMPLE APPLICATION While you will learn in the message -s, --start-type Mode in which the service is installed. AUTO_START or DEMAND_START (Default: AUTO_START) (defaults to AUTO_START) -n, --name The service name that will be used when installing OPTIONS -s, --start-type Mode in which the service is installed. AUTO_START or DEMAND_START (Default: AUTO_START) (defaults to AUTO_START) --help Display this help message -n, --name The service

Auto-Encoders 主讲：龙良曲 Outline Supervised Learning https://towardsdatascience.com/supervised-vs-unsupervised-learning-14f68e32ea8d Massive Unlabeled data Unsupervised Learning https://medium.com/ tensorflow.org/ ▪ Taking advantages of unsupervised data ▪ Compression, denoising, super-resolution … Auto-Encoders https://towardsdatascience.com/applied-deep-learning-part-3-autoencoders- 1c083af4d798 com/a-wizards-guide-to-adversarial-autoencoders-part-1- autoencoder-d9a5f8795af4 How to Train? PCA V.S. Auto-Encoders ▪ PCA, which finds the directions of maximal variance in high- dimensional data, select